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| Author | SHA1 | Date | |
|---|---|---|---|
 Henry Li
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fa20cc9a98 |
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---
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name: smoke-test
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description: End-to-end smoke test skill for DeerFlow. Guides through: 1) Pulling latest code, 2) Docker OR Local installation and deployment (user preference, default to Local if Docker network issues), 3) Service availability verification, 4) Health check, 5) Final test report. Use when the user says "run smoke test", "smoke test deployment", "verify installation", "test service availability", "end-to-end test", or similar.
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---
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# DeerFlow Smoke Test Skill
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This skill guides the Agent through DeerFlow's full end-to-end smoke test workflow, including code updates, deployment (supporting both Docker and local installation modes), service availability verification, and health checks.
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## Deployment Mode Selection
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This skill supports two deployment modes:
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- **Local installation mode** (recommended, especially when network issues occur) - Run all services directly on the local machine
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- **Docker mode** - Run all services inside Docker containers
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**Selection strategy**:
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- If the user explicitly asks for Docker mode, use Docker
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- If network issues occur (such as slow image pulls), automatically switch to local mode
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- Default to local mode whenever possible
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## Structure
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```
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smoke-test/
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├── SKILL.md ← You are here - core workflow and logic
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├── scripts/
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│ ├── check_docker.sh ← Check the Docker environment
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│ ├── check_local_env.sh ← Check local environment dependencies
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│ ├── frontend_check.sh ← Frontend page smoke check
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│ ├── pull_code.sh ← Pull the latest code
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│ ├── deploy_docker.sh ← Docker deployment
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│ ├── deploy_local.sh ← Local deployment
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│ └── health_check.sh ← Service health check
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├── references/
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│ ├── SOP.md ← Standard operating procedure
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│ └── troubleshooting.md ← Troubleshooting guide
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└── templates/
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├── report.local.template.md ← Local mode smoke test report template
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└── report.docker.template.md ← Docker mode smoke test report template
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```
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## Standard Operating Procedure (SOP)
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### Phase 1: Code Update Check
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1. **Confirm current directory** - Verify that the current working directory is the DeerFlow project root
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2. **Check Git status** - See whether there are uncommitted changes
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3. **Pull the latest code** - Use `git pull origin main` to get the latest updates
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4. **Confirm code update** - Verify that the latest code was pulled successfully
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### Phase 2: Deployment Mode Selection and Environment Check
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**Choose deployment mode**:
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- Ask for user preference, or choose automatically based on network conditions
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- Default to local installation mode
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**Local mode environment check**:
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1. **Check Node.js version** - Requires 22+
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2. **Check pnpm** - Package manager
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3. **Check uv** - Python package manager
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4. **Check nginx** - Reverse proxy
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5. **Check required ports** - Confirm that ports 2026, 3000, 8001, and 2024 are not occupied
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**Docker mode environment check** (if Docker is selected):
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1. **Check whether Docker is installed** - Run `docker --version`
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2. **Check Docker daemon status** - Run `docker info`
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3. **Check Docker Compose availability** - Run `docker compose version`
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4. **Check required ports** - Confirm that port 2026 is not occupied
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### Phase 3: Configuration Preparation
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1. **Check whether config.yaml exists**
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- If it does not exist, run `make config` to generate it
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- If it already exists, check whether it needs an upgrade with `make config-upgrade`
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2. **Check the .env file**
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- Verify that required environment variables are configured
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- Especially model API keys such as `OPENAI_API_KEY`
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### Phase 4: Deployment Execution
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**Local mode deployment**:
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1. **Check dependencies** - Run `make check`
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2. **Install dependencies** - Run `make install`
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3. **(Optional) Pre-pull the sandbox image** - If needed, run `make setup-sandbox`
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4. **Start services** - Run `make dev-daemon` (background mode, recommended) or `make dev` (foreground mode)
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5. **Wait for startup** - Give all services enough time to start completely (90-120 seconds recommended)
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**Docker mode deployment** (if Docker is selected):
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1. **Initialize Docker environment** - Run `make docker-init`
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2. **Start Docker services** - Run `make docker-start`
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3. **Wait for startup** - Give all containers enough time to start completely (60 seconds recommended)
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### Phase 5: Service Health Check
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**Local mode health check**:
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1. **Check process status** - Confirm that LangGraph, Gateway, Frontend, and Nginx processes are all running
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2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
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3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
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4. **Check LangGraph service** - Verify the availability of relevant endpoints
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5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
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**Docker mode health check** (when using Docker):
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1. **Check container status** - Run `docker ps` and confirm that all containers are running
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2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
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3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
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4. **Check LangGraph service** - Verify the availability of relevant endpoints
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5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
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### Optional Functional Verification
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1. **List available models** - Verify that model configuration loads correctly
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2. **List available skills** - Verify that the skill directory is mounted correctly
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3. **Simple chat test** - Send a simple message to verify the end-to-end flow
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### Phase 6: Generate Test Report
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1. **Collect all test results** - Summarize execution status for each phase
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2. **Record encountered issues** - If anything fails, record the error details
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3. **Generate the final report** - Use the template that matches the selected deployment mode to create the complete test report, including overall conclusion, detailed key test cases, and explicit frontend page / route results
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4. **Provide follow-up recommendations** - Offer suggestions based on the test results
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## Execution Rules
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- **Follow the sequence** - Execute strictly in the order described above
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- **Idempotency** - Every step should be safe to repeat
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- **Error handling** - If a step fails, stop and report the issue, then provide troubleshooting suggestions
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- **Detailed logging** - Record the execution result and status of each step
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- **User confirmation** - Ask for confirmation before potentially risky operations such as overwriting config
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- **Mode preference** - Prefer local mode to avoid network-related issues
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- **Template requirement** - The final report must use the matching template under `templates/`; do not output a free-form summary instead of the template-based report
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- **Report clarity** - The execution summary must include the overall pass/fail conclusion plus per-case result explanations, and frontend smoke check results must be listed explicitly in the report
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- **Optional phase handling** - If functional verification is not executed, do not present it as a separate skipped phase in the final report
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## Known Acceptable Warnings
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The following warnings can appear during smoke testing and do not block a successful result:
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- Feishu/Lark SSL errors in Gateway logs (certificate verification failure) can be ignored if that channel is not enabled
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- Warnings in LangGraph logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
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## Key Tools
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Use the following tools during execution:
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1. **bash** - Run shell commands
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2. **present_file** - Show generated reports and important files
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3. **task_tool** - Organize complex steps with subtasks when needed
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## Success Criteria
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Smoke test pass criteria (local mode):
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- [x] Latest code is pulled successfully
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- [x] Local environment check passes (Node.js 22+, pnpm, uv, nginx)
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- [x] Configuration files are set up correctly
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- [x] `make check` passes
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- [x] `make install` completes successfully
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- [x] `make dev` starts successfully
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- [x] All service processes run normally
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- [x] Frontend page is accessible
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- [x] Frontend route smoke check passes (`/workspace` key routes)
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- [x] API Gateway health check passes
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- [x] Test report is generated completely
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Smoke test pass criteria (Docker mode):
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- [x] Latest code is pulled successfully
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- [x] Docker environment check passes
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- [x] Configuration files are set up correctly
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- [x] `make docker-init` completes successfully
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- [x] `make docker-start` completes successfully
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- [x] All Docker containers run normally
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- [x] Frontend page is accessible
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- [x] Frontend route smoke check passes (`/workspace` key routes)
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- [x] API Gateway health check passes
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- [x] Test report is generated completely
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## Read Reference Files
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Before starting execution, read the following reference files:
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1. `references/SOP.md` - Detailed step-by-step operating instructions
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2. `references/troubleshooting.md` - Common issues and solutions
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3. `templates/report.local.template.md` - Local mode test report template
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4. `templates/report.docker.template.md` - Docker mode test report template
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@@ -1,452 +0,0 @@
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# DeerFlow Smoke Test Standard Operating Procedure (SOP)
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This document describes the detailed operating steps for each phase of the DeerFlow smoke test.
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## Phase 1: Code Update Check
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### 1.1 Confirm Current Directory
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**Objective**: Verify that the current working directory is the DeerFlow project root.
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**Steps**:
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1. Run `pwd` to view the current working directory
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2. Check whether the directory contains the following files/directories:
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- `Makefile`
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- `backend/`
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- `frontend/`
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- `config.example.yaml`
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**Success Criteria**: The current directory contains all of the files/directories listed above.
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---
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### 1.2 Check Git Status
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**Objective**: Check whether there are uncommitted changes.
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**Steps**:
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1. Run `git status`
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2. Check whether the output includes "Changes not staged for commit" or "Untracked files"
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**Notes**:
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- If there are uncommitted changes, recommend that the user commit or stash them first to avoid conflicts while pulling
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- If the user confirms that they want to continue, this step can be skipped
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---
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### 1.3 Pull the Latest Code
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**Objective**: Fetch the latest code updates.
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**Steps**:
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1. Run `git fetch origin main`
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2. Run `git pull origin main`
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**Success Criteria**:
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- The commands succeed without errors
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- The output shows "Already up to date" or indicates that new commits were pulled successfully
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---
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### 1.4 Confirm Code Update
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**Objective**: Verify that the latest code was pulled successfully.
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**Steps**:
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1. Run `git log -1 --oneline` to view the latest commit
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2. Record the commit hash and message
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---
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## Phase 2: Deployment Mode Selection and Environment Check
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### 2.1 Choose Deployment Mode
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**Objective**: Decide whether to use local mode or Docker mode.
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**Decision Flow**:
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1. Prefer local mode first to avoid network-related issues
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2. If the user explicitly requests Docker, use Docker
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3. If Docker network issues occur, switch to local mode automatically
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---
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### 2.2 Local Mode Environment Check
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**Objective**: Verify that local development environment dependencies are satisfied.
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#### 2.2.1 Check Node.js Version
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**Steps**:
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1. If nvm is used, run `nvm use 22` to switch to Node 22+
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2. Run `node --version`
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**Success Criteria**: Version >= 22.x
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**Failure Handling**:
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- If the version is too low, ask the user to install/switch Node.js with nvm:
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```bash
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nvm install 22
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nvm use 22
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```
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- Or install it from the official website: https://nodejs.org/
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---
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#### 2.2.2 Check pnpm
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**Steps**:
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1. Run `pnpm --version`
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**Success Criteria**: The command returns pnpm version information.
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**Failure Handling**:
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- If pnpm is not installed, ask the user to install it with `npm install -g pnpm`
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---
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#### 2.2.3 Check uv
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**Steps**:
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1. Run `uv --version`
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**Success Criteria**: The command returns uv version information.
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**Failure Handling**:
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- If uv is not installed, ask the user to install uv
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---
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#### 2.2.4 Check nginx
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**Steps**:
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1. Run `nginx -v`
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**Success Criteria**: The command returns nginx version information.
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**Failure Handling**:
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- macOS: install with Homebrew using `brew install nginx`
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- Linux: install using the system package manager
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---
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#### 2.2.5 Check Required Ports
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**Steps**:
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1. Run the following commands to check ports:
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```bash
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lsof -i :2026 # Main port
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lsof -i :3000 # Frontend
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lsof -i :8001 # Gateway
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lsof -i :2024 # LangGraph
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```
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**Success Criteria**: All ports are free, or they are occupied only by DeerFlow-related processes.
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**Failure Handling**:
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- If a port is occupied, ask the user to stop the related process
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---
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### 2.3 Docker Mode Environment Check (If Docker Is Selected)
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#### 2.3.1 Check Whether Docker Is Installed
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**Steps**:
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1. Run `docker --version`
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**Success Criteria**: The command returns Docker version information, such as "Docker version 24.x.x".
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---
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#### 2.3.2 Check Docker Daemon Status
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**Steps**:
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1. Run `docker info`
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**Success Criteria**: The command runs successfully and shows Docker system information.
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**Failure Handling**:
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- If it fails, ask the user to start Docker Desktop or the Docker service
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---
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#### 2.3.3 Check Docker Compose Availability
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**Steps**:
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1. Run `docker compose version`
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**Success Criteria**: The command returns Docker Compose version information.
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---
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#### 2.3.4 Check Required Ports
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**Steps**:
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1. Run `lsof -i :2026` (macOS/Linux) or `netstat -ano | findstr :2026` (Windows)
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**Success Criteria**: Port 2026 is free, or it is occupied only by a DeerFlow-related process.
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**Failure Handling**:
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- If the port is occupied by another process, ask the user to stop that process or change the configuration
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---
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## Phase 3: Configuration Preparation
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### 3.1 Check config.yaml
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**Steps**:
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1. Check whether `config.yaml` exists
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2. If it does not exist, run `make config`
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3. If it already exists, consider running `make config-upgrade` to merge new fields
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**Validation**:
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- Check whether at least one model is configured in config.yaml
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- Check whether the model configuration references the correct environment variables
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||||
---
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### 3.2 Check the .env File
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**Steps**:
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1. Check whether the `.env` file exists
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2. If it does not exist, copy it from `.env.example`
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3. Check whether the following environment variables are configured:
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- `OPENAI_API_KEY` (or other model API keys)
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- Other required settings
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||||
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||||
---
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||||
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## Phase 4: Deployment Execution
|
||||
|
||||
### 4.1 Local Mode Deployment
|
||||
|
||||
#### 4.1.1 Check Dependencies
|
||||
|
||||
**Steps**:
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||||
1. Run `make check`
|
||||
|
||||
**Description**: This command validates all required tools (Node.js 22+, pnpm, uv, nginx).
|
||||
|
||||
---
|
||||
|
||||
#### 4.1.2 Install Dependencies
|
||||
|
||||
**Steps**:
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||||
1. Run `make install`
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||||
|
||||
**Description**: This command installs both backend and frontend dependencies.
|
||||
|
||||
**Notes**:
|
||||
- This step may take some time
|
||||
- If network issues cause failures, try using a closer or mirrored package registry
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||||
|
||||
---
|
||||
|
||||
#### 4.1.3 (Optional) Pre-pull the Sandbox Image
|
||||
|
||||
**Steps**:
|
||||
1. If Docker / Container sandbox is used, run `make setup-sandbox`
|
||||
|
||||
**Description**: This step is optional and not needed for local sandbox mode.
|
||||
|
||||
---
|
||||
|
||||
#### 4.1.4 Start Services
|
||||
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||||
**Steps**:
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||||
1. Run `make dev-daemon` (background mode)
|
||||
|
||||
**Description**: This command starts all services (LangGraph, Gateway, Frontend, Nginx).
|
||||
|
||||
**Notes**:
|
||||
- `make dev` runs in the foreground and stops with Ctrl+C
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||||
- `make dev-daemon` runs in the background
|
||||
- Use `make stop` to stop services
|
||||
|
||||
---
|
||||
|
||||
#### 4.1.5 Wait for Services to Start
|
||||
|
||||
**Steps**:
|
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1. Wait 90-120 seconds for all services to start completely
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2. You can monitor startup progress by checking these log files:
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||||
- `logs/langgraph.log`
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- `logs/gateway.log`
|
||||
- `logs/frontend.log`
|
||||
- `logs/nginx.log`
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||||
|
||||
---
|
||||
|
||||
### 4.2 Docker Mode Deployment (If Docker Is Selected)
|
||||
|
||||
#### 4.2.1 Initialize the Docker Environment
|
||||
|
||||
**Steps**:
|
||||
1. Run `make docker-init`
|
||||
|
||||
**Description**: This command pulls the sandbox image if needed.
|
||||
|
||||
---
|
||||
|
||||
#### 4.2.2 Start Docker Services
|
||||
|
||||
**Steps**:
|
||||
1. Run `make docker-start`
|
||||
|
||||
**Description**: This command builds and starts all required Docker containers.
|
||||
|
||||
---
|
||||
|
||||
#### 4.2.3 Wait for Services to Start
|
||||
|
||||
**Steps**:
|
||||
1. Wait 60-90 seconds for all services to start completely
|
||||
2. You can run `make docker-logs` to monitor startup progress
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Service Health Check
|
||||
|
||||
### 5.1 Local Mode Health Check
|
||||
|
||||
#### 5.1.1 Check Process Status
|
||||
|
||||
**Steps**:
|
||||
1. Run the following command to check processes:
|
||||
```bash
|
||||
ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
|
||||
```
|
||||
|
||||
**Success Criteria**: Confirm that the following processes are running:
|
||||
- LangGraph (`langgraph dev`)
|
||||
- Gateway (`uvicorn app.gateway.app:app`)
|
||||
- Frontend (`next dev` or `next start`)
|
||||
- Nginx (`nginx`)
|
||||
|
||||
---
|
||||
|
||||
#### 5.1.2 Check Frontend Service
|
||||
|
||||
**Steps**:
|
||||
1. Use curl or a browser to visit `http://localhost:2026`
|
||||
2. Verify that the page loads normally
|
||||
|
||||
**Example curl command**:
|
||||
```bash
|
||||
curl -I http://localhost:2026
|
||||
```
|
||||
|
||||
**Success Criteria**: Returns an HTTP 200 status code.
|
||||
|
||||
---
|
||||
|
||||
#### 5.1.3 Check API Gateway
|
||||
|
||||
**Steps**:
|
||||
1. Visit `http://localhost:2026/health`
|
||||
|
||||
**Example curl command**:
|
||||
```bash
|
||||
curl http://localhost:2026/health
|
||||
```
|
||||
|
||||
**Success Criteria**: Returns health status JSON.
|
||||
|
||||
---
|
||||
|
||||
#### 5.1.4 Check LangGraph Service
|
||||
|
||||
**Steps**:
|
||||
1. Visit relevant LangGraph endpoints to verify availability
|
||||
|
||||
---
|
||||
|
||||
### 5.2 Docker Mode Health Check (When Using Docker)
|
||||
|
||||
#### 5.2.1 Check Container Status
|
||||
|
||||
**Steps**:
|
||||
1. Run `docker ps`
|
||||
2. Confirm that the following containers are running:
|
||||
- `deer-flow-nginx`
|
||||
- `deer-flow-frontend`
|
||||
- `deer-flow-gateway`
|
||||
- `deer-flow-langgraph` (if not in gateway mode)
|
||||
|
||||
---
|
||||
|
||||
#### 5.2.2 Check Frontend Service
|
||||
|
||||
**Steps**:
|
||||
1. Use curl or a browser to visit `http://localhost:2026`
|
||||
2. Verify that the page loads normally
|
||||
|
||||
**Example curl command**:
|
||||
```bash
|
||||
curl -I http://localhost:2026
|
||||
```
|
||||
|
||||
**Success Criteria**: Returns an HTTP 200 status code.
|
||||
|
||||
---
|
||||
|
||||
#### 5.2.3 Check API Gateway
|
||||
|
||||
**Steps**:
|
||||
1. Visit `http://localhost:2026/health`
|
||||
|
||||
**Example curl command**:
|
||||
```bash
|
||||
curl http://localhost:2026/health
|
||||
```
|
||||
|
||||
**Success Criteria**: Returns health status JSON.
|
||||
|
||||
---
|
||||
|
||||
#### 5.2.4 Check LangGraph Service
|
||||
|
||||
**Steps**:
|
||||
1. Visit relevant LangGraph endpoints to verify availability
|
||||
|
||||
---
|
||||
|
||||
## Optional Functional Verification
|
||||
|
||||
### 6.1 List Available Models
|
||||
|
||||
**Steps**: Verify the model list through the API or UI.
|
||||
|
||||
---
|
||||
|
||||
### 6.2 List Available Skills
|
||||
|
||||
**Steps**: Verify the skill list through the API or UI.
|
||||
|
||||
---
|
||||
|
||||
### 6.3 Simple Chat Test
|
||||
|
||||
**Steps**: Send a simple message to test the complete workflow.
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: Generate the Test Report
|
||||
|
||||
### 6.1 Collect Test Results
|
||||
|
||||
Summarize the execution status of each phase and record successful and failed items.
|
||||
|
||||
### 6.2 Record Issues
|
||||
|
||||
If anything fails, record detailed error information.
|
||||
|
||||
### 6.3 Generate the Report
|
||||
|
||||
Use the template to create a complete test report.
|
||||
|
||||
### 6.4 Provide Recommendations
|
||||
|
||||
Provide follow-up recommendations based on the test results.
|
||||
@@ -1,612 +0,0 @@
|
||||
# Troubleshooting Guide
|
||||
|
||||
This document lists common issues encountered during DeerFlow smoke testing and how to resolve them.
|
||||
|
||||
## Code Update Issues
|
||||
|
||||
### Issue: `git pull` Fails with a Merge Conflict Warning
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
error: Your local changes to the following files would be overwritten by merge
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Option A: Commit local changes first
|
||||
```bash
|
||||
git add .
|
||||
git commit -m "Save local changes"
|
||||
git pull origin main
|
||||
```
|
||||
|
||||
2. Option B: Stash local changes
|
||||
```bash
|
||||
git stash
|
||||
git pull origin main
|
||||
git stash pop # Restore changes later if needed
|
||||
```
|
||||
|
||||
3. Option C: Discard local changes (use with caution)
|
||||
```bash
|
||||
git reset --hard HEAD
|
||||
git pull origin main
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Local Mode Environment Issues
|
||||
|
||||
### Issue: Node.js Version Is Too Old
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
Node.js version is too old. Requires 22+, got x.x.x
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Install or upgrade Node.js with nvm:
|
||||
```bash
|
||||
nvm install 22
|
||||
nvm use 22
|
||||
```
|
||||
|
||||
2. Or download and install it from the official website: https://nodejs.org/
|
||||
|
||||
3. Verify the version:
|
||||
```bash
|
||||
node --version
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: pnpm Is Not Installed
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
command not found: pnpm
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Install pnpm with npm:
|
||||
```bash
|
||||
npm install -g pnpm
|
||||
```
|
||||
|
||||
2. Or use the official installation script:
|
||||
```bash
|
||||
curl -fsSL https://get.pnpm.io/install.sh | sh -
|
||||
```
|
||||
|
||||
3. Verify the installation:
|
||||
```bash
|
||||
pnpm --version
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: uv Is Not Installed
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
command not found: uv
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Use the official installation script:
|
||||
```bash
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
```
|
||||
|
||||
2. macOS users can also install it with Homebrew:
|
||||
```bash
|
||||
brew install uv
|
||||
```
|
||||
|
||||
3. Verify the installation:
|
||||
```bash
|
||||
uv --version
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: nginx Is Not Installed
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
command not found: nginx
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. macOS (Homebrew):
|
||||
```bash
|
||||
brew install nginx
|
||||
```
|
||||
|
||||
2. Ubuntu/Debian:
|
||||
```bash
|
||||
sudo apt update
|
||||
sudo apt install nginx
|
||||
```
|
||||
|
||||
3. CentOS/RHEL:
|
||||
```bash
|
||||
sudo yum install nginx
|
||||
```
|
||||
|
||||
4. Verify the installation:
|
||||
```bash
|
||||
nginx -v
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Port Is Already in Use
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
Error: listen EADDRINUSE: address already in use :::2026
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Find the process using the port:
|
||||
```bash
|
||||
lsof -i :2026 # macOS/Linux
|
||||
netstat -ano | findstr :2026 # Windows
|
||||
```
|
||||
|
||||
2. Stop that process:
|
||||
```bash
|
||||
kill -9 <PID> # macOS/Linux
|
||||
taskkill /PID <PID> /F # Windows
|
||||
```
|
||||
|
||||
3. Or stop DeerFlow services first:
|
||||
```bash
|
||||
make stop
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Local Mode Dependency Installation Issues
|
||||
|
||||
### Issue: `make install` Fails Due to Network Timeout
|
||||
|
||||
**Symptoms**:
|
||||
Network timeouts or connection failures occur during dependency installation.
|
||||
|
||||
**Solutions**:
|
||||
1. Configure pnpm to use a mirror registry:
|
||||
```bash
|
||||
pnpm config set registry https://registry.npmmirror.com
|
||||
```
|
||||
|
||||
2. Configure uv to use a mirror registry:
|
||||
```bash
|
||||
uv pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
3. Retry the installation:
|
||||
```bash
|
||||
make install
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Python Dependency Installation Fails
|
||||
|
||||
**Symptoms**:
|
||||
Errors occur during `uv sync`.
|
||||
|
||||
**Solutions**:
|
||||
1. Clean the uv cache:
|
||||
```bash
|
||||
cd backend
|
||||
uv cache clean
|
||||
```
|
||||
|
||||
2. Resync dependencies:
|
||||
```bash
|
||||
cd backend
|
||||
uv sync
|
||||
```
|
||||
|
||||
3. View detailed error logs:
|
||||
```bash
|
||||
cd backend
|
||||
uv sync --verbose
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Frontend Dependency Installation Fails
|
||||
|
||||
**Symptoms**:
|
||||
Errors occur during `pnpm install`.
|
||||
|
||||
**Solutions**:
|
||||
1. Clean the pnpm cache:
|
||||
```bash
|
||||
cd frontend
|
||||
pnpm store prune
|
||||
```
|
||||
|
||||
2. Remove node_modules and the lock file:
|
||||
```bash
|
||||
cd frontend
|
||||
rm -rf node_modules pnpm-lock.yaml
|
||||
```
|
||||
|
||||
3. Reinstall:
|
||||
```bash
|
||||
cd frontend
|
||||
pnpm install
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Local Mode Service Startup Issues
|
||||
|
||||
### Issue: Services Exit Immediately After Startup
|
||||
|
||||
**Symptoms**:
|
||||
Processes exit quickly after running `make dev-daemon`.
|
||||
|
||||
**Solutions**:
|
||||
1. Check log files:
|
||||
```bash
|
||||
tail -f logs/langgraph.log
|
||||
tail -f logs/gateway.log
|
||||
tail -f logs/frontend.log
|
||||
tail -f logs/nginx.log
|
||||
```
|
||||
|
||||
2. Check whether config.yaml is configured correctly
|
||||
3. Check environment variables in the .env file
|
||||
4. Confirm that required ports are not occupied
|
||||
5. Stop all services and restart:
|
||||
```bash
|
||||
make stop
|
||||
make dev-daemon
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Nginx Fails to Start Because Temp Directories Do Not Exist
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
nginx: [emerg] mkdir() "/opt/homebrew/var/run/nginx/client_body_temp" failed (2: No such file or directory)
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
Add local temp directory configuration to `docker/nginx/nginx.local.conf` so nginx uses the repository's temp directory.
|
||||
|
||||
Add the following at the beginning of the `http` block:
|
||||
```nginx
|
||||
client_body_temp_path temp/client_body_temp;
|
||||
proxy_temp_path temp/proxy_temp;
|
||||
fastcgi_temp_path temp/fastcgi_temp;
|
||||
uwsgi_temp_path temp/uwsgi_temp;
|
||||
scgi_temp_path temp/scgi_temp;
|
||||
```
|
||||
|
||||
Note: The `temp/` directory under the repository root is created automatically by `make dev` or `make dev-daemon`.
|
||||
|
||||
---
|
||||
|
||||
### Issue: Nginx Fails to Start (General)
|
||||
|
||||
**Symptoms**:
|
||||
The nginx process fails to start or reports an error.
|
||||
|
||||
**Solutions**:
|
||||
1. Check the nginx configuration:
|
||||
```bash
|
||||
nginx -t -c docker/nginx/nginx.local.conf -p .
|
||||
```
|
||||
|
||||
2. Check nginx logs:
|
||||
```bash
|
||||
tail -f logs/nginx.log
|
||||
```
|
||||
|
||||
3. Ensure no other nginx process is running:
|
||||
```bash
|
||||
ps aux | grep nginx
|
||||
```
|
||||
|
||||
4. If needed, stop existing nginx processes:
|
||||
```bash
|
||||
pkill -9 nginx
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Frontend Compilation Fails
|
||||
|
||||
**Symptoms**:
|
||||
Compilation errors appear in `frontend.log`.
|
||||
|
||||
**Solutions**:
|
||||
1. Check frontend logs:
|
||||
```bash
|
||||
tail -f logs/frontend.log
|
||||
```
|
||||
|
||||
2. Check whether Node.js version is 22+
|
||||
3. Reinstall frontend dependencies:
|
||||
```bash
|
||||
cd frontend
|
||||
rm -rf node_modules .next
|
||||
pnpm install
|
||||
```
|
||||
|
||||
4. Restart services:
|
||||
```bash
|
||||
make stop
|
||||
make dev-daemon
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Issue: Gateway Fails to Start
|
||||
|
||||
**Symptoms**:
|
||||
Errors appear in `gateway.log`.
|
||||
|
||||
**Solutions**:
|
||||
1. Check gateway logs:
|
||||
```bash
|
||||
tail -f logs/gateway.log
|
||||
```
|
||||
|
||||
2. Check whether config.yaml exists and has valid formatting
|
||||
3. Check whether Python dependencies are complete:
|
||||
```bash
|
||||
cd backend
|
||||
uv sync
|
||||
```
|
||||
|
||||
4. Confirm that the LangGraph service is running normally (if not in gateway mode)
|
||||
|
||||
---
|
||||
|
||||
### Issue: LangGraph Fails to Start
|
||||
|
||||
**Symptoms**:
|
||||
Errors appear in `langgraph.log`.
|
||||
|
||||
**Solutions**:
|
||||
1. Check LangGraph logs:
|
||||
```bash
|
||||
tail -f logs/langgraph.log
|
||||
```
|
||||
|
||||
2. Check config.yaml
|
||||
3. Check whether Python dependencies are complete
|
||||
4. Confirm that port 2024 is not occupied
|
||||
|
||||
---
|
||||
|
||||
## Docker-Related Issues
|
||||
|
||||
### Issue: Docker Commands Cannot Run
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
Cannot connect to the Docker daemon
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Confirm that Docker Desktop is running
|
||||
2. macOS: check whether the Docker icon appears in the top menu bar
|
||||
3. Linux: run `sudo systemctl start docker`
|
||||
4. Run `docker info` again to verify
|
||||
|
||||
---
|
||||
|
||||
### Issue: `make docker-init` Fails to Pull the Image
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
Error pulling image: connection refused
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Check network connectivity
|
||||
2. Configure a Docker image mirror if needed
|
||||
3. Check whether a proxy is required
|
||||
4. Switch to local installation mode if necessary (recommended)
|
||||
|
||||
---
|
||||
|
||||
## Configuration File Issues
|
||||
|
||||
### Issue: config.yaml Is Missing or Invalid
|
||||
|
||||
**Symptoms**:
|
||||
```
|
||||
Error: could not read config.yaml
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Regenerate the configuration file:
|
||||
```bash
|
||||
make config
|
||||
```
|
||||
|
||||
2. Check YAML syntax:
|
||||
- Make sure indentation is correct (use 2 spaces)
|
||||
- Make sure there are no tab characters
|
||||
- Check that there is a space after each colon
|
||||
|
||||
3. Use a YAML validation tool to check the format
|
||||
|
||||
---
|
||||
|
||||
### Issue: Model API Key Is Not Configured
|
||||
|
||||
**Symptoms**:
|
||||
After services start, API requests fail with authentication errors.
|
||||
|
||||
**Solutions**:
|
||||
1. Edit the .env file and add the API key:
|
||||
```bash
|
||||
OPENAI_API_KEY=your-actual-api-key-here
|
||||
```
|
||||
|
||||
2. Restart services (local mode):
|
||||
```bash
|
||||
make stop
|
||||
make dev-daemon
|
||||
```
|
||||
|
||||
3. Restart services (Docker mode):
|
||||
```bash
|
||||
make docker-stop
|
||||
make docker-start
|
||||
```
|
||||
|
||||
4. Confirm that the model configuration in config.yaml references the environment variable correctly
|
||||
|
||||
---
|
||||
|
||||
## Service Health Check Issues
|
||||
|
||||
### Issue: Frontend Page Is Not Accessible
|
||||
|
||||
**Symptoms**:
|
||||
The browser shows a connection failure when visiting http://localhost:2026.
|
||||
|
||||
**Solutions** (local mode):
|
||||
1. Confirm that the nginx process is running:
|
||||
```bash
|
||||
ps aux | grep nginx
|
||||
```
|
||||
|
||||
2. Check nginx logs:
|
||||
```bash
|
||||
tail -f logs/nginx.log
|
||||
```
|
||||
|
||||
3. Check firewall settings
|
||||
|
||||
**Solutions** (Docker mode):
|
||||
1. Confirm that the nginx container is running:
|
||||
```bash
|
||||
docker ps | grep nginx
|
||||
```
|
||||
|
||||
2. Check nginx logs:
|
||||
```bash
|
||||
cd docker && docker compose -p deer-flow-dev -f docker-compose-dev.yaml logs nginx
|
||||
```
|
||||
|
||||
3. Check firewall settings
|
||||
|
||||
---
|
||||
|
||||
### Issue: API Gateway Health Check Fails
|
||||
|
||||
**Symptoms**:
|
||||
Accessing `/health` returns an error or times out.
|
||||
|
||||
**Solutions** (local mode):
|
||||
1. Check gateway logs:
|
||||
```bash
|
||||
tail -f logs/gateway.log
|
||||
```
|
||||
|
||||
2. Confirm that config.yaml exists and has valid formatting
|
||||
3. Check whether Python dependencies are complete
|
||||
4. Confirm that the LangGraph service is running normally
|
||||
|
||||
**Solutions** (Docker mode):
|
||||
1. Check gateway container logs:
|
||||
```bash
|
||||
make docker-logs-gateway
|
||||
```
|
||||
|
||||
2. Confirm that config.yaml is mounted correctly
|
||||
3. Check whether Python dependencies are complete
|
||||
4. Confirm that the LangGraph service is running normally
|
||||
|
||||
---
|
||||
|
||||
## Common Diagnostic Commands
|
||||
|
||||
### Local Mode Diagnostics
|
||||
|
||||
#### View All Service Processes
|
||||
```bash
|
||||
ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
|
||||
```
|
||||
|
||||
#### View Service Logs
|
||||
```bash
|
||||
# View all logs
|
||||
tail -f logs/*.log
|
||||
|
||||
# View specific service logs
|
||||
tail -f logs/langgraph.log
|
||||
tail -f logs/gateway.log
|
||||
tail -f logs/frontend.log
|
||||
tail -f logs/nginx.log
|
||||
```
|
||||
|
||||
#### Stop All Services
|
||||
```bash
|
||||
make stop
|
||||
```
|
||||
|
||||
#### Fully Reset the Local Environment
|
||||
```bash
|
||||
make stop
|
||||
make clean
|
||||
make config
|
||||
make install
|
||||
make dev-daemon
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Docker Mode Diagnostics
|
||||
|
||||
#### View All Container Status
|
||||
```bash
|
||||
docker ps -a
|
||||
```
|
||||
|
||||
#### View Container Resource Usage
|
||||
```bash
|
||||
docker stats
|
||||
```
|
||||
|
||||
#### Enter a Container for Debugging
|
||||
```bash
|
||||
docker exec -it deer-flow-gateway sh
|
||||
```
|
||||
|
||||
#### Clean Up All DeerFlow-Related Containers and Images
|
||||
```bash
|
||||
make docker-stop
|
||||
cd docker && docker compose -p deer-flow-dev -f docker-compose-dev.yaml down -v
|
||||
```
|
||||
|
||||
#### Fully Reset the Docker Environment
|
||||
```bash
|
||||
make docker-stop
|
||||
make clean
|
||||
make config
|
||||
make docker-init
|
||||
make docker-start
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Get More Help
|
||||
|
||||
If the solutions above do not resolve the issue:
|
||||
1. Check the GitHub issues for the project: https://github.com/bytedance/deer-flow/issues
|
||||
2. Review the project documentation: README.md and the `backend/docs/` directory
|
||||
3. Open a new issue and include detailed error logs
|
||||
@@ -1,80 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Checking Docker Environment"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Check whether Docker is installed
|
||||
if command -v docker >/dev/null 2>&1; then
|
||||
echo "✓ Docker is installed"
|
||||
docker --version
|
||||
else
|
||||
echo "✗ Docker is not installed"
|
||||
exit 1
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check the Docker daemon
|
||||
if docker info >/dev/null 2>&1; then
|
||||
echo "✓ Docker daemon is running normally"
|
||||
else
|
||||
echo "✗ Docker daemon is not running"
|
||||
echo " Please start Docker Desktop or the Docker service"
|
||||
exit 1
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check Docker Compose
|
||||
if docker compose version >/dev/null 2>&1; then
|
||||
echo "✓ Docker Compose is available"
|
||||
docker compose version
|
||||
else
|
||||
echo "✗ Docker Compose is not available"
|
||||
exit 1
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check port 2026
|
||||
if ! command -v lsof >/dev/null 2>&1; then
|
||||
echo "✗ lsof is required to check whether port 2026 is available"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
port_2026_usage="$(lsof -nP -iTCP:2026 -sTCP:LISTEN 2>/dev/null || true)"
|
||||
if [ -n "$port_2026_usage" ]; then
|
||||
echo "⚠ Port 2026 is already in use"
|
||||
echo " Occupying process:"
|
||||
echo "$port_2026_usage"
|
||||
|
||||
deerflow_process_found=0
|
||||
while IFS= read -r pid; do
|
||||
if [ -z "$pid" ]; then
|
||||
continue
|
||||
fi
|
||||
|
||||
process_command="$(ps -p "$pid" -o command= 2>/dev/null || true)"
|
||||
case "$process_command" in
|
||||
*[Dd]eer[Ff]low*|*[Dd]eerflow*|*[Nn]ginx*deerflow*|*deerflow/*[Nn]ginx*)
|
||||
deerflow_process_found=1
|
||||
;;
|
||||
esac
|
||||
done <<EOF
|
||||
$(printf '%s\n' "$port_2026_usage" | awk 'NR > 1 {print $2}')
|
||||
EOF
|
||||
|
||||
if [ "$deerflow_process_found" -eq 1 ]; then
|
||||
echo "✓ Port 2026 is occupied by DeerFlow"
|
||||
else
|
||||
echo "✗ Port 2026 must be free before starting DeerFlow"
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
echo "✓ Port 2026 is available"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Docker Environment Check Complete"
|
||||
echo "=========================================="
|
||||
@@ -1,93 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Checking Local Development Environment"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
all_passed=true
|
||||
|
||||
# Check Node.js
|
||||
echo "1. Checking Node.js..."
|
||||
if command -v node >/dev/null 2>&1; then
|
||||
NODE_VERSION=$(node --version | sed 's/v//')
|
||||
NODE_MAJOR=$(echo "$NODE_VERSION" | cut -d. -f1)
|
||||
if [ "$NODE_MAJOR" -ge 22 ]; then
|
||||
echo "✓ Node.js is installed (version: $NODE_VERSION)"
|
||||
else
|
||||
echo "✗ Node.js version is too old (current: $NODE_VERSION, required: 22+)"
|
||||
all_passed=false
|
||||
fi
|
||||
else
|
||||
echo "✗ Node.js is not installed"
|
||||
all_passed=false
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check pnpm
|
||||
echo "2. Checking pnpm..."
|
||||
if command -v pnpm >/dev/null 2>&1; then
|
||||
echo "✓ pnpm is installed (version: $(pnpm --version))"
|
||||
else
|
||||
echo "✗ pnpm is not installed"
|
||||
echo " Install command: npm install -g pnpm"
|
||||
all_passed=false
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check uv
|
||||
echo "3. Checking uv..."
|
||||
if command -v uv >/dev/null 2>&1; then
|
||||
echo "✓ uv is installed (version: $(uv --version))"
|
||||
else
|
||||
echo "✗ uv is not installed"
|
||||
all_passed=false
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check nginx
|
||||
echo "4. Checking nginx..."
|
||||
if command -v nginx >/dev/null 2>&1; then
|
||||
echo "✓ nginx is installed (version: $(nginx -v 2>&1))"
|
||||
else
|
||||
echo "✗ nginx is not installed"
|
||||
echo " macOS: brew install nginx"
|
||||
echo " Linux: install it with the system package manager"
|
||||
all_passed=false
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check ports
|
||||
echo "5. Checking ports..."
|
||||
if ! command -v lsof >/dev/null 2>&1; then
|
||||
echo "✗ lsof is not installed, so port availability cannot be verified"
|
||||
echo " Install lsof and rerun this check"
|
||||
all_passed=false
|
||||
else
|
||||
for port in 2026 3000 8001 2024; do
|
||||
if lsof -i :$port >/dev/null 2>&1; then
|
||||
echo "⚠ Port $port is already in use:"
|
||||
lsof -i :$port | head -2
|
||||
all_passed=false
|
||||
else
|
||||
echo "✓ Port $port is available"
|
||||
fi
|
||||
done
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Summary
|
||||
echo "=========================================="
|
||||
echo " Environment Check Summary"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
if [ "$all_passed" = true ]; then
|
||||
echo "✅ All environment checks passed!"
|
||||
echo ""
|
||||
echo "Next step: run make install to install dependencies"
|
||||
exit 0
|
||||
else
|
||||
echo "❌ Some checks failed. Please fix the issues above first"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,65 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Docker Deployment"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Check config.yaml
|
||||
if [ ! -f "config.yaml" ]; then
|
||||
echo "config.yaml does not exist. Generating it..."
|
||||
make config
|
||||
echo ""
|
||||
echo "⚠ Please edit config.yaml to configure your models and API keys"
|
||||
echo " Then run this script again"
|
||||
exit 1
|
||||
else
|
||||
echo "✓ config.yaml exists"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check the .env file
|
||||
if [ ! -f ".env" ]; then
|
||||
echo ".env does not exist. Copying it from the example..."
|
||||
if [ -f ".env.example" ]; then
|
||||
cp .env.example .env
|
||||
echo "✓ Created the .env file"
|
||||
else
|
||||
echo "⚠ .env.example does not exist. Please create the .env file manually"
|
||||
fi
|
||||
else
|
||||
echo "✓ .env file exists"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check the frontend .env file
|
||||
if [ ! -f "frontend/.env" ]; then
|
||||
echo "frontend/.env does not exist. Copying it from the example..."
|
||||
if [ -f "frontend/.env.example" ]; then
|
||||
cp frontend/.env.example frontend/.env
|
||||
echo "✓ Created the frontend/.env file"
|
||||
else
|
||||
echo "⚠ frontend/.env.example does not exist. Please create frontend/.env manually"
|
||||
fi
|
||||
else
|
||||
echo "✓ frontend/.env file exists"
|
||||
fi
|
||||
echo ""
|
||||
# Initialize the Docker environment
|
||||
echo "Initializing the Docker environment..."
|
||||
make docker-init
|
||||
echo ""
|
||||
|
||||
# Start Docker services
|
||||
echo "Starting Docker services..."
|
||||
make docker-start
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Deployment Complete"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
echo "🌐 Access URL: http://localhost:2026"
|
||||
echo "📋 View logs: make docker-logs"
|
||||
echo "🛑 Stop services: make docker-stop"
|
||||
@@ -1,63 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Local Mode Deployment"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Check config.yaml
|
||||
if [ ! -f "config.yaml" ]; then
|
||||
echo "config.yaml does not exist. Generating it..."
|
||||
make config
|
||||
echo ""
|
||||
echo "⚠ Please edit config.yaml to configure your models and API keys"
|
||||
echo " Then run this script again"
|
||||
exit 1
|
||||
else
|
||||
echo "✓ config.yaml exists"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check the .env file
|
||||
if [ ! -f ".env" ]; then
|
||||
echo ".env does not exist. Copying it from the example..."
|
||||
if [ -f ".env.example" ]; then
|
||||
cp .env.example .env
|
||||
echo "✓ Created the .env file"
|
||||
else
|
||||
echo "⚠ .env.example does not exist. Please create the .env file manually"
|
||||
fi
|
||||
else
|
||||
echo "✓ .env file exists"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Check dependencies
|
||||
echo "Checking dependencies..."
|
||||
make check
|
||||
echo ""
|
||||
|
||||
# Install dependencies
|
||||
echo "Installing dependencies..."
|
||||
make install
|
||||
echo ""
|
||||
|
||||
# Start services
|
||||
echo "Starting services (background mode)..."
|
||||
make dev-daemon
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Deployment Complete"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
echo "🌐 Access URL: http://localhost:2026"
|
||||
echo "📋 View logs:"
|
||||
echo " - logs/langgraph.log"
|
||||
echo " - logs/gateway.log"
|
||||
echo " - logs/frontend.log"
|
||||
echo " - logs/nginx.log"
|
||||
echo "🛑 Stop services: make stop"
|
||||
echo ""
|
||||
echo "Please wait 90-120 seconds for all services to start completely, then run the health check"
|
||||
@@ -1,70 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set +e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Frontend Page Smoke Check"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
BASE_URL="${BASE_URL:-http://localhost:2026}"
|
||||
DOC_PATH="${DOC_PATH:-/en/docs}"
|
||||
|
||||
all_passed=true
|
||||
|
||||
check_status() {
|
||||
local name="$1"
|
||||
local url="$2"
|
||||
local expected_re="$3"
|
||||
|
||||
local status
|
||||
status="$(curl -s -o /dev/null -w "%{http_code}" -L "$url")"
|
||||
if echo "$status" | grep -Eq "$expected_re"; then
|
||||
echo "✓ $name ($url) -> $status"
|
||||
else
|
||||
echo "✗ $name ($url) -> $status (expected: $expected_re)"
|
||||
all_passed=false
|
||||
fi
|
||||
}
|
||||
|
||||
check_final_url() {
|
||||
local name="$1"
|
||||
local url="$2"
|
||||
local expected_path_re="$3"
|
||||
|
||||
local effective
|
||||
effective="$(curl -s -o /dev/null -w "%{url_effective}" -L "$url")"
|
||||
if echo "$effective" | grep -Eq "$expected_path_re"; then
|
||||
echo "✓ $name redirect target -> $effective"
|
||||
else
|
||||
echo "✗ $name redirect target -> $effective (expected path: $expected_path_re)"
|
||||
all_passed=false
|
||||
fi
|
||||
}
|
||||
|
||||
echo "1. Checking entry pages..."
|
||||
check_status "Landing page" "${BASE_URL}/" "200"
|
||||
check_status "Workspace redirect" "${BASE_URL}/workspace" "200|301|302|307|308"
|
||||
check_final_url "Workspace redirect" "${BASE_URL}/workspace" "/workspace/chats/"
|
||||
echo ""
|
||||
|
||||
echo "2. Checking key workspace routes..."
|
||||
check_status "New chat page" "${BASE_URL}/workspace/chats/new" "200"
|
||||
check_status "Chats list page" "${BASE_URL}/workspace/chats" "200"
|
||||
check_status "Agents gallery page" "${BASE_URL}/workspace/agents" "200"
|
||||
echo ""
|
||||
|
||||
echo "3. Checking docs route (optional)..."
|
||||
check_status "Docs page" "${BASE_URL}${DOC_PATH}" "200|404"
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Frontend Smoke Check Summary"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
if [ "$all_passed" = true ]; then
|
||||
echo "✅ Frontend smoke checks passed!"
|
||||
exit 0
|
||||
else
|
||||
echo "❌ Frontend smoke checks failed"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,125 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set +e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Service Health Check"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
all_passed=true
|
||||
mode="${SMOKE_TEST_MODE:-auto}"
|
||||
summary_hint="make logs"
|
||||
|
||||
print_step() {
|
||||
echo "$1"
|
||||
}
|
||||
|
||||
check_http_status() {
|
||||
local name="$1"
|
||||
local url="$2"
|
||||
local expected_re="$3"
|
||||
local status
|
||||
|
||||
status="$(curl -s -o /dev/null -w "%{http_code}" "$url" 2>/dev/null)"
|
||||
if echo "$status" | grep -Eq "$expected_re"; then
|
||||
echo "✓ $name is accessible ($url -> $status)"
|
||||
else
|
||||
echo "✗ $name is not accessible ($url -> ${status:-000})"
|
||||
all_passed=false
|
||||
fi
|
||||
}
|
||||
|
||||
check_listen_port() {
|
||||
local name="$1"
|
||||
local port="$2"
|
||||
|
||||
if lsof -nP -iTCP:"$port" -sTCP:LISTEN >/dev/null 2>&1; then
|
||||
echo "✓ $name is listening on port $port"
|
||||
else
|
||||
echo "✗ $name is not listening on port $port"
|
||||
all_passed=false
|
||||
fi
|
||||
}
|
||||
|
||||
docker_available() {
|
||||
command -v docker >/dev/null 2>&1 && docker info >/dev/null 2>&1
|
||||
}
|
||||
|
||||
detect_mode() {
|
||||
case "$mode" in
|
||||
local|docker)
|
||||
echo "$mode"
|
||||
return
|
||||
;;
|
||||
esac
|
||||
|
||||
if docker_available && docker ps --format "{{.Names}}" | grep -q "deer-flow"; then
|
||||
echo "docker"
|
||||
else
|
||||
echo "local"
|
||||
fi
|
||||
}
|
||||
|
||||
mode="$(detect_mode)"
|
||||
|
||||
echo "Deployment mode: $mode"
|
||||
echo ""
|
||||
|
||||
if [ "$mode" = "docker" ]; then
|
||||
summary_hint="make docker-logs"
|
||||
print_step "1. Checking container status..."
|
||||
if docker ps --format "{{.Names}}" | grep -q "deer-flow"; then
|
||||
echo "✓ Containers are running:"
|
||||
docker ps --format " - {{.Names}} ({{.Status}})"
|
||||
else
|
||||
echo "✗ No DeerFlow-related containers are running"
|
||||
all_passed=false
|
||||
fi
|
||||
else
|
||||
summary_hint="logs/{langgraph,gateway,frontend,nginx}.log"
|
||||
print_step "1. Checking local service ports..."
|
||||
check_listen_port "Nginx" 2026
|
||||
check_listen_port "Frontend" 3000
|
||||
check_listen_port "Gateway" 8001
|
||||
check_listen_port "LangGraph" 2024
|
||||
fi
|
||||
echo ""
|
||||
|
||||
echo "2. Waiting for services to fully start (30 seconds)..."
|
||||
sleep 30
|
||||
echo ""
|
||||
|
||||
echo "3. Checking frontend service..."
|
||||
check_http_status "Frontend service" "http://localhost:2026" "200|301|302|307|308"
|
||||
echo ""
|
||||
|
||||
echo "4. Checking API Gateway..."
|
||||
health_response=$(curl -s http://localhost:2026/health 2>/dev/null)
|
||||
if [ $? -eq 0 ] && [ -n "$health_response" ]; then
|
||||
echo "✓ API Gateway health check passed"
|
||||
echo " Response: $health_response"
|
||||
else
|
||||
echo "✗ API Gateway health check failed"
|
||||
all_passed=false
|
||||
fi
|
||||
echo ""
|
||||
|
||||
echo "5. Checking LangGraph service..."
|
||||
check_http_status "LangGraph service" "http://localhost:2024/" "200|301|302|307|308|404"
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Health Check Summary"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
if [ "$all_passed" = true ]; then
|
||||
echo "✅ All checks passed!"
|
||||
echo ""
|
||||
echo "🌐 Application URL: http://localhost:2026"
|
||||
exit 0
|
||||
else
|
||||
echo "❌ Some checks failed"
|
||||
echo ""
|
||||
echo "Please review: $summary_hint"
|
||||
exit 1
|
||||
fi
|
||||
@@ -1,49 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo " Pulling the Latest Code"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Check whether the current directory is a Git repository
|
||||
if [ ! -d ".git" ]; then
|
||||
echo "✗ The current directory is not a Git repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check Git status
|
||||
echo "Checking Git status..."
|
||||
if git status --porcelain | grep -q .; then
|
||||
echo "⚠ Uncommitted changes detected:"
|
||||
git status --short
|
||||
echo ""
|
||||
echo "Please commit or stash your changes before continuing"
|
||||
echo "Options:"
|
||||
echo " 1. git add . && git commit -m 'Save changes'"
|
||||
echo " 2. git stash (stash changes and restore them later)"
|
||||
echo " 3. git reset --hard HEAD (discard local changes - use with caution)"
|
||||
exit 1
|
||||
else
|
||||
echo "✓ Working tree is clean"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
# Fetch remote updates
|
||||
echo "Fetching remote updates..."
|
||||
git fetch origin main
|
||||
echo ""
|
||||
|
||||
# Pull the latest code
|
||||
echo "Pulling the latest code..."
|
||||
git pull origin main
|
||||
echo ""
|
||||
|
||||
# Show the latest commit
|
||||
echo "Latest commit:"
|
||||
git log -1 --oneline
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo " Code Update Complete"
|
||||
echo "=========================================="
|
||||
@@ -1,180 +0,0 @@
|
||||
# DeerFlow Smoke Test Report
|
||||
|
||||
**Test Date**: {{test_date}}
|
||||
**Test Environment**: {{test_environment}}
|
||||
**Deployment Mode**: Docker
|
||||
**Test Version**: {{git_commit}}
|
||||
|
||||
---
|
||||
|
||||
## Execution Summary
|
||||
|
||||
| Metric | Status |
|
||||
|------|------|
|
||||
| Total Test Phases | 6 |
|
||||
| Passed Phases | {{passed_stages}} |
|
||||
| Failed Phases | {{failed_stages}} |
|
||||
| Overall Conclusion | **{{overall_status}}** |
|
||||
|
||||
### Key Test Cases
|
||||
|
||||
| Case | Result | Details |
|
||||
|------|--------|---------|
|
||||
| Code update check | {{case_code_update}} | {{case_code_update_details}} |
|
||||
| Environment check | {{case_env_check}} | {{case_env_check_details}} |
|
||||
| Configuration preparation | {{case_config_prep}} | {{case_config_prep_details}} |
|
||||
| Deployment | {{case_deploy}} | {{case_deploy_details}} |
|
||||
| Health check | {{case_health_check}} | {{case_health_check_details}} |
|
||||
| Frontend routes | {{case_frontend_routes_overall}} | {{case_frontend_routes_details}} |
|
||||
|
||||
---
|
||||
|
||||
## Detailed Test Results
|
||||
|
||||
### Phase 1: Code Update Check
|
||||
|
||||
- [x] Confirm current directory - {{status_dir_check}}
|
||||
- [x] Check Git status - {{status_git_status}}
|
||||
- [x] Pull latest code - {{status_git_pull}}
|
||||
- [x] Confirm code update - {{status_git_verify}}
|
||||
|
||||
**Phase Status**: {{stage1_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: Docker Environment Check
|
||||
|
||||
- [x] Docker version - {{status_docker_version}}
|
||||
- [x] Docker daemon - {{status_docker_daemon}}
|
||||
- [x] Docker Compose - {{status_docker_compose}}
|
||||
- [x] Port check - {{status_port_check}}
|
||||
|
||||
**Phase Status**: {{stage2_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 3: Configuration Preparation
|
||||
|
||||
- [x] config.yaml - {{status_config_yaml}}
|
||||
- [x] .env file - {{status_env_file}}
|
||||
- [x] Model configuration - {{status_model_config}}
|
||||
|
||||
**Phase Status**: {{stage3_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 4: Docker Deployment
|
||||
|
||||
- [x] docker-init - {{status_docker_init}}
|
||||
- [x] docker-start - {{status_docker_start}}
|
||||
- [x] Service startup wait - {{status_wait_startup}}
|
||||
|
||||
**Phase Status**: {{stage4_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 5: Service Health Check
|
||||
|
||||
- [x] Container status - {{status_containers}}
|
||||
- [x] Frontend service - {{status_frontend}}
|
||||
- [x] API Gateway - {{status_api_gateway}}
|
||||
- [x] LangGraph service - {{status_langgraph}}
|
||||
|
||||
**Phase Status**: {{stage5_status}}
|
||||
|
||||
---
|
||||
|
||||
### Frontend Routes Smoke Results
|
||||
|
||||
| Route | Status | Details |
|
||||
|-------|--------|---------|
|
||||
| Landing `/` | {{landing_status}} | {{landing_details}} |
|
||||
| Workspace redirect `/workspace` | {{workspace_redirect_status}} | target {{workspace_redirect_target}} |
|
||||
| New chat `/workspace/chats/new` | {{new_chat_status}} | {{new_chat_details}} |
|
||||
| Chats list `/workspace/chats` | {{chats_list_status}} | {{chats_list_details}} |
|
||||
| Agents gallery `/workspace/agents` | {{agents_gallery_status}} | {{agents_gallery_details}} |
|
||||
| Docs `{{docs_path}}` | {{docs_status}} | {{docs_details}} |
|
||||
|
||||
**Summary**: {{frontend_routes_summary}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 6: Test Report Generation
|
||||
|
||||
- [x] Result summary - {{status_summary}}
|
||||
- [x] Issue log - {{status_issues}}
|
||||
- [x] Report generation - {{status_report}}
|
||||
|
||||
**Phase Status**: {{stage6_status}}
|
||||
|
||||
---
|
||||
|
||||
## Issue Log
|
||||
|
||||
### Issue 1
|
||||
**Description**: {{issue1_description}}
|
||||
**Severity**: {{issue1_severity}}
|
||||
**Solution**: {{issue1_solution}}
|
||||
|
||||
---
|
||||
|
||||
## Environment Information
|
||||
|
||||
### Docker Version
|
||||
```text
|
||||
{{docker_version_output}}
|
||||
```
|
||||
|
||||
### Git Information
|
||||
```text
|
||||
Repository: {{git_repo}}
|
||||
Branch: {{git_branch}}
|
||||
Commit: {{git_commit}}
|
||||
Commit Message: {{git_commit_message}}
|
||||
```
|
||||
|
||||
### Configuration Summary
|
||||
- config.yaml exists: {{config_exists}}
|
||||
- .env file exists: {{env_exists}}
|
||||
- Number of configured models: {{model_count}}
|
||||
|
||||
---
|
||||
|
||||
## Container Status
|
||||
|
||||
| Container Name | Status | Uptime |
|
||||
|----------|------|----------|
|
||||
| deer-flow-nginx | {{nginx_status}} | {{nginx_uptime}} |
|
||||
| deer-flow-frontend | {{frontend_status}} | {{frontend_uptime}} |
|
||||
| deer-flow-gateway | {{gateway_status}} | {{gateway_uptime}} |
|
||||
| deer-flow-langgraph | {{langgraph_status}} | {{langgraph_uptime}} |
|
||||
|
||||
---
|
||||
|
||||
## Recommendations and Next Steps
|
||||
|
||||
### If the Test Passes
|
||||
1. [ ] Visit http://localhost:2026 to start using DeerFlow
|
||||
2. [ ] Configure your preferred model if it is not configured yet
|
||||
3. [ ] Explore available skills
|
||||
4. [ ] Refer to the documentation to learn more features
|
||||
|
||||
### If the Test Fails
|
||||
1. [ ] Review references/troubleshooting.md for common solutions
|
||||
2. [ ] Check Docker logs: `make docker-logs`
|
||||
3. [ ] Verify configuration file format and content
|
||||
4. [ ] If needed, fully reset the environment: `make clean && make config && make docker-init && make docker-start`
|
||||
|
||||
---
|
||||
|
||||
## Appendix
|
||||
|
||||
### Full Logs
|
||||
{{full_logs}}
|
||||
|
||||
### Tester
|
||||
{{tester_name}}
|
||||
|
||||
---
|
||||
|
||||
*Report generated at: {{report_time}}*
|
||||
@@ -1,185 +0,0 @@
|
||||
# DeerFlow Smoke Test Report
|
||||
|
||||
**Test Date**: {{test_date}}
|
||||
**Test Environment**: {{test_environment}}
|
||||
**Deployment Mode**: Local
|
||||
**Test Version**: {{git_commit}}
|
||||
|
||||
---
|
||||
|
||||
## Execution Summary
|
||||
|
||||
| Metric | Status |
|
||||
|------|------|
|
||||
| Total Test Phases | 6 |
|
||||
| Passed Phases | {{passed_stages}} |
|
||||
| Failed Phases | {{failed_stages}} |
|
||||
| Overall Conclusion | **{{overall_status}}** |
|
||||
|
||||
### Key Test Cases
|
||||
|
||||
| Case | Result | Details |
|
||||
|------|--------|---------|
|
||||
| Code update check | {{case_code_update}} | {{case_code_update_details}} |
|
||||
| Environment check | {{case_env_check}} | {{case_env_check_details}} |
|
||||
| Configuration preparation | {{case_config_prep}} | {{case_config_prep_details}} |
|
||||
| Deployment | {{case_deploy}} | {{case_deploy_details}} |
|
||||
| Health check | {{case_health_check}} | {{case_health_check_details}} |
|
||||
| Frontend routes | {{case_frontend_routes_overall}} | {{case_frontend_routes_details}} |
|
||||
|
||||
---
|
||||
|
||||
## Detailed Test Results
|
||||
|
||||
### Phase 1: Code Update Check
|
||||
|
||||
- [x] Confirm current directory - {{status_dir_check}}
|
||||
- [x] Check Git status - {{status_git_status}}
|
||||
- [x] Pull latest code - {{status_git_pull}}
|
||||
- [x] Confirm code update - {{status_git_verify}}
|
||||
|
||||
**Phase Status**: {{stage1_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: Local Environment Check
|
||||
|
||||
- [x] Node.js version - {{status_node_version}}
|
||||
- [x] pnpm - {{status_pnpm}}
|
||||
- [x] uv - {{status_uv}}
|
||||
- [x] nginx - {{status_nginx}}
|
||||
- [x] Port check - {{status_port_check}}
|
||||
|
||||
**Phase Status**: {{stage2_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 3: Configuration Preparation
|
||||
|
||||
- [x] config.yaml - {{status_config_yaml}}
|
||||
- [x] .env file - {{status_env_file}}
|
||||
- [x] Model configuration - {{status_model_config}}
|
||||
|
||||
**Phase Status**: {{stage3_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 4: Local Deployment
|
||||
|
||||
- [x] make check - {{status_make_check}}
|
||||
- [x] make install - {{status_make_install}}
|
||||
- [x] make dev-daemon / make dev - {{status_local_start}}
|
||||
- [x] Service startup wait - {{status_wait_startup}}
|
||||
|
||||
**Phase Status**: {{stage4_status}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 5: Service Health Check
|
||||
|
||||
- [x] Process status - {{status_processes}}
|
||||
- [x] Frontend service - {{status_frontend}}
|
||||
- [x] API Gateway - {{status_api_gateway}}
|
||||
- [x] LangGraph service - {{status_langgraph}}
|
||||
|
||||
**Phase Status**: {{stage5_status}}
|
||||
|
||||
---
|
||||
|
||||
### Frontend Routes Smoke Results
|
||||
|
||||
| Route | Status | Details |
|
||||
|-------|--------|---------|
|
||||
| Landing `/` | {{landing_status}} | {{landing_details}} |
|
||||
| Workspace redirect `/workspace` | {{workspace_redirect_status}} | target {{workspace_redirect_target}} |
|
||||
| New chat `/workspace/chats/new` | {{new_chat_status}} | {{new_chat_details}} |
|
||||
| Chats list `/workspace/chats` | {{chats_list_status}} | {{chats_list_details}} |
|
||||
| Agents gallery `/workspace/agents` | {{agents_gallery_status}} | {{agents_gallery_details}} |
|
||||
| Docs `{{docs_path}}` | {{docs_status}} | {{docs_details}} |
|
||||
|
||||
**Summary**: {{frontend_routes_summary}}
|
||||
|
||||
---
|
||||
|
||||
### Phase 6: Test Report Generation
|
||||
|
||||
- [x] Result summary - {{status_summary}}
|
||||
- [x] Issue log - {{status_issues}}
|
||||
- [x] Report generation - {{status_report}}
|
||||
|
||||
**Phase Status**: {{stage6_status}}
|
||||
|
||||
---
|
||||
|
||||
## Issue Log
|
||||
|
||||
### Issue 1
|
||||
**Description**: {{issue1_description}}
|
||||
**Severity**: {{issue1_severity}}
|
||||
**Solution**: {{issue1_solution}}
|
||||
|
||||
---
|
||||
|
||||
## Environment Information
|
||||
|
||||
### Local Dependency Versions
|
||||
```text
|
||||
Node.js: {{node_version_output}}
|
||||
pnpm: {{pnpm_version_output}}
|
||||
uv: {{uv_version_output}}
|
||||
nginx: {{nginx_version_output}}
|
||||
```
|
||||
|
||||
### Git Information
|
||||
```text
|
||||
Repository: {{git_repo}}
|
||||
Branch: {{git_branch}}
|
||||
Commit: {{git_commit}}
|
||||
Commit Message: {{git_commit_message}}
|
||||
```
|
||||
|
||||
### Configuration Summary
|
||||
- config.yaml exists: {{config_exists}}
|
||||
- .env file exists: {{env_exists}}
|
||||
- Number of configured models: {{model_count}}
|
||||
|
||||
---
|
||||
|
||||
## Local Service Status
|
||||
|
||||
| Service | Status | Endpoint |
|
||||
|---------|--------|----------|
|
||||
| Nginx | {{nginx_status}} | {{nginx_endpoint}} |
|
||||
| Frontend | {{frontend_status}} | {{frontend_endpoint}} |
|
||||
| Gateway | {{gateway_status}} | {{gateway_endpoint}} |
|
||||
| LangGraph | {{langgraph_status}} | {{langgraph_endpoint}} |
|
||||
|
||||
---
|
||||
|
||||
## Recommendations and Next Steps
|
||||
|
||||
### If the Test Passes
|
||||
1. [ ] Visit http://localhost:2026 to start using DeerFlow
|
||||
2. [ ] Configure your preferred model if it is not configured yet
|
||||
3. [ ] Explore available skills
|
||||
4. [ ] Refer to the documentation to learn more features
|
||||
|
||||
### If the Test Fails
|
||||
1. [ ] Review references/troubleshooting.md for common solutions
|
||||
2. [ ] Check local logs: `logs/{langgraph,gateway,frontend,nginx}.log`
|
||||
3. [ ] Verify configuration file format and content
|
||||
4. [ ] If needed, fully reset the environment: `make stop && make clean && make install && make dev-daemon`
|
||||
|
||||
---
|
||||
|
||||
## Appendix
|
||||
|
||||
### Full Logs
|
||||
{{full_logs}}
|
||||
|
||||
### Tester
|
||||
{{tester_name}}
|
||||
|
||||
---
|
||||
|
||||
*Report generated at: {{report_time}}*
|
||||
@@ -3,7 +3,6 @@ Dockerfile
|
||||
.dockerignore
|
||||
.git
|
||||
.gitignore
|
||||
docker/
|
||||
|
||||
# Python
|
||||
__pycache__/
|
||||
@@ -52,20 +51,3 @@ examples/
|
||||
assets/
|
||||
tests/
|
||||
*.log
|
||||
|
||||
# Exclude directories not needed in Docker context
|
||||
# Frontend build only needs frontend/
|
||||
# Backend build only needs backend/
|
||||
scripts/
|
||||
logs/
|
||||
docker/
|
||||
skills/
|
||||
frontend/.next
|
||||
frontend/node_modules
|
||||
backend/.venv
|
||||
backend/htmlcov
|
||||
backend/.coverage
|
||||
*.md
|
||||
!README.md
|
||||
!frontend/README.md
|
||||
!backend/README.md
|
||||
|
||||
+45
-31
@@ -1,38 +1,52 @@
|
||||
# TAVILY API Key
|
||||
TAVILY_API_KEY=your-tavily-api-key
|
||||
# Application Settings
|
||||
DEBUG=True
|
||||
APP_ENV=development
|
||||
|
||||
# Jina API Key
|
||||
JINA_API_KEY=your-jina-api-key
|
||||
# docker build args
|
||||
NEXT_PUBLIC_API_URL="http://localhost:8000/api"
|
||||
|
||||
# InfoQuest API Key
|
||||
INFOQUEST_API_KEY=your-infoquest-api-key
|
||||
# CORS Origins (comma-separated) - e.g., http://localhost:3000,http://localhost:3001
|
||||
# CORS_ORIGINS=http://localhost:3000
|
||||
AGENT_RECURSION_LIMIT=30
|
||||
|
||||
# Optional:
|
||||
# FIRECRAWL_API_KEY=your-firecrawl-api-key
|
||||
# VOLCENGINE_API_KEY=your-volcengine-api-key
|
||||
# OPENAI_API_KEY=your-openai-api-key
|
||||
# GEMINI_API_KEY=your-gemini-api-key
|
||||
# DEEPSEEK_API_KEY=your-deepseek-api-key
|
||||
# NOVITA_API_KEY=your-novita-api-key # OpenAI-compatible, see https://novita.ai
|
||||
# MINIMAX_API_KEY=your-minimax-api-key # OpenAI-compatible, see https://platform.minimax.io
|
||||
# VLLM_API_KEY=your-vllm-api-key # OpenAI-compatible
|
||||
# FEISHU_APP_ID=your-feishu-app-id
|
||||
# FEISHU_APP_SECRET=your-feishu-app-secret
|
||||
# CORS settings
|
||||
# Comma-separated list of allowed origins for CORS requests
|
||||
# Example: ALLOWED_ORIGINS=http://localhost:3000,http://example.com
|
||||
ALLOWED_ORIGINS=http://localhost:3000
|
||||
|
||||
# SLACK_BOT_TOKEN=your-slack-bot-token
|
||||
# SLACK_APP_TOKEN=your-slack-app-token
|
||||
# TELEGRAM_BOT_TOKEN=your-telegram-bot-token
|
||||
# DISCORD_BOT_TOKEN=your-discord-bot-token
|
||||
# Enable or disable MCP server configuration, the default is false.
|
||||
# Please enable this feature before securing your front-end and back-end in a managed environment.
|
||||
|
||||
# Enable LangSmith to monitor and debug your LLM calls, agent runs, and tool executions.
|
||||
# Otherwise, you system could be compromised.
|
||||
ENABLE_MCP_SERVER_CONFIGURATION=false
|
||||
|
||||
# Search Engine, Supported values: tavily (recommended), duckduckgo, brave_search, arxiv
|
||||
SEARCH_API=tavily
|
||||
TAVILY_API_KEY=tvly-xxx
|
||||
# BRAVE_SEARCH_API_KEY=xxx # Required only if SEARCH_API is brave_search
|
||||
# JINA_API_KEY=jina_xxx # Optional, default is None
|
||||
|
||||
# Optional, RAG provider
|
||||
# RAG_PROVIDER=vikingdb_knowledge_base
|
||||
# VIKINGDB_KNOWLEDGE_BASE_API_URL="api-knowledgebase.mlp.cn-beijing.volces.com"
|
||||
# VIKINGDB_KNOWLEDGE_BASE_API_AK="AKxxx"
|
||||
# VIKINGDB_KNOWLEDGE_BASE_API_SK=""
|
||||
# VIKINGDB_KNOWLEDGE_BASE_RETRIEVAL_SIZE=15
|
||||
|
||||
# RAG_PROVIDER=ragflow
|
||||
# RAGFLOW_API_URL="http://localhost:9388"
|
||||
# RAGFLOW_API_KEY="ragflow-xxx"
|
||||
# RAGFLOW_RETRIEVAL_SIZE=10
|
||||
|
||||
# Optional, volcengine TTS for generating podcast
|
||||
VOLCENGINE_TTS_APPID=xxx
|
||||
VOLCENGINE_TTS_ACCESS_TOKEN=xxx
|
||||
# VOLCENGINE_TTS_CLUSTER=volcano_tts # Optional, default is volcano_tts
|
||||
# VOLCENGINE_TTS_VOICE_TYPE=BV700_V2_streaming # Optional, default is BV700_V2_streaming
|
||||
|
||||
# Option, for langsmith tracing and monitoring
|
||||
# LANGSMITH_TRACING=true
|
||||
# LANGSMITH_ENDPOINT=https://api.smith.langchain.com
|
||||
# LANGSMITH_API_KEY=your-langsmith-api-key
|
||||
# LANGSMITH_PROJECT=your-langsmith-project
|
||||
# LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
|
||||
# LANGSMITH_API_KEY="xxx"
|
||||
# LANGSMITH_PROJECT="xxx"
|
||||
|
||||
# GitHub API Token
|
||||
# GITHUB_TOKEN=your-github-token
|
||||
# WECOM_BOT_ID=your-wecom-bot-id
|
||||
# WECOM_BOT_SECRET=your-wecom-bot-secret
|
||||
# [!NOTE]
|
||||
# For model settings and other configurations, please refer to `docs/configuration_guide.md`
|
||||
|
||||
@@ -1,43 +0,0 @@
|
||||
# Normalize line endings to LF for all text files
|
||||
* text=auto eol=lf
|
||||
|
||||
# Shell scripts and makefiles must always use LF
|
||||
*.sh text eol=lf
|
||||
Makefile text eol=lf
|
||||
**/Makefile text eol=lf
|
||||
|
||||
# Common config/source files
|
||||
*.yml text eol=lf
|
||||
*.yaml text eol=lf
|
||||
*.toml text eol=lf
|
||||
*.json text eol=lf
|
||||
*.md text eol=lf
|
||||
*.py text eol=lf
|
||||
*.ts text eol=lf
|
||||
*.tsx text eol=lf
|
||||
*.js text eol=lf
|
||||
*.jsx text eol=lf
|
||||
*.css text eol=lf
|
||||
*.scss text eol=lf
|
||||
*.html text eol=lf
|
||||
*.env text eol=lf
|
||||
|
||||
# Windows scripts
|
||||
*.bat text eol=crlf
|
||||
*.cmd text eol=crlf
|
||||
|
||||
# Binary assets
|
||||
*.png binary
|
||||
*.jpg binary
|
||||
*.jpeg binary
|
||||
*.gif binary
|
||||
*.webp binary
|
||||
*.ico binary
|
||||
*.pdf binary
|
||||
*.zip binary
|
||||
*.tar binary
|
||||
*.gz binary
|
||||
*.mp4 binary
|
||||
*.mov binary
|
||||
*.woff binary
|
||||
*.woff2 binary
|
||||
@@ -1,128 +0,0 @@
|
||||
name: Runtime Information
|
||||
description: Report runtime/environment details to help reproduce an issue.
|
||||
title: "[runtime] "
|
||||
labels:
|
||||
- needs-triage
|
||||
body:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Thanks for sharing runtime details.
|
||||
Complete this form so maintainers can quickly reproduce and diagnose the problem.
|
||||
|
||||
- type: input
|
||||
id: summary
|
||||
attributes:
|
||||
label: Problem summary
|
||||
description: Short summary of the issue.
|
||||
placeholder: e.g. make dev fails to start gateway service
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: expected
|
||||
attributes:
|
||||
label: Expected behavior
|
||||
placeholder: What did you expect to happen?
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: actual
|
||||
attributes:
|
||||
label: Actual behavior
|
||||
placeholder: What happened instead? Include key error lines.
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: dropdown
|
||||
id: os
|
||||
attributes:
|
||||
label: Operating system
|
||||
options:
|
||||
- macOS
|
||||
- Linux
|
||||
- Windows
|
||||
- Other
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: input
|
||||
id: platform_details
|
||||
attributes:
|
||||
label: Platform details
|
||||
description: Add architecture and shell if relevant.
|
||||
placeholder: e.g. arm64, zsh
|
||||
|
||||
- type: input
|
||||
id: python_version
|
||||
attributes:
|
||||
label: Python version
|
||||
placeholder: e.g. Python 3.12.9
|
||||
|
||||
- type: input
|
||||
id: node_version
|
||||
attributes:
|
||||
label: Node.js version
|
||||
placeholder: e.g. v23.11.0
|
||||
|
||||
- type: input
|
||||
id: pnpm_version
|
||||
attributes:
|
||||
label: pnpm version
|
||||
placeholder: e.g. 10.26.2
|
||||
|
||||
- type: input
|
||||
id: uv_version
|
||||
attributes:
|
||||
label: uv version
|
||||
placeholder: e.g. 0.7.20
|
||||
|
||||
- type: dropdown
|
||||
id: run_mode
|
||||
attributes:
|
||||
label: How are you running DeerFlow?
|
||||
options:
|
||||
- Local (make dev)
|
||||
- Docker (make docker-dev)
|
||||
- CI
|
||||
- Other
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: reproduce
|
||||
attributes:
|
||||
label: Reproduction steps
|
||||
description: Provide exact commands and sequence.
|
||||
placeholder: |
|
||||
1. make check
|
||||
2. make install
|
||||
3. make dev
|
||||
4. ...
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: logs
|
||||
attributes:
|
||||
label: Relevant logs
|
||||
description: Paste key lines from logs (for example logs/gateway.log, logs/frontend.log).
|
||||
render: shell
|
||||
validations:
|
||||
required: true
|
||||
|
||||
- type: textarea
|
||||
id: git_info
|
||||
attributes:
|
||||
label: Git state
|
||||
description: Share output of git branch and latest commit SHA.
|
||||
placeholder: |
|
||||
branch: feature/my-branch
|
||||
commit: abcdef1
|
||||
|
||||
- type: textarea
|
||||
id: additional
|
||||
attributes:
|
||||
label: Additional context
|
||||
description: Add anything else that might help triage.
|
||||
@@ -1,213 +0,0 @@
|
||||
# Copilot Onboarding Instructions for DeerFlow
|
||||
|
||||
Use this file as the default operating guide for this repository. Follow it first, and only search the codebase when this file is incomplete or incorrect.
|
||||
|
||||
## 1) Repository Summary
|
||||
|
||||
DeerFlow is a full-stack "super agent harness".
|
||||
|
||||
- Backend: Python 3.12, LangGraph + FastAPI gateway, sandbox/tool system, memory, MCP integration.
|
||||
- Frontend: Next.js 16 + React 19 + TypeScript + pnpm.
|
||||
- Local dev entrypoint: root `Makefile` starts backend + frontend + nginx on `http://localhost:2026`.
|
||||
- Docker dev entrypoint: `make docker-*` (mode-aware provisioner startup from `config.yaml`).
|
||||
|
||||
Current repo footprint is medium-large (backend service, frontend app, docker stack, skills library, docs).
|
||||
|
||||
## 2) Runtime and Toolchain Requirements
|
||||
|
||||
Validated in this repo on macOS:
|
||||
|
||||
- Node.js `>=22` (validated with Node `23.11.0`)
|
||||
- pnpm (repo expects lockfile generated by pnpm 10; validated with pnpm `10.26.2` and `10.15.0`)
|
||||
- Python `>=3.12` (CI uses `3.12`)
|
||||
- `uv` (validated with `0.7.20`)
|
||||
- `nginx` (required for `make dev` unified local endpoint)
|
||||
|
||||
Always run from repo root unless a command explicitly says otherwise.
|
||||
|
||||
## 3) Build/Test/Lint/Run - Verified Command Sequences
|
||||
|
||||
These were executed and validated in this repository.
|
||||
|
||||
### A. Bootstrap and install
|
||||
|
||||
1. Check prerequisites:
|
||||
|
||||
```bash
|
||||
make check
|
||||
```
|
||||
|
||||
Observed: passes when required tools are installed.
|
||||
|
||||
2. Install dependencies (recommended order: backend then frontend, as implemented by `make install`):
|
||||
|
||||
```bash
|
||||
make install
|
||||
```
|
||||
|
||||
### B. Backend CI-equivalent validation
|
||||
|
||||
Run from `backend/`:
|
||||
|
||||
```bash
|
||||
make lint
|
||||
make test
|
||||
```
|
||||
|
||||
Validated results:
|
||||
|
||||
- `make lint`: pass (`ruff check .`)
|
||||
- `make test`: pass (`277 passed, 15 warnings in ~76.6s`)
|
||||
|
||||
CI parity:
|
||||
|
||||
- `.github/workflows/backend-unit-tests.yml` runs on pull requests.
|
||||
- CI executes `uv sync --group dev`, then `make lint`, then `make test` in `backend/`.
|
||||
|
||||
### C. Frontend validation
|
||||
|
||||
Run from `frontend/`.
|
||||
|
||||
Recommended reliable sequence:
|
||||
|
||||
```bash
|
||||
pnpm lint
|
||||
pnpm typecheck
|
||||
BETTER_AUTH_SECRET=local-dev-secret pnpm build
|
||||
```
|
||||
|
||||
Observed failure modes and workarounds:
|
||||
|
||||
- `pnpm build` fails without `BETTER_AUTH_SECRET` in production-mode env validation.
|
||||
- Workaround: set `BETTER_AUTH_SECRET` (best) or set `SKIP_ENV_VALIDATION=1`.
|
||||
- Even with `SKIP_ENV_VALIDATION=1`, Better Auth can still warn/error in logs about default secret; prefer setting a real non-default secret.
|
||||
- `pnpm check` currently fails (`next lint` invocation is incompatible here and resolves to an invalid directory). Do not rely on `pnpm check`; run `pnpm lint` and `pnpm typecheck` explicitly.
|
||||
|
||||
### D. Run locally (all services)
|
||||
|
||||
From root:
|
||||
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
Behavior:
|
||||
|
||||
- Stops existing local services first.
|
||||
- Starts LangGraph (`2024`), Gateway (`8001`), Frontend (`3000`), nginx (`2026`).
|
||||
- Unified app endpoint: `http://localhost:2026`.
|
||||
- Logs: `logs/langgraph.log`, `logs/gateway.log`, `logs/frontend.log`, `logs/nginx.log`.
|
||||
|
||||
Stop services:
|
||||
|
||||
```bash
|
||||
make stop
|
||||
```
|
||||
|
||||
If tool sessions/timeouts interrupt `make dev`, run `make stop` again to ensure cleanup.
|
||||
|
||||
### E. Config bootstrap
|
||||
|
||||
From root:
|
||||
|
||||
```bash
|
||||
make config
|
||||
```
|
||||
|
||||
Important behavior:
|
||||
|
||||
- This intentionally aborts if `config.yaml` (or `config.yml`/`configure.yml`) already exists.
|
||||
- Use `make config` only for first-time setup in a clean clone.
|
||||
|
||||
## 4) Command Order That Minimizes Failures
|
||||
|
||||
Use this exact order for local code changes:
|
||||
|
||||
1. `make check`
|
||||
2. `make install` (if frontend fails with proxy errors, rerun frontend install with proxy vars unset)
|
||||
3. Backend checks: `cd backend && make lint && make test`
|
||||
4. Frontend checks: `cd frontend && pnpm lint && pnpm typecheck`
|
||||
5. Frontend build (if UI changes or release-sensitive changes): `BETTER_AUTH_SECRET=... pnpm build`
|
||||
|
||||
Always run backend lint/tests before opening PRs because that is what CI enforces.
|
||||
|
||||
## 5) Project Layout and Architecture (High-Value Paths)
|
||||
|
||||
Root-level orchestration and config:
|
||||
|
||||
- `Makefile` - main local/dev/docker command entrypoints
|
||||
- `config.example.yaml` - primary app config template
|
||||
- `config.yaml` - local active config (gitignored)
|
||||
- `docker/docker-compose-dev.yaml` - Docker dev topology
|
||||
- `.github/workflows/backend-unit-tests.yml` - PR validation workflow
|
||||
|
||||
Backend core:
|
||||
|
||||
- `backend/packages/harness/deerflow/agents/` - lead agent, middleware chain, memory
|
||||
- `backend/app/gateway/` - FastAPI gateway API
|
||||
- `backend/packages/harness/deerflow/sandbox/` - sandbox provider + tool wrappers
|
||||
- `backend/packages/harness/deerflow/subagents/` - subagent registry/execution
|
||||
- `backend/packages/harness/deerflow/mcp/` - MCP integration
|
||||
- `backend/langgraph.json` - graph entrypoint (`deerflow.agents:make_lead_agent`)
|
||||
- `backend/pyproject.toml` - Python deps and `requires-python`
|
||||
- `backend/ruff.toml` - lint/format policy
|
||||
- `backend/tests/` - backend unit and integration-like tests
|
||||
|
||||
Frontend core:
|
||||
|
||||
- `frontend/src/app/` - Next.js routes/pages
|
||||
- `frontend/src/components/` - UI components
|
||||
- `frontend/src/core/` - app logic (threads, tools, API, models)
|
||||
- `frontend/src/env.js` - env schema/validation (critical for build behavior)
|
||||
- `frontend/package.json` - scripts/deps
|
||||
- `frontend/eslint.config.js` - lint rules
|
||||
- `frontend/tsconfig.json` - TS config
|
||||
|
||||
Skills and assets:
|
||||
|
||||
- `skills/public/` - built-in skill packs loaded by agent runtime
|
||||
|
||||
## 6) Pre-Checkin / Validation Expectations
|
||||
|
||||
Before submitting changes, run at minimum:
|
||||
|
||||
- Backend: `cd backend && make lint && make test`
|
||||
- Frontend (if touched): `cd frontend && pnpm lint && pnpm typecheck`
|
||||
- Frontend build when changing env/auth/routing/build-sensitive files: `BETTER_AUTH_SECRET=... pnpm build`
|
||||
|
||||
If touching orchestration/config (`Makefile`, `docker/*`, `config*.yaml`), also run `make dev` and verify the four services start.
|
||||
|
||||
## 7) Non-Obvious Dependencies and Gotchas
|
||||
|
||||
- Proxy env vars can silently break frontend network operations (`pnpm install`/registry access).
|
||||
- `BETTER_AUTH_SECRET` is effectively required for reliable frontend production build validation.
|
||||
- Next.js may warn about multiple lockfiles and workspace root inference; this is currently a warning, not a build blocker.
|
||||
- `make config` is non-idempotent by design when config already exists.
|
||||
- `make dev` includes process cleanup and can emit shutdown logs/noise if interrupted; this is expected.
|
||||
|
||||
## 8) Root Inventory (quick reference)
|
||||
|
||||
Important root entries:
|
||||
|
||||
- `.github/`
|
||||
- `backend/`
|
||||
- `frontend/`
|
||||
- `docker/`
|
||||
- `skills/`
|
||||
- `scripts/`
|
||||
- `docs/`
|
||||
- `README.md`
|
||||
- `CONTRIBUTING.md`
|
||||
- `Makefile`
|
||||
- `config.example.yaml`
|
||||
- `extensions_config.example.json`
|
||||
|
||||
## 9) Instruction Priority
|
||||
|
||||
Trust this onboarding guide first.
|
||||
|
||||
Only do broad repo searches (`grep/find/code search`) when:
|
||||
|
||||
- you need file-level implementation details not listed here,
|
||||
- a command here fails and you need updated replacement behavior,
|
||||
- or CI/workflow definitions have changed since this file was written.
|
||||
@@ -1,40 +0,0 @@
|
||||
name: Unit Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened, ready_for_review]
|
||||
|
||||
concurrency:
|
||||
group: unit-tests-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
backend-unit-tests:
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v7
|
||||
|
||||
- name: Install backend dependencies
|
||||
working-directory: backend
|
||||
run: uv sync --group dev
|
||||
|
||||
- name: Run unit tests of backend
|
||||
working-directory: backend
|
||||
run: make test
|
||||
@@ -0,0 +1,95 @@
|
||||
name: Publish Containers
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
release:
|
||||
types: [published]
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
|
||||
backend-container:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
packages: write
|
||||
attestations: write
|
||||
id-token: write
|
||||
env:
|
||||
REGISTRY: ghcr.io
|
||||
IMAGE_NAME: ${{ github.repository }}
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
- name: Log in to the Container registry
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
registry: ${{ env.REGISTRY }}
|
||||
username: ${{ github.actor }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
- name: Extract metadata (tags, labels) for Docker
|
||||
id: meta
|
||||
uses: docker/metadata-action@v5
|
||||
with:
|
||||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||
- name: Build and push Docker image
|
||||
id: push
|
||||
uses: docker/build-push-action@v6
|
||||
with:
|
||||
context: .
|
||||
file: Dockerfile
|
||||
push: true
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
|
||||
- name: Generate artifact attestation
|
||||
uses: actions/attest-build-provenance@v2
|
||||
with:
|
||||
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
|
||||
subject-digest: ${{ steps.push.outputs.digest }}
|
||||
push-to-registry: true
|
||||
|
||||
frontend-container:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
packages: write
|
||||
attestations: write
|
||||
id-token: write
|
||||
env:
|
||||
REGISTRY: ghcr.io
|
||||
IMAGE_NAME: ${{ github.repository }}-web
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
- name: Log in to the Container registry
|
||||
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
|
||||
with:
|
||||
registry: ${{ env.REGISTRY }}
|
||||
username: ${{ github.actor }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
- name: Extract metadata (tags, labels) for Docker
|
||||
id: meta
|
||||
uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
|
||||
with:
|
||||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||
- name: Build and push Docker image
|
||||
id: push
|
||||
uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
|
||||
with:
|
||||
context: web
|
||||
file: web/Dockerfile
|
||||
push: true
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
|
||||
- name: Generate artifact attestation
|
||||
uses: actions/attest-build-provenance@v2
|
||||
with:
|
||||
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
|
||||
subject-digest: ${{ steps.push.outputs.digest }}
|
||||
push-to-registry: true
|
||||
@@ -1,63 +0,0 @@
|
||||
name: E2E Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
paths:
|
||||
- 'frontend/**'
|
||||
- '.github/workflows/e2e-tests.yml'
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened, ready_for_review]
|
||||
paths:
|
||||
- 'frontend/**'
|
||||
- '.github/workflows/e2e-tests.yml'
|
||||
|
||||
concurrency:
|
||||
group: e2e-tests-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
e2e-tests:
|
||||
if: ${{ github.event_name != 'pull_request' || github.event.pull_request.draft == false }}
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: '22'
|
||||
|
||||
- name: Enable Corepack
|
||||
run: corepack enable
|
||||
|
||||
- name: Use pinned pnpm version
|
||||
run: corepack prepare pnpm@10.26.2 --activate
|
||||
|
||||
- name: Install frontend dependencies
|
||||
working-directory: frontend
|
||||
run: pnpm install --frozen-lockfile
|
||||
|
||||
- name: Install Playwright Chromium
|
||||
working-directory: frontend
|
||||
run: npx playwright install chromium --with-deps
|
||||
|
||||
- name: Run E2E tests
|
||||
working-directory: frontend
|
||||
run: pnpm exec playwright test
|
||||
env:
|
||||
SKIP_ENV_VALIDATION: '1'
|
||||
|
||||
- name: Upload Playwright report
|
||||
uses: actions/upload-artifact@v4
|
||||
if: ${{ !cancelled() }}
|
||||
with:
|
||||
name: playwright-report
|
||||
path: frontend/playwright-report/
|
||||
retention-days: 7
|
||||
@@ -1,43 +0,0 @@
|
||||
name: Frontend Unit Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened, ready_for_review]
|
||||
|
||||
concurrency:
|
||||
group: frontend-unit-tests-${{ github.event.pull_request.number || github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
frontend-unit-tests:
|
||||
if: github.event.pull_request.draft == false
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v6
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: '22'
|
||||
|
||||
- name: Enable Corepack
|
||||
run: corepack enable
|
||||
|
||||
- name: Use pinned pnpm version
|
||||
run: corepack prepare pnpm@10.26.2 --activate
|
||||
|
||||
- name: Install frontend dependencies
|
||||
working-directory: frontend
|
||||
run: pnpm install --frozen-lockfile
|
||||
|
||||
- name: Run unit tests of frontend
|
||||
working-directory: frontend
|
||||
run: make test
|
||||
@@ -1,74 +0,0 @@
|
||||
name: Lint Check
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
pull_request:
|
||||
branches: [ '*' ]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
lint:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v6
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v7
|
||||
|
||||
- name: Install dependencies
|
||||
working-directory: backend
|
||||
run: |
|
||||
uv sync --group dev
|
||||
|
||||
- name: Lint backend
|
||||
working-directory: backend
|
||||
run: make lint
|
||||
|
||||
lint-frontend:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v6
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: '22'
|
||||
|
||||
- name: Enable Corepack
|
||||
run: corepack enable
|
||||
|
||||
- name: Use pinned pnpm version
|
||||
run: corepack prepare pnpm@10.26.2 --activate
|
||||
|
||||
- name: Install frontend dependencies
|
||||
run: |
|
||||
cd frontend
|
||||
pnpm install --frozen-lockfile
|
||||
|
||||
- name: Check frontend formatting
|
||||
run: |
|
||||
cd frontend
|
||||
pnpm format
|
||||
|
||||
- name: Run frontend linting
|
||||
run: |
|
||||
cd frontend
|
||||
pnpm lint
|
||||
|
||||
- name: Check TypeScript types
|
||||
run: |
|
||||
cd frontend
|
||||
pnpm typecheck
|
||||
|
||||
- name: Build frontend
|
||||
run: |
|
||||
cd frontend
|
||||
BETTER_AUTH_SECRET=local-dev-secret pnpm build
|
||||
@@ -0,0 +1,31 @@
|
||||
name: Lint Check
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
pull_request:
|
||||
branches: [ '*' ]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
lint:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
|
||||
- name: Install the latest version of uv
|
||||
uses: astral-sh/setup-uv@v6.3.1
|
||||
with:
|
||||
version: "latest"
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
uv venv --python 3.12
|
||||
uv pip install -e ".[dev]"
|
||||
|
||||
- name: Run linters
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
make lint
|
||||
@@ -0,0 +1,48 @@
|
||||
name: Test Cases Check
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'main' ]
|
||||
pull_request:
|
||||
branches: [ '*' ]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
test:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
|
||||
- name: Install the latest version of uv
|
||||
uses: astral-sh/setup-uv@v6.3.1
|
||||
with:
|
||||
version: "latest"
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
uv venv --python 3.12
|
||||
uv pip install -e ".[dev]"
|
||||
uv pip install -e ".[test]"
|
||||
|
||||
- name: Run test cases with coverage
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
TAVILY_API_KEY=mock-key make coverage
|
||||
|
||||
- name: Generate HTML Coverage Report
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
python -m coverage html -d coverage_html
|
||||
|
||||
- name: Upload Coverage Report
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: coverage-report
|
||||
path: coverage_html/
|
||||
|
||||
- name: Display Coverage Summary
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
python -m coverage report
|
||||
+14
-47
@@ -1,20 +1,14 @@
|
||||
# DeerFlow docker image cache
|
||||
docker/.cache/
|
||||
# oh-my-claudecode state
|
||||
.omc/
|
||||
# OS generated files
|
||||
.DS_Store
|
||||
*.local
|
||||
._*
|
||||
.Spotlight-V100
|
||||
.Trashes
|
||||
ehthumbs.db
|
||||
Thumbs.db
|
||||
|
||||
# Python cache
|
||||
# Python-generated files
|
||||
__pycache__/
|
||||
*.pyc
|
||||
*.pyo
|
||||
*.py[oc]
|
||||
build/
|
||||
dist/
|
||||
wheels/
|
||||
*.egg-info
|
||||
.coverage
|
||||
.coverage.*
|
||||
agent_history.gif
|
||||
static/browser_history/*.gif
|
||||
|
||||
# Virtual environments
|
||||
.venv
|
||||
@@ -23,39 +17,12 @@ venv/
|
||||
# Environment variables
|
||||
.env
|
||||
|
||||
# Configuration files
|
||||
config.yaml
|
||||
mcp_config.json
|
||||
extensions_config.json
|
||||
# user conf
|
||||
conf.yaml
|
||||
|
||||
# IDE
|
||||
.idea/
|
||||
.vscode/
|
||||
.langgraph_api/
|
||||
|
||||
# Coverage report
|
||||
# coverage report
|
||||
coverage.xml
|
||||
coverage/
|
||||
.deer-flow/
|
||||
.claude/
|
||||
skills/custom/*
|
||||
logs/
|
||||
log/
|
||||
|
||||
# Local git hooks (keep only on this machine, do not push)
|
||||
.githooks/
|
||||
|
||||
# pnpm
|
||||
.pnpm-store
|
||||
sandbox_image_cache.tar
|
||||
|
||||
# ignore the legacy `web` folder
|
||||
web/
|
||||
|
||||
# Deployment artifacts
|
||||
backend/Dockerfile.langgraph
|
||||
config.yaml.bak
|
||||
.playwright-mcp
|
||||
/frontend/test-results/
|
||||
/frontend/playwright-report/
|
||||
.gstack/
|
||||
.worktrees
|
||||
|
||||
Vendored
+90
@@ -0,0 +1,90 @@
|
||||
{
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
|
||||
{
|
||||
"name": "Debug Tests",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"module": "pytest",
|
||||
"args": [
|
||||
"${workspaceFolder}/tests",
|
||||
"-v",
|
||||
"-s"
|
||||
],
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": false,
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "Debug Current Test File",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"module": "pytest",
|
||||
"args": [
|
||||
"${file}",
|
||||
"-v",
|
||||
"-s"
|
||||
],
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": false
|
||||
},
|
||||
{
|
||||
"name": "Python: 当前文件",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${file}",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": true
|
||||
},
|
||||
{
|
||||
"name": "Python: main.py",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${workspaceFolder}/main.py",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": false,
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}"
|
||||
},
|
||||
"args": [
|
||||
"--debug", "--max_plan_iterations", "1", "--max_step_num", "1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "Python: llm.py",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${workspaceFolder}/src/llms/llm.py",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": true,
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "Python: server.py",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${workspaceFolder}/server.py",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": false,
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "Python: graph.py",
|
||||
"type": "debugpy",
|
||||
"request": "launch",
|
||||
"program": "${workspaceFolder}/src/ppt/graph/builder.py",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": false,
|
||||
"env": {
|
||||
"PYTHONPATH": "${workspaceFolder}"
|
||||
}
|
||||
},
|
||||
]
|
||||
}
|
||||
Vendored
+7
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"python.testing.pytestArgs": [
|
||||
"tests"
|
||||
],
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.pytestEnabled": true
|
||||
}
|
||||
@@ -1,128 +0,0 @@
|
||||
# Contributor Covenant Code of Conduct
|
||||
|
||||
## Our Pledge
|
||||
|
||||
We as members, contributors, and leaders pledge to make participation in our
|
||||
community a harassment-free experience for everyone, regardless of age, body
|
||||
size, visible or invisible disability, ethnicity, sex characteristics, gender
|
||||
identity and expression, level of experience, education, socio-economic status,
|
||||
nationality, personal appearance, race, religion, or sexual identity
|
||||
and orientation.
|
||||
|
||||
We pledge to act and interact in ways that contribute to an open, welcoming,
|
||||
diverse, inclusive, and healthy community.
|
||||
|
||||
## Our Standards
|
||||
|
||||
Examples of behavior that contributes to a positive environment for our
|
||||
community include:
|
||||
|
||||
* Demonstrating empathy and kindness toward other people
|
||||
* Being respectful of differing opinions, viewpoints, and experiences
|
||||
* Giving and gracefully accepting constructive feedback
|
||||
* Accepting responsibility and apologizing to those affected by our mistakes,
|
||||
and learning from the experience
|
||||
* Focusing on what is best not just for us as individuals, but for the
|
||||
overall community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
* The use of sexualized language or imagery, and sexual attention or
|
||||
advances of any kind
|
||||
* Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or email
|
||||
address, without their explicit permission
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
|
||||
Community leaders are responsible for clarifying and enforcing our standards of
|
||||
acceptable behavior and will take appropriate and fair corrective action in
|
||||
response to any behavior that they deem inappropriate, threatening, offensive,
|
||||
or harmful.
|
||||
|
||||
Community leaders have the right and responsibility to remove, edit, or reject
|
||||
comments, commits, code, wiki edits, issues, and other contributions that are
|
||||
not aligned to this Code of Conduct, and will communicate reasons for moderation
|
||||
decisions when appropriate.
|
||||
|
||||
## Scope
|
||||
|
||||
This Code of Conduct applies within all community spaces, and also applies when
|
||||
an individual is officially representing the community in public spaces.
|
||||
Examples of representing our community include using an official e-mail address,
|
||||
posting via an official social media account, or acting as an appointed
|
||||
representative at an online or offline event.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Instances of abusive, harassing, or otherwise unacceptable behavior may be
|
||||
reported to the community leaders responsible for enforcement at
|
||||
willem.jiang@gmail.com.
|
||||
All complaints will be reviewed and investigated promptly and fairly.
|
||||
|
||||
All community leaders are obligated to respect the privacy and security of the
|
||||
reporter of any incident.
|
||||
|
||||
## Enforcement Guidelines
|
||||
|
||||
Community leaders will follow these Community Impact Guidelines in determining
|
||||
the consequences for any action they deem in violation of this Code of Conduct:
|
||||
|
||||
### 1. Correction
|
||||
|
||||
**Community Impact**: Use of inappropriate language or other behavior deemed
|
||||
unprofessional or unwelcome in the community.
|
||||
|
||||
**Consequence**: A private, written warning from community leaders, providing
|
||||
clarity around the nature of the violation and an explanation of why the
|
||||
behavior was inappropriate. A public apology may be requested.
|
||||
|
||||
### 2. Warning
|
||||
|
||||
**Community Impact**: A violation through a single incident or series
|
||||
of actions.
|
||||
|
||||
**Consequence**: A warning with consequences for continued behavior. No
|
||||
interaction with the people involved, including unsolicited interaction with
|
||||
those enforcing the Code of Conduct, for a specified period of time. This
|
||||
includes avoiding interactions in community spaces as well as external channels
|
||||
like social media. Violating these terms may lead to a temporary or
|
||||
permanent ban.
|
||||
|
||||
### 3. Temporary Ban
|
||||
|
||||
**Community Impact**: A serious violation of community standards, including
|
||||
sustained inappropriate behavior.
|
||||
|
||||
**Consequence**: A temporary ban from any sort of interaction or public
|
||||
communication with the community for a specified period of time. No public or
|
||||
private interaction with the people involved, including unsolicited interaction
|
||||
with those enforcing the Code of Conduct, is allowed during this period.
|
||||
Violating these terms may lead to a permanent ban.
|
||||
|
||||
### 4. Permanent Ban
|
||||
|
||||
**Community Impact**: Demonstrating a pattern of violation of community
|
||||
standards, including sustained inappropriate behavior, harassment of an
|
||||
individual, or aggression toward or disparagement of classes of individuals.
|
||||
|
||||
**Consequence**: A permanent ban from any sort of public interaction within
|
||||
the community.
|
||||
|
||||
## Attribution
|
||||
|
||||
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
|
||||
version 2.0, available at
|
||||
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
|
||||
|
||||
Community Impact Guidelines were inspired by [Mozilla's code of conduct
|
||||
enforcement ladder](https://github.com/mozilla/diversity).
|
||||
|
||||
[homepage]: https://www.contributor-covenant.org
|
||||
|
||||
For answers to common questions about this code of conduct, see the FAQ at
|
||||
https://www.contributor-covenant.org/faq. Translations are available at
|
||||
https://www.contributor-covenant.org/translations.
|
||||
+144
@@ -0,0 +1,144 @@
|
||||
# Contributing to DeerFlow
|
||||
|
||||
Thank you for your interest in contributing to DeerFlow! We welcome contributions of all kinds from the community.
|
||||
|
||||
## Ways to Contribute
|
||||
|
||||
There are many ways you can contribute to DeerFlow:
|
||||
|
||||
- **Code Contributions**: Add new features, fix bugs, or improve performance
|
||||
- **Documentation**: Improve README, add code comments, or create examples
|
||||
- **Bug Reports**: Submit detailed bug reports through issues
|
||||
- **Feature Requests**: Suggest new features or improvements
|
||||
- **Code Reviews**: Review pull requests from other contributors
|
||||
- **Community Support**: Help others in discussions and issues
|
||||
|
||||
## Development Setup
|
||||
|
||||
1. Fork the repository
|
||||
2. Clone your fork:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
```
|
||||
|
||||
3. Set up your development environment:
|
||||
|
||||
```bash
|
||||
# Install dependencies, uv will take care of the python interpreter and venv creation
|
||||
uv sync
|
||||
|
||||
# For development, install additional dependencies
|
||||
uv pip install -e ".[dev]"
|
||||
uv pip install -e ".[test]"
|
||||
```
|
||||
|
||||
4. Configure pre-commit hooks:
|
||||
|
||||
```bash
|
||||
chmod +x pre-commit
|
||||
ln -s ../../pre-commit .git/hooks/pre-commit
|
||||
```
|
||||
|
||||
## Development Process
|
||||
|
||||
1. Create a new branch:
|
||||
|
||||
```bash
|
||||
git checkout -b feature/amazing-feature
|
||||
```
|
||||
|
||||
2. Make your changes following our coding standards:
|
||||
- Write clear, documented code
|
||||
- Follow PEP 8 style guidelines
|
||||
- Add tests for new features
|
||||
- Update documentation as needed
|
||||
|
||||
3. Run tests and checks:
|
||||
|
||||
```bash
|
||||
make test # Run tests
|
||||
make lint # Run linting
|
||||
make format # Format code
|
||||
make coverage # Check test coverage
|
||||
```
|
||||
|
||||
4. Commit your changes:
|
||||
|
||||
```bash
|
||||
git commit -m 'Add some amazing feature'
|
||||
```
|
||||
|
||||
5. Push to your fork:
|
||||
|
||||
```bash
|
||||
git push origin feature/amazing-feature
|
||||
```
|
||||
|
||||
6. Open a Pull Request
|
||||
|
||||
## Pull Request Guidelines
|
||||
|
||||
- Fill in the pull request template completely
|
||||
- Include tests for new features
|
||||
- Update documentation as needed
|
||||
- Ensure all tests pass and there are no linting errors
|
||||
- Keep pull requests focused on a single feature or fix
|
||||
- Reference any related issues
|
||||
|
||||
## Code Style
|
||||
|
||||
- Follow PEP 8 guidelines
|
||||
- Use type hints where possible
|
||||
- Write descriptive docstrings
|
||||
- Keep functions and methods focused and single-purpose
|
||||
- Comment complex logic
|
||||
- Python version requirement: >= 3.12
|
||||
|
||||
## Testing
|
||||
|
||||
Run the test suite:
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
make test
|
||||
|
||||
# Run specific test file
|
||||
pytest tests/integration/test_workflow.py
|
||||
|
||||
# Run with coverage
|
||||
make coverage
|
||||
```
|
||||
|
||||
## Code Quality
|
||||
|
||||
```bash
|
||||
# Run linting
|
||||
make lint
|
||||
|
||||
# Format code
|
||||
make format
|
||||
```
|
||||
|
||||
## Community Guidelines
|
||||
|
||||
- Be respectful and inclusive
|
||||
- Follow our code of conduct
|
||||
- Help others learn and grow
|
||||
- Give constructive feedback
|
||||
- Stay focused on improving the project
|
||||
|
||||
## Need Help?
|
||||
|
||||
If you need help with anything:
|
||||
|
||||
- Check existing issues and discussions
|
||||
- Join our community channels
|
||||
- Ask questions in discussions
|
||||
|
||||
## License
|
||||
|
||||
By contributing to DeerFlow, you agree that your contributions will be licensed under the MIT License.
|
||||
|
||||
We appreciate your contributions to making DeerFlow better!
|
||||
-340
@@ -1,340 +0,0 @@
|
||||
# Contributing to DeerFlow
|
||||
|
||||
Thank you for your interest in contributing to DeerFlow! This guide will help you set up your development environment and understand our development workflow.
|
||||
|
||||
## Development Environment Setup
|
||||
|
||||
We offer two development environments. **Docker is recommended** for the most consistent and hassle-free experience.
|
||||
|
||||
### Option 1: Docker Development (Recommended)
|
||||
|
||||
Docker provides a consistent, isolated environment with all dependencies pre-configured. No need to install Node.js, Python, or nginx on your local machine.
|
||||
|
||||
#### Prerequisites
|
||||
|
||||
- Docker Desktop or Docker Engine
|
||||
- pnpm (for caching optimization)
|
||||
|
||||
#### Setup Steps
|
||||
|
||||
1. **Configure the application**:
|
||||
```bash
|
||||
# Copy example configuration
|
||||
cp config.example.yaml config.yaml
|
||||
|
||||
# Set your API keys
|
||||
export OPENAI_API_KEY="your-key-here"
|
||||
# or edit config.yaml directly
|
||||
```
|
||||
|
||||
2. **Initialize Docker environment** (first time only):
|
||||
```bash
|
||||
make docker-init
|
||||
```
|
||||
This will:
|
||||
- Build Docker images
|
||||
- Install frontend dependencies (pnpm)
|
||||
- Install backend dependencies (uv)
|
||||
- Share pnpm cache with host for faster builds
|
||||
|
||||
3. **Start development services**:
|
||||
```bash
|
||||
make docker-start
|
||||
```
|
||||
`make docker-start` reads `config.yaml` and starts `provisioner` only for provisioner/Kubernetes sandbox mode.
|
||||
|
||||
All services will start with hot-reload enabled:
|
||||
- Frontend changes are automatically reloaded
|
||||
- Backend changes trigger automatic restart
|
||||
- LangGraph server supports hot-reload
|
||||
|
||||
4. **Access the application**:
|
||||
- Web Interface: http://localhost:2026
|
||||
- API Gateway: http://localhost:2026/api/*
|
||||
- LangGraph: http://localhost:2026/api/langgraph/*
|
||||
|
||||
#### Docker Commands
|
||||
|
||||
```bash
|
||||
# Build the custom k3s image (with pre-cached sandbox image)
|
||||
make docker-init
|
||||
# Start Docker services (mode-aware, localhost:2026)
|
||||
make docker-start
|
||||
# Stop Docker development services
|
||||
make docker-stop
|
||||
# View Docker development logs
|
||||
make docker-logs
|
||||
# View Docker frontend logs
|
||||
make docker-logs-frontend
|
||||
# View Docker gateway logs
|
||||
make docker-logs-gateway
|
||||
```
|
||||
|
||||
If Docker builds are slow in your network, you can override the default package registries before running `make docker-init` or `make docker-start`:
|
||||
|
||||
```bash
|
||||
export UV_INDEX_URL=https://pypi.org/simple
|
||||
export NPM_REGISTRY=https://registry.npmjs.org
|
||||
```
|
||||
|
||||
#### Recommended host resources
|
||||
|
||||
Use these as practical starting points for development and review environments:
|
||||
|
||||
| Scenario | Starting point | Recommended | Notes |
|
||||
|---------|-----------|------------|-------|
|
||||
| `make dev` on one machine | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Best when DeerFlow uses hosted model APIs. |
|
||||
| `make docker-start` review environment | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Docker image builds and sandbox containers need extra headroom. |
|
||||
| Shared Linux test server | 8 vCPU, 16 GB RAM | 16 vCPU, 32 GB RAM | Prefer this for heavier multi-agent runs or multiple reviewers. |
|
||||
|
||||
`2 vCPU / 4 GB` environments often fail to start reliably or become unresponsive under normal DeerFlow workloads.
|
||||
|
||||
#### Linux: Docker daemon permission denied
|
||||
|
||||
If `make docker-init`, `make docker-start`, or `make docker-stop` fails on Linux with an error like below, your current user likely does not have permission to access the Docker daemon socket:
|
||||
|
||||
```text
|
||||
unable to get image 'deer-flow-dev-langgraph': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
|
||||
```
|
||||
|
||||
Recommended fix: add your current user to the `docker` group so Docker commands work without `sudo`.
|
||||
|
||||
1. Confirm the `docker` group exists:
|
||||
```bash
|
||||
getent group docker
|
||||
```
|
||||
2. Add your current user to the `docker` group:
|
||||
```bash
|
||||
sudo usermod -aG docker $USER
|
||||
```
|
||||
3. Apply the new group membership. The most reliable option is to log out completely and then log back in. If you want to refresh the current shell session instead, run:
|
||||
```bash
|
||||
newgrp docker
|
||||
```
|
||||
4. Verify Docker access:
|
||||
```bash
|
||||
docker ps
|
||||
```
|
||||
5. Retry the DeerFlow command:
|
||||
```bash
|
||||
make docker-stop
|
||||
make docker-start
|
||||
```
|
||||
|
||||
If `docker ps` still reports a permission error after `usermod`, fully log out and log back in before retrying.
|
||||
|
||||
#### Docker Architecture
|
||||
|
||||
```
|
||||
Host Machine
|
||||
↓
|
||||
Docker Compose (deer-flow-dev)
|
||||
├→ nginx (port 2026) ← Reverse proxy
|
||||
├→ web (port 3000) ← Frontend with hot-reload
|
||||
├→ api (port 8001) ← Gateway API with hot-reload
|
||||
├→ langgraph (port 2024) ← LangGraph server with hot-reload
|
||||
└→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
|
||||
```
|
||||
|
||||
**Benefits of Docker Development**:
|
||||
- ✅ Consistent environment across different machines
|
||||
- ✅ No need to install Node.js, Python, or nginx locally
|
||||
- ✅ Isolated dependencies and services
|
||||
- ✅ Easy cleanup and reset
|
||||
- ✅ Hot-reload for all services
|
||||
- ✅ Production-like environment
|
||||
|
||||
### Option 2: Local Development
|
||||
|
||||
If you prefer to run services directly on your machine:
|
||||
|
||||
#### Prerequisites
|
||||
|
||||
Check that you have all required tools installed:
|
||||
|
||||
```bash
|
||||
make check
|
||||
```
|
||||
|
||||
Required tools:
|
||||
- Node.js 22+
|
||||
- pnpm
|
||||
- uv (Python package manager)
|
||||
- nginx
|
||||
|
||||
#### Setup Steps
|
||||
|
||||
1. **Configure the application** (same as Docker setup above)
|
||||
|
||||
2. **Install dependencies**:
|
||||
```bash
|
||||
make install
|
||||
```
|
||||
|
||||
3. **Run development server** (starts all services with nginx):
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
4. **Access the application**:
|
||||
- Web Interface: http://localhost:2026
|
||||
- All API requests are automatically proxied through nginx
|
||||
|
||||
#### Manual Service Control
|
||||
|
||||
If you need to start services individually:
|
||||
|
||||
1. **Start backend services**:
|
||||
```bash
|
||||
# Terminal 1: Start LangGraph Server (port 2024)
|
||||
cd backend
|
||||
make dev
|
||||
|
||||
# Terminal 2: Start Gateway API (port 8001)
|
||||
cd backend
|
||||
make gateway
|
||||
|
||||
# Terminal 3: Start Frontend (port 3000)
|
||||
cd frontend
|
||||
pnpm dev
|
||||
```
|
||||
|
||||
2. **Start nginx**:
|
||||
```bash
|
||||
make nginx
|
||||
# or directly: nginx -c $(pwd)/docker/nginx/nginx.local.conf -g 'daemon off;'
|
||||
```
|
||||
|
||||
3. **Access the application**:
|
||||
- Web Interface: http://localhost:2026
|
||||
|
||||
#### Nginx Configuration
|
||||
|
||||
The nginx configuration provides:
|
||||
- Unified entry point on port 2026
|
||||
- Routes `/api/langgraph/*` to LangGraph Server (2024)
|
||||
- Routes other `/api/*` endpoints to Gateway API (8001)
|
||||
- Routes non-API requests to Frontend (3000)
|
||||
- Centralized CORS handling
|
||||
- SSE/streaming support for real-time agent responses
|
||||
- Optimized timeouts for long-running operations
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
deer-flow/
|
||||
├── config.example.yaml # Configuration template
|
||||
├── extensions_config.example.json # MCP and Skills configuration template
|
||||
├── Makefile # Build and development commands
|
||||
├── scripts/
|
||||
│ └── docker.sh # Docker management script
|
||||
├── docker/
|
||||
│ ├── docker-compose-dev.yaml # Docker Compose configuration
|
||||
│ └── nginx/
|
||||
│ ├── nginx.conf # Nginx config for Docker
|
||||
│ └── nginx.local.conf # Nginx config for local dev
|
||||
├── backend/ # Backend application
|
||||
│ ├── src/
|
||||
│ │ ├── gateway/ # Gateway API (port 8001)
|
||||
│ │ ├── agents/ # LangGraph agents (port 2024)
|
||||
│ │ ├── mcp/ # Model Context Protocol integration
|
||||
│ │ ├── skills/ # Skills system
|
||||
│ │ └── sandbox/ # Sandbox execution
|
||||
│ ├── docs/ # Backend documentation
|
||||
│ └── Makefile # Backend commands
|
||||
├── frontend/ # Frontend application
|
||||
│ └── Makefile # Frontend commands
|
||||
└── skills/ # Agent skills
|
||||
├── public/ # Public skills
|
||||
└── custom/ # Custom skills
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
Browser
|
||||
↓
|
||||
Nginx (port 2026) ← Unified entry point
|
||||
├→ Frontend (port 3000) ← / (non-API requests)
|
||||
├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
|
||||
└→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
|
||||
```
|
||||
|
||||
## Development Workflow
|
||||
|
||||
1. **Create a feature branch**:
|
||||
```bash
|
||||
git checkout -b feature/your-feature-name
|
||||
```
|
||||
|
||||
2. **Make your changes** with hot-reload enabled
|
||||
|
||||
3. **Format and lint your code** (CI will reject unformatted code):
|
||||
```bash
|
||||
# Backend
|
||||
cd backend
|
||||
make format # ruff check --fix + ruff format
|
||||
|
||||
# Frontend
|
||||
cd frontend
|
||||
pnpm format:write # Prettier
|
||||
```
|
||||
|
||||
4. **Test your changes** thoroughly
|
||||
|
||||
5. **Commit your changes**:
|
||||
```bash
|
||||
git add .
|
||||
git commit -m "feat: description of your changes"
|
||||
```
|
||||
|
||||
6. **Push and create a Pull Request**:
|
||||
```bash
|
||||
git push origin feature/your-feature-name
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
```bash
|
||||
# Backend tests
|
||||
cd backend
|
||||
make test
|
||||
|
||||
# Frontend unit tests
|
||||
cd frontend
|
||||
make test
|
||||
|
||||
# Frontend E2E tests (requires Chromium; builds and auto-starts the Next.js production server)
|
||||
cd frontend
|
||||
make test-e2e
|
||||
```
|
||||
|
||||
### PR Regression Checks
|
||||
|
||||
Every pull request triggers the following CI workflows:
|
||||
|
||||
- **Backend unit tests** — [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml)
|
||||
- **Frontend unit tests** — [.github/workflows/frontend-unit-tests.yml](.github/workflows/frontend-unit-tests.yml)
|
||||
- **Frontend E2E tests** — [.github/workflows/e2e-tests.yml](.github/workflows/e2e-tests.yml) (triggered only when `frontend/` files change)
|
||||
|
||||
## Code Style
|
||||
|
||||
- **Backend (Python)**: We use `ruff` for linting and formatting. Run `make format` before committing.
|
||||
- **Frontend (TypeScript)**: We use ESLint and Prettier. Run `pnpm format:write` before committing.
|
||||
- CI enforces formatting — PRs with unformatted code will fail the lint check.
|
||||
|
||||
## Documentation
|
||||
|
||||
- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration
|
||||
- [Architecture Overview](backend/CLAUDE.md) - Technical architecture
|
||||
- [MCP Setup Guide](backend/docs/MCP_SERVER.md) - Model Context Protocol configuration
|
||||
|
||||
## Need Help?
|
||||
|
||||
- Check existing [Issues](https://github.com/bytedance/deer-flow/issues)
|
||||
- Read the [Documentation](backend/docs/)
|
||||
- Ask questions in [Discussions](https://github.com/bytedance/deer-flow/discussions)
|
||||
|
||||
## License
|
||||
|
||||
By contributing to DeerFlow, you agree that your contributions will be licensed under the [MIT License](./LICENSE).
|
||||
+24
@@ -0,0 +1,24 @@
|
||||
FROM ghcr.io/astral-sh/uv:python3.12-bookworm
|
||||
|
||||
# Install uv.
|
||||
COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Pre-cache the application dependencies.
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
--mount=type=bind,source=uv.lock,target=uv.lock \
|
||||
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
|
||||
uv sync --locked --no-install-project
|
||||
|
||||
# Copy the application into the container.
|
||||
COPY . /app
|
||||
|
||||
# Install the application dependencies.
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
uv sync --locked
|
||||
|
||||
EXPOSE 8000
|
||||
|
||||
# Run the application.
|
||||
CMD ["uv", "run", "python", "server.py", "--host", "0.0.0.0", "--port", "8000"]
|
||||
-87
@@ -1,87 +0,0 @@
|
||||
# DeerFlow Install
|
||||
|
||||
This file is for coding agents. If the DeerFlow repository is not already cloned and open, clone `https://github.com/bytedance/deer-flow.git` first, then continue from the repository root.
|
||||
|
||||
## Goal
|
||||
|
||||
Bootstrap a DeerFlow local development workspace on the user's machine with the least risky path available.
|
||||
|
||||
Default preference:
|
||||
|
||||
1. Docker development environment
|
||||
2. Local development environment
|
||||
|
||||
Do not assume API keys or model credentials exist. Set up everything that can be prepared safely, then stop with a concise summary of what the user still needs to provide.
|
||||
|
||||
## Operating Rules
|
||||
|
||||
- Be idempotent. Re-running this document should not damage an existing setup.
|
||||
- Prefer existing repo commands over ad hoc shell commands.
|
||||
- Do not use `sudo` or install system packages without explicit user approval.
|
||||
- Do not overwrite existing user config values unless the user asks.
|
||||
- If a step fails, stop, explain the blocker, and provide the smallest next action.
|
||||
- If multiple setup paths are possible, prefer Docker when Docker is already available.
|
||||
|
||||
## Success Criteria
|
||||
|
||||
Consider the setup successful when all of the following are true:
|
||||
|
||||
- The DeerFlow repository is cloned and the current working directory is the repo root.
|
||||
- `config.yaml` exists.
|
||||
- For Docker setup, `make docker-init` completed successfully and Docker prerequisites are prepared, but services are not assumed to be running yet.
|
||||
- For local setup, `make check` passed or reported no missing prerequisites, and `make install` completed successfully.
|
||||
- The user receives the exact next command to launch DeerFlow.
|
||||
- The user also receives any missing model configuration or referenced environment variable names from `config.yaml`, without inspecting secret-bearing files for actual values.
|
||||
|
||||
## Steps
|
||||
|
||||
- If the current directory is not the DeerFlow repository root, clone `https://github.com/bytedance/deer-flow.git` if needed, then change into the repository root.
|
||||
- Confirm the current directory is the DeerFlow repository root by checking that `Makefile`, `backend/`, `frontend/`, and `config.example.yaml` exist.
|
||||
- Detect whether `config.yaml` already exists.
|
||||
- If `config.yaml` does not exist, run `make config`.
|
||||
- Detect whether Docker is available and the daemon is reachable with `docker info`.
|
||||
- If Docker is available:
|
||||
- Run `make docker-init`.
|
||||
- Treat this as Docker prerequisite preparation only. Do not claim that app services, compose validation, or image builds have already succeeded.
|
||||
- Do not start long-running services unless the user explicitly asks or this setup request clearly includes launch verification.
|
||||
- Tell the user the recommended next command is `make docker-start`.
|
||||
- If Docker is not available:
|
||||
- Run `make check`.
|
||||
- If `make check` reports missing system dependencies such as `node`, `pnpm`, `uv`, or `nginx`, stop and report the missing tools instead of attempting privileged installs.
|
||||
- If prerequisites are satisfied, run `make install`.
|
||||
- Tell the user the recommended next command is `make dev`.
|
||||
- Inspect `config.yaml` only for missing model entries or referenced environment variable placeholders. Do not read `.env`, `frontend/.env`, or other secret-bearing files.
|
||||
- If no model is configured, tell the user they must add at least one entry under `models` in `config.yaml`.
|
||||
- If `config.yaml` references variables such as `$OPENAI_API_KEY`, tell the user which variable names still need real values, but do not verify them by opening secret-bearing files.
|
||||
- If the repository already appears configured, avoid repeating expensive work unless it is necessary to verify the environment.
|
||||
|
||||
## Verification
|
||||
|
||||
Use the lightest verification that matches the chosen setup path.
|
||||
|
||||
For Docker setup:
|
||||
|
||||
- Confirm `make docker-init` completed successfully.
|
||||
- Confirm `config.yaml` exists.
|
||||
- State explicitly that Docker services were not started and `make docker-start` is still the first real launch step.
|
||||
- Do not leave background services running unless the user asked for that.
|
||||
|
||||
For local setup:
|
||||
|
||||
- Confirm `make install` completed successfully.
|
||||
- Confirm `config.yaml` exists.
|
||||
- Do not leave background services running unless the user asked for that.
|
||||
|
||||
## Final Response Format
|
||||
|
||||
Return a short status report with:
|
||||
|
||||
1. Setup path used: Docker or local
|
||||
2. Setup level reached: Docker prerequisites prepared or local dependencies installed
|
||||
3. Files created or detected: for example `config.yaml`
|
||||
4. Remaining user action: model config, env var values, auth files, or nothing
|
||||
5. Exact next command to start DeerFlow
|
||||
|
||||
## EXECUTE NOW
|
||||
|
||||
Complete the steps above. When finished, stop after the setup boundary and report status instead of continuing into unrelated project work.
|
||||
@@ -1,7 +1,6 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
|
||||
Copyright (c) 2025-2026 DeerFlow Authors
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
@@ -19,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
SOFTWARE.
|
||||
@@ -1,215 +1,23 @@
|
||||
# DeerFlow - Unified Development Environment
|
||||
.PHONY: lint format install-dev serve test coverage
|
||||
|
||||
.PHONY: help config config-upgrade check install setup doctor dev dev-pro dev-daemon dev-daemon-pro start start-pro start-daemon start-daemon-pro stop up up-pro down clean docker-init docker-start docker-start-pro docker-stop docker-logs docker-logs-frontend docker-logs-gateway
|
||||
install-dev:
|
||||
uv pip install -e ".[dev]" && uv pip install -e ".[test]"
|
||||
|
||||
BASH ?= bash
|
||||
BACKEND_UV_RUN = cd backend && uv run
|
||||
format:
|
||||
uv run black --preview .
|
||||
|
||||
# Detect OS for Windows compatibility
|
||||
ifeq ($(OS),Windows_NT)
|
||||
SHELL := cmd.exe
|
||||
PYTHON ?= python
|
||||
# Run repo shell scripts through Git Bash when Make is launched from cmd.exe / PowerShell.
|
||||
RUN_WITH_GIT_BASH = call scripts\run-with-git-bash.cmd
|
||||
else
|
||||
PYTHON ?= python3
|
||||
RUN_WITH_GIT_BASH =
|
||||
endif
|
||||
lint:
|
||||
uv run black --check .
|
||||
uv run ruff check .
|
||||
|
||||
help:
|
||||
@echo "DeerFlow Development Commands:"
|
||||
@echo " make setup - Interactive setup wizard (recommended for new users)"
|
||||
@echo " make doctor - Check configuration and system requirements"
|
||||
@echo " make config - Generate local config files (aborts if config already exists)"
|
||||
@echo " make config-upgrade - Merge new fields from config.example.yaml into config.yaml"
|
||||
@echo " make check - Check if all required tools are installed"
|
||||
@echo " make install - Install all dependencies (frontend + backend)"
|
||||
@echo " make setup-sandbox - Pre-pull sandbox container image (recommended)"
|
||||
@echo " make dev - Start all services in development mode (with hot-reloading)"
|
||||
@echo " make dev-pro - Start in dev + Gateway mode (experimental, no LangGraph server)"
|
||||
@echo " make dev-daemon - Start dev services in background (daemon mode)"
|
||||
@echo " make dev-daemon-pro - Start dev daemon + Gateway mode (experimental)"
|
||||
@echo " make start - Start all services in production mode (optimized, no hot-reloading)"
|
||||
@echo " make start-pro - Start in prod + Gateway mode (experimental)"
|
||||
@echo " make start-daemon - Start prod services in background (daemon mode)"
|
||||
@echo " make start-daemon-pro - Start prod daemon + Gateway mode (experimental)"
|
||||
@echo " make stop - Stop all running services"
|
||||
@echo " make clean - Clean up processes and temporary files"
|
||||
@echo ""
|
||||
@echo "Docker Production Commands:"
|
||||
@echo " make up - Build and start production Docker services (localhost:2026)"
|
||||
@echo " make up-pro - Build and start production Docker in Gateway mode (experimental)"
|
||||
@echo " make down - Stop and remove production Docker containers"
|
||||
@echo ""
|
||||
@echo "Docker Development Commands:"
|
||||
@echo " make docker-init - Pull the sandbox image"
|
||||
@echo " make docker-start - Start Docker services (mode-aware from config.yaml, localhost:2026)"
|
||||
@echo " make docker-start-pro - Start Docker in Gateway mode (experimental, no LangGraph container)"
|
||||
@echo " make docker-stop - Stop Docker development services"
|
||||
@echo " make docker-logs - View Docker development logs"
|
||||
@echo " make docker-logs-frontend - View Docker frontend logs"
|
||||
@echo " make docker-logs-gateway - View Docker gateway logs"
|
||||
serve:
|
||||
uv run server.py --reload
|
||||
|
||||
## Setup & Diagnosis
|
||||
setup:
|
||||
@$(BACKEND_UV_RUN) python ../scripts/setup_wizard.py
|
||||
test:
|
||||
uv run pytest tests/
|
||||
|
||||
doctor:
|
||||
@$(BACKEND_UV_RUN) python ../scripts/doctor.py
|
||||
langgraph-dev:
|
||||
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.12 langgraph dev --allow-blocking
|
||||
|
||||
config:
|
||||
@$(PYTHON) ./scripts/configure.py
|
||||
|
||||
config-upgrade:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/config-upgrade.sh
|
||||
|
||||
# Check required tools
|
||||
check:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
|
||||
# Install all dependencies
|
||||
install:
|
||||
@echo "Installing backend dependencies..."
|
||||
@cd backend && uv sync
|
||||
@echo "Installing frontend dependencies..."
|
||||
@cd frontend && pnpm install
|
||||
@echo "✓ All dependencies installed"
|
||||
@echo ""
|
||||
@echo "=========================================="
|
||||
@echo " Optional: Pre-pull Sandbox Image"
|
||||
@echo "=========================================="
|
||||
@echo ""
|
||||
@echo "If you plan to use Docker/Container-based sandbox, you can pre-pull the image:"
|
||||
@echo " make setup-sandbox"
|
||||
@echo ""
|
||||
|
||||
# Pre-pull sandbox Docker image (optional but recommended)
|
||||
setup-sandbox:
|
||||
@echo "=========================================="
|
||||
@echo " Pre-pulling Sandbox Container Image"
|
||||
@echo "=========================================="
|
||||
@echo ""
|
||||
@IMAGE=$$(grep -A 20 "# sandbox:" config.yaml 2>/dev/null | grep "image:" | awk '{print $$2}' | head -1); \
|
||||
if [ -z "$$IMAGE" ]; then \
|
||||
IMAGE="enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"; \
|
||||
echo "Using default image: $$IMAGE"; \
|
||||
else \
|
||||
echo "Using configured image: $$IMAGE"; \
|
||||
fi; \
|
||||
echo ""; \
|
||||
if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
|
||||
echo "Detected Apple Container on macOS, pulling image..."; \
|
||||
container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
|
||||
fi; \
|
||||
if command -v docker >/dev/null 2>&1; then \
|
||||
echo "Pulling image using Docker..."; \
|
||||
if docker pull "$$IMAGE"; then \
|
||||
echo ""; \
|
||||
echo "✓ Sandbox image pulled successfully"; \
|
||||
else \
|
||||
echo ""; \
|
||||
echo "⚠ Failed to pull sandbox image (this is OK for local sandbox mode)"; \
|
||||
fi; \
|
||||
else \
|
||||
echo "✗ Neither Docker nor Apple Container is available"; \
|
||||
echo " Please install Docker: https://docs.docker.com/get-docker/"; \
|
||||
exit 1; \
|
||||
fi
|
||||
|
||||
# Start all services in development mode (with hot-reloading)
|
||||
dev:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev
|
||||
|
||||
# Start all services in dev + Gateway mode (experimental: agent runtime embedded in Gateway)
|
||||
dev-pro:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway
|
||||
|
||||
# Start all services in production mode (with optimizations)
|
||||
start:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod
|
||||
|
||||
# Start all services in prod + Gateway mode (experimental)
|
||||
start-pro:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway
|
||||
|
||||
# Start all services in daemon mode (background)
|
||||
dev-daemon:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --daemon
|
||||
|
||||
# Start daemon + Gateway mode (experimental)
|
||||
dev-daemon-pro:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway --daemon
|
||||
|
||||
# Start prod services in daemon mode (background)
|
||||
start-daemon:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --daemon
|
||||
|
||||
# Start prod daemon + Gateway mode (experimental)
|
||||
start-daemon-pro:
|
||||
@$(PYTHON) ./scripts/check.py
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway --daemon
|
||||
|
||||
# Stop all services
|
||||
stop:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --stop
|
||||
|
||||
# Clean up
|
||||
clean: stop
|
||||
@echo "Cleaning up..."
|
||||
@-rm -rf backend/.deer-flow 2>/dev/null || true
|
||||
@-rm -rf backend/.langgraph_api 2>/dev/null || true
|
||||
@-rm -rf logs/*.log 2>/dev/null || true
|
||||
@echo "✓ Cleanup complete"
|
||||
|
||||
# ==========================================
|
||||
# Docker Development Commands
|
||||
# ==========================================
|
||||
|
||||
# Initialize Docker containers and install dependencies
|
||||
docker-init:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh init
|
||||
|
||||
# Start Docker development environment
|
||||
docker-start:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start
|
||||
|
||||
# Start Docker in Gateway mode (experimental)
|
||||
docker-start-pro:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start --gateway
|
||||
|
||||
# Stop Docker development environment
|
||||
docker-stop:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh stop
|
||||
|
||||
# View Docker development logs
|
||||
docker-logs:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs
|
||||
|
||||
# View Docker development logs
|
||||
docker-logs-frontend:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs --frontend
|
||||
docker-logs-gateway:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs --gateway
|
||||
|
||||
# ==========================================
|
||||
# Production Docker Commands
|
||||
# ==========================================
|
||||
|
||||
# Build and start production services
|
||||
up:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh
|
||||
|
||||
# Build and start production services in Gateway mode
|
||||
up-pro:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh --gateway
|
||||
|
||||
# Stop and remove production containers
|
||||
down:
|
||||
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh down
|
||||
coverage:
|
||||
uv run pytest --cov=src tests/ --cov-report=term-missing --cov-report=xml
|
||||
|
||||
+504
@@ -0,0 +1,504 @@
|
||||
# 🦌 DeerFlow
|
||||
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://deepwiki.com/bytedance/deer-flow)
|
||||
<!-- DeepWiki badge generated by https://deepwiki.ryoppippi.com/ -->
|
||||
|
||||
[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md) | [Español](./README_es.md) | [Русский](./README_ru.md) | [Portuguese](./README_pt.md)
|
||||
|
||||
> Aus Open Source entstanden, an Open Source zurückgeben.
|
||||
|
||||
**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) ist ein Community-getriebenes Framework für tiefgehende Recherche, das auf der großartigen Arbeit der Open-Source-Community aufbaut. Unser Ziel ist es, Sprachmodelle mit spezialisierten Werkzeugen für Aufgaben wie Websuche, Crawling und Python-Code-Ausführung zu kombinieren und gleichzeitig der Community, die dies möglich gemacht hat, etwas zurückzugeben.
|
||||
|
||||
Derzeit ist DeerFlow offiziell in das FaaS-Anwendungszentrum von Volcengine eingezogen. Benutzer können es über den Erfahrungslink online erleben, um seine leistungsstarken Funktionen und bequemen Operationen intuitiv zu spüren. Gleichzeitig unterstützt DeerFlow zur Erfüllung der Bereitstellungsanforderungen verschiedener Benutzer die Ein-Klick-Bereitstellung basierend auf Volcengine. Klicken Sie auf den Bereitstellungslink, um den Bereitstellungsprozess schnell abzuschließen und eine effiziente Forschungsreise zu beginnen.
|
||||
|
||||
Besuchen Sie [unsere offizielle Website](https://deerflow.tech/) für weitere Details.
|
||||
|
||||
## Demo
|
||||
|
||||
### Video
|
||||
|
||||
<https://github.com/user-attachments/assets/f3786598-1f2a-4d07-919e-8b99dfa1de3e>
|
||||
|
||||
In dieser Demo zeigen wir, wie man DeerFlow nutzt, um:
|
||||
|
||||
- Nahtlos mit MCP-Diensten zu integrieren
|
||||
- Den Prozess der tiefgehenden Recherche durchzuführen und einen umfassenden Bericht mit Bildern zu erstellen
|
||||
- Podcast-Audio basierend auf dem generierten Bericht zu erstellen
|
||||
|
||||
### Wiedergaben
|
||||
|
||||
- [Wie hoch ist der Eiffelturm im Vergleich zum höchsten Gebäude?](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)
|
||||
- [Was sind die angesagtesten Repositories auf GitHub?](https://deerflow.tech/chat?replay=github-top-trending-repo)
|
||||
- [Einen Artikel über traditionelle Gerichte aus Nanjing schreiben](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)
|
||||
- [Wie dekoriert man eine Mietwohnung?](https://deerflow.tech/chat?replay=rental-apartment-decoration)
|
||||
- [Besuchen Sie unsere offizielle Website, um weitere Wiedergaben zu entdecken.](https://deerflow.tech/#case-studies)
|
||||
|
||||
---
|
||||
|
||||
## 📑 Inhaltsverzeichnis
|
||||
|
||||
- [🚀 Schnellstart](#schnellstart)
|
||||
- [🌟 Funktionen](#funktionen)
|
||||
- [🏗️ Architektur](#architektur)
|
||||
- [🛠️ Entwicklung](#entwicklung)
|
||||
- [🗣️ Text-zu-Sprache-Integration](#text-zu-sprache-integration)
|
||||
- [📚 Beispiele](#beispiele)
|
||||
- [❓ FAQ](#faq)
|
||||
- [📜 Lizenz](#lizenz)
|
||||
- [💖 Danksagungen](#danksagungen)
|
||||
- [⭐ Star-Verlauf](#star-verlauf)
|
||||
|
||||
## Schnellstart
|
||||
|
||||
DeerFlow ist in Python entwickelt und kommt mit einer in Node.js geschriebenen Web-UI. Um einen reibungslosen Einrichtungsprozess zu gewährleisten, empfehlen wir die Verwendung der folgenden Tools:
|
||||
|
||||
### Empfohlene Tools
|
||||
|
||||
- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**
|
||||
Vereinfacht die Verwaltung von Python-Umgebungen und Abhängigkeiten. `uv` erstellt automatisch eine virtuelle Umgebung im Stammverzeichnis und installiert alle erforderlichen Pakete für Sie—keine manuelle Installation von Python-Umgebungen notwendig.
|
||||
|
||||
- **[`nvm`](https://github.com/nvm-sh/nvm):**
|
||||
Verwalten Sie mühelos mehrere Versionen der Node.js-Laufzeit.
|
||||
|
||||
- **[`pnpm`](https://pnpm.io/installation):**
|
||||
Installieren und verwalten Sie Abhängigkeiten des Node.js-Projekts.
|
||||
|
||||
### Umgebungsanforderungen
|
||||
|
||||
Stellen Sie sicher, dass Ihr System die folgenden Mindestanforderungen erfüllt:
|
||||
|
||||
- **[Python](https://www.python.org/downloads/):** Version `3.12+`
|
||||
- **[Node.js](https://nodejs.org/en/download/):** Version `22+`
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
# Repository klonen
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
|
||||
# Abhängigkeiten installieren, uv kümmert sich um den Python-Interpreter und die Erstellung der venv sowie die Installation der erforderlichen Pakete
|
||||
uv sync
|
||||
|
||||
# Konfigurieren Sie .env mit Ihren API-Schlüsseln
|
||||
# Tavily: https://app.tavily.com/home
|
||||
# Brave_SEARCH: https://brave.com/search/api/
|
||||
# volcengine TTS: Fügen Sie Ihre TTS-Anmeldedaten hinzu, falls vorhanden
|
||||
cp .env.example .env
|
||||
|
||||
# Siehe die Abschnitte 'Unterstützte Suchmaschinen' und 'Text-zu-Sprache-Integration' unten für alle verfügbaren Optionen
|
||||
|
||||
# Konfigurieren Sie conf.yaml für Ihr LLM-Modell und API-Schlüssel
|
||||
# Weitere Details finden Sie unter 'docs/configuration_guide.md'
|
||||
cp conf.yaml.example conf.yaml
|
||||
|
||||
# Installieren Sie marp für PPT-Generierung
|
||||
# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager
|
||||
brew install marp-cli
|
||||
```
|
||||
|
||||
Optional können Sie Web-UI-Abhängigkeiten über [pnpm](https://pnpm.io/installation) installieren:
|
||||
|
||||
```bash
|
||||
cd deer-flow/web
|
||||
pnpm install
|
||||
```
|
||||
|
||||
### Konfigurationen
|
||||
|
||||
Weitere Informationen finden Sie im [Konfigurationsleitfaden](docs/configuration_guide.md).
|
||||
|
||||
> [!HINWEIS]
|
||||
> Lesen Sie den Leitfaden sorgfältig, bevor Sie das Projekt starten, und aktualisieren Sie die Konfigurationen entsprechend Ihren spezifischen Einstellungen und Anforderungen.
|
||||
|
||||
### Konsolen-UI
|
||||
|
||||
Der schnellste Weg, um das Projekt auszuführen, ist die Verwendung der Konsolen-UI.
|
||||
|
||||
```bash
|
||||
# Führen Sie das Projekt in einer bash-ähnlichen Shell aus
|
||||
uv run main.py
|
||||
```
|
||||
|
||||
### Web-UI
|
||||
|
||||
Dieses Projekt enthält auch eine Web-UI, die ein dynamischeres und ansprechenderes interaktives Erlebnis bietet.
|
||||
> [!HINWEIS]
|
||||
> Sie müssen zuerst die Abhängigkeiten der Web-UI installieren.
|
||||
|
||||
```bash
|
||||
# Führen Sie sowohl den Backend- als auch den Frontend-Server im Entwicklungsmodus aus
|
||||
# Unter macOS/Linux
|
||||
./bootstrap.sh -d
|
||||
|
||||
# Unter Windows
|
||||
bootstrap.bat -d
|
||||
```
|
||||
|
||||
Öffnen Sie Ihren Browser und besuchen Sie [`http://localhost:3000`](http://localhost:3000), um die Web-UI zu erkunden.
|
||||
|
||||
Weitere Details finden Sie im Verzeichnis [`web`](./web/).
|
||||
|
||||
## Unterstützte Suchmaschinen
|
||||
|
||||
DeerFlow unterstützt mehrere Suchmaschinen, die in Ihrer `.env`-Datei über die Variable `SEARCH_API` konfiguriert werden können:
|
||||
|
||||
- **Tavily** (Standard): Eine spezialisierte Such-API für KI-Anwendungen
|
||||
- Erfordert `TAVILY_API_KEY` in Ihrer `.env`-Datei
|
||||
- Registrieren Sie sich unter: <https://app.tavily.com/home>
|
||||
|
||||
- **DuckDuckGo**: Datenschutzorientierte Suchmaschine
|
||||
- Kein API-Schlüssel erforderlich
|
||||
|
||||
- **Brave Search**: Datenschutzorientierte Suchmaschine mit erweiterten Funktionen
|
||||
- Erfordert `BRAVE_SEARCH_API_KEY` in Ihrer `.env`-Datei
|
||||
- Registrieren Sie sich unter: <https://brave.com/search/api/>
|
||||
|
||||
- **Arxiv**: Wissenschaftliche Papiersuche für akademische Forschung
|
||||
- Kein API-Schlüssel erforderlich
|
||||
- Spezialisiert auf wissenschaftliche und akademische Papiere
|
||||
|
||||
Um Ihre bevorzugte Suchmaschine zu konfigurieren, setzen Sie die Variable `SEARCH_API` in Ihrer `.env`-Datei:
|
||||
|
||||
```bash
|
||||
# Wählen Sie eine: tavily, duckduckgo, brave_search, arxiv
|
||||
SEARCH_API=tavily
|
||||
```
|
||||
|
||||
## Funktionen
|
||||
|
||||
### Kernfähigkeiten
|
||||
|
||||
- 🤖 **LLM-Integration**
|
||||
- Unterstützt die Integration der meisten Modelle über [litellm](https://docs.litellm.ai/docs/providers).
|
||||
- Unterstützung für Open-Source-Modelle wie Qwen
|
||||
- OpenAI-kompatible API-Schnittstelle
|
||||
- Mehrstufiges LLM-System für unterschiedliche Aufgabenkomplexitäten
|
||||
|
||||
### Tools und MCP-Integrationen
|
||||
|
||||
- 🔍 **Suche und Abruf**
|
||||
- Websuche über Tavily, Brave Search und mehr
|
||||
- Crawling mit Jina
|
||||
- Fortgeschrittene Inhaltsextraktion
|
||||
|
||||
- 🔗 **MCP Nahtlose Integration**
|
||||
- Erweiterte Fähigkeiten für privaten Domänenzugriff, Wissensgraphen, Webbrowsing und mehr
|
||||
- Erleichtert die Integration verschiedener Forschungswerkzeuge und -methoden
|
||||
|
||||
### Menschliche Zusammenarbeit
|
||||
|
||||
- 🧠 **Mensch-in-der-Schleife**
|
||||
- Unterstützt interaktive Modifikation von Forschungsplänen mit natürlicher Sprache
|
||||
- Unterstützt automatische Akzeptanz von Forschungsplänen
|
||||
|
||||
- 📝 **Bericht-Nachbearbeitung**
|
||||
- Unterstützt Notion-ähnliche Blockbearbeitung
|
||||
- Ermöglicht KI-Verfeinerungen, einschließlich KI-unterstützter Polierung, Satzkürzung und -erweiterung
|
||||
- Angetrieben von [tiptap](https://tiptap.dev/)
|
||||
|
||||
### Inhaltserstellung
|
||||
|
||||
- 🎙️ **Podcast- und Präsentationserstellung**
|
||||
- KI-gestützte Podcast-Skripterstellung und Audiosynthese
|
||||
- Automatisierte Erstellung einfacher PowerPoint-Präsentationen
|
||||
- Anpassbare Vorlagen für maßgeschneiderte Inhalte
|
||||
|
||||
## Architektur
|
||||
|
||||
DeerFlow implementiert eine modulare Multi-Agenten-Systemarchitektur, die für automatisierte Forschung und Codeanalyse konzipiert ist. Das System basiert auf LangGraph und ermöglicht einen flexiblen zustandsbasierten Workflow, bei dem Komponenten über ein klar definiertes Nachrichtenübermittlungssystem kommunizieren.
|
||||
|
||||
|
||||
> Sehen Sie es live auf [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)
|
||||
|
||||
Das System verwendet einen optimierten Workflow mit den folgenden Komponenten:
|
||||
|
||||
1. **Koordinator**: Der Einstiegspunkt, der den Workflow-Lebenszyklus verwaltet
|
||||
- Initiiert den Forschungsprozess basierend auf Benutzereingaben
|
||||
- Delegiert Aufgaben bei Bedarf an den Planer
|
||||
- Fungiert als primäre Schnittstelle zwischen dem Benutzer und dem System
|
||||
|
||||
2. **Planer**: Strategische Komponente für Aufgabenzerlegung und -planung
|
||||
- Analysiert Forschungsziele und erstellt strukturierte Ausführungspläne
|
||||
- Bestimmt, ob ausreichend Kontext verfügbar ist oder ob weitere Forschung benötigt wird
|
||||
- Verwaltet den Forschungsablauf und entscheidet, wann der endgültige Bericht erstellt wird
|
||||
|
||||
3. **Forschungsteam**: Eine Sammlung spezialisierter Agenten, die den Plan ausführen:
|
||||
- **Forscher**: Führt Websuchen und Informationssammlung mit Tools wie Websuchmaschinen, Crawling und sogar MCP-Diensten durch.
|
||||
- **Codierer**: Behandelt Codeanalyse, -ausführung und technische Aufgaben mit dem Python REPL Tool.
|
||||
Jeder Agent hat Zugriff auf spezifische Tools, die für seine Rolle optimiert sind, und operiert innerhalb des LangGraph-Frameworks
|
||||
|
||||
4. **Reporter**: Endphasenprozessor für Forschungsergebnisse
|
||||
- Aggregiert Erkenntnisse vom Forschungsteam
|
||||
- Verarbeitet und strukturiert die gesammelten Informationen
|
||||
- Erstellt umfassende Forschungsberichte
|
||||
|
||||
## Text-zu-Sprache-Integration
|
||||
|
||||
DeerFlow enthält jetzt eine Text-zu-Sprache (TTS)-Funktion, mit der Sie Forschungsberichte in Sprache umwandeln können. Diese Funktion verwendet die volcengine TTS API, um hochwertige Audios aus Text zu generieren. Funktionen wie Geschwindigkeit, Lautstärke und Tonhöhe können ebenfalls angepasst werden.
|
||||
|
||||
### Verwendung der TTS API
|
||||
|
||||
Sie können auf die TTS-Funktionalität über den Endpunkt `/api/tts` zugreifen:
|
||||
|
||||
```bash
|
||||
# Beispiel API-Aufruf mit curl
|
||||
curl --location 'http://localhost:8000/api/tts' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"text": "Dies ist ein Test der Text-zu-Sprache-Funktionalität.",
|
||||
"speed_ratio": 1.0,
|
||||
"volume_ratio": 1.0,
|
||||
"pitch_ratio": 1.0
|
||||
}' \
|
||||
--output speech.mp3
|
||||
```
|
||||
|
||||
## Entwicklung
|
||||
|
||||
### Testen
|
||||
|
||||
Führen Sie die Testsuite aus:
|
||||
|
||||
```bash
|
||||
# Alle Tests ausführen
|
||||
make test
|
||||
|
||||
# Spezifische Testdatei ausführen
|
||||
pytest tests/integration/test_workflow.py
|
||||
|
||||
# Mit Abdeckung ausführen
|
||||
make coverage
|
||||
```
|
||||
|
||||
### Codequalität
|
||||
|
||||
```bash
|
||||
# Lint ausführen
|
||||
make lint
|
||||
|
||||
# Code formatieren
|
||||
make format
|
||||
```
|
||||
|
||||
### Debugging mit LangGraph Studio
|
||||
|
||||
DeerFlow verwendet LangGraph für seine Workflow-Architektur. Sie können LangGraph Studio verwenden, um den Workflow in Echtzeit zu debuggen und zu visualisieren.
|
||||
|
||||
#### LangGraph Studio lokal ausführen
|
||||
|
||||
DeerFlow enthält eine `langgraph.json`-Konfigurationsdatei, die die Graphstruktur und Abhängigkeiten für das LangGraph Studio definiert. Diese Datei verweist auf die im Projekt definierten Workflow-Graphen und lädt automatisch Umgebungsvariablen aus der `.env`-Datei.
|
||||
|
||||
##### Mac
|
||||
|
||||
```bash
|
||||
# Installieren Sie den uv-Paketmanager, wenn Sie ihn noch nicht haben
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
# Installieren Sie Abhängigkeiten und starten Sie den LangGraph-Server
|
||||
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.12 langgraph dev --allow-blocking
|
||||
```
|
||||
|
||||
##### Windows / Linux
|
||||
|
||||
```bash
|
||||
# Abhängigkeiten installieren
|
||||
pip install -e .
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
|
||||
# LangGraph-Server starten
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Nach dem Start des LangGraph-Servers sehen Sie mehrere URLs im Terminal:
|
||||
|
||||
- API: <http://127.0.0.1:2024>
|
||||
- Studio UI: <https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024>
|
||||
- API-Dokumentation: <http://127.0.0.1:2024/docs>
|
||||
|
||||
Öffnen Sie den Studio UI-Link in Ihrem Browser, um auf die Debugging-Schnittstelle zuzugreifen.
|
||||
|
||||
#### Verwendung von LangGraph Studio
|
||||
|
||||
In der Studio UI können Sie:
|
||||
|
||||
1. Den Workflow-Graphen visualisieren und sehen, wie Komponenten verbunden sind
|
||||
2. Die Ausführung in Echtzeit verfolgen, um zu sehen, wie Daten durch das System fließen
|
||||
3. Den Zustand in jedem Schritt des Workflows inspizieren
|
||||
4. Probleme durch Untersuchung von Ein- und Ausgaben jeder Komponente debuggen
|
||||
5. Feedback während der Planungsphase geben, um Forschungspläne zu verfeinern
|
||||
|
||||
Wenn Sie ein Forschungsthema in der Studio UI einreichen, können Sie die gesamte Workflow-Ausführung sehen, einschließlich:
|
||||
|
||||
- Die Planungsphase, in der der Forschungsplan erstellt wird
|
||||
- Die Feedback-Schleife, in der Sie den Plan ändern können
|
||||
- Die Forschungs- und Schreibphasen für jeden Abschnitt
|
||||
- Die Erstellung des endgültigen Berichts
|
||||
|
||||
### Aktivieren von LangSmith-Tracing
|
||||
|
||||
DeerFlow unterstützt LangSmith-Tracing, um Ihnen beim Debuggen und Überwachen Ihrer Workflows zu helfen. Um LangSmith-Tracing zu aktivieren:
|
||||
|
||||
1. Stellen Sie sicher, dass Ihre `.env`-Datei die folgenden Konfigurationen enthält (siehe `.env.example`):
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
|
||||
LANGSMITH_API_KEY="xxx"
|
||||
LANGSMITH_PROJECT="xxx"
|
||||
```
|
||||
|
||||
2. Starten Sie das Tracing mit LangSmith lokal, indem Sie folgenden Befehl ausführen:
|
||||
|
||||
```bash
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Dies aktiviert die Trace-Visualisierung in LangGraph Studio und sendet Ihre Traces zur Überwachung und Analyse an LangSmith.
|
||||
|
||||
## Beispiele
|
||||
|
||||
Die folgenden Beispiele demonstrieren die Fähigkeiten von DeerFlow:
|
||||
|
||||
### Forschungsberichte
|
||||
|
||||
1. **OpenAI Sora Bericht** - Analyse von OpenAIs Sora KI-Tool
|
||||
- Diskutiert Funktionen, Zugang, Prompt-Engineering, Einschränkungen und ethische Überlegungen
|
||||
- [Vollständigen Bericht ansehen](examples/openai_sora_report.md)
|
||||
|
||||
2. **Googles Agent-to-Agent-Protokoll Bericht** - Überblick über Googles Agent-to-Agent (A2A)-Protokoll
|
||||
- Diskutiert seine Rolle in der KI-Agentenkommunikation und seine Beziehung zum Model Context Protocol (MCP) von Anthropic
|
||||
- [Vollständigen Bericht ansehen](examples/what_is_agent_to_agent_protocol.md)
|
||||
|
||||
3. **Was ist MCP?** - Eine umfassende Analyse des Begriffs "MCP" in mehreren Kontexten
|
||||
- Untersucht Model Context Protocol in KI, Monocalciumphosphat in der Chemie und Micro-channel Plate in der Elektronik
|
||||
- [Vollständigen Bericht ansehen](examples/what_is_mcp.md)
|
||||
|
||||
4. **Bitcoin-Preisschwankungen** - Analyse der jüngsten Bitcoin-Preisbewegungen
|
||||
- Untersucht Markttrends, regulatorische Einflüsse und technische Indikatoren
|
||||
- Bietet Empfehlungen basierend auf historischen Daten
|
||||
- [Vollständigen Bericht ansehen](examples/bitcoin_price_fluctuation.md)
|
||||
|
||||
5. **Was ist LLM?** - Eine eingehende Erforschung großer Sprachmodelle
|
||||
- Diskutiert Architektur, Training, Anwendungen und ethische Überlegungen
|
||||
- [Vollständigen Bericht ansehen](examples/what_is_llm.md)
|
||||
|
||||
6. **Wie nutzt man Claude für tiefgehende Recherche?** - Best Practices und Workflows für die Verwendung von Claude in der tiefgehenden Forschung
|
||||
- Behandelt Prompt-Engineering, Datenanalyse und Integration mit anderen Tools
|
||||
- [Vollständigen Bericht ansehen](examples/how_to_use_claude_deep_research.md)
|
||||
|
||||
7. **KI-Adoption im Gesundheitswesen: Einflussfaktoren** - Analyse der Faktoren, die die KI-Adoption im Gesundheitswesen vorantreiben
|
||||
- Diskutiert KI-Technologien, Datenqualität, ethische Überlegungen, wirtschaftliche Bewertungen, organisatorische Bereitschaft und digitale Infrastruktur
|
||||
- [Vollständigen Bericht ansehen](examples/AI_adoption_in_healthcare.md)
|
||||
|
||||
8. **Auswirkungen des Quantencomputing auf die Kryptographie** - Analyse der Auswirkungen des Quantencomputing auf die Kryptographie
|
||||
- Diskutiert Schwachstellen der klassischen Kryptographie, Post-Quanten-Kryptographie und quantenresistente kryptographische Lösungen
|
||||
- [Vollständigen Bericht ansehen](examples/Quantum_Computing_Impact_on_Cryptography.md)
|
||||
|
||||
9. **Cristiano Ronaldos Leistungshöhepunkte** - Analyse der Leistungshöhepunkte von Cristiano Ronaldo
|
||||
- Diskutiert seine Karriereerfolge, internationalen Tore und Leistungen in verschiedenen Spielen
|
||||
- [Vollständigen Bericht ansehen](examples/Cristiano_Ronaldo's_Performance_Highlights.md)
|
||||
|
||||
Um diese Beispiele auszuführen oder Ihre eigenen Forschungsberichte zu erstellen, können Sie die folgenden Befehle verwenden:
|
||||
|
||||
```bash
|
||||
# Mit einer spezifischen Anfrage ausführen
|
||||
uv run main.py "Welche Faktoren beeinflussen die KI-Adoption im Gesundheitswesen?"
|
||||
|
||||
# Mit benutzerdefinierten Planungsparametern ausführen
|
||||
uv run main.py --max_plan_iterations 3 "Wie wirkt sich Quantencomputing auf die Kryptographie aus?"
|
||||
|
||||
# Im interaktiven Modus mit eingebauten Fragen ausführen
|
||||
uv run main.py --interactive
|
||||
|
||||
# Oder mit grundlegendem interaktiven Prompt ausführen
|
||||
uv run main.py
|
||||
|
||||
# Alle verfügbaren Optionen anzeigen
|
||||
uv run main.py --help
|
||||
```
|
||||
|
||||
### Interaktiver Modus
|
||||
|
||||
Die Anwendung unterstützt jetzt einen interaktiven Modus mit eingebauten Fragen in Englisch und Chinesisch:
|
||||
|
||||
1. Starten Sie den interaktiven Modus:
|
||||
|
||||
```bash
|
||||
uv run main.py --interactive
|
||||
```
|
||||
|
||||
2. Wählen Sie Ihre bevorzugte Sprache (English oder 中文)
|
||||
|
||||
3. Wählen Sie aus einer Liste von eingebauten Fragen oder wählen Sie die Option, Ihre eigene Frage zu stellen
|
||||
|
||||
4. Das System wird Ihre Frage verarbeiten und einen umfassenden Forschungsbericht generieren
|
||||
|
||||
### Mensch-in-der-Schleife
|
||||
|
||||
DeerFlow enthält einen Mensch-in-der-Schleife-Mechanismus, der es Ihnen ermöglicht, Forschungspläne vor ihrer Ausführung zu überprüfen, zu bearbeiten und zu genehmigen:
|
||||
|
||||
1. **Planüberprüfung**: Wenn Mensch-in-der-Schleife aktiviert ist, präsentiert das System den generierten Forschungsplan zur Überprüfung vor der Ausführung
|
||||
|
||||
2. **Feedback geben**: Sie können:
|
||||
- Den Plan akzeptieren, indem Sie mit `[ACCEPTED]` antworten
|
||||
- Den Plan bearbeiten, indem Sie Feedback geben (z.B., `[EDIT PLAN] Fügen Sie mehr Schritte zur technischen Implementierung hinzu`)
|
||||
- Das System wird Ihr Feedback einarbeiten und einen überarbeiteten Plan generieren
|
||||
|
||||
3. **Automatische Akzeptanz**: Sie können die automatische Akzeptanz aktivieren, um den Überprüfungsprozess zu überspringen:
|
||||
- Über API: Setzen Sie `auto_accepted_plan: true` in Ihrer Anfrage
|
||||
|
||||
4. **API-Integration**: Bei Verwendung der API können Sie Feedback über den Parameter `feedback` geben:
|
||||
|
||||
```json
|
||||
{
|
||||
"messages": [{"role": "user", "content": "Was ist Quantencomputing?"}],
|
||||
"thread_id": "my_thread_id",
|
||||
"auto_accepted_plan": false,
|
||||
"feedback": "[EDIT PLAN] Mehr über Quantenalgorithmen aufnehmen"
|
||||
}
|
||||
```
|
||||
|
||||
### Kommandozeilenargumente
|
||||
|
||||
Die Anwendung unterstützt mehrere Kommandozeilenargumente, um ihr Verhalten anzupassen:
|
||||
|
||||
- **query**: Die zu verarbeitende Forschungsanfrage (kann mehrere Wörter umfassen)
|
||||
- **--interactive**: Im interaktiven Modus mit eingebauten Fragen ausführen
|
||||
- **--max_plan_iterations**: Maximale Anzahl von Planungszyklen (Standard: 1)
|
||||
- **--max_step_num**: Maximale Anzahl von Schritten in einem Forschungsplan (Standard: 3)
|
||||
- **--debug**: Detaillierte Debug-Protokollierung aktivieren
|
||||
|
||||
## FAQ
|
||||
|
||||
Weitere Informationen finden Sie in der [FAQ.md](docs/FAQ.md).
|
||||
|
||||
## Lizenz
|
||||
|
||||
Dieses Projekt ist Open Source und unter der [MIT-Lizenz](./LICENSE) verfügbar.
|
||||
|
||||
## Danksagungen
|
||||
|
||||
DeerFlow baut auf der unglaublichen Arbeit der Open-Source-Community auf. Wir sind allen Projekten und Mitwirkenden zutiefst dankbar, deren Bemühungen DeerFlow möglich gemacht haben. Wahrhaftig stehen wir auf den Schultern von Riesen.
|
||||
|
||||
Wir möchten unsere aufrichtige Wertschätzung den folgenden Projekten für ihre unschätzbaren Beiträge aussprechen:
|
||||
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)**: Ihr außergewöhnliches Framework unterstützt unsere LLM-Interaktionen und -Ketten und ermöglicht nahtlose Integration und Funktionalität.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)**: Ihr innovativer Ansatz zur Multi-Agenten-Orchestrierung war maßgeblich für die Ermöglichung der ausgeklügelten Workflows von DeerFlow.
|
||||
|
||||
Diese Projekte veranschaulichen die transformative Kraft der Open-Source-Zusammenarbeit, und wir sind stolz darauf, auf ihren Grundlagen aufzubauen.
|
||||
|
||||
### Hauptmitwirkende
|
||||
|
||||
Ein herzliches Dankeschön geht an die Hauptautoren von `DeerFlow`, deren Vision, Leidenschaft und Engagement dieses Projekt zum Leben erweckt haben:
|
||||
|
||||
- **[Daniel Walnut](https://github.com/hetaoBackend/)**
|
||||
- **[Henry Li](https://github.com/magiccube/)**
|
||||
|
||||
Ihr unerschütterliches Engagement und Fachwissen waren die treibende Kraft hinter dem Erfolg von DeerFlow. Wir fühlen uns geehrt, Sie an der Spitze dieser Reise zu haben.
|
||||
|
||||
## Star-Verlauf
|
||||
|
||||
[](https://star-history.com/#bytedance/deer-flow&Date)
|
||||
+559
@@ -0,0 +1,559 @@
|
||||
# 🦌 DeerFlow
|
||||
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://deepwiki.com/bytedance/deer-flow)
|
||||
<!-- DeepWiki badge generated by https://deepwiki.ryoppippi.com/ -->
|
||||
|
||||
[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md) | [Español](./README_es.md) | [Русский](./README_ru.md) | [Portuguese](./README_pt.md)
|
||||
|
||||
> Originado del código abierto, retribuido al código abierto.
|
||||
|
||||
**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) es un marco de Investigación Profunda impulsado por la comunidad que se basa en el increíble trabajo de la comunidad de código abierto. Nuestro objetivo es combinar modelos de lenguaje con herramientas especializadas para tareas como búsqueda web, rastreo y ejecución de código Python, mientras devolvemos a la comunidad que hizo esto posible.
|
||||
|
||||
Actualmente, DeerFlow ha ingresado oficialmente al Centro de Aplicaciones FaaS de Volcengine. Los usuarios pueden experimentarlo en línea a través del enlace de experiencia para sentir intuitivamente sus potentes funciones y operaciones convenientes. Al mismo tiempo, para satisfacer las necesidades de implementación de diferentes usuarios, DeerFlow admite la implementación con un clic basada en Volcengine. Haga clic en el enlace de implementación para completar rápidamente el proceso de implementación y comenzar un viaje de investigación eficiente.
|
||||
|
||||
Por favor, visita [nuestra página web oficial](https://deerflow.tech/) para más detalles.
|
||||
|
||||
## Demostración
|
||||
|
||||
### Video
|
||||
|
||||
<https://github.com/user-attachments/assets/f3786598-1f2a-4d07-919e-8b99dfa1de3e>
|
||||
|
||||
En esta demostración, mostramos cómo usar DeerFlow para:
|
||||
|
||||
- Integrar perfectamente con servicios MCP
|
||||
- Realizar el proceso de Investigación Profunda y producir un informe completo con imágenes
|
||||
- Crear audio de podcast basado en el informe generado
|
||||
|
||||
### Repeticiones
|
||||
|
||||
- [¿Qué altura tiene la Torre Eiffel comparada con el edificio más alto?](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)
|
||||
- [¿Cuáles son los repositorios más populares en GitHub?](https://deerflow.tech/chat?replay=github-top-trending-repo)
|
||||
- [Escribir un artículo sobre los platos tradicionales de Nanjing](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)
|
||||
- [¿Cómo decorar un apartamento de alquiler?](https://deerflow.tech/chat?replay=rental-apartment-decoration)
|
||||
- [Visita nuestra página web oficial para explorar más repeticiones.](https://deerflow.tech/#case-studies)
|
||||
|
||||
---
|
||||
|
||||
## 📑 Tabla de Contenidos
|
||||
|
||||
- [🚀 Inicio Rápido](#inicio-rápido)
|
||||
- [🌟 Características](#características)
|
||||
- [🏗️ Arquitectura](#arquitectura)
|
||||
- [🛠️ Desarrollo](#desarrollo)
|
||||
- [🐳 Docker](#docker)
|
||||
- [🗣️ Integración de Texto a Voz](#integración-de-texto-a-voz)
|
||||
- [📚 Ejemplos](#ejemplos)
|
||||
- [❓ Preguntas Frecuentes](#preguntas-frecuentes)
|
||||
- [📜 Licencia](#licencia)
|
||||
- [💖 Agradecimientos](#agradecimientos)
|
||||
- [⭐ Historial de Estrellas](#historial-de-estrellas)
|
||||
|
||||
## Inicio Rápido
|
||||
|
||||
DeerFlow está desarrollado en Python y viene con una interfaz web escrita en Node.js. Para garantizar un proceso de configuración sin problemas, recomendamos utilizar las siguientes herramientas:
|
||||
|
||||
### Herramientas Recomendadas
|
||||
|
||||
- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**
|
||||
Simplifica la gestión del entorno Python y las dependencias. `uv` crea automáticamente un entorno virtual en el directorio raíz e instala todos los paquetes necesarios por ti—sin necesidad de instalar entornos Python manualmente.
|
||||
|
||||
- **[`nvm`](https://github.com/nvm-sh/nvm):**
|
||||
Gestiona múltiples versiones del entorno de ejecución Node.js sin esfuerzo.
|
||||
|
||||
- **[`pnpm`](https://pnpm.io/installation):**
|
||||
Instala y gestiona dependencias del proyecto Node.js.
|
||||
|
||||
### Requisitos del Entorno
|
||||
|
||||
Asegúrate de que tu sistema cumple con los siguientes requisitos mínimos:
|
||||
|
||||
- **[Python](https://www.python.org/downloads/):** Versión `3.12+`
|
||||
- **[Node.js](https://nodejs.org/en/download/):** Versión `22+`
|
||||
|
||||
### Instalación
|
||||
|
||||
```bash
|
||||
# Clonar el repositorio
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
|
||||
# Instalar dependencias, uv se encargará del intérprete de python, la creación del entorno virtual y la instalación de los paquetes necesarios
|
||||
uv sync
|
||||
|
||||
# Configurar .env con tus claves API
|
||||
# Tavily: https://app.tavily.com/home
|
||||
# Brave_SEARCH: https://brave.com/search/api/
|
||||
# volcengine TTS: Añade tus credenciales TTS si las tienes
|
||||
cp .env.example .env
|
||||
|
||||
# Ver las secciones 'Motores de Búsqueda Compatibles' e 'Integración de Texto a Voz' a continuación para todas las opciones disponibles
|
||||
|
||||
# Configurar conf.yaml para tu modelo LLM y claves API
|
||||
# Por favor, consulta 'docs/configuration_guide.md' para más detalles
|
||||
cp conf.yaml.example conf.yaml
|
||||
|
||||
# Instalar marp para la generación de presentaciones
|
||||
# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager
|
||||
brew install marp-cli
|
||||
```
|
||||
|
||||
Opcionalmente, instala las dependencias de la interfaz web vía [pnpm](https://pnpm.io/installation):
|
||||
|
||||
```bash
|
||||
cd deer-flow/web
|
||||
pnpm install
|
||||
```
|
||||
|
||||
### Configuraciones
|
||||
|
||||
Por favor, consulta la [Guía de Configuración](docs/configuration_guide.md) para más detalles.
|
||||
|
||||
> [!NOTA]
|
||||
> Antes de iniciar el proyecto, lee la guía cuidadosamente y actualiza las configuraciones para que coincidan con tus ajustes y requisitos específicos.
|
||||
|
||||
### Interfaz de Consola
|
||||
|
||||
La forma más rápida de ejecutar el proyecto es utilizar la interfaz de consola.
|
||||
|
||||
```bash
|
||||
# Ejecutar el proyecto en un shell tipo bash
|
||||
uv run main.py
|
||||
```
|
||||
|
||||
### Interfaz Web
|
||||
|
||||
Este proyecto también incluye una Interfaz Web, que ofrece una experiencia interactiva más dinámica y atractiva.
|
||||
|
||||
> [!NOTA]
|
||||
> Necesitas instalar primero las dependencias de la interfaz web.
|
||||
|
||||
```bash
|
||||
# Ejecutar tanto el servidor backend como el frontend en modo desarrollo
|
||||
# En macOS/Linux
|
||||
./bootstrap.sh -d
|
||||
|
||||
# En Windows
|
||||
bootstrap.bat -d
|
||||
```
|
||||
|
||||
Abre tu navegador y visita [`http://localhost:3000`](http://localhost:3000) para explorar la interfaz web.
|
||||
|
||||
Explora más detalles en el directorio [`web`](./web/).
|
||||
|
||||
## Motores de Búsqueda Compatibles
|
||||
|
||||
DeerFlow soporta múltiples motores de búsqueda que pueden configurarse en tu archivo `.env` usando la variable `SEARCH_API`:
|
||||
|
||||
- **Tavily** (predeterminado): Una API de búsqueda especializada para aplicaciones de IA
|
||||
|
||||
- Requiere `TAVILY_API_KEY` en tu archivo `.env`
|
||||
- Regístrate en: <https://app.tavily.com/home>
|
||||
|
||||
- **DuckDuckGo**: Motor de búsqueda centrado en la privacidad
|
||||
|
||||
- No requiere clave API
|
||||
|
||||
- **Brave Search**: Motor de búsqueda centrado en la privacidad con características avanzadas
|
||||
|
||||
- Requiere `BRAVE_SEARCH_API_KEY` en tu archivo `.env`
|
||||
- Regístrate en: <https://brave.com/search/api/>
|
||||
|
||||
- **Arxiv**: Búsqueda de artículos científicos para investigación académica
|
||||
- No requiere clave API
|
||||
- Especializado en artículos científicos y académicos
|
||||
|
||||
Para configurar tu motor de búsqueda preferido, establece la variable `SEARCH_API` en tu archivo `.env`:
|
||||
|
||||
```bash
|
||||
# Elige uno: tavily, duckduckgo, brave_search, arxiv
|
||||
SEARCH_API=tavily
|
||||
```
|
||||
|
||||
## Características
|
||||
|
||||
### Capacidades Principales
|
||||
|
||||
- 🤖 **Integración de LLM**
|
||||
- Soporta la integración de la mayoría de los modelos a través de [litellm](https://docs.litellm.ai/docs/providers).
|
||||
- Soporte para modelos de código abierto como Qwen
|
||||
- Interfaz API compatible con OpenAI
|
||||
- Sistema LLM de múltiples niveles para diferentes complejidades de tareas
|
||||
|
||||
### Herramientas e Integraciones MCP
|
||||
|
||||
- 🔍 **Búsqueda y Recuperación**
|
||||
|
||||
- Búsqueda web a través de Tavily, Brave Search y más
|
||||
- Rastreo con Jina
|
||||
- Extracción avanzada de contenido
|
||||
|
||||
- 🔗 **Integración Perfecta con MCP**
|
||||
- Amplía capacidades para acceso a dominio privado, gráfico de conocimiento, navegación web y más
|
||||
- Facilita la integración de diversas herramientas y metodologías de investigación
|
||||
|
||||
### Colaboración Humana
|
||||
|
||||
- 🧠 **Humano en el Bucle**
|
||||
|
||||
- Soporta modificación interactiva de planes de investigación usando lenguaje natural
|
||||
- Soporta aceptación automática de planes de investigación
|
||||
|
||||
- 📝 **Post-Edición de Informes**
|
||||
- Soporta edición de bloques tipo Notion
|
||||
- Permite refinamientos por IA, incluyendo pulido asistido por IA, acortamiento y expansión de oraciones
|
||||
- Impulsado por [tiptap](https://tiptap.dev/)
|
||||
|
||||
### Creación de Contenido
|
||||
|
||||
- 🎙️ **Generación de Podcasts y Presentaciones**
|
||||
- Generación de guiones de podcast y síntesis de audio impulsadas por IA
|
||||
- Creación automatizada de presentaciones PowerPoint simples
|
||||
- Plantillas personalizables para contenido a medida
|
||||
|
||||
## Arquitectura
|
||||
|
||||
DeerFlow implementa una arquitectura modular de sistema multi-agente diseñada para investigación automatizada y análisis de código. El sistema está construido sobre LangGraph, permitiendo un flujo de trabajo flexible basado en estados donde los componentes se comunican a través de un sistema de paso de mensajes bien definido.
|
||||
|
||||
|
||||
|
||||
> Vélo en vivo en [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)
|
||||
|
||||
El sistema emplea un flujo de trabajo racionalizado con los siguientes componentes:
|
||||
|
||||
1. **Coordinador**: El punto de entrada que gestiona el ciclo de vida del flujo de trabajo
|
||||
|
||||
- Inicia el proceso de investigación basado en la entrada del usuario
|
||||
- Delega tareas al planificador cuando corresponde
|
||||
- Actúa como la interfaz principal entre el usuario y el sistema
|
||||
|
||||
2. **Planificador**: Componente estratégico para descomposición y planificación de tareas
|
||||
|
||||
- Analiza objetivos de investigación y crea planes de ejecución estructurados
|
||||
- Determina si hay suficiente contexto disponible o si se necesita más investigación
|
||||
- Gestiona el flujo de investigación y decide cuándo generar el informe final
|
||||
|
||||
3. **Equipo de Investigación**: Una colección de agentes especializados que ejecutan el plan:
|
||||
|
||||
- **Investigador**: Realiza búsquedas web y recopilación de información utilizando herramientas como motores de búsqueda web, rastreo e incluso servicios MCP.
|
||||
- **Programador**: Maneja análisis de código, ejecución y tareas técnicas utilizando la herramienta Python REPL.
|
||||
Cada agente tiene acceso a herramientas específicas optimizadas para su rol y opera dentro del marco LangGraph
|
||||
|
||||
4. **Reportero**: Procesador de etapa final para los resultados de la investigación
|
||||
- Agrega hallazgos del equipo de investigación
|
||||
- Procesa y estructura la información recopilada
|
||||
- Genera informes de investigación completos
|
||||
|
||||
## Integración de Texto a Voz
|
||||
|
||||
DeerFlow ahora incluye una función de Texto a Voz (TTS) que te permite convertir informes de investigación a voz. Esta función utiliza la API TTS de volcengine para generar audio de alta calidad a partir de texto. Características como velocidad, volumen y tono también son personalizables.
|
||||
|
||||
### Usando la API TTS
|
||||
|
||||
Puedes acceder a la funcionalidad TTS a través del punto final `/api/tts`:
|
||||
|
||||
```bash
|
||||
# Ejemplo de llamada API usando curl
|
||||
curl --location 'http://localhost:8000/api/tts' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"text": "Esto es una prueba de la funcionalidad de texto a voz.",
|
||||
"speed_ratio": 1.0,
|
||||
"volume_ratio": 1.0,
|
||||
"pitch_ratio": 1.0
|
||||
}' \
|
||||
--output speech.mp3
|
||||
```
|
||||
|
||||
## Desarrollo
|
||||
|
||||
### Pruebas
|
||||
|
||||
Ejecuta el conjunto de pruebas:
|
||||
|
||||
```bash
|
||||
# Ejecutar todas las pruebas
|
||||
make test
|
||||
|
||||
# Ejecutar archivo de prueba específico
|
||||
pytest tests/integration/test_workflow.py
|
||||
|
||||
# Ejecutar con cobertura
|
||||
make coverage
|
||||
```
|
||||
|
||||
### Calidad del Código
|
||||
|
||||
```bash
|
||||
# Ejecutar linting
|
||||
make lint
|
||||
|
||||
# Formatear código
|
||||
make format
|
||||
```
|
||||
|
||||
### Depuración con LangGraph Studio
|
||||
|
||||
DeerFlow utiliza LangGraph para su arquitectura de flujo de trabajo. Puedes usar LangGraph Studio para depurar y visualizar el flujo de trabajo en tiempo real.
|
||||
|
||||
#### Ejecutando LangGraph Studio Localmente
|
||||
|
||||
DeerFlow incluye un archivo de configuración `langgraph.json` que define la estructura del grafo y las dependencias para LangGraph Studio. Este archivo apunta a los grafos de flujo de trabajo definidos en el proyecto y carga automáticamente variables de entorno desde el archivo `.env`.
|
||||
|
||||
##### Mac
|
||||
|
||||
```bash
|
||||
# Instala el gestor de paquetes uv si no lo tienes
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
# Instala dependencias e inicia el servidor LangGraph
|
||||
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.12 langgraph dev --allow-blocking
|
||||
```
|
||||
|
||||
##### Windows / Linux
|
||||
|
||||
```bash
|
||||
# Instalar dependencias
|
||||
pip install -e .
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
|
||||
# Iniciar el servidor LangGraph
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Después de iniciar el servidor LangGraph, verás varias URLs en la terminal:
|
||||
|
||||
- API: <http://127.0.0.1:2024>
|
||||
- UI de Studio: <https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024>
|
||||
- Docs de API: <http://127.0.0.1:2024/docs>
|
||||
|
||||
Abre el enlace de UI de Studio en tu navegador para acceder a la interfaz de depuración.
|
||||
|
||||
#### Usando LangGraph Studio
|
||||
|
||||
En la UI de Studio, puedes:
|
||||
|
||||
1. Visualizar el grafo de flujo de trabajo y ver cómo se conectan los componentes
|
||||
2. Rastrear la ejecución en tiempo real para ver cómo fluyen los datos a través del sistema
|
||||
3. Inspeccionar el estado en cada paso del flujo de trabajo
|
||||
4. Depurar problemas examinando entradas y salidas de cada componente
|
||||
5. Proporcionar retroalimentación durante la fase de planificación para refinar planes de investigación
|
||||
|
||||
Cuando envías un tema de investigación en la UI de Studio, podrás ver toda la ejecución del flujo de trabajo, incluyendo:
|
||||
|
||||
- La fase de planificación donde se crea el plan de investigación
|
||||
- El bucle de retroalimentación donde puedes modificar el plan
|
||||
- Las fases de investigación y escritura para cada sección
|
||||
- La generación del informe final
|
||||
|
||||
### Habilitando el Rastreo de LangSmith
|
||||
|
||||
DeerFlow soporta el rastreo de LangSmith para ayudarte a depurar y monitorear tus flujos de trabajo. Para habilitar el rastreo de LangSmith:
|
||||
|
||||
1. Asegúrate de que tu archivo `.env` tenga las siguientes configuraciones (ver `.env.example`):
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
|
||||
LANGSMITH_API_KEY="xxx"
|
||||
LANGSMITH_PROJECT="xxx"
|
||||
```
|
||||
|
||||
2. Inicia el rastreo y visualiza el grafo localmente con LangSmith ejecutando:
|
||||
|
||||
```bash
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Esto habilitará la visualización de rastros en LangGraph Studio y enviará tus rastros a LangSmith para monitoreo y análisis.
|
||||
|
||||
## Docker
|
||||
|
||||
También puedes ejecutar este proyecto con Docker.
|
||||
|
||||
Primero, necesitas leer la [configuración](docs/configuration_guide.md) a continuación. Asegúrate de que los archivos `.env` y `.conf.yaml` estén listos.
|
||||
|
||||
Segundo, para construir una imagen Docker de tu propio servidor web:
|
||||
|
||||
```bash
|
||||
docker build -t deer-flow-api .
|
||||
```
|
||||
|
||||
Finalmente, inicia un contenedor Docker que ejecute el servidor web:
|
||||
|
||||
```bash
|
||||
# Reemplaza deer-flow-api-app con tu nombre de contenedor preferido
|
||||
docker run -d -t -p 8000:8000 --env-file .env --name deer-flow-api-app deer-flow-api
|
||||
|
||||
# detener el servidor
|
||||
docker stop deer-flow-api-app
|
||||
```
|
||||
|
||||
### Docker Compose (incluye tanto backend como frontend)
|
||||
|
||||
DeerFlow proporciona una configuración docker-compose para ejecutar fácilmente tanto el backend como el frontend juntos:
|
||||
|
||||
```bash
|
||||
# construir imagen docker
|
||||
docker compose build
|
||||
|
||||
# iniciar el servidor
|
||||
docker compose up
|
||||
```
|
||||
|
||||
## Ejemplos
|
||||
|
||||
Los siguientes ejemplos demuestran las capacidades de DeerFlow:
|
||||
|
||||
### Informes de Investigación
|
||||
|
||||
1. **Informe sobre OpenAI Sora** - Análisis de la herramienta IA Sora de OpenAI
|
||||
|
||||
- Discute características, acceso, ingeniería de prompts, limitaciones y consideraciones éticas
|
||||
- [Ver informe completo](examples/openai_sora_report.md)
|
||||
|
||||
2. **Informe sobre el Protocolo Agent to Agent de Google** - Visión general del protocolo Agent to Agent (A2A) de Google
|
||||
|
||||
- Discute su papel en la comunicación de agentes IA y su relación con el Model Context Protocol (MCP) de Anthropic
|
||||
- [Ver informe completo](examples/what_is_agent_to_agent_protocol.md)
|
||||
|
||||
3. **¿Qué es MCP?** - Un análisis completo del término "MCP" en múltiples contextos
|
||||
|
||||
- Explora Model Context Protocol en IA, Fosfato Monocálcico en química y Placa de Microcanales en electrónica
|
||||
- [Ver informe completo](examples/what_is_mcp.md)
|
||||
|
||||
4. **Fluctuaciones del Precio de Bitcoin** - Análisis de los movimientos recientes del precio de Bitcoin
|
||||
|
||||
- Examina tendencias del mercado, influencias regulatorias e indicadores técnicos
|
||||
- Proporciona recomendaciones basadas en datos históricos
|
||||
- [Ver informe completo](examples/bitcoin_price_fluctuation.md)
|
||||
|
||||
5. **¿Qué es LLM?** - Una exploración en profundidad de los Modelos de Lenguaje Grandes
|
||||
|
||||
- Discute arquitectura, entrenamiento, aplicaciones y consideraciones éticas
|
||||
- [Ver informe completo](examples/what_is_llm.md)
|
||||
|
||||
6. **¿Cómo usar Claude para Investigación Profunda?** - Mejores prácticas y flujos de trabajo para usar Claude en investigación profunda
|
||||
|
||||
- Cubre ingeniería de prompts, análisis de datos e integración con otras herramientas
|
||||
- [Ver informe completo](examples/how_to_use_claude_deep_research.md)
|
||||
|
||||
7. **Adopción de IA en Salud: Factores de Influencia** - Análisis de factores que impulsan la adopción de IA en salud
|
||||
|
||||
- Discute tecnologías IA, calidad de datos, consideraciones éticas, evaluaciones económicas, preparación organizativa e infraestructura digital
|
||||
- [Ver informe completo](examples/AI_adoption_in_healthcare.md)
|
||||
|
||||
8. **Impacto de la Computación Cuántica en la Criptografía** - Análisis del impacto de la computación cuántica en la criptografía
|
||||
|
||||
- Discute vulnerabilidades de la criptografía clásica, criptografía post-cuántica y soluciones criptográficas resistentes a la cuántica
|
||||
- [Ver informe completo](examples/Quantum_Computing_Impact_on_Cryptography.md)
|
||||
|
||||
9. **Aspectos Destacados del Rendimiento de Cristiano Ronaldo** - Análisis de los aspectos destacados del rendimiento de Cristiano Ronaldo
|
||||
- Discute sus logros profesionales, goles internacionales y rendimiento en varios partidos
|
||||
- [Ver informe completo](examples/Cristiano_Ronaldo's_Performance_Highlights.md)
|
||||
|
||||
Para ejecutar estos ejemplos o crear tus propios informes de investigación, puedes usar los siguientes comandos:
|
||||
|
||||
```bash
|
||||
# Ejecutar con una consulta específica
|
||||
uv run main.py "¿Qué factores están influyendo en la adopción de IA en salud?"
|
||||
|
||||
# Ejecutar con parámetros de planificación personalizados
|
||||
uv run main.py --max_plan_iterations 3 "¿Cómo impacta la computación cuántica en la criptografía?"
|
||||
|
||||
# Ejecutar en modo interactivo con preguntas integradas
|
||||
uv run main.py --interactive
|
||||
|
||||
# O ejecutar con prompt interactivo básico
|
||||
uv run main.py
|
||||
|
||||
# Ver todas las opciones disponibles
|
||||
uv run main.py --help
|
||||
```
|
||||
|
||||
### Modo Interactivo
|
||||
|
||||
La aplicación ahora soporta un modo interactivo con preguntas integradas tanto en inglés como en chino:
|
||||
|
||||
1. Lanza el modo interactivo:
|
||||
|
||||
```bash
|
||||
uv run main.py --interactive
|
||||
```
|
||||
|
||||
2. Selecciona tu idioma preferido (English o 中文)
|
||||
|
||||
3. Elige de una lista de preguntas integradas o selecciona la opción para hacer tu propia pregunta
|
||||
|
||||
4. El sistema procesará tu pregunta y generará un informe de investigación completo
|
||||
|
||||
### Humano en el Bucle
|
||||
|
||||
DeerFlow incluye un mecanismo de humano en el bucle que te permite revisar, editar y aprobar planes de investigación antes de que sean ejecutados:
|
||||
|
||||
1. **Revisión del Plan**: Cuando el humano en el bucle está habilitado, el sistema presentará el plan de investigación generado para tu revisión antes de la ejecución
|
||||
|
||||
2. **Proporcionando Retroalimentación**: Puedes:
|
||||
|
||||
- Aceptar el plan respondiendo con `[ACCEPTED]`
|
||||
- Editar el plan proporcionando retroalimentación (p.ej., `[EDIT PLAN] Añadir más pasos sobre implementación técnica`)
|
||||
- El sistema incorporará tu retroalimentación y generará un plan revisado
|
||||
|
||||
3. **Auto-aceptación**: Puedes habilitar la auto-aceptación para omitir el proceso de revisión:
|
||||
|
||||
- Vía API: Establece `auto_accepted_plan: true` en tu solicitud
|
||||
|
||||
4. **Integración API**: Cuando uses la API, puedes proporcionar retroalimentación a través del parámetro `feedback`:
|
||||
|
||||
```json
|
||||
{
|
||||
"messages": [{ "role": "user", "content": "¿Qué es la computación cuántica?" }],
|
||||
"thread_id": "my_thread_id",
|
||||
"auto_accepted_plan": false,
|
||||
"feedback": "[EDIT PLAN] Incluir más sobre algoritmos cuánticos"
|
||||
}
|
||||
```
|
||||
|
||||
### Argumentos de Línea de Comandos
|
||||
|
||||
La aplicación soporta varios argumentos de línea de comandos para personalizar su comportamiento:
|
||||
|
||||
- **query**: La consulta de investigación a procesar (puede ser múltiples palabras)
|
||||
- **--interactive**: Ejecutar en modo interactivo con preguntas integradas
|
||||
- **--max_plan_iterations**: Número máximo de ciclos de planificación (predeterminado: 1)
|
||||
- **--max_step_num**: Número máximo de pasos en un plan de investigación (predeterminado: 3)
|
||||
- **--debug**: Habilitar registro detallado de depuración
|
||||
|
||||
## Preguntas Frecuentes
|
||||
|
||||
Por favor, consulta [FAQ.md](docs/FAQ.md) para más detalles.
|
||||
|
||||
## Licencia
|
||||
|
||||
Este proyecto es de código abierto y está disponible bajo la [Licencia MIT](./LICENSE).
|
||||
|
||||
## Agradecimientos
|
||||
|
||||
DeerFlow está construido sobre el increíble trabajo de la comunidad de código abierto. Estamos profundamente agradecidos a todos los proyectos y contribuyentes cuyos esfuerzos han hecho posible DeerFlow. Verdaderamente, nos apoyamos en hombros de gigantes.
|
||||
|
||||
Nos gustaría extender nuestro sincero agradecimiento a los siguientes proyectos por sus invaluables contribuciones:
|
||||
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)**: Su excepcional marco impulsa nuestras interacciones y cadenas LLM, permitiendo integración y funcionalidad sin problemas.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)**: Su enfoque innovador para la orquestación multi-agente ha sido instrumental en permitir los sofisticados flujos de trabajo de DeerFlow.
|
||||
|
||||
Estos proyectos ejemplifican el poder transformador de la colaboración de código abierto, y estamos orgullosos de construir sobre sus cimientos.
|
||||
|
||||
### Contribuyentes Clave
|
||||
|
||||
Un sentido agradecimiento va para los autores principales de `DeerFlow`, cuya visión, pasión y dedicación han dado vida a este proyecto:
|
||||
|
||||
- **[Daniel Walnut](https://github.com/hetaoBackend/)**
|
||||
- **[Henry Li](https://github.com/magiccube/)**
|
||||
|
||||
Su compromiso inquebrantable y experiencia han sido la fuerza impulsora detrás del éxito de DeerFlow. Nos sentimos honrados de tenerlos al timón de este viaje.
|
||||
|
||||
## Historial de Estrellas
|
||||
|
||||
[](https://star-history.com/#bytedance/deer-flow&Date)
|
||||
-610
@@ -1,610 +0,0 @@
|
||||
# 🦌 DeerFlow - 2.0
|
||||
|
||||
[English](./README.md) | [中文](./README_zh.md) | [日本語](./README_ja.md) | Français | [Русский](./README_ru.md)
|
||||
|
||||
[](./backend/pyproject.toml)
|
||||
[](./Makefile)
|
||||
[](./LICENSE)
|
||||
|
||||
<a href="https://trendshift.io/repositories/14699" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14699" alt="bytedance%2Fdeer-flow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
> Le 28 février 2026, DeerFlow a décroché la 🏆 1re place sur GitHub Trending suite au lancement de la version 2. Un immense merci à notre incroyable communauté — c'est grâce à vous ! 💪🔥
|
||||
|
||||
DeerFlow (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) est un **super agent harness** open source qui orchestre des **sub-agents**, de la **mémoire** et des **sandboxes** pour accomplir pratiquement n'importe quelle tâche — le tout propulsé par des **skills extensibles**.
|
||||
|
||||
https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
|
||||
|
||||
> [!NOTE]
|
||||
> **DeerFlow 2.0 est une réécriture complète.** Il ne partage aucun code avec la v1. Si vous cherchez le framework Deep Research original, il est maintenu sur la [branche `1.x`](https://github.com/bytedance/deer-flow/tree/main-1.x) — les contributions y sont toujours les bienvenues. Le développement actif a migré vers la 2.0.
|
||||
|
||||
## Site officiel
|
||||
|
||||
[<img width="2880" height="1600" alt="image" src="https://github.com/user-attachments/assets/a598c49f-3b2f-41ea-a052-05e21349188a" />](https://deerflow.tech)
|
||||
|
||||
Découvrez-en plus et regardez des **démos réelles** sur notre [**site officiel**](https://deerflow.tech).
|
||||
|
||||
## Coding Plan de ByteDance Volcengine
|
||||
|
||||
<img width="4808" height="2400" alt="英文方舟" src="https://github.com/user-attachments/assets/2ecc7b9d-50be-4185-b1f7-5542d222fb2d" />
|
||||
|
||||
- Nous recommandons fortement d'utiliser Doubao-Seed-2.0-Code, DeepSeek v3.2 et Kimi 2.5 pour exécuter DeerFlow
|
||||
- [En savoir plus](https://www.byteplus.com/en/activity/codingplan?utm_campaign=deer_flow&utm_content=deer_flow&utm_medium=devrel&utm_source=OWO&utm_term=deer_flow)
|
||||
- [Développeurs en Chine continentale, cliquez ici](https://www.volcengine.com/activity/codingplan?utm_campaign=deer_flow&utm_content=deer_flow&utm_medium=devrel&utm_source=OWO&utm_term=deer_flow)
|
||||
|
||||
## InfoQuest
|
||||
|
||||
DeerFlow intègre désormais le toolkit de recherche et de crawling intelligent développé par BytePlus — [InfoQuest (essai gratuit en ligne)](https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest)
|
||||
|
||||
<a href="https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest" target="_blank">
|
||||
<img
|
||||
src="https://sf16-sg.tiktokcdn.com/obj/eden-sg/hubseh7bsbps/20251208-160108.png" alt="InfoQuest_banner"
|
||||
/>
|
||||
</a>
|
||||
|
||||
---
|
||||
|
||||
## Table des matières
|
||||
|
||||
- [🦌 DeerFlow - 2.0](#-deerflow---20)
|
||||
- [Site officiel](#site-officiel)
|
||||
- [InfoQuest](#infoquest)
|
||||
- [Table des matières](#table-des-matières)
|
||||
- [Installation en une phrase pour un coding agent](#installation-en-une-phrase-pour-un-coding-agent)
|
||||
- [Démarrage rapide](#démarrage-rapide)
|
||||
- [Configuration](#configuration)
|
||||
- [Lancer l'application](#lancer-lapplication)
|
||||
- [Option 1 : Docker (recommandé)](#option-1--docker-recommandé)
|
||||
- [Option 2 : Développement local](#option-2--développement-local)
|
||||
- [Avancé](#avancé)
|
||||
- [Mode Sandbox](#mode-sandbox)
|
||||
- [Serveur MCP](#serveur-mcp)
|
||||
- [Canaux de messagerie](#canaux-de-messagerie)
|
||||
- [Traçage LangSmith](#traçage-langsmith)
|
||||
- [Du Deep Research au Super Agent Harness](#du-deep-research-au-super-agent-harness)
|
||||
- [Fonctionnalités principales](#fonctionnalités-principales)
|
||||
- [Skills et outils](#skills-et-outils)
|
||||
- [Intégration Claude Code](#intégration-claude-code)
|
||||
- [Sub-Agents](#sub-agents)
|
||||
- [Sandbox et système de fichiers](#sandbox-et-système-de-fichiers)
|
||||
- [Context Engineering](#context-engineering)
|
||||
- [Mémoire à long terme](#mémoire-à-long-terme)
|
||||
- [Modèles recommandés](#modèles-recommandés)
|
||||
- [Client Python intégré](#client-python-intégré)
|
||||
- [Documentation](#documentation)
|
||||
- [⚠️ Avertissement de sécurité](#️-avertissement-de-sécurité)
|
||||
- [Contribuer](#contribuer)
|
||||
- [Licence](#licence)
|
||||
- [Remerciements](#remerciements)
|
||||
- [Contributeurs principaux](#contributeurs-principaux)
|
||||
- [Star History](#star-history)
|
||||
|
||||
## Installation en une phrase pour un coding agent
|
||||
|
||||
Si vous utilisez Claude Code, Codex, Cursor, Windsurf ou un autre coding agent, vous pouvez simplement lui envoyer cette phrase :
|
||||
|
||||
```text
|
||||
Aide-moi à cloner DeerFlow si nécessaire, puis à initialiser son environnement de développement local en suivant https://raw.githubusercontent.com/bytedance/deer-flow/main/Install.md
|
||||
```
|
||||
|
||||
Ce prompt est destiné aux coding agents. Il leur demande de cloner le dépôt si nécessaire, de privilégier Docker quand il est disponible, puis de s'arrêter avec la commande exacte pour lancer DeerFlow et la liste des configurations encore manquantes.
|
||||
|
||||
## Démarrage rapide
|
||||
|
||||
### Configuration
|
||||
|
||||
1. **Cloner le dépôt DeerFlow**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
```
|
||||
|
||||
2. **Générer les fichiers de configuration locaux**
|
||||
|
||||
Depuis le répertoire racine du projet (`deer-flow/`), exécutez :
|
||||
|
||||
```bash
|
||||
make config
|
||||
```
|
||||
|
||||
Cette commande crée les fichiers de configuration locaux à partir des templates fournis.
|
||||
|
||||
3. **Configurer le(s) modèle(s) de votre choix**
|
||||
|
||||
Éditez `config.yaml` et définissez au moins un modèle :
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4 # Internal identifier
|
||||
display_name: GPT-4 # Human-readable name
|
||||
use: langchain_openai:ChatOpenAI # LangChain class path
|
||||
model: gpt-4 # Model identifier for API
|
||||
api_key: $OPENAI_API_KEY # API key (recommended: use env var)
|
||||
max_tokens: 4096 # Maximum tokens per request
|
||||
temperature: 0.7 # Sampling temperature
|
||||
|
||||
- name: openrouter-gemini-2.5-flash
|
||||
display_name: Gemini 2.5 Flash (OpenRouter)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: google/gemini-2.5-flash-preview
|
||||
api_key: $OPENAI_API_KEY # OpenRouter still uses the OpenAI-compatible field name here
|
||||
base_url: https://openrouter.ai/api/v1
|
||||
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
```
|
||||
|
||||
OpenRouter et les passerelles compatibles OpenAI similaires doivent être configurés avec `langchain_openai:ChatOpenAI` et `base_url`. Si vous préférez utiliser un nom de variable d'environnement propre au fournisseur, pointez `api_key` vers cette variable explicitement (par exemple `api_key: $OPENROUTER_API_KEY`).
|
||||
|
||||
Pour router les modèles OpenAI via `/v1/responses`, continuez d'utiliser `langchain_openai:ChatOpenAI` et définissez `use_responses_api: true` avec `output_version: responses/v1`.
|
||||
|
||||
Exemples de providers basés sur un CLI :
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5.4
|
||||
display_name: GPT-5.4 (Codex CLI)
|
||||
use: deerflow.models.openai_codex_provider:CodexChatModel
|
||||
model: gpt-5.4
|
||||
supports_thinking: true
|
||||
supports_reasoning_effort: true
|
||||
|
||||
- name: claude-sonnet-4.6
|
||||
display_name: Claude Sonnet 4.6 (Claude Code OAuth)
|
||||
use: deerflow.models.claude_provider:ClaudeChatModel
|
||||
model: claude-sonnet-4-6
|
||||
max_tokens: 4096
|
||||
supports_thinking: true
|
||||
```
|
||||
|
||||
- Codex CLI lit `~/.codex/auth.json`
|
||||
- L'endpoint Responses de Codex rejette actuellement `max_tokens` et `max_output_tokens`, donc `CodexChatModel` n'expose pas de limite de tokens par requête
|
||||
- Claude Code accepte `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR`, `CLAUDE_CODE_CREDENTIALS_PATH`, ou en clair `~/.claude/.credentials.json`
|
||||
- Sur macOS, DeerFlow ne sonde pas le Keychain automatiquement. Exportez l'auth Claude Code explicitement si nécessaire :
|
||||
|
||||
```bash
|
||||
eval "$(python3 scripts/export_claude_code_oauth.py --print-export)"
|
||||
```
|
||||
|
||||
4. **Définir les clés API pour le(s) modèle(s) configuré(s)**
|
||||
|
||||
Choisissez l'une des méthodes suivantes :
|
||||
|
||||
- Option A : Éditer le fichier `.env` à la racine du projet (recommandé)
|
||||
|
||||
|
||||
```bash
|
||||
TAVILY_API_KEY=your-tavily-api-key
|
||||
OPENAI_API_KEY=your-openai-api-key
|
||||
# OpenRouter also uses OPENAI_API_KEY when your config uses langchain_openai:ChatOpenAI + base_url.
|
||||
# Add other provider keys as needed
|
||||
INFOQUEST_API_KEY=your-infoquest-api-key
|
||||
```
|
||||
|
||||
- Option B : Exporter les variables d'environnement dans votre shell
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-openai-api-key
|
||||
```
|
||||
|
||||
Pour les providers basés sur un CLI :
|
||||
- Codex CLI : `~/.codex/auth.json`
|
||||
- Claude Code OAuth : handoff explicite via env/fichier ou `~/.claude/.credentials.json`
|
||||
|
||||
- Option C : Éditer `config.yaml` directement (non recommandé en production)
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4
|
||||
api_key: your-actual-api-key-here # Replace placeholder
|
||||
```
|
||||
|
||||
### Lancer l'application
|
||||
|
||||
#### Option 1 : Docker (recommandé)
|
||||
|
||||
**Développement** (hot-reload, montage des sources) :
|
||||
|
||||
```bash
|
||||
make docker-init # Pull sandbox image (only once or when image updates)
|
||||
make docker-start # Start services (auto-detects sandbox mode from config.yaml)
|
||||
```
|
||||
|
||||
`make docker-start` ne lance `provisioner` que si `config.yaml` utilise le mode provisioner (`sandbox.use: deerflow.community.aio_sandbox:AioSandboxProvider` avec `provisioner_url`).
|
||||
Les processus backend récupèrent automatiquement les changements dans `config.yaml` au prochain accès à la configuration, donc les mises à jour de métadonnées des modèles ne nécessitent pas de redémarrage manuel en développement.
|
||||
|
||||
> [!TIP]
|
||||
> Sous Linux, si les commandes Docker échouent avec `permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock`, ajoutez votre utilisateur au groupe `docker` et reconnectez-vous avant de réessayer. Voir [CONTRIBUTING.md](CONTRIBUTING.md#linux-docker-daemon-permission-denied) pour la solution complète.
|
||||
|
||||
**Production** (build des images en local, montage de la config et des données) :
|
||||
|
||||
```bash
|
||||
make up # Build images and start all production services
|
||||
make down # Stop and remove containers
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> Le serveur d'agents LangGraph fonctionne actuellement via `langgraph dev` (le serveur CLI open source).
|
||||
|
||||
Accès : http://localhost:2026
|
||||
|
||||
Voir [CONTRIBUTING.md](CONTRIBUTING.md) pour le guide complet de développement avec Docker.
|
||||
|
||||
#### Option 2 : Développement local
|
||||
|
||||
Si vous préférez lancer les services en local :
|
||||
|
||||
Prérequis : complétez d'abord les étapes de « Configuration » ci-dessus (`make config` et clés API des modèles). `make dev` nécessite un fichier de configuration valide (par défaut `config.yaml` à la racine du projet ; modifiable via `DEER_FLOW_CONFIG_PATH`).
|
||||
|
||||
1. **Vérifier les prérequis** :
|
||||
```bash
|
||||
make check # Verifies Node.js 22+, pnpm, uv, nginx
|
||||
```
|
||||
|
||||
2. **Installer les dépendances** :
|
||||
```bash
|
||||
make install # Install backend + frontend dependencies
|
||||
```
|
||||
|
||||
3. **(Optionnel) Pré-télécharger l'image sandbox** :
|
||||
```bash
|
||||
# Recommended if using Docker/Container-based sandbox
|
||||
make setup-sandbox
|
||||
```
|
||||
|
||||
4. **Démarrer les services** :
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
5. **Accès** : http://localhost:2026
|
||||
|
||||
### Avancé
|
||||
#### Mode Sandbox
|
||||
|
||||
DeerFlow supporte plusieurs modes d'exécution sandbox :
|
||||
- **Exécution locale** (exécute le code sandbox directement sur la machine hôte)
|
||||
- **Exécution Docker** (exécute le code sandbox dans des conteneurs Docker isolés)
|
||||
- **Exécution Docker avec Kubernetes** (exécute le code sandbox dans des pods Kubernetes via le service provisioner)
|
||||
|
||||
En développement Docker, le démarrage des services suit le mode sandbox défini dans `config.yaml`. En mode Local/Docker, `provisioner` n'est pas démarré.
|
||||
|
||||
Voir le [Guide de configuration Sandbox](backend/docs/CONFIGURATION.md#sandbox) pour configurer le mode de votre choix.
|
||||
|
||||
#### Serveur MCP
|
||||
|
||||
DeerFlow supporte des serveurs MCP et des skills configurables pour étendre ses capacités.
|
||||
Pour les serveurs MCP HTTP/SSE, les flux de tokens OAuth sont supportés (`client_credentials`, `refresh_token`).
|
||||
Voir le [Guide MCP Server](backend/docs/MCP_SERVER.md) pour les instructions détaillées.
|
||||
|
||||
#### Canaux de messagerie
|
||||
|
||||
DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les canaux démarrent automatiquement une fois configurés — aucune IP publique n'est requise.
|
||||
|
||||
| Canal | Transport | Difficulté |
|
||||
|---------|-----------|------------|
|
||||
| Telegram | Bot API (long-polling) | Facile |
|
||||
| Slack | Socket Mode | Modérée |
|
||||
| Feishu / Lark | WebSocket | Modérée |
|
||||
|
||||
**Configuration dans `config.yaml` :**
|
||||
|
||||
```yaml
|
||||
channels:
|
||||
# LangGraph Server URL (default: http://localhost:2024)
|
||||
langgraph_url: http://localhost:2024
|
||||
# Gateway API URL (default: http://localhost:8001)
|
||||
gateway_url: http://localhost:8001
|
||||
|
||||
# Optional: global session defaults for all mobile channels
|
||||
session:
|
||||
assistant_id: lead_agent
|
||||
config:
|
||||
recursion_limit: 100
|
||||
context:
|
||||
thinking_enabled: true
|
||||
is_plan_mode: false
|
||||
subagent_enabled: false
|
||||
|
||||
feishu:
|
||||
enabled: true
|
||||
app_id: $FEISHU_APP_ID
|
||||
app_secret: $FEISHU_APP_SECRET
|
||||
# domain: https://open.feishu.cn # China (default)
|
||||
# domain: https://open.larksuite.com # International
|
||||
|
||||
slack:
|
||||
enabled: true
|
||||
bot_token: $SLACK_BOT_TOKEN # xoxb-...
|
||||
app_token: $SLACK_APP_TOKEN # xapp-... (Socket Mode)
|
||||
allowed_users: [] # empty = allow all
|
||||
|
||||
telegram:
|
||||
enabled: true
|
||||
bot_token: $TELEGRAM_BOT_TOKEN
|
||||
allowed_users: [] # empty = allow all
|
||||
|
||||
# Optional: per-channel / per-user session settings
|
||||
session:
|
||||
assistant_id: mobile_agent
|
||||
context:
|
||||
thinking_enabled: false
|
||||
users:
|
||||
"123456789":
|
||||
assistant_id: vip_agent
|
||||
config:
|
||||
recursion_limit: 150
|
||||
context:
|
||||
thinking_enabled: true
|
||||
subagent_enabled: true
|
||||
```
|
||||
|
||||
Définissez les clés API correspondantes dans votre fichier `.env` :
|
||||
|
||||
```bash
|
||||
# Telegram
|
||||
TELEGRAM_BOT_TOKEN=123456789:ABCdefGHIjklMNOpqrSTUvwxYZ
|
||||
|
||||
# Slack
|
||||
SLACK_BOT_TOKEN=xoxb-...
|
||||
SLACK_APP_TOKEN=xapp-...
|
||||
|
||||
# Feishu / Lark
|
||||
FEISHU_APP_ID=cli_xxxx
|
||||
FEISHU_APP_SECRET=your_app_secret
|
||||
```
|
||||
|
||||
**Configuration Telegram**
|
||||
|
||||
1. Ouvrez une conversation avec [@BotFather](https://t.me/BotFather), envoyez `/newbot`, et copiez le token HTTP API.
|
||||
2. Définissez `TELEGRAM_BOT_TOKEN` dans `.env` et activez le canal dans `config.yaml`.
|
||||
|
||||
**Configuration Slack**
|
||||
|
||||
1. Créez une Slack App sur [api.slack.com/apps](https://api.slack.com/apps) → Create New App → From scratch.
|
||||
2. Dans **OAuth & Permissions**, ajoutez les Bot Token Scopes : `app_mentions:read`, `chat:write`, `im:history`, `im:read`, `im:write`, `files:write`.
|
||||
3. Activez le **Socket Mode** → générez un App-Level Token (`xapp-…`) avec le scope `connections:write`.
|
||||
4. Dans **Event Subscriptions**, abonnez-vous aux bot events : `app_mention`, `message.im`.
|
||||
5. Définissez `SLACK_BOT_TOKEN` et `SLACK_APP_TOKEN` dans `.env` et activez le canal dans `config.yaml`.
|
||||
|
||||
**Configuration Feishu / Lark**
|
||||
|
||||
1. Créez une application sur [Feishu Open Platform](https://open.feishu.cn/) → activez la capacité **Bot**.
|
||||
2. Ajoutez les permissions : `im:message`, `im:message.p2p_msg:readonly`, `im:resource`.
|
||||
3. Dans **Events**, abonnez-vous à `im.message.receive_v1` et sélectionnez le mode **Long Connection**.
|
||||
4. Copiez l'App ID et l'App Secret. Définissez `FEISHU_APP_ID` et `FEISHU_APP_SECRET` dans `.env` et activez le canal dans `config.yaml`.
|
||||
|
||||
**Commandes**
|
||||
|
||||
Une fois un canal connecté, vous pouvez interagir avec DeerFlow directement depuis le chat :
|
||||
|
||||
| Commande | Description |
|
||||
|---------|-------------|
|
||||
| `/new` | Démarrer une nouvelle conversation |
|
||||
| `/status` | Afficher les infos du thread en cours |
|
||||
| `/models` | Lister les modèles disponibles |
|
||||
| `/memory` | Consulter la mémoire |
|
||||
| `/help` | Afficher l'aide |
|
||||
|
||||
> Les messages sans préfixe de commande sont traités comme du chat classique — DeerFlow crée un thread et répond de manière conversationnelle.
|
||||
|
||||
#### Traçage LangSmith
|
||||
|
||||
DeerFlow intègre nativement [LangSmith](https://smith.langchain.com) pour l'observabilité. Une fois activé, tous les appels LLM, les exécutions d'agents et les exécutions d'outils sont tracés et visibles dans le tableau de bord LangSmith.
|
||||
|
||||
Ajoutez les lignes suivantes à votre fichier `.env` :
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
|
||||
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
|
||||
LANGSMITH_PROJECT=xxx
|
||||
```
|
||||
|
||||
Pour les déploiements Docker, le traçage est désactivé par défaut. Définissez `LANGSMITH_TRACING=true` et `LANGSMITH_API_KEY` dans votre `.env` pour l'activer.
|
||||
|
||||
## Du Deep Research au Super Agent Harness
|
||||
|
||||
DeerFlow a démarré comme un framework de Deep Research — et la communauté s'en est emparée. Depuis le lancement, les développeurs l'ont poussé bien au-delà de la recherche : construction de pipelines de données, génération de présentations, mise en place de dashboards, automatisation de workflows de contenu. Des usages qu'on n'avait jamais anticipés.
|
||||
|
||||
Ça nous a révélé quelque chose d'important : DeerFlow n'était pas qu'un simple outil de recherche. C'était un **harness** — un runtime qui donne aux agents l'infrastructure nécessaire pour vraiment accomplir du travail.
|
||||
|
||||
On l'a donc reconstruit de zéro.
|
||||
|
||||
DeerFlow 2.0 n'est plus un framework à assembler soi-même. C'est un super agent harness — clé en main et entièrement extensible. Construit sur LangGraph et LangChain, il embarque tout ce dont un agent a besoin out of the box : un système de fichiers, de la mémoire, des skills, une exécution sandboxée, et la capacité de planifier et de lancer des sub-agents pour les tâches complexes et multi-étapes.
|
||||
|
||||
Utilisez-le tel quel. Ou démontez-le et faites-en le vôtre.
|
||||
|
||||
## Fonctionnalités principales
|
||||
|
||||
### Skills et outils
|
||||
|
||||
Les skills sont ce qui permet à DeerFlow de faire *pratiquement n'importe quoi*.
|
||||
|
||||
Un Agent Skill standard est un module de capacité structuré — un fichier Markdown qui définit un workflow, des bonnes pratiques et des références vers des ressources associées. DeerFlow est livré avec des skills intégrés pour la recherche, la génération de rapports, la création de présentations, les pages web, la génération d'images et de vidéos, et bien plus. Mais la vraie force réside dans l'extensibilité : ajoutez vos propres skills, remplacez ceux fournis, ou combinez-les en workflows composites.
|
||||
|
||||
Les skills sont chargés progressivement — uniquement quand la tâche le nécessite, pas tous en même temps. Ça permet de garder la fenêtre de contexte légère et de bien fonctionner même avec des modèles sensibles au nombre de tokens.
|
||||
|
||||
Quand vous installez des archives `.skill` via le Gateway, DeerFlow accepte les métadonnées frontmatter optionnelles standard comme `version`, `author` et `compatibility`, plutôt que de rejeter des skills externes par ailleurs valides.
|
||||
|
||||
Les outils suivent la même philosophie. DeerFlow est livré avec un ensemble d'outils de base — recherche web, fetch de pages web, opérations sur les fichiers, exécution bash — et supporte les outils custom via des serveurs MCP et des fonctions Python. Remplacez n'importe quoi. Ajoutez n'importe quoi.
|
||||
|
||||
Les suggestions de suivi générées par le Gateway normalisent désormais aussi bien la sortie texte brut du modèle que le contenu riche au format bloc/liste avant de parser la réponse en tableau JSON, de sorte que les wrappers de contenu propres à chaque provider ne suppriment plus silencieusement les suggestions.
|
||||
|
||||
```
|
||||
# Paths inside the sandbox container
|
||||
/mnt/skills/public
|
||||
├── research/SKILL.md
|
||||
├── report-generation/SKILL.md
|
||||
├── slide-creation/SKILL.md
|
||||
├── web-page/SKILL.md
|
||||
└── image-generation/SKILL.md
|
||||
|
||||
/mnt/skills/custom
|
||||
└── your-custom-skill/SKILL.md ← yours
|
||||
```
|
||||
|
||||
#### Intégration Claude Code
|
||||
|
||||
Le skill `claude-to-deerflow` vous permet d'interagir avec une instance DeerFlow en cours d'exécution directement depuis [Claude Code](https://docs.anthropic.com/en/docs/claude-code). Envoyez des tâches de recherche, vérifiez le statut, gérez les threads — le tout sans quitter le terminal.
|
||||
|
||||
**Installer le skill** :
|
||||
|
||||
```bash
|
||||
npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow
|
||||
```
|
||||
|
||||
Assurez-vous ensuite que DeerFlow tourne (par défaut sur `http://localhost:2026`) et utilisez la commande `/claude-to-deerflow` dans Claude Code.
|
||||
|
||||
**Ce que vous pouvez faire** :
|
||||
- Envoyer des messages à DeerFlow et recevoir des réponses en streaming
|
||||
- Choisir le mode d'exécution : flash (rapide), standard, pro (planification), ultra (sub-agents)
|
||||
- Vérifier la santé de DeerFlow, lister les modèles/skills/agents
|
||||
- Gérer les threads et l'historique des conversations
|
||||
- Upload des fichiers pour analyse
|
||||
|
||||
**Variables d'environnement** (optionnel, pour des endpoints custom) :
|
||||
|
||||
```bash
|
||||
DEERFLOW_URL=http://localhost:2026 # Unified proxy base URL
|
||||
DEERFLOW_GATEWAY_URL=http://localhost:2026 # Gateway API
|
||||
DEERFLOW_LANGGRAPH_URL=http://localhost:2026/api/langgraph # LangGraph API
|
||||
```
|
||||
|
||||
Voir [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerflow/SKILL.md) pour la référence API complète.
|
||||
|
||||
### Sub-Agents
|
||||
|
||||
Les tâches complexes tiennent rarement en une seule passe. DeerFlow les décompose.
|
||||
|
||||
L'agent principal peut lancer des sub-agents à la volée — chacun avec son propre contexte délimité, ses outils et ses conditions d'arrêt. Les sub-agents s'exécutent en parallèle quand c'est possible, remontent des résultats structurés, et l'agent principal synthétise le tout en une sortie cohérente.
|
||||
|
||||
C'est comme ça que DeerFlow gère les tâches qui prennent de quelques minutes à plusieurs heures : une tâche de recherche peut se déployer en une dizaine de sub-agents, chacun explorant un angle différent, puis converger vers un seul rapport — ou un site web — ou un jeu de slides avec des visuels générés. Un seul harness, de nombreuses mains.
|
||||
|
||||
### Sandbox et système de fichiers
|
||||
|
||||
DeerFlow ne se contente pas de *parler* de faire les choses. Il dispose de son propre ordinateur.
|
||||
|
||||
Chaque tâche s'exécute dans un conteneur Docker isolé avec un système de fichiers complet — skills, workspace, uploads, outputs. L'agent lit, écrit et édite des fichiers. Il exécute des commandes bash et du code. Il visualise des images. Le tout sandboxé, le tout auditable, zéro contamination entre les sessions.
|
||||
|
||||
C'est la différence entre un chatbot avec accès à des outils et un agent doté d'un véritable environnement d'exécution.
|
||||
|
||||
```
|
||||
# Paths inside the sandbox container
|
||||
/mnt/user-data/
|
||||
├── uploads/ ← your files
|
||||
├── workspace/ ← agents' working directory
|
||||
└── outputs/ ← final deliverables
|
||||
```
|
||||
|
||||
### Context Engineering
|
||||
|
||||
**Contexte isolé des Sub-Agents** : chaque sub-agent s'exécute dans son propre contexte isolé. Il ne peut voir ni le contexte de l'agent principal, ni celui des autres sub-agents. L'objectif est de garantir que chaque sub-agent reste concentré sur sa tâche sans être parasité par des informations non pertinentes.
|
||||
|
||||
**Résumé** : au sein d'une session, DeerFlow gère le contexte de manière agressive — en résumant les sous-tâches terminées, en déchargeant les résultats intermédiaires vers le système de fichiers, en compressant ce qui n'est plus immédiatement pertinent. Ça lui permet de rester efficace sur des tâches longues et multi-étapes sans faire exploser la fenêtre de contexte.
|
||||
|
||||
### Mémoire à long terme
|
||||
|
||||
La plupart des agents oublient tout dès qu'une conversation se termine. DeerFlow, lui, se souvient.
|
||||
|
||||
D'une session à l'autre, DeerFlow construit une mémoire persistante de votre profil, de vos préférences et de vos connaissances accumulées. Plus vous l'utilisez, mieux il vous connaît — votre style d'écriture, votre stack technique, vos workflows récurrents. La mémoire est stockée localement et reste sous votre contrôle.
|
||||
|
||||
Les mises à jour de la mémoire ignorent désormais les entrées de faits en double au moment de l'application, de sorte que les préférences et le contexte répétés ne s'accumulent plus indéfiniment entre les sessions.
|
||||
|
||||
## Modèles recommandés
|
||||
|
||||
DeerFlow est agnostique en termes de modèle — il fonctionne avec n'importe quel LLM implémentant l'API compatible OpenAI. Cela dit, il offre de meilleures performances avec des modèles qui supportent :
|
||||
|
||||
- **De longues fenêtres de contexte** (100k+ tokens) pour la recherche approfondie et les tâches multi-étapes
|
||||
- **Des capacités de raisonnement** pour la planification adaptative et la décomposition de tâches complexes
|
||||
- **Des entrées multimodales** pour la compréhension d'images et de vidéos
|
||||
- **Un usage fiable des outils (tool use)** pour des appels de fonctions et des sorties structurées fiables
|
||||
|
||||
## Client Python intégré
|
||||
|
||||
DeerFlow peut être utilisé comme bibliothèque Python intégrée sans lancer l'ensemble des services HTTP. Le `DeerFlowClient` fournit un accès direct in-process à toutes les capacités d'agent et de Gateway, en retournant les mêmes schémas de réponse que l'API HTTP Gateway. Le HTTP Gateway expose également `DELETE /api/threads/{thread_id}` pour supprimer les données de thread locales gérées par DeerFlow après la suppression du thread LangGraph :
|
||||
|
||||
```python
|
||||
from deerflow.client import DeerFlowClient
|
||||
|
||||
client = DeerFlowClient()
|
||||
|
||||
# Chat
|
||||
response = client.chat("Analyze this paper for me", thread_id="my-thread")
|
||||
|
||||
# Streaming (LangGraph SSE protocol: values, messages-tuple, end)
|
||||
for event in client.stream("hello"):
|
||||
if event.type == "messages-tuple" and event.data.get("type") == "ai":
|
||||
print(event.data["content"])
|
||||
|
||||
# Configuration & management — returns Gateway-aligned dicts
|
||||
models = client.list_models() # {"models": [...]}
|
||||
skills = client.list_skills() # {"skills": [...]}
|
||||
client.update_skill("web-search", enabled=True)
|
||||
client.upload_files("thread-1", ["./report.pdf"]) # {"success": True, "files": [...]}
|
||||
```
|
||||
|
||||
Toutes les méthodes retournant des dicts sont validées en CI contre les modèles de réponse Pydantic du Gateway (`TestGatewayConformance`), garantissant que le client intégré reste synchronisé avec les schémas de l'API HTTP. Voir `backend/packages/harness/deerflow/client.py` pour la documentation API complète.
|
||||
|
||||
## Documentation
|
||||
|
||||
- [Guide de contribution](CONTRIBUTING.md) - Mise en place de l'environnement de développement et workflow
|
||||
- [Guide de configuration](backend/docs/CONFIGURATION.md) - Instructions d'installation et de configuration
|
||||
- [Vue d'ensemble de l'architecture](backend/CLAUDE.md) - Détails de l'architecture technique
|
||||
- [Architecture backend](backend/README.md) - Architecture backend et référence API
|
||||
|
||||
## ⚠️ Avertissement de sécurité
|
||||
|
||||
### Un déploiement inapproprié peut introduire des risques de sécurité
|
||||
|
||||
DeerFlow dispose de capacités clés à hauts privilèges, notamment **l'exécution de commandes système, les opérations sur les ressources et l'invocation de logique métier**. Il est conçu par défaut pour être **déployé dans un environnement local de confiance (accessible uniquement via l'interface de loopback 127.0.0.1)**. Si vous déployez l'agent dans des environnements non fiables — tels que des réseaux LAN, des serveurs cloud publics ou d'autres environnements accessibles depuis plusieurs terminaux — sans mesures de sécurité strictes, cela peut introduire des risques, notamment :
|
||||
|
||||
- **Invocation non autorisée** : les fonctionnalités de l'agent pourraient être découvertes par des tiers non autorisés ou des scanners malveillants, déclenchant des requêtes non autorisées en masse qui exécutent des opérations à haut risque (commandes système, lecture/écriture de fichiers), pouvant causer de graves conséquences.
|
||||
- **Risques juridiques et de conformité** : si l'agent est utilisé illégalement pour mener des cyberattaques, du vol de données ou d'autres activités illicites, cela peut entraîner des responsabilités juridiques et des risques de conformité.
|
||||
|
||||
### Recommandations de sécurité
|
||||
|
||||
**Note : nous recommandons fortement de déployer DeerFlow dans un environnement réseau local de confiance.** Si vous avez besoin d'un déploiement multi-appareils ou multi-réseaux, vous devez mettre en place des mesures de sécurité strictes, par exemple :
|
||||
|
||||
- **Liste blanche d'IP** : utilisez `iptables`, ou déployez des pare-feux matériels / commutateurs avec ACL, pour **configurer des règles de liste blanche d'IP** et refuser l'accès à toutes les autres adresses IP.
|
||||
- **Passerelle d'authentification** : configurez un proxy inverse (ex. nginx) et **activez une authentification forte en amont**, bloquant tout accès non authentifié.
|
||||
- **Isolation réseau** : si possible, placez l'agent et les appareils de confiance dans le **même VLAN dédié**, isolé des autres équipements réseau.
|
||||
- **Restez informé** : continuez à suivre les mises à jour de sécurité du projet DeerFlow.
|
||||
|
||||
## Contribuer
|
||||
|
||||
Les contributions sont les bienvenues ! Consultez [CONTRIBUTING.md](CONTRIBUTING.md) pour la mise en place de l'environnement de développement, le workflow et les conventions.
|
||||
|
||||
La couverture de tests de régression inclut la détection du mode sandbox Docker et les tests de gestion du kubeconfig-path du provisioner dans `backend/tests/`.
|
||||
|
||||
## Licence
|
||||
|
||||
Ce projet est open source et disponible sous la [Licence MIT](./LICENSE).
|
||||
|
||||
## Remerciements
|
||||
|
||||
DeerFlow est construit sur le travail remarquable de la communauté open source. Nous sommes profondément reconnaissants envers tous les projets et contributeurs dont les efforts ont rendu DeerFlow possible. Nous nous tenons véritablement sur les épaules de géants.
|
||||
|
||||
Nous tenons à exprimer notre sincère gratitude aux projets suivants pour leurs contributions inestimables :
|
||||
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)** : leur excellent framework propulse nos interactions LLM et nos chaînes, permettant une intégration et des fonctionnalités fluides.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)** : leur approche innovante de l'orchestration multi-agents a été déterminante pour les workflows sophistiqués de DeerFlow.
|
||||
|
||||
Ces projets illustrent le pouvoir transformateur de la collaboration open source, et nous sommes fiers de bâtir sur leurs fondations.
|
||||
|
||||
### Contributeurs principaux
|
||||
|
||||
Un grand merci aux auteurs principaux de `DeerFlow`, dont la vision, la passion et le dévouement ont donné vie à ce projet :
|
||||
|
||||
- **[Daniel Walnut](https://github.com/hetaoBackend/)**
|
||||
- **[Henry Li](https://github.com/magiccube/)**
|
||||
|
||||
Votre engagement sans faille et votre expertise sont le moteur du succès de DeerFlow. Nous sommes honorés de vous avoir à la barre de cette aventure.
|
||||
|
||||
## Star History
|
||||
|
||||
[](https://star-history.com/#bytedance/deer-flow&Date)
|
||||
+503
-490
File diff suppressed because it is too large
Load Diff
+545
@@ -0,0 +1,545 @@
|
||||
# 🦌 DeerFlow
|
||||
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://deepwiki.com/bytedance/deer-flow)
|
||||
|
||||
<!-- DeepWiki badge generated by https://deepwiki.ryoppippi.com/ -->
|
||||
|
||||
[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md) | [Español](./README_es.md) | [Русский](./README_ru.md) | [Portuguese](./README_pt.md)
|
||||
|
||||
> Originado do Open Source, de volta ao Open Source
|
||||
|
||||
**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) é um framework de Pesquisa Profunda orientado-a-comunidade que baseia-se em um íncrivel trabalho da comunidade open source. Nosso objetivo é combinar modelos de linguagem com ferramentas especializadas para tarefas como busca na web, crawling, e execução de código Python, enquanto retribui com a comunidade que o tornou possível.
|
||||
|
||||
Atualmente, o DeerFlow entrou oficialmente no Centro de Aplicações FaaS da Volcengine. Os usuários podem experimentá-lo online através do link de experiência para sentir intuitivamente suas funções poderosas e operações convenientes. Ao mesmo tempo, para atender às necessidades de implantação de diferentes usuários, o DeerFlow suporta implantação com um clique baseada na Volcengine. Clique no link de implantação para completar rapidamente o processo de implantação e iniciar uma jornada de pesquisa eficiente.
|
||||
|
||||
Por favor, visite [Nosso Site Oficial](https://deerflow.tech/) para maiores detalhes.
|
||||
|
||||
## Demo
|
||||
|
||||
### Video
|
||||
|
||||
<https://github.com/user-attachments/assets/f3786598-1f2a-4d07-919e-8b99dfa1de3e>
|
||||
|
||||
Nesse demo, nós demonstramos como usar o DeerFlow para:
|
||||
In this demo, we showcase how to use DeerFlow to:
|
||||
|
||||
- Integração fácil com serviços MCP
|
||||
- Conduzir o processo de Pesquisa Profunda e produzir um relatório abrangente com imagens
|
||||
- Criar um áudio podcast baseado no relatório gerado
|
||||
|
||||
### Replays
|
||||
|
||||
- [Quão alta é a Torre Eiffel comparada ao prédio mais alto?](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)
|
||||
- [Quais são os top repositórios tendência no GitHub?](https://deerflow.tech/chat?replay=github-top-trending-repo)
|
||||
- [Escreva um artigo sobre os pratos tradicionais de Nanjing's](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)
|
||||
- [Como decorar um apartamento alugado?](https://deerflow.tech/chat?replay=rental-apartment-decoration)
|
||||
- [Visite nosso site oficial para explorar mais replays.](https://deerflow.tech/#case-studies)
|
||||
|
||||
---
|
||||
|
||||
## 📑 Tabela de Conteúdos
|
||||
|
||||
- [🚀 Início Rápido](#Início-Rápido)
|
||||
- [🌟 Funcionalidades](#funcionalidades)
|
||||
- [🏗️ Arquitetura](#arquitetura)
|
||||
- [🛠️ Desenvolvimento](#desenvolvimento)
|
||||
- [🐳 Docker](#docker)
|
||||
- [🗣️ Texto-para-fala Integração](#texto-para-fala-integração)
|
||||
- [📚 Exemplos](#exemplos)
|
||||
- [❓ FAQ](#faq)
|
||||
- [📜 Licença](#licença)
|
||||
- [💖 Agradecimentos](#agradecimentos)
|
||||
- [🏆 Contribuidores-Chave](#contribuidores-chave)
|
||||
- [⭐ Histórico de Estrelas](#Histórico-Estrelas)
|
||||
|
||||
## Início-Rápido
|
||||
|
||||
DeerFlow é desenvolvido em Python, e vem com uma IU web escrita em Node.js. Para garantir um processo de configuração fácil, nós recomendamos o uso das seguintes ferramentas:
|
||||
|
||||
### Ferramentas Recomendadas
|
||||
|
||||
- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**
|
||||
Simplifica o gerenciamento de dependência de ambientes Python. `uv` automaticamente cria um ambiente virtual no diretório raiz e instala todos os pacotes necessários para não haver a necessidade de instalar ambientes Python manualmente
|
||||
|
||||
- **[`nvm`](https://github.com/nvm-sh/nvm):**
|
||||
Gerencia múltiplas versões do ambiente de execução do Node.js sem esforço.
|
||||
|
||||
- **[`pnpm`](https://pnpm.io/installation):**
|
||||
Instala e gerencia dependências do projeto Node.js.
|
||||
|
||||
### Requisitos de Ambiente
|
||||
|
||||
Certifique-se de que seu sistema atenda os seguintes requisitos mínimos:
|
||||
|
||||
- **[Python](https://www.python.org/downloads/):** Versão `3.12+`
|
||||
- **[Node.js](https://nodejs.org/en/download/):** Versão `22+`
|
||||
|
||||
### Instalação
|
||||
|
||||
```bash
|
||||
# Clone o repositório
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
|
||||
# Instale as dependências, uv irá lidar com o interpretador do python e a criação do venv, e instalar os pacotes necessários
|
||||
uv sync
|
||||
|
||||
# Configure .env com suas chaves de API
|
||||
# Tavily: https://app.tavily.com/home
|
||||
# Brave_SEARCH: https://brave.com/search/api/
|
||||
# volcengine TTS: Adicione sua credencial TTS caso você a possua
|
||||
cp .env.example .env
|
||||
|
||||
# Veja as seções abaixo 'Supported Search Engines' and 'Texto-para-Fala Integração' para todas as opções disponíveis
|
||||
|
||||
# Configure o conf.yaml para o seu modelo LLM e chaves API
|
||||
# Por favor, consulte 'docs/configuration_guide.md' para maiores detalhes
|
||||
cp conf.yaml.example conf.yaml
|
||||
|
||||
# Instale marp para geração de ppt
|
||||
# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager
|
||||
brew install marp-cli
|
||||
```
|
||||
|
||||
Opcionalmente, instale as dependências IU web via [pnpm](https://pnpm.io/installation):
|
||||
|
||||
```bash
|
||||
cd deer-flow/web
|
||||
pnpm install
|
||||
```
|
||||
|
||||
### Configurações
|
||||
|
||||
Por favor, consulte o [Guia de Configuração](docs/configuration_guide.md) para maiores detalhes.
|
||||
|
||||
> [!NOTA]
|
||||
> Antes de iniciar o projeto, leia o guia detalhadamente, e atualize as configurações para baterem com os seus requisitos e configurações específicas.
|
||||
|
||||
### Console IU
|
||||
|
||||
A maneira mais rápida de rodar o projeto é usar o console IU.
|
||||
|
||||
```bash
|
||||
# Execute o projeto em um shell tipo-bash
|
||||
uv run main.py
|
||||
```
|
||||
|
||||
### Web IU
|
||||
|
||||
Esse projeto também inclui uma IU Web, trazendo uma experiência mais interativa, dinâmica e engajadora.
|
||||
|
||||
> [!NOTA]
|
||||
> Você precisa instalar as dependências do IU web primeiro.
|
||||
|
||||
```bash
|
||||
# Execute ambos os servidores de backend e frontend em modo desenvolvimento
|
||||
# No macOS/Linux
|
||||
./bootstrap.sh -d
|
||||
|
||||
# No Windows
|
||||
bootstrap.bat -d
|
||||
```
|
||||
|
||||
Abra seu navegador e visite [`http://localhost:3000`](http://localhost:3000) para explorar a IU web.
|
||||
|
||||
Explore mais detalhes no diretório [`web`](./web/) .
|
||||
|
||||
## Mecanismos de Busca Suportados
|
||||
|
||||
DeerFlow suporta múltiplos mecanismos de busca que podem ser configurados no seu arquivo `.env` usando a variável `SEARCH_API`:
|
||||
|
||||
- **Tavily** (padrão): Uma API de busca especializada para aplicações de IA
|
||||
|
||||
- Requer `TAVILY_API_KEY` no seu arquivo `.env`
|
||||
- Inscreva-se em: <https://app.tavily.com/home>
|
||||
|
||||
- **DuckDuckGo**: Mecanismo de busca focado em privacidade
|
||||
|
||||
- Não requer chave API
|
||||
|
||||
- **Brave Search**: Mecanismo de busca focado em privacidade com funcionalidades avançadas
|
||||
|
||||
- Requer `BRAVE_SEARCH_API_KEY` no seu arquivo `.env`
|
||||
- Inscreva-se em: <https://brave.com/search/api/>
|
||||
|
||||
- **Arxiv**: Busca de artigos científicos para pesquisa acadêmica
|
||||
- Não requer chave API
|
||||
- Especializado em artigos científicos e acadêmicos
|
||||
|
||||
Para configurar o seu mecanismo preferido, defina a variável `SEARCH_API` no seu arquivo:
|
||||
|
||||
```bash
|
||||
# Escolha uma: tavily, duckduckgo, brave_search, arxiv
|
||||
SEARCH_API=tavily
|
||||
```
|
||||
|
||||
## Funcionalidades
|
||||
|
||||
### Principais Funcionalidades
|
||||
|
||||
- 🤖 **Integração LLM**
|
||||
|
||||
- Suporta a integração da maioria dos modelos através de [litellm](https://docs.litellm.ai/docs/providers).
|
||||
- Suporte a modelos open source como Qwen
|
||||
- Interface API compatível com a OpenAI
|
||||
- Sistema LLM multicamadas para diferentes complexidades de tarefa
|
||||
|
||||
### Ferramentas e Integrações MCP
|
||||
|
||||
- 🔍 **Busca e Recuperação**
|
||||
|
||||
- Busca web com Tavily, Brave Search e mais
|
||||
- Crawling com Jina
|
||||
- Extração de Conteúdo avançada
|
||||
|
||||
- 🔗 **Integração MCP perfeita**
|
||||
|
||||
- Expansão de capacidades de acesso para acesso a domínios privados, grafo de conhecimento, navegação web e mais
|
||||
- Integração facilitdade de diversas ferramentas de pesquisa e metodologias
|
||||
|
||||
### Colaboração Humana
|
||||
|
||||
- 🧠 **Humano-no-processo**
|
||||
|
||||
- Suporta modificação interativa de planos de pesquisa usando linguagem natural
|
||||
- Suporta auto-aceite de planos de pesquisa
|
||||
|
||||
- 📝 **Relatório Pós-Edição**
|
||||
- Suporta edição de edição de blocos estilo Notion
|
||||
- Permite refinamentos de IA, incluindo polimento de IA assistida, encurtamento de frase, e expansão
|
||||
- Distribuído por [tiptap](https://tiptap.dev/)
|
||||
|
||||
### Criação de Conteúdo
|
||||
|
||||
- 🎙️ **Geração de Podcast e apresentação**
|
||||
|
||||
- Script de geração de podcast e síntese de áudio movido por IA
|
||||
- Criação automatizada de apresentações PowerPoint simples
|
||||
- Templates customizáveis para conteúdo personalizado
|
||||
|
||||
## Arquitetura
|
||||
|
||||
DeerFlow implementa uma arquitetura de sistema multi-agente modular designada para pesquisa e análise de código automatizada. O sistema é construído em LangGraph, possibilitando um fluxo de trabalho flexível baseado-em-estado onde os componentes se comunicam através de um sistema de transmissão de mensagens bem-definido.
|
||||
|
||||
|
||||
|
||||
> Veja ao vivo em [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)
|
||||
|
||||
O sistema emprega um fluxo de trabalho simplificado com os seguintes componentes:
|
||||
|
||||
1. **Coordenador**: O ponto de entrada que gerencia o ciclo de vida do fluxo de trabalho
|
||||
|
||||
- Inicia o processo de pesquisa baseado na entrada do usuário
|
||||
- Delega tarefas so planejador quando apropriado
|
||||
- Atua como a interface primária entre o usuário e o sistema
|
||||
|
||||
2. **Planejador**: Componente estratégico para a decomposição e planejamento
|
||||
|
||||
- Analisa objetivos de pesquisa e cria planos de execução estruturados
|
||||
- Determina se há contexto suficiente disponível ou se mais pesquisa é necessária
|
||||
- Gerencia o fluxo de pesquisa e decide quando gerar o relatório final
|
||||
|
||||
3. **Time de Pesquisa**: Uma coleção de agentes especializados que executam o plano:
|
||||
|
||||
- **Pesquisador**: Conduz buscas web e coleta informações utilizando ferramentas como mecanismos de busca web, crawling e mesmo serviços MCP.
|
||||
- **Programador**: Lida com a análise de código, execução e tarefas técnicas como usar a ferramenta Python REPL.
|
||||
Cada agente tem acesso à ferramentas específicas otimizadas para seu papel e opera dentro do fluxo de trabalho LangGraph.
|
||||
|
||||
4. **Repórter**: Estágio final do processador de estágio para saídas de pesquisa
|
||||
- Resultados agregados do time de pesquisa
|
||||
- Processa e estrutura as informações coletadas
|
||||
- Gera relatórios abrangentes de pesquisas
|
||||
|
||||
## Texto-para-Fala Integração
|
||||
|
||||
DeerFlow agora inclui uma funcionalidade Texto-para-Fala (TTS) que permite que você converta relatórios de busca para voz. Essa funcionalidade usa o mecanismo de voz da API TTS para gerar áudio de alta qualidade a partir do texto. Funcionalidades como velocidade, volume e tom também são customizáveis.
|
||||
|
||||
### Usando a API TTS
|
||||
|
||||
Você pode acessar a funcionalidade TTS através do endpoint `/api/tts`:
|
||||
|
||||
```bash
|
||||
# Exemplo de chamada da API usando curl
|
||||
curl --location 'http://localhost:8000/api/tts' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"text": "This is a test of the text-to-speech functionality.",
|
||||
"speed_ratio": 1.0,
|
||||
"volume_ratio": 1.0,
|
||||
"pitch_ratio": 1.0
|
||||
}' \
|
||||
--output speech.mp3
|
||||
```
|
||||
|
||||
## Desenvolvimento
|
||||
|
||||
### Testando
|
||||
|
||||
Rode o conjunto de testes:
|
||||
|
||||
```bash
|
||||
# Roda todos os testes
|
||||
make test
|
||||
|
||||
# Roda um arquivo de teste específico
|
||||
pytest tests/integration/test_workflow.py
|
||||
|
||||
# Roda com coverage
|
||||
make coverage
|
||||
```
|
||||
|
||||
### Qualidade de Código
|
||||
|
||||
```bash
|
||||
# Roda o linting
|
||||
make lint
|
||||
|
||||
# Formata de código
|
||||
make format
|
||||
```
|
||||
|
||||
### Debugando com o LangGraph Studio
|
||||
|
||||
DeerFlow usa LangGraph para sua arquitetura de fluxo de trabalho. Nós podemos usar o LangGraph Studio para debugar e visualizar o fluxo de trabalho em tempo real.
|
||||
|
||||
#### Rodando o LangGraph Studio Localmente
|
||||
|
||||
DeerFlow inclui um arquivo de configuração `langgraph.json` que define a estrutura do grafo e dependências para o LangGraph Studio. Esse arquivo aponta para o grafo do fluxo de trabalho definido no projeto e automaticamente carrega as variáveis de ambiente do arquivo `.env`.
|
||||
|
||||
##### Mac
|
||||
|
||||
```bash
|
||||
# Instala o gerenciador de pacote uv caso você não o possua
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
# Instala as dependências e inicia o servidor LangGraph
|
||||
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.12 langgraph dev --allow-blocking
|
||||
```
|
||||
|
||||
##### Windows / Linux
|
||||
|
||||
```bash
|
||||
# Instala as dependências
|
||||
pip install -e .
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
|
||||
# Inicia o servidor LangGraph
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Após iniciar o servidor LangGraph, você verá diversas URLs no seu terminal:
|
||||
|
||||
- API: <http://127.0.0.1:2024>
|
||||
- Studio UI: <https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024>
|
||||
- API Docs: <http://127.0.0.1:2024/docs>
|
||||
|
||||
Abra o link do Studio UI no seu navegador para acessar a interface de depuração.
|
||||
|
||||
#### Usando o LangGraph Studio
|
||||
|
||||
No Studio UI, você pode:
|
||||
|
||||
1. Visualizar o grafo do fluxo de trabalho e como seus componentes se conectam
|
||||
2. Rastrear a execução em tempo-real e ver como os dados fluem através do sistema
|
||||
3. Inspecionar o estado de cada passo do fluxo de trabalho
|
||||
4. Depurar problemas ao examinar entradas e saídas de cada componente
|
||||
5. Coletar feedback durante a fase de planejamento para refinar os planos de pesquisa
|
||||
|
||||
Quando você envia um tópico de pesquisa ao Studio UI, você será capaz de ver toda a execução do fluxo de trabalho, incluindo:
|
||||
|
||||
- A fase de planejamento onde o plano de pesquisa foi criado
|
||||
- O processo de feedback onde você pode modificar o plano
|
||||
- As fases de pesquisa e escrita de cada seção
|
||||
- A geração do relatório final
|
||||
|
||||
## Docker
|
||||
|
||||
Você também pode executar esse projeto via Docker.
|
||||
|
||||
Primeiro, voce deve ler a [configuração](#configuration) below. Make sure `.env`, `.conf.yaml` files are ready.
|
||||
|
||||
Segundo, para fazer o build de sua imagem docker em seu próprio servidor:
|
||||
|
||||
```bash
|
||||
docker build -t deer-flow-api .
|
||||
```
|
||||
|
||||
E por fim, inicie um container docker rodando o servidor web:
|
||||
|
||||
```bash
|
||||
# substitua deer-flow-api-app com seu nome de container preferido
|
||||
docker run -d -t -p 8000:8000 --env-file .env --name deer-flow-api-app deer-flow-api
|
||||
|
||||
# pare o servidor
|
||||
docker stop deer-flow-api-app
|
||||
```
|
||||
|
||||
### Docker Compose (inclui ambos backend e frontend)
|
||||
|
||||
DeerFlow fornece uma estrutura docker-compose para facilmente executar ambos o backend e frontend juntos:
|
||||
|
||||
```bash
|
||||
# building docker image
|
||||
docker compose build
|
||||
|
||||
# start the server
|
||||
docker compose up
|
||||
```
|
||||
|
||||
## Exemplos
|
||||
|
||||
Os seguintes exemplos demonstram as capacidades do DeerFlow:
|
||||
|
||||
### Relatórios de Pesquisa
|
||||
|
||||
1. **Relatório OpenAI Sora** - Análise da ferramenta Sora da OpenAI
|
||||
|
||||
- Discute funcionalidades, acesso, engenharia de prompt, limitações e considerações éticas
|
||||
|
||||
- [Veja o relatório completo](examples/openai_sora_report.md)
|
||||
|
||||
2. **Relatório Protocolo Agent-to-Agent do Google** - Visão geral do protocolo Agent-to-Agent (A2A) do Google
|
||||
|
||||
- Discute o seu papel na comunicação de Agente de IA e seu relacionamento com o Protocolo de Contexto de Modelo ( MCP ) da Anthropic
|
||||
- [Veja o relatório completo](examples/what_is_agent_to_agent_protocol.md)
|
||||
|
||||
3. **O que é MCP?** - Uma análise abrangente to termo "MCP" através de múltiplos contextos
|
||||
|
||||
- Explora o Protocolo de Contexto de Modelo em IA, Fosfato Monocálcio em Química, e placa de microcanal em eletrônica
|
||||
- [Veja o relatório completo](examples/what_is_mcp.md)
|
||||
|
||||
4. **Bitcoin Price Fluctuations** - Análise das recentes movimentações de preço do Bitcoin
|
||||
|
||||
- Examina tendências de mercado, influências regulatórias, e indicadores técnicos
|
||||
- Fornece recomendações baseadas nos dados históricos
|
||||
- [Veja o relatório completo](examples/bitcoin_price_fluctuation.md)
|
||||
|
||||
5. **O que é LLM?** - Uma exploração em profundidade de Large Language Models
|
||||
|
||||
- Discute arquitetura, treinamento, aplicações, e considerações éticas
|
||||
- [Veja o relatório completo](examples/what_is_llm.md)
|
||||
|
||||
6. **Como usar Claude para Pesquisa Aprofundada?** - Melhores práticas e fluxos de trabalho para usar Claude em pesquisa aprofundada
|
||||
|
||||
- Cobre engenharia de prompt, análise de dados, e integração com outras ferramentas
|
||||
- [Veja o relatório completo](examples/how_to_use_claude_deep_research.md)
|
||||
|
||||
7. **Adoção de IA na Área da Saúde: Fatores de Influência** - Análise dos fatores que levam à adoção de IA na área da saúde
|
||||
|
||||
- Discute tecnologias de IA, qualidade de dados, considerações éticas, avaliações econômicas, prontidão organizacional, e infraestrutura digital
|
||||
- [Veja o relatório completo](examples/AI_adoption_in_healthcare.md)
|
||||
|
||||
8. **Impacto da Computação Quântica em Criptografia** - Análise dos impactos da computação quântica em criptografia
|
||||
|
||||
- Discture vulnerabilidades da criptografia clássica, criptografia pós-quântica, e soluções criptográficas de resistência-quântica
|
||||
- [Veja o relatório completo](examples/Quantum_Computing_Impact_on_Cryptography.md)
|
||||
|
||||
9. **Destaques da Performance do Cristiano Ronaldo** - Análise dos destaques da performance do Cristiano Ronaldo
|
||||
- Discute as suas conquistas de carreira, objetivos internacionais, e performance em diversas partidas
|
||||
- [Veja o relatório completo](examples/Cristiano_Ronaldo's_Performance_Highlights.md)
|
||||
|
||||
Para executar esses exemplos ou criar seus próprios relatórios de pesquisa, você deve utilizar os seguintes comandos:
|
||||
|
||||
```bash
|
||||
# Executa com uma consulta específica
|
||||
uv run main.py "Quais fatores estão influenciando a adoção de IA na área da saúde?"
|
||||
|
||||
# Executa com parâmetros de planejamento customizados
|
||||
uv run main.py --max_plan_iterations 3 "Como a computação quântica impacta na criptografia?"
|
||||
|
||||
# Executa em modo interativo com questões embutidas
|
||||
uv run main.py --interactive
|
||||
|
||||
# Ou executa com um prompt interativo básico
|
||||
uv run main.py
|
||||
|
||||
# Vê todas as opções disponíveis
|
||||
uv run main.py --help
|
||||
```
|
||||
|
||||
### Modo Interativo
|
||||
|
||||
A aplicação agora suporta um modo interativo com questões embutidas tanto em Inglês quanto Chinês:
|
||||
|
||||
1. Inicie o modo interativo:
|
||||
|
||||
```bash
|
||||
uv run main.py --interactive
|
||||
```
|
||||
|
||||
2. Selecione sua linguagem de preferência (English or 中文)
|
||||
|
||||
3. Escolha uma das questões embutidas da lista ou selecione a opção para perguntar sua própria questão
|
||||
|
||||
4. O sistema irá processar sua questão e gerar um relatório abrangente de pesquisa
|
||||
|
||||
### Humano no processo
|
||||
|
||||
DeerFlow inclue um mecanismo de humano no processo que permite a você revisar, editar e aprovar planos de pesquisa antes que estes sejam executados:
|
||||
|
||||
1. **Revisão de Plano**: Quando o humano no processo está habilitado, o sistema irá apresentar o plano de pesquisa gerado para sua revisão antes da execução
|
||||
|
||||
2. **Fornecimento de Feedback**: Você pode:
|
||||
|
||||
- Aceitar o plano respondendo com `[ACCEPTED]`
|
||||
- Edite o plano fornecendo feedback (e.g., `[EDIT PLAN] Adicione mais passos sobre a implementação técnica`)
|
||||
- O sistema irá incorporar seu feedback e gerar um plano revisado
|
||||
|
||||
3. **Auto-aceite**: Você pode habilitar o auto-aceite ou pular o processo de revisão:
|
||||
|
||||
- Via API: Defina `auto_accepted_plan: true` na sua requisição
|
||||
|
||||
4. **Integração de API**: Quanto usar a API, você pode fornecer um feedback através do parâmetro `feedback`:
|
||||
|
||||
```json
|
||||
{
|
||||
"messages": [{ "role": "user", "content": "O que é computação quântica?" }],
|
||||
"thread_id": "my_thread_id",
|
||||
"auto_accepted_plan": false,
|
||||
"feedback": "[EDIT PLAN] Inclua mais sobre algoritmos quânticos"
|
||||
}
|
||||
```
|
||||
|
||||
### Argumentos via Linha de Comando
|
||||
|
||||
A aplicação suporta diversos argumentos via linha de comando para customizar o seu comportamento:
|
||||
|
||||
- **consulta**: A consulta de pesquisa a ser processada (podem ser múltiplas palavras)
|
||||
- **--interativo**: Roda no modo interativo com questões embutidas
|
||||
- **--max_plan_iterations**: Número máximo de ciclos de planejamento (padrão: 1)
|
||||
- **--max_step_num**: Número máximo de passos em um plano de pesquisa (padrão: 3)
|
||||
- **--debug**: Habilita Enable um log de depuração detalhado
|
||||
|
||||
## FAQ
|
||||
|
||||
Por favor consulte a [FAQ.md](docs/FAQ.md) para maiores detalhes.
|
||||
|
||||
## Licença
|
||||
|
||||
Esse projeto é open source e disponível sob a [MIT License](./LICENSE).
|
||||
|
||||
## Agradecimentos
|
||||
|
||||
DeerFlow é construído através do incrível trabalho da comunidade open-source. Nós somos profundamente gratos a todos os projetos e contribuidores cujos esforços tornaram o DeerFlow possível. Realmente, nós estamos apoiados nos ombros de gigantes.
|
||||
|
||||
Nós gostaríamos de extender nossos sinceros agradecimentos aos seguintes projetos por suas invaloráveis contribuições:
|
||||
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)**: O framework excepcional deles empodera nossas interações via LLM e correntes, permitindo uma integração perfeita e funcional.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)**: A abordagem inovativa para orquestração multi-agente deles tem sido foi fundamental em permitir o acesso dos fluxos de trabalho sofisticados do DeerFlow.
|
||||
|
||||
Esses projetos exemplificam o poder transformador da colaboração open-source, e nós temos orgulho de construir baseado em suas fundações.
|
||||
|
||||
### Contribuidores-Chave
|
||||
|
||||
Um sincero muito obrigado vai para os principais autores do `DeerFlow`, cuja visão, paixão, e dedicação trouxe esse projeto à vida:
|
||||
|
||||
- **[Daniel Walnut](https://github.com/hetaoBackend/)**
|
||||
- **[Henry Li](https://github.com/magiccube/)**
|
||||
|
||||
O seu compromisso inabalável e experiência tem sido a força por trás do sucesso do DeerFlow. Nós estamos honrados em tê-los no comando dessa trajetória.
|
||||
|
||||
## Histórico-Estrelas
|
||||
|
||||
[](https://star-history.com/#bytedance/deer-flow&Date)
|
||||
+453
-384
@@ -1,490 +1,559 @@
|
||||
# 🦌 DeerFlow - 2.0
|
||||
# 🦌 DeerFlow
|
||||
|
||||
[English](./README.md) | [中文](./README_zh.md) | [日本語](./README_ja.md) | [Français](./README_fr.md) | Русский
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://deepwiki.com/bytedance/deer-flow)
|
||||
<!-- DeepWiki badge generated by https://deepwiki.ryoppippi.com/ -->
|
||||
|
||||
[](./backend/pyproject.toml)
|
||||
[](./Makefile)
|
||||
[](./LICENSE)
|
||||
[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md) | [Español](./README_es.md) | [Русский](./README_ru.md) | [Portuguese](./README_pt.md)
|
||||
|
||||
<a href="https://trendshift.io/repositories/14699" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14699" alt="bytedance%2Fdeer-flow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
> Создано на базе открытого кода, возвращено в открытый код.
|
||||
|
||||
> 28 февраля 2026 года DeerFlow занял 🏆 #1 в GitHub Trending после релиза версии 2. Спасибо огромное нашему сообществу — всё благодаря вам! 💪🔥
|
||||
**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) - это фреймворк для глубокого исследования, разработанный сообществом и основанный на впечатляющей работе сообщества открытого кода. Наша цель - объединить языковые модели со специализированными инструментами для таких задач, как веб-поиск, сканирование и выполнение кода Python, одновременно возвращая пользу сообществу, которое сделало это возможным.
|
||||
|
||||
DeerFlow (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) — open-source **Super Agent Harness**, который управляет **Sub-Agents**, **Memory** и **Sandbox** для решения почти любой задачи. Всё на основе расширяемых **Skills**.
|
||||
В настоящее время DeerFlow официально вошел в Центр приложений FaaS Volcengine. Пользователи могут испытать его онлайн через ссылку для опыта, чтобы интуитивно почувствовать его мощные функции и удобные операции. В то же время, для удовлетворения потребностей развертывания различных пользователей, DeerFlow поддерживает развертывание одним кликом на основе Volcengine. Нажмите на ссылку развертывания, чтобы быстро завершить процесс развертывания и начать эффективное исследовательское путешествие.
|
||||
|
||||
https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
|
||||
Пожалуйста, посетите [наш официальный сайт](https://deerflow.tech/) для получения дополнительной информации.
|
||||
|
||||
> [!NOTE]
|
||||
> **DeerFlow 2.0 — проект переписан с нуля.** Общего кода с v1 нет. Если нужен оригинальный Deep Research фреймворк — он живёт в ветке [`1.x`](https://github.com/bytedance/deer-flow/tree/main-1.x), туда тоже принимают контрибьюты. Активная разработка идёт в 2.0.
|
||||
## Демонстрация
|
||||
|
||||
## Официальный сайт
|
||||
### Видео
|
||||
|
||||
[<img width="2880" height="1600" alt="image" src="https://github.com/user-attachments/assets/a598c49f-3b2f-41ea-a052-05e21349188a" />](https://deerflow.tech)
|
||||
<https://github.com/user-attachments/assets/f3786598-1f2a-4d07-919e-8b99dfa1de3e>
|
||||
|
||||
Больше информации и живые демо на [**официальном сайте**](https://deerflow.tech).
|
||||
В этой демонстрации мы показываем, как использовать DeerFlow для:
|
||||
|
||||
## Coding Plan от ByteDance Volcengine
|
||||
- Бесшовной интеграции с сервисами MCP
|
||||
- Проведения процесса глубокого исследования и создания комплексного отчета с изображениями
|
||||
- Создания аудио подкаста на основе сгенерированного отчета
|
||||
|
||||
<img width="4808" height="2400" alt="英文方舟" src="https://github.com/user-attachments/assets/2ecc7b9d-50be-4185-b1f7-5542d222fb2d" />
|
||||
### Повторы
|
||||
|
||||
- Рекомендуем Doubao-Seed-2.0-Code, DeepSeek v3.2 и Kimi 2.5 для запуска DeerFlow
|
||||
- [Подробнее](https://www.byteplus.com/en/activity/codingplan?utm_campaign=deer_flow&utm_content=deer_flow&utm_medium=devrel&utm_source=OWO&utm_term=deer_flow)
|
||||
- [Для разработчиков из материкового Китая](https://www.volcengine.com/activity/codingplan?utm_campaign=deer_flow&utm_content=deer_flow&utm_medium=devrel&utm_source=OWO&utm_term=deer_flow)
|
||||
|
||||
## InfoQuest
|
||||
|
||||
DeerFlow интегрирован с инструментарием для умного поиска и краулинга от BytePlus — [InfoQuest (есть бесплатный онлайн-доступ)](https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest)
|
||||
|
||||
<a href="https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest" target="_blank">
|
||||
<img
|
||||
src="https://sf16-sg.tiktokcdn.com/obj/eden-sg/hubseh7bsbps/20251208-160108.png"
|
||||
alt="InfoQuest_banner"
|
||||
/>
|
||||
</a>
|
||||
- [Какова высота Эйфелевой башни по сравнению с самым высоким зданием?](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)
|
||||
- [Какие репозитории самые популярные на GitHub?](https://deerflow.tech/chat?replay=github-top-trending-repo)
|
||||
- [Написать статью о традиционных блюдах Нанкина](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)
|
||||
- [Как украсить съемную квартиру?](https://deerflow.tech/chat?replay=rental-apartment-decoration)
|
||||
- [Посетите наш официальный сайт, чтобы изучить больше повторов.](https://deerflow.tech/#case-studies)
|
||||
|
||||
---
|
||||
|
||||
## Содержание
|
||||
## 📑 Оглавление
|
||||
|
||||
- [🦌 DeerFlow - 2.0](#-deerflow---20)
|
||||
- [Официальный сайт](#официальный-сайт)
|
||||
- [InfoQuest](#infoquest)
|
||||
- [Содержание](#содержание)
|
||||
- [Установка одной фразой для coding agent](#установка-одной-фразой-для-coding-agent)
|
||||
- [Быстрый старт](#быстрый-старт)
|
||||
- [Конфигурация](#конфигурация)
|
||||
- [Запуск](#запуск)
|
||||
- [Вариант 1: Docker (рекомендуется)](#вариант-1-docker-рекомендуется)
|
||||
- [Вариант 2: Локальная разработка](#вариант-2-локальная-разработка)
|
||||
- [Дополнительно](#дополнительно)
|
||||
- [Режим Sandbox](#режим-sandbox)
|
||||
- [MCP-сервер](#mcp-сервер)
|
||||
- [Мессенджеры](#мессенджеры)
|
||||
- [Трассировка LangSmith](#трассировка-langsmith)
|
||||
- [От Deep Research к Super Agent Harness](#от-deep-research-к-super-agent-harness)
|
||||
- [Core Features](#core-features)
|
||||
- [Skills & Tools](#skills--tools)
|
||||
- [Интеграция с Claude Code](#интеграция-с-claude-code)
|
||||
- [Sub-Agents](#sub-agents)
|
||||
- [Sandbox & файловая система](#sandbox--файловая-система)
|
||||
- [Context Engineering](#context-engineering)
|
||||
- [Long-Term Memory](#long-term-memory)
|
||||
- [Рекомендуемые модели](#рекомендуемые-модели)
|
||||
- [Встроенный Python-клиент](#встроенный-python-клиент)
|
||||
- [Документация](#документация)
|
||||
- [⚠️ Безопасность](#️-безопасность)
|
||||
- [Участие в разработке](#участие-в-разработке)
|
||||
- [Лицензия](#лицензия)
|
||||
- [Благодарности](#благодарности)
|
||||
- [Ключевые контрибьюторы](#ключевые-контрибьюторы)
|
||||
- [История звёзд](#история-звёзд)
|
||||
|
||||
## Установка одной фразой для coding agent
|
||||
|
||||
Если вы используете Claude Code, Codex, Cursor, Windsurf или другой coding agent, просто отправьте ему эту фразу:
|
||||
|
||||
```text
|
||||
Если DeerFlow еще не клонирован, сначала клонируй его, а затем подготовь локальное окружение разработки по инструкции https://raw.githubusercontent.com/bytedance/deer-flow/main/Install.md
|
||||
```
|
||||
|
||||
Этот prompt предназначен для coding agent. Он просит агента при необходимости сначала клонировать репозиторий, предпочесть Docker, если он доступен, и в конце вернуть точную команду запуска и список недостающих настроек.
|
||||
- [🚀 Быстрый старт](#быстрый-старт)
|
||||
- [🌟 Особенности](#особенности)
|
||||
- [🏗️ Архитектура](#архитектура)
|
||||
- [🛠️ Разработка](#разработка)
|
||||
- [🐳 Docker](#docker)
|
||||
- [🗣️ Интеграция преобразования текста в речь](#интеграция-преобразования-текста-в-речь)
|
||||
- [📚 Примеры](#примеры)
|
||||
- [❓ FAQ](#faq)
|
||||
- [📜 Лицензия](#лицензия)
|
||||
- [💖 Благодарности](#благодарности)
|
||||
- [⭐ История звезд](#история-звезд)
|
||||
|
||||
## Быстрый старт
|
||||
|
||||
### Конфигурация
|
||||
DeerFlow разработан на Python и поставляется с веб-интерфейсом, написанным на Node.js. Для обеспечения плавного процесса настройки мы рекомендуем использовать следующие инструменты:
|
||||
|
||||
1. **Склонировать репозиторий DeerFlow**
|
||||
### Рекомендуемые инструменты
|
||||
|
||||
```bash
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
```
|
||||
- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**
|
||||
Упрощает управление средой Python и зависимостями. `uv` автоматически создает виртуальную среду в корневом каталоге и устанавливает все необходимые пакеты за вас—без необходимости вручную устанавливать среды Python.
|
||||
|
||||
2. **Сгенерировать локальные конфиги**
|
||||
- **[`nvm`](https://github.com/nvm-sh/nvm):**
|
||||
Легко управляйте несколькими версиями среды выполнения Node.js.
|
||||
|
||||
Из корня проекта (`deer-flow/`) запустите:
|
||||
- **[`pnpm`](https://pnpm.io/installation):**
|
||||
Установка и управление зависимостями проекта Node.js.
|
||||
|
||||
```bash
|
||||
make config
|
||||
```
|
||||
### Требования к среде
|
||||
|
||||
Команда создаёт локальные конфиги на основе шаблонов.
|
||||
Убедитесь, что ваша система соответствует следующим минимальным требованиям:
|
||||
|
||||
3. **Настроить модель**
|
||||
- **[Python](https://www.python.org/downloads/):** Версия `3.12+`
|
||||
- **[Node.js](https://nodejs.org/en/download/):** Версия `22+`
|
||||
|
||||
Отредактируйте `config.yaml` и задайте хотя бы одну модель:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4 # Внутренний идентификатор
|
||||
display_name: GPT-4 # Отображаемое имя
|
||||
use: langchain_openai:ChatOpenAI # Путь к классу LangChain
|
||||
model: gpt-4 # Идентификатор модели для API
|
||||
api_key: $OPENAI_API_KEY # API-ключ (рекомендуется: переменная окружения)
|
||||
max_tokens: 4096 # Максимальное количество токенов на запрос
|
||||
temperature: 0.7 # Температура сэмплирования
|
||||
|
||||
- name: openrouter-gemini-2.5-flash
|
||||
display_name: Gemini 2.5 Flash (OpenRouter)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: google/gemini-2.5-flash-preview
|
||||
api_key: $OPENAI_API_KEY
|
||||
base_url: https://openrouter.ai/api/v1
|
||||
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
```
|
||||
|
||||
OpenRouter и аналогичные OpenAI-совместимые шлюзы настраиваются через `langchain_openai:ChatOpenAI` с параметром `base_url`. Для CLI-провайдеров:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5.4
|
||||
display_name: GPT-5.4 (Codex CLI)
|
||||
use: deerflow.models.openai_codex_provider:CodexChatModel
|
||||
model: gpt-5.4
|
||||
supports_thinking: true
|
||||
supports_reasoning_effort: true
|
||||
|
||||
- name: claude-sonnet-4.6
|
||||
display_name: Claude Sonnet 4.6 (Claude Code OAuth)
|
||||
use: deerflow.models.claude_provider:ClaudeChatModel
|
||||
model: claude-sonnet-4-6
|
||||
max_tokens: 4096
|
||||
supports_thinking: true
|
||||
```
|
||||
|
||||
- Codex CLI читает `~/.codex/auth.json`
|
||||
- Claude Code принимает `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN` или `~/.claude/.credentials.json`
|
||||
- На macOS при необходимости экспортируйте аутентификацию Claude Code явно:
|
||||
|
||||
```bash
|
||||
eval "$(python3 scripts/export_claude_code_oauth.py --print-export)"
|
||||
```
|
||||
|
||||
4. **Указать API-ключи**
|
||||
|
||||
- **Вариант А**: файл `.env` в корне проекта (рекомендуется)
|
||||
|
||||
```bash
|
||||
TAVILY_API_KEY=your-tavily-api-key
|
||||
OPENAI_API_KEY=your-openai-api-key
|
||||
INFOQUEST_API_KEY=your-infoquest-api-key
|
||||
```
|
||||
|
||||
- **Вариант Б**: переменные окружения в терминале
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-openai-api-key
|
||||
```
|
||||
|
||||
- **Вариант В**: напрямую в `config.yaml` (не рекомендуется для продакшена)
|
||||
|
||||
### Запуск
|
||||
|
||||
#### Вариант 1: Docker (рекомендуется)
|
||||
|
||||
**Разработка** (hot-reload, монтирование исходников):
|
||||
### Установка
|
||||
|
||||
```bash
|
||||
make docker-init # Загрузить образ Sandbox (один раз или при обновлении)
|
||||
make docker-start # Запустить сервисы
|
||||
# Клонировать репозиторий
|
||||
git clone https://github.com/bytedance/deer-flow.git
|
||||
cd deer-flow
|
||||
|
||||
# Установить зависимости, uv позаботится об интерпретаторе python и создании venv, и установит необходимые пакеты
|
||||
uv sync
|
||||
|
||||
# Настроить .env с вашими API-ключами
|
||||
# Tavily: https://app.tavily.com/home
|
||||
# Brave_SEARCH: https://brave.com/search/api/
|
||||
# volcengine TTS: Добавьте ваши учетные данные TTS, если они у вас есть
|
||||
cp .env.example .env
|
||||
|
||||
# См. разделы 'Поддерживаемые поисковые системы' и 'Интеграция преобразования текста в речь' ниже для всех доступных опций
|
||||
|
||||
# Настроить conf.yaml для вашей модели LLM и API-ключей
|
||||
# Пожалуйста, обратитесь к 'docs/configuration_guide.md' для получения дополнительной информации
|
||||
cp conf.yaml.example conf.yaml
|
||||
|
||||
# Установить marp для генерации презентаций
|
||||
# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager
|
||||
brew install marp-cli
|
||||
```
|
||||
|
||||
**Продакшен** (собирает образы локально):
|
||||
По желанию установите зависимости веб-интерфейса через [pnpm](https://pnpm.io/installation):
|
||||
|
||||
```bash
|
||||
make up # Собрать образы и запустить все сервисы
|
||||
make down # Остановить и удалить контейнеры
|
||||
cd deer-flow/web
|
||||
pnpm install
|
||||
```
|
||||
|
||||
> [!TIP]
|
||||
> На Linux при ошибке `permission denied` для Docker daemon добавьте пользователя в группу `docker` и перелогиньтесь. Подробнее в [CONTRIBUTING.md](CONTRIBUTING.md#linux-docker-daemon-permission-denied).
|
||||
### Конфигурации
|
||||
|
||||
Адрес: http://localhost:2026
|
||||
Пожалуйста, обратитесь к [Руководству по конфигурации](docs/configuration_guide.md) для получения дополнительной информации.
|
||||
|
||||
#### Вариант 2: Локальная разработка
|
||||
> [!ПРИМЕЧАНИЕ]
|
||||
> Прежде чем запустить проект, внимательно прочитайте руководство и обновите конфигурации в соответствии с вашими конкретными настройками и требованиями.
|
||||
|
||||
1. **Проверить зависимости**:
|
||||
```bash
|
||||
make check # Проверяет Node.js 22+, pnpm, uv, nginx
|
||||
```
|
||||
### Консольный интерфейс
|
||||
|
||||
2. **Установить зависимости**:
|
||||
```bash
|
||||
make install
|
||||
```
|
||||
|
||||
3. **(Опционально) Загрузить образ Sandbox заранее**:
|
||||
```bash
|
||||
make setup-sandbox
|
||||
```
|
||||
|
||||
4. **Запустить сервисы**:
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
5. **Адрес**: http://localhost:2026
|
||||
|
||||
### Дополнительно
|
||||
|
||||
#### Режим Sandbox
|
||||
|
||||
DeerFlow поддерживает несколько режимов выполнения:
|
||||
- **Локальное выполнение** — код запускается прямо на хосте
|
||||
- **Docker** — код выполняется в изолированных Docker-контейнерах
|
||||
- **Docker + Kubernetes** — выполнение в Kubernetes-подах через provisioner
|
||||
|
||||
Подробнее в [руководстве по конфигурации Sandbox](backend/docs/CONFIGURATION.md#sandbox).
|
||||
|
||||
#### MCP-сервер
|
||||
|
||||
DeerFlow поддерживает настраиваемые MCP-серверы для расширения возможностей. Для HTTP/SSE MCP-серверов поддерживаются OAuth-токены (`client_credentials`, `refresh_token`). Подробнее в [руководстве по MCP-серверу](backend/docs/MCP_SERVER.md).
|
||||
|
||||
#### Мессенджеры
|
||||
|
||||
DeerFlow принимает задачи прямо из мессенджеров. Каналы запускаются автоматически при настройке, публичный IP не нужен.
|
||||
|
||||
| Канал | Транспорт | Сложность |
|
||||
|-------|-----------|-----------|
|
||||
| Telegram | Bot API (long-polling) | Просто |
|
||||
| Slack | Socket Mode | Средне |
|
||||
| Feishu / Lark | WebSocket | Средне |
|
||||
|
||||
**Конфигурация в `config.yaml`:**
|
||||
|
||||
```yaml
|
||||
channels:
|
||||
feishu:
|
||||
enabled: true
|
||||
app_id: $FEISHU_APP_ID
|
||||
app_secret: $FEISHU_APP_SECRET
|
||||
# domain: https://open.feishu.cn # China (default)
|
||||
# domain: https://open.larksuite.com # International
|
||||
|
||||
slack:
|
||||
enabled: true
|
||||
bot_token: $SLACK_BOT_TOKEN
|
||||
app_token: $SLACK_APP_TOKEN
|
||||
allowed_users: []
|
||||
|
||||
telegram:
|
||||
enabled: true
|
||||
bot_token: $TELEGRAM_BOT_TOKEN
|
||||
allowed_users: []
|
||||
```
|
||||
|
||||
**Настройка Telegram**
|
||||
|
||||
1. Напишите [@BotFather](https://t.me/BotFather), отправьте `/newbot` и скопируйте HTTP API-токен.
|
||||
2. Укажите `TELEGRAM_BOT_TOKEN` в `.env` и включите канал в `config.yaml`.
|
||||
|
||||
**Доступные команды**
|
||||
|
||||
| Команда | Описание |
|
||||
|---------|----------|
|
||||
| `/new` | Начать новый диалог |
|
||||
| `/status` | Показать информацию о текущем треде |
|
||||
| `/models` | Список доступных моделей |
|
||||
| `/memory` | Просмотреть память |
|
||||
| `/help` | Показать справку |
|
||||
|
||||
> Сообщения без команды воспринимаются как обычный чат — DeerFlow создаёт тред и отвечает.
|
||||
|
||||
#### Трассировка LangSmith
|
||||
|
||||
DeerFlow имеет встроенную интеграцию с [LangSmith](https://smith.langchain.com) для наблюдаемости. При включении все вызовы LLM, запуски агентов и выполнения инструментов отслеживаются и отображаются в дашборде LangSmith.
|
||||
|
||||
Добавьте в файл `.env` в корне проекта:
|
||||
Самый быстрый способ запустить проект - использовать консольный интерфейс.
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
|
||||
LANGSMITH_PROJECT=deer-flow
|
||||
# Запустить проект в оболочке, похожей на bash
|
||||
uv run main.py
|
||||
```
|
||||
|
||||
`LANGSMITH_ENDPOINT` по умолчанию `https://api.smith.langchain.com` и может быть переопределён при необходимости. Устаревшие переменные `LANGCHAIN_*` (`LANGCHAIN_TRACING_V2`, `LANGCHAIN_API_KEY` и т.д.) также поддерживаются для обратной совместимости; `LANGSMITH_*` имеет приоритет, когда заданы обе.
|
||||
### Веб-интерфейс
|
||||
|
||||
В Docker-развёртываниях трассировка отключена по умолчанию. Установите `LANGSMITH_TRACING=true` и `LANGSMITH_API_KEY` в `.env` для включения.
|
||||
Этот проект также включает веб-интерфейс, предлагающий более динамичный и привлекательный интерактивный опыт.
|
||||
|
||||
## От Deep Research к Super Agent Harness
|
||||
|
||||
DeerFlow начинался как фреймворк для Deep Research, и сообщество вышло далеко за эти рамки. После запуска разработчики строили пайплайны, генерировали презентации, поднимали дашборды, автоматизировали контент. То, чего мы не ожидали.
|
||||
|
||||
Стало понятно: DeerFlow не просто research-инструмент. Это **harness**: runtime, который даёт агентам необходимую инфраструктуру.
|
||||
|
||||
Поэтому мы переписали всё с нуля.
|
||||
|
||||
DeerFlow 2.0 — это Super Agent Harness «из коробки». Batteries included, полностью расширяемый. Построен на LangGraph и LangChain. По умолчанию есть всё, что нужно агенту: файловая система, memory, skills, sandbox-выполнение и возможность планировать и запускать sub-agents для сложных многошаговых задач.
|
||||
|
||||
Используйте как есть. Или разберите и переделайте под себя.
|
||||
|
||||
## Core Features
|
||||
|
||||
### Skills & Tools
|
||||
|
||||
Skills — это то, что позволяет DeerFlow делать почти что угодно.
|
||||
|
||||
Agent Skill — это структурированный модуль: Markdown-файл с описанием воркфлоу, лучших практик и ссылок на ресурсы. DeerFlow поставляется со встроенными skills для ресёрча, генерации отчётов, слайдов, веб-страниц, изображений и видео. Но главное — расширяемость: добавляйте свои skills, заменяйте встроенные или собирайте из них составные воркфлоу.
|
||||
|
||||
Skills загружаются по мере необходимости, только когда задача их требует. Это держит контекстное окно чистым.
|
||||
|
||||
```
|
||||
# Пути внутри контейнера sandbox
|
||||
/mnt/skills/public
|
||||
├── research/SKILL.md
|
||||
├── report-generation/SKILL.md
|
||||
├── slide-creation/SKILL.md
|
||||
├── web-page/SKILL.md
|
||||
└── image-generation/SKILL.md
|
||||
|
||||
/mnt/skills/custom
|
||||
└── your-custom-skill/SKILL.md ← ваш skill
|
||||
```
|
||||
|
||||
#### Интеграция с Claude Code
|
||||
|
||||
Skill `claude-to-deerflow` позволяет работать с DeerFlow прямо из [Claude Code](https://docs.anthropic.com/en/docs/claude-code). Отправляйте задачи, проверяйте статус, управляйте тредами, не выходя из терминала.
|
||||
|
||||
**Установка скилла**:
|
||||
> [!ПРИМЕЧАНИЕ]
|
||||
> Сначала вам нужно установить зависимости веб-интерфейса.
|
||||
|
||||
```bash
|
||||
npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow
|
||||
# Запустить оба сервера, бэкенд и фронтенд, в режиме разработки
|
||||
# На macOS/Linux
|
||||
./bootstrap.sh -d
|
||||
|
||||
# На Windows
|
||||
bootstrap.bat -d
|
||||
```
|
||||
|
||||
**Что можно делать**:
|
||||
- Отправлять сообщения в DeerFlow и получать потоковые ответы
|
||||
- Выбирать режимы выполнения: flash (быстро), standard, pro (planning), ultra (sub-agents)
|
||||
- Проверять статус DeerFlow, просматривать модели, скиллы, агентов
|
||||
- Управлять тредами и историей диалога
|
||||
- Загружать файлы для анализа
|
||||
Откройте ваш браузер и посетите [`http://localhost:3000`](http://localhost:3000), чтобы исследовать веб-интерфейс.
|
||||
|
||||
Полный справочник API в [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerflow/SKILL.md).
|
||||
Исследуйте больше деталей в каталоге [`web`](./web/).
|
||||
|
||||
### Sub-Agents
|
||||
## Поддерживаемые поисковые системы
|
||||
|
||||
Сложные задачи редко решаются за один проход. DeerFlow их декомпозирует.
|
||||
DeerFlow поддерживает несколько поисковых систем, которые можно настроить в файле `.env` с помощью переменной `SEARCH_API`:
|
||||
|
||||
Lead agent запускает sub-agents на лету, каждый со своим изолированным контекстом, инструментами и условиями завершения. Sub-agents работают параллельно, возвращают структурированные результаты, а lead agent собирает всё в единый итог.
|
||||
- **Tavily** (по умолчанию): Специализированный поисковый API для приложений ИИ
|
||||
|
||||
Вот как DeerFlow справляется с задачами на минуты и часы: research-задача разветвляется в дюжину sub-agents, каждый копает свой угол, потом всё сходится в один отчёт, или сайт, или слайддек со сгенерированными визуалами. Один harness, много рук.
|
||||
- Требуется `TAVILY_API_KEY` в вашем файле `.env`
|
||||
- Зарегистрируйтесь на: <https://app.tavily.com/home>
|
||||
|
||||
### Sandbox & файловая система
|
||||
- **DuckDuckGo**: Поисковая система, ориентированная на конфиденциальность
|
||||
|
||||
DeerFlow не просто *говорит* о том, что умеет что-то делать. У него есть собственный компьютер.
|
||||
- Не требуется API-ключ
|
||||
|
||||
Каждая задача выполняется внутри изолированного Docker-контейнера с полной файловой системой: skills, workspace, uploads, outputs. Агент читает, пишет и редактирует файлы. Выполняет bash-команды и пишет код. Смотрит на изображения. Всё изолировано, всё прозрачно, никакого пересечения между сессиями.
|
||||
- **Brave Search**: Поисковая система, ориентированная на конфиденциальность, с расширенными функциями
|
||||
|
||||
Это разница между чатботом с доступом к инструментам и агентом с реальной средой выполнения.
|
||||
- Требуется `BRAVE_SEARCH_API_KEY` в вашем файле `.env`
|
||||
- Зарегистрируйтесь на: <https://brave.com/search/api/>
|
||||
|
||||
```
|
||||
# Пути внутри контейнера sandbox
|
||||
/mnt/user-data/
|
||||
├── uploads/ ← ваши файлы
|
||||
├── workspace/ ← рабочая директория агентов
|
||||
└── outputs/ ← результаты
|
||||
- **Arxiv**: Поиск научных статей для академических исследований
|
||||
- Не требуется API-ключ
|
||||
- Специализируется на научных и академических статьях
|
||||
|
||||
Чтобы настроить предпочитаемую поисковую систему, установите переменную `SEARCH_API` в вашем файле `.env`:
|
||||
|
||||
```bash
|
||||
# Выберите одно: tavily, duckduckgo, brave_search, arxiv
|
||||
SEARCH_API=tavily
|
||||
```
|
||||
|
||||
### Context Engineering
|
||||
## Особенности
|
||||
|
||||
**Изолированный контекст**: каждый sub-agent работает в своём контексте и не видит контекст главного агента или других sub-agents. Агент фокусируется на своей задаче.
|
||||
### Ключевые возможности
|
||||
|
||||
**Управление контекстом**: внутри сессии DeerFlow агрессивно сжимает контекст и суммирует завершённые подзадачи, выгружает промежуточные результаты в файловую систему, сжимает то, что уже не актуально. На длинных многошаговых задачах контекстное окно не переполняется.
|
||||
- 🤖 **Интеграция LLM**
|
||||
- Поддерживает интеграцию большинства моделей через [litellm](https://docs.litellm.ai/docs/providers).
|
||||
- Поддержка моделей с открытым исходным кодом, таких как Qwen
|
||||
- API-интерфейс, совместимый с OpenAI
|
||||
- Многоуровневая система LLM для задач различной сложности
|
||||
|
||||
### Long-Term Memory
|
||||
### Инструменты и интеграции MCP
|
||||
|
||||
Большинство агентов забывают всё, когда диалог заканчивается. DeerFlow помнит.
|
||||
- 🔍 **Поиск и извлечение**
|
||||
|
||||
DeerFlow сохраняет ваш профиль, предпочтения и накопленные знания между сессиями. Чем больше используете, тем лучше он вас знает: стиль, технологический стек, повторяющиеся воркфлоу. Всё хранится локально и остаётся под вашим контролем.
|
||||
- Веб-поиск через Tavily, Brave Search и другие
|
||||
- Сканирование с Jina
|
||||
- Расширенное извлечение контента
|
||||
|
||||
## Рекомендуемые модели
|
||||
- 🔗 **Бесшовная интеграция MCP**
|
||||
- Расширение возможностей для доступа к частным доменам, графам знаний, веб-браузингу и многому другому
|
||||
- Облегчает интеграцию различных исследовательских инструментов и методологий
|
||||
|
||||
DeerFlow работает с любым LLM через OpenAI-совместимый API. Лучше всего — с моделями, которые поддерживают:
|
||||
### Человеческое взаимодействие
|
||||
|
||||
- **Большое контекстное окно** (100k+ токенов) — для deep research и многошаговых задач
|
||||
- **Reasoning capabilities** — для адаптивного планирования и сложной декомпозиции
|
||||
- **Multimodal inputs** — для работы с изображениями и видео
|
||||
- **Strong tool-use** — для надёжного вызова функций и структурированных ответов
|
||||
- 🧠 **Человек в контуре**
|
||||
|
||||
## Встроенный Python-клиент
|
||||
- Поддерживает интерактивное изменение планов исследования с использованием естественного языка
|
||||
- Поддерживает автоматическое принятие планов исследования
|
||||
|
||||
DeerFlow можно использовать как Python-библиотеку прямо в коде — без запуска HTTP-сервисов. `DeerFlowClient` даёт доступ ко всем возможностям агента и Gateway, возвращает те же схемы ответов, что и HTTP Gateway API:
|
||||
- 📝 **Пост-редактирование отчетов**
|
||||
- Поддерживает блочное редактирование в стиле Notion
|
||||
- Позволяет совершенствовать с помощью ИИ, включая полировку, сокращение и расширение предложений
|
||||
- Работает на [tiptap](https://tiptap.dev/)
|
||||
|
||||
```python
|
||||
from deerflow.client import DeerFlowClient
|
||||
### Создание контента
|
||||
|
||||
client = DeerFlowClient()
|
||||
- 🎙️ **Генерация подкастов и презентаций**
|
||||
- Генерация сценариев подкастов и синтез аудио с помощью ИИ
|
||||
- Автоматическое создание простых презентаций PowerPoint
|
||||
- Настраиваемые шаблоны для индивидуального контента
|
||||
|
||||
# Chat
|
||||
response = client.chat("Analyze this paper for me", thread_id="my-thread")
|
||||
## Архитектура
|
||||
|
||||
# Streaming (LangGraph SSE protocol: values, messages-tuple, end)
|
||||
for event in client.stream("hello"):
|
||||
if event.type == "messages-tuple" and event.data.get("type") == "ai":
|
||||
print(event.data["content"])
|
||||
DeerFlow реализует модульную архитектуру системы с несколькими агентами, предназначенную для автоматизированных исследований и анализа кода. Система построена на LangGraph, обеспечивающей гибкий рабочий процесс на основе состояний, где компоненты взаимодействуют через четко определенную систему передачи сообщений.
|
||||
|
||||
# Configuration & management — returns Gateway-aligned dicts
|
||||
models = client.list_models() # {"models": [...]}
|
||||
skills = client.list_skills() # {"skills": [...]}
|
||||
client.update_skill("web-search", enabled=True)
|
||||
client.upload_files("thread-1", ["./report.pdf"]) # {"success": True, "files": [...]}
|
||||
|
||||
|
||||
> Посмотрите вживую на [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)
|
||||
|
||||
В системе используется оптимизированный рабочий процесс со следующими компонентами:
|
||||
|
||||
1. **Координатор**: Точка входа, управляющая жизненным циклом рабочего процесса
|
||||
|
||||
- Инициирует процесс исследования на основе пользовательского ввода
|
||||
- Делегирует задачи планировщику, когда это необходимо
|
||||
- Выступает в качестве основного интерфейса между пользователем и системой
|
||||
|
||||
2. **Планировщик**: Стратегический компонент для декомпозиции и планирования задач
|
||||
|
||||
- Анализирует цели исследования и создает структурированные планы выполнения
|
||||
- Определяет, достаточно ли доступного контекста или требуется дополнительное исследование
|
||||
- Управляет потоком исследования и решает, когда генерировать итоговый отчет
|
||||
|
||||
3. **Исследовательская команда**: Набор специализированных агентов, которые выполняют план:
|
||||
|
||||
- **Исследователь**: Проводит веб-поиск и сбор информации с использованием таких инструментов, как поисковые системы, сканирование и даже сервисы MCP.
|
||||
- **Программист**: Обрабатывает анализ кода, выполнение и технические задачи с помощью инструмента Python REPL.
|
||||
Каждый агент имеет доступ к определенным инструментам, оптимизированным для его роли, и работает в рамках фреймворка LangGraph
|
||||
|
||||
4. **Репортер**: Процессор финальной стадии для результатов исследования
|
||||
- Агрегирует находки исследовательской команды
|
||||
- Обрабатывает и структурирует собранную информацию
|
||||
- Генерирует комплексные исследовательские отчеты
|
||||
|
||||
## Интеграция преобразования текста в речь
|
||||
|
||||
DeerFlow теперь включает функцию преобразования текста в речь (TTS), которая позволяет конвертировать исследовательские отчеты в речь. Эта функция использует API TTS volcengine для генерации высококачественного аудио из текста. Также можно настраивать такие параметры, как скорость, громкость и тон.
|
||||
|
||||
### Использование API TTS
|
||||
|
||||
Вы можете получить доступ к функциональности TTS через конечную точку `/api/tts`:
|
||||
|
||||
```bash
|
||||
# Пример вызова API с использованием curl
|
||||
curl --location 'http://localhost:8000/api/tts' \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"text": "Это тест функциональности преобразования текста в речь.",
|
||||
"speed_ratio": 1.0,
|
||||
"volume_ratio": 1.0,
|
||||
"pitch_ratio": 1.0
|
||||
}' \
|
||||
--output speech.mp3
|
||||
```
|
||||
|
||||
## Документация
|
||||
## Разработка
|
||||
|
||||
- [Руководство по участию](CONTRIBUTING.md) — настройка среды разработки, воркфлоу и гайдлайны
|
||||
- [Руководство по конфигурации](backend/docs/CONFIGURATION.md) — инструкции по настройке
|
||||
- [Обзор архитектуры](backend/CLAUDE.md) — технические детали
|
||||
- [Архитектура бэкенда](backend/README.md) — бэкенд и справочник API
|
||||
### Тестирование
|
||||
|
||||
## ⚠️ Безопасность
|
||||
Запустите набор тестов:
|
||||
|
||||
### Неправильное развёртывание может привести к угрозам безопасности
|
||||
```bash
|
||||
# Запустить все тесты
|
||||
make test
|
||||
|
||||
DeerFlow обладает ключевыми высокопривилегированными возможностями, включая **выполнение системных команд, операции с ресурсами и вызов бизнес-логики**. По умолчанию он рассчитан на **развёртывание в локальной доверенной среде (доступ только через loopback-адрес 127.0.0.1)**. Если вы разворачиваете агент в недоверенных средах — локальных сетях, публичных облачных серверах или других окружениях, доступных с нескольких устройств — без строгих мер безопасности, это может привести к следующим угрозам:
|
||||
# Запустить определенный тестовый файл
|
||||
pytest tests/integration/test_workflow.py
|
||||
|
||||
- **Несанкционированные вызовы**: функциональность агента может быть обнаружена неавторизованными третьими лицами или вредоносными сканерами, что приведёт к массовым несанкционированным запросам с выполнением высокорисковых операций (системные команды, чтение/запись файлов) и серьёзным последствиям для безопасности.
|
||||
- **Юридические и compliance-риски**: если агент будет незаконно использован для кибератак, кражи данных или других противоправных действий, это может повлечь юридическую ответственность и compliance-риски.
|
||||
# Запустить с покрытием
|
||||
make coverage
|
||||
```
|
||||
|
||||
### Рекомендации по безопасности
|
||||
### Качество кода
|
||||
|
||||
**Примечание: настоятельно рекомендуем развёртывать DeerFlow только в локальной доверенной сети.** Если вам необходимо развёртывание через несколько устройств или сетей, обязательно реализуйте строгие меры безопасности, например:
|
||||
```bash
|
||||
# Запустить линтинг
|
||||
make lint
|
||||
|
||||
- **Белый список IP-адресов**: используйте `iptables` или аппаратные межсетевые экраны / коммутаторы с ACL, чтобы **настроить правила белого списка IP** и заблокировать доступ со всех остальных адресов.
|
||||
- **Шлюз аутентификации**: настройте обратный прокси (nginx и др.) и **включите строгую предварительную аутентификацию**, запрещающую любой доступ без авторизации.
|
||||
- **Сетевая изоляция**: по возможности разместите агент и доверенные устройства в **одном выделенном VLAN**, изолированном от остальной сети.
|
||||
- **Следите за обновлениями**: регулярно отслеживайте обновления безопасности проекта DeerFlow.
|
||||
# Форматировать код
|
||||
make format
|
||||
```
|
||||
|
||||
## Участие в разработке
|
||||
### Отладка с LangGraph Studio
|
||||
|
||||
Приветствуем контрибьюторов! Настройка среды разработки, воркфлоу и гайдлайны — в [CONTRIBUTING.md](CONTRIBUTING.md).
|
||||
DeerFlow использует LangGraph для своей архитектуры рабочего процесса. Вы можете использовать LangGraph Studio для отладки и визуализации рабочего процесса в реальном времени.
|
||||
|
||||
#### Запуск LangGraph Studio локально
|
||||
|
||||
DeerFlow включает конфигурационный файл `langgraph.json`, который определяет структуру графа и зависимости для LangGraph Studio. Этот файл указывает на графы рабочего процесса, определенные в проекте, и автоматически загружает переменные окружения из файла `.env`.
|
||||
|
||||
##### Mac
|
||||
|
||||
```bash
|
||||
# Установите менеджер пакетов uv, если у вас его нет
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
# Установите зависимости и запустите сервер LangGraph
|
||||
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.12 langgraph dev --allow-blocking
|
||||
```
|
||||
|
||||
##### Windows / Linux
|
||||
|
||||
```bash
|
||||
# Установить зависимости
|
||||
pip install -e .
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
|
||||
# Запустить сервер LangGraph
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
После запуска сервера LangGraph вы увидите несколько URL в терминале:
|
||||
|
||||
- API: <http://127.0.0.1:2024>
|
||||
- Studio UI: <https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024>
|
||||
- API Docs: <http://127.0.0.1:2024/docs>
|
||||
|
||||
Откройте ссылку Studio UI в вашем браузере для доступа к интерфейсу отладки.
|
||||
|
||||
#### Использование LangGraph Studio
|
||||
|
||||
В интерфейсе Studio вы можете:
|
||||
|
||||
1. Визуализировать граф рабочего процесса и видеть, как соединяются компоненты
|
||||
2. Отслеживать выполнение в реальном времени, чтобы видеть, как данные проходят через систему
|
||||
3. Исследовать состояние на каждом шаге рабочего процесса
|
||||
4. Отлаживать проблемы путем изучения входов и выходов каждого компонента
|
||||
5. Предоставлять обратную связь во время фазы планирования для уточнения планов исследования
|
||||
|
||||
Когда вы отправляете тему исследования в интерфейсе Studio, вы сможете увидеть весь процесс выполнения рабочего процесса, включая:
|
||||
|
||||
- Фазу планирования, где создается план исследования
|
||||
- Цикл обратной связи, где вы можете модифицировать план
|
||||
- Фазы исследования и написания для каждого раздела
|
||||
- Генерацию итогового отчета
|
||||
|
||||
### Включение трассировки LangSmith
|
||||
|
||||
DeerFlow поддерживает трассировку LangSmith, чтобы помочь вам отладить и контролировать ваши рабочие процессы. Чтобы включить трассировку LangSmith:
|
||||
|
||||
1. Убедитесь, что в вашем файле `.env` есть следующие конфигурации (см. `.env.example`):
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
|
||||
LANGSMITH_API_KEY="xxx"
|
||||
LANGSMITH_PROJECT="xxx"
|
||||
```
|
||||
|
||||
2. Запустите трассировку и визуализируйте граф локально с LangSmith, выполнив:
|
||||
|
||||
```bash
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
Это включит визуализацию трассировки в LangGraph Studio и отправит ваши трассировки в LangSmith для мониторинга и анализа.
|
||||
|
||||
## Docker
|
||||
|
||||
Вы также можете запустить этот проект с Docker.
|
||||
|
||||
Во-первых, вам нужно прочитать [конфигурацию](docs/configuration_guide.md) ниже. Убедитесь, что файлы `.env`, `.conf.yaml` готовы.
|
||||
|
||||
Во-вторых, чтобы построить Docker-образ вашего собственного веб-сервера:
|
||||
|
||||
```bash
|
||||
docker build -t deer-flow-api .
|
||||
```
|
||||
|
||||
Наконец, запустите Docker-контейнер с веб-сервером:
|
||||
|
||||
```bash
|
||||
# Замените deer-flow-api-app на предпочитаемое вами имя контейнера
|
||||
docker run -d -t -p 8000:8000 --env-file .env --name deer-flow-api-app deer-flow-api
|
||||
|
||||
# остановить сервер
|
||||
docker stop deer-flow-api-app
|
||||
```
|
||||
|
||||
### Docker Compose (включает как бэкенд, так и фронтенд)
|
||||
|
||||
DeerFlow предоставляет настройку docker-compose для легкого запуска бэкенда и фронтенда вместе:
|
||||
|
||||
```bash
|
||||
# сборка docker-образа
|
||||
docker compose build
|
||||
|
||||
# запуск сервера
|
||||
docker compose up
|
||||
```
|
||||
|
||||
## Примеры
|
||||
|
||||
Следующие примеры демонстрируют возможности DeerFlow:
|
||||
|
||||
### Исследовательские отчеты
|
||||
|
||||
1. **Отчет о OpenAI Sora** - Анализ инструмента ИИ Sora от OpenAI
|
||||
|
||||
- Обсуждаются функции, доступ, инженерия промптов, ограничения и этические соображения
|
||||
- [Просмотреть полный отчет](examples/openai_sora_report.md)
|
||||
|
||||
2. **Отчет о протоколе Agent to Agent от Google** - Обзор протокола Agent to Agent (A2A) от Google
|
||||
|
||||
- Обсуждается его роль в коммуникации агентов ИИ и его отношение к протоколу Model Context Protocol (MCP) от Anthropic
|
||||
- [Просмотреть полный отчет](examples/what_is_agent_to_agent_protocol.md)
|
||||
|
||||
3. **Что такое MCP?** - Комплексный анализ термина "MCP" в различных контекстах
|
||||
|
||||
- Исследует Model Context Protocol в ИИ, Монокальцийфосфат в химии и Микроканальные пластины в электронике
|
||||
- [Просмотреть полный отчет](examples/what_is_mcp.md)
|
||||
|
||||
4. **Колебания цены Биткоина** - Анализ недавних движений цены Биткоина
|
||||
|
||||
- Исследует рыночные тренды, регуляторные влияния и технические индикаторы
|
||||
- Предоставляет рекомендации на основе исторических данных
|
||||
- [Просмотреть полный отчет](examples/bitcoin_price_fluctuation.md)
|
||||
|
||||
5. **Что такое LLM?** - Углубленное исследование больших языковых моделей
|
||||
|
||||
- Обсуждаются архитектура, обучение, приложения и этические соображения
|
||||
- [Просмотреть полный отчет](examples/what_is_llm.md)
|
||||
|
||||
6. **Как использовать Claude для глубокого исследования?** - Лучшие практики и рабочие процессы для использования Claude в глубоком исследовании
|
||||
|
||||
- Охватывает инженерию промптов, анализ данных и интеграцию с другими инструментами
|
||||
- [Просмотреть полный отчет](examples/how_to_use_claude_deep_research.md)
|
||||
|
||||
7. **Внедрение ИИ в здравоохранении: Влияющие факторы** - Анализ факторов, движущих внедрением ИИ в здравоохранении
|
||||
|
||||
- Обсуждаются технологии ИИ, качество данных, этические соображения, экономические оценки, организационная готовность и цифровая инфраструктура
|
||||
- [Просмотреть полный отчет](examples/AI_adoption_in_healthcare.md)
|
||||
|
||||
8. **Влияние квантовых вычислений на криптографию** - Анализ влияния квантовых вычислений на криптографию
|
||||
|
||||
- Обсуждаются уязвимости классической криптографии, пост-квантовая криптография и криптографические решения, устойчивые к квантовым вычислениям
|
||||
- [Просмотреть полный отчет](examples/Quantum_Computing_Impact_on_Cryptography.md)
|
||||
|
||||
9. **Ключевые моменты выступлений Криштиану Роналду** - Анализ выдающихся выступлений Криштиану Роналду
|
||||
- Обсуждаются его карьерные достижения, международные голы и выступления в различных матчах
|
||||
- [Просмотреть полный отчет](examples/Cristiano_Ronaldo's_Performance_Highlights.md)
|
||||
|
||||
Чтобы запустить эти примеры или создать собственные исследовательские отчеты, вы можете использовать следующие команды:
|
||||
|
||||
```bash
|
||||
# Запустить с определенным запросом
|
||||
uv run main.py "Какие факторы влияют на внедрение ИИ в здравоохранении?"
|
||||
|
||||
# Запустить с пользовательскими параметрами планирования
|
||||
uv run main.py --max_plan_iterations 3 "Как квантовые вычисления влияют на криптографию?"
|
||||
|
||||
# Запустить в интерактивном режиме с встроенными вопросами
|
||||
uv run main.py --interactive
|
||||
|
||||
# Или запустить с базовым интерактивным приглашением
|
||||
uv run main.py
|
||||
|
||||
# Посмотреть все доступные опции
|
||||
uv run main.py --help
|
||||
```
|
||||
|
||||
### Интерактивный режим
|
||||
|
||||
Приложение теперь поддерживает интерактивный режим с встроенными вопросами как на английском, так и на китайском языках:
|
||||
|
||||
1. Запустите интерактивный режим:
|
||||
|
||||
```bash
|
||||
uv run main.py --interactive
|
||||
```
|
||||
|
||||
2. Выберите предпочитаемый язык (English или 中文)
|
||||
|
||||
3. Выберите из списка встроенных вопросов или выберите опцию задать собственный вопрос
|
||||
|
||||
4. Система обработает ваш вопрос и сгенерирует комплексный исследовательский отчет
|
||||
|
||||
### Человек в контуре
|
||||
|
||||
DeerFlow включает механизм "человек в контуре", который позволяет вам просматривать, редактировать и утверждать планы исследования перед их выполнением:
|
||||
|
||||
1. **Просмотр плана**: Когда активирован режим "человек в контуре", система представит сгенерированный план исследования для вашего просмотра перед выполнением
|
||||
|
||||
2. **Предоставление обратной связи**: Вы можете:
|
||||
|
||||
- Принять план, ответив `[ACCEPTED]`
|
||||
- Отредактировать план, предоставив обратную связь (например, `[EDIT PLAN] Добавить больше шагов о технической реализации`)
|
||||
- Система включит вашу обратную связь и сгенерирует пересмотренный план
|
||||
|
||||
3. **Автоматическое принятие**: Вы можете включить автоматическое принятие, чтобы пропустить процесс просмотра:
|
||||
|
||||
- Через API: Установите `auto_accepted_plan: true` в вашем запросе
|
||||
|
||||
4. **Интеграция API**: При использовании API вы можете предоставить обратную связь через параметр `feedback`:
|
||||
|
||||
```json
|
||||
{
|
||||
"messages": [{ "role": "user", "content": "Что такое квантовые вычисления?" }],
|
||||
"thread_id": "my_thread_id",
|
||||
"auto_accepted_plan": false,
|
||||
"feedback": "[EDIT PLAN] Включить больше о квантовых алгоритмах"
|
||||
}
|
||||
```
|
||||
|
||||
### Аргументы командной строки
|
||||
|
||||
Приложение поддерживает несколько аргументов командной строки для настройки его поведения:
|
||||
|
||||
- **query**: Запрос исследования для обработки (может состоять из нескольких слов)
|
||||
- **--interactive**: Запустить в интерактивном режиме с встроенными вопросами
|
||||
- **--max_plan_iterations**: Максимальное количество циклов планирования (по умолчанию: 1)
|
||||
- **--max_step_num**: Максимальное количество шагов в плане исследования (по умолчанию: 3)
|
||||
- **--debug**: Включить подробное логирование отладки
|
||||
|
||||
## FAQ
|
||||
|
||||
Пожалуйста, обратитесь к [FAQ.md](docs/FAQ.md) для получения дополнительной информации.
|
||||
|
||||
## Лицензия
|
||||
|
||||
Проект распространяется под [лицензией MIT](./LICENSE).
|
||||
Этот проект имеет открытый исходный код и доступен под [Лицензией MIT](./LICENSE).
|
||||
|
||||
## Благодарности
|
||||
|
||||
DeerFlow стоит на плечах open-source сообщества. Спасибо всем проектам и разработчикам, чья работа сделала его возможным.
|
||||
DeerFlow создан на основе невероятной работы сообщества открытого кода. Мы глубоко благодарны всем проектам и контрибьюторам, чьи усилия сделали DeerFlow возможным. Поистине, мы стоим на плечах гигантов.
|
||||
|
||||
Отдельная благодарность:
|
||||
Мы хотели бы выразить искреннюю признательность следующим проектам за их неоценимый вклад:
|
||||
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)** — фреймворк для взаимодействия с LLM и построения цепочек.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)** — многоагентная оркестрация, на которой держатся сложные воркфлоу DeerFlow.
|
||||
- **[LangChain](https://github.com/langchain-ai/langchain)**: Их исключительный фреймворк обеспечивает наши взаимодействия и цепочки LLM, позволяя бесшовную интеграцию и функциональность.
|
||||
- **[LangGraph](https://github.com/langchain-ai/langgraph)**: Их инновационный подход к оркестровке многоагентных систем сыграл решающую роль в обеспечении сложных рабочих процессов DeerFlow.
|
||||
|
||||
Эти проекты являются примером преобразующей силы сотрудничества в области открытого кода, и мы гордимся тем, что строим на их основе.
|
||||
|
||||
### Ключевые контрибьюторы
|
||||
|
||||
Авторы DeerFlow, без которых проекта бы не было:
|
||||
Сердечная благодарность основным авторам `DeerFlow`, чье видение, страсть и преданность делу вдохнули жизнь в этот проект:
|
||||
|
||||
- **[Daniel Walnut](https://github.com/hetaoBackend/)**
|
||||
- **[Henry Li](https://github.com/magiccube/)**
|
||||
|
||||
## История звёзд
|
||||
Ваша непоколебимая приверженность и опыт стали движущей силой успеха DeerFlow. Мы считаем за честь иметь вас во главе этого путешествия.
|
||||
|
||||
## История звезд
|
||||
|
||||
[](https://star-history.com/#bytedance/deer-flow&Date)
|
||||
|
||||
+473
-464
File diff suppressed because it is too large
Load Diff
-12
@@ -1,12 +0,0 @@
|
||||
# Security Policy
|
||||
|
||||
## Supported Versions
|
||||
|
||||
As deer-flow doesn't provide an official release yet, please use the latest version for the security updates.
|
||||
Currently, we have two branches to maintain:
|
||||
* main branch for deer-flow 2.x
|
||||
* main-1.x branch for deer-flow 1.x
|
||||
|
||||
## Reporting a Vulnerability
|
||||
|
||||
Please go to https://github.com/bytedance/deer-flow/security to report the vulnerability you find.
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 151 KiB |
@@ -1,28 +0,0 @@
|
||||
# Python-generated files
|
||||
__pycache__/
|
||||
*.py[oc]
|
||||
build/
|
||||
dist/
|
||||
wheels/
|
||||
*.egg-info
|
||||
.coverage
|
||||
.coverage.*
|
||||
.ruff_cache
|
||||
agent_history.gif
|
||||
static/browser_history/*.gif
|
||||
|
||||
log/
|
||||
log/*
|
||||
|
||||
# Virtual environments
|
||||
.venv
|
||||
venv/
|
||||
|
||||
# User config file
|
||||
config.yaml
|
||||
|
||||
# Langgraph
|
||||
.langgraph_api
|
||||
|
||||
# Claude Code settings
|
||||
.claude/settings.local.json
|
||||
Vendored
-3
@@ -1,3 +0,0 @@
|
||||
{
|
||||
"recommendations": ["charliermarsh.ruff"]
|
||||
}
|
||||
Vendored
-11
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"window.title": "${activeEditorShort}${separator}${separator}deer-flow/backend",
|
||||
"[python]": {
|
||||
"editor.formatOnSave": true,
|
||||
"editor.codeActionsOnSave": {
|
||||
"source.fixAll": "explicit",
|
||||
"source.organizeImports": "explicit"
|
||||
},
|
||||
"editor.defaultFormatter": "charliermarsh.ruff"
|
||||
}
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
For the backend architecture and design patterns:
|
||||
@./CLAUDE.md
|
||||
@@ -1,563 +0,0 @@
|
||||
# CLAUDE.md
|
||||
|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
DeerFlow is a LangGraph-based AI super agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution, persistent memory, subagent delegation, and extensible tool integration - all operating in per-thread isolated environments.
|
||||
|
||||
**Architecture**:
|
||||
- **LangGraph Server** (port 2024): Agent runtime and workflow execution
|
||||
- **Gateway API** (port 8001): REST API for models, MCP, skills, memory, artifacts, uploads, and local thread cleanup
|
||||
- **Frontend** (port 3000): Next.js web interface
|
||||
- **Nginx** (port 2026): Unified reverse proxy entry point
|
||||
- **Provisioner** (port 8002, optional in Docker dev): Started only when sandbox is configured for provisioner/Kubernetes mode
|
||||
|
||||
**Runtime Modes**:
|
||||
- **Standard mode** (`make dev`): LangGraph Server handles agent execution as a separate process. 4 processes total.
|
||||
- **Gateway mode** (`make dev-pro`, experimental): Agent runtime embedded in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Service manages its own concurrency via async tasks. 3 processes total, no LangGraph Server.
|
||||
|
||||
**Project Structure**:
|
||||
```
|
||||
deer-flow/
|
||||
├── Makefile # Root commands (check, install, dev, stop)
|
||||
├── config.yaml # Main application configuration
|
||||
├── extensions_config.json # MCP servers and skills configuration
|
||||
├── backend/ # Backend application (this directory)
|
||||
│ ├── Makefile # Backend-only commands (dev, gateway, lint)
|
||||
│ ├── langgraph.json # LangGraph server configuration
|
||||
│ ├── packages/
|
||||
│ │ └── harness/ # deerflow-harness package (import: deerflow.*)
|
||||
│ │ ├── pyproject.toml
|
||||
│ │ └── deerflow/
|
||||
│ │ ├── agents/ # LangGraph agent system
|
||||
│ │ │ ├── lead_agent/ # Main agent (factory + system prompt)
|
||||
│ │ │ ├── middlewares/ # 10 middleware components
|
||||
│ │ │ ├── memory/ # Memory extraction, queue, prompts
|
||||
│ │ │ └── thread_state.py # ThreadState schema
|
||||
│ │ ├── sandbox/ # Sandbox execution system
|
||||
│ │ │ ├── local/ # Local filesystem provider
|
||||
│ │ │ ├── sandbox.py # Abstract Sandbox interface
|
||||
│ │ │ ├── tools.py # bash, ls, read/write/str_replace
|
||||
│ │ │ └── middleware.py # Sandbox lifecycle management
|
||||
│ │ ├── subagents/ # Subagent delegation system
|
||||
│ │ │ ├── builtins/ # general-purpose, bash agents
|
||||
│ │ │ ├── executor.py # Background execution engine
|
||||
│ │ │ └── registry.py # Agent registry
|
||||
│ │ ├── tools/builtins/ # Built-in tools (present_files, ask_clarification, view_image)
|
||||
│ │ ├── mcp/ # MCP integration (tools, cache, client)
|
||||
│ │ ├── models/ # Model factory with thinking/vision support
|
||||
│ │ ├── skills/ # Skills discovery, loading, parsing
|
||||
│ │ ├── config/ # Configuration system (app, model, sandbox, tool, etc.)
|
||||
│ │ ├── community/ # Community tools (tavily, jina_ai, firecrawl, image_search, aio_sandbox)
|
||||
│ │ ├── reflection/ # Dynamic module loading (resolve_variable, resolve_class)
|
||||
│ │ ├── utils/ # Utilities (network, readability)
|
||||
│ │ └── client.py # Embedded Python client (DeerFlowClient)
|
||||
│ ├── app/ # Application layer (import: app.*)
|
||||
│ │ ├── gateway/ # FastAPI Gateway API
|
||||
│ │ │ ├── app.py # FastAPI application
|
||||
│ │ │ └── routers/ # FastAPI route modules (models, mcp, memory, skills, uploads, threads, artifacts, agents, suggestions, channels)
|
||||
│ │ └── channels/ # IM platform integrations
|
||||
│ ├── tests/ # Test suite
|
||||
│ └── docs/ # Documentation
|
||||
├── frontend/ # Next.js frontend application
|
||||
└── skills/ # Agent skills directory
|
||||
├── public/ # Public skills (committed)
|
||||
└── custom/ # Custom skills (gitignored)
|
||||
```
|
||||
|
||||
## Important Development Guidelines
|
||||
|
||||
### Documentation Update Policy
|
||||
**CRITICAL: Always update README.md and CLAUDE.md after every code change**
|
||||
|
||||
When making code changes, you MUST update the relevant documentation:
|
||||
- Update `README.md` for user-facing changes (features, setup, usage instructions)
|
||||
- Update `CLAUDE.md` for development changes (architecture, commands, workflows, internal systems)
|
||||
- Keep documentation synchronized with the codebase at all times
|
||||
- Ensure accuracy and timeliness of all documentation
|
||||
|
||||
## Commands
|
||||
|
||||
**Root directory** (for full application):
|
||||
```bash
|
||||
make check # Check system requirements
|
||||
make install # Install all dependencies (frontend + backend)
|
||||
make dev # Start all services (LangGraph + Gateway + Frontend + Nginx), with config.yaml preflight
|
||||
make dev-pro # Gateway mode (experimental): skip LangGraph, agent runtime embedded in Gateway
|
||||
make start-pro # Production + Gateway mode (experimental)
|
||||
make stop # Stop all services
|
||||
```
|
||||
|
||||
**Backend directory** (for backend development only):
|
||||
```bash
|
||||
make install # Install backend dependencies
|
||||
make dev # Run LangGraph server only (port 2024)
|
||||
make gateway # Run Gateway API only (port 8001)
|
||||
make test # Run all backend tests
|
||||
make lint # Lint with ruff
|
||||
make format # Format code with ruff
|
||||
```
|
||||
|
||||
Regression tests related to Docker/provisioner behavior:
|
||||
- `tests/test_docker_sandbox_mode_detection.py` (mode detection from `config.yaml`)
|
||||
- `tests/test_provisioner_kubeconfig.py` (kubeconfig file/directory handling)
|
||||
|
||||
Boundary check (harness → app import firewall):
|
||||
- `tests/test_harness_boundary.py` — ensures `packages/harness/deerflow/` never imports from `app.*`
|
||||
|
||||
CI runs these regression tests for every pull request via [.github/workflows/backend-unit-tests.yml](../.github/workflows/backend-unit-tests.yml).
|
||||
|
||||
## Architecture
|
||||
|
||||
### Harness / App Split
|
||||
|
||||
The backend is split into two layers with a strict dependency direction:
|
||||
|
||||
- **Harness** (`packages/harness/deerflow/`): Publishable agent framework package (`deerflow-harness`). Import prefix: `deerflow.*`. Contains agent orchestration, tools, sandbox, models, MCP, skills, config — everything needed to build and run agents.
|
||||
- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram).
|
||||
|
||||
**Dependency rule**: App imports deerflow, but deerflow never imports app. This boundary is enforced by `tests/test_harness_boundary.py` which runs in CI.
|
||||
|
||||
**Import conventions**:
|
||||
```python
|
||||
# Harness internal
|
||||
from deerflow.agents import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
# App internal
|
||||
from app.gateway.app import app
|
||||
from app.channels.service import start_channel_service
|
||||
|
||||
# App → Harness (allowed)
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
# Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
|
||||
# from app.gateway.routers.uploads import ... # ← will fail CI
|
||||
```
|
||||
|
||||
### Agent System
|
||||
|
||||
**Lead Agent** (`packages/harness/deerflow/agents/lead_agent/agent.py`):
|
||||
- Entry point: `make_lead_agent(config: RunnableConfig)` registered in `langgraph.json`
|
||||
- Dynamic model selection via `create_chat_model()` with thinking/vision support
|
||||
- Tools loaded via `get_available_tools()` - combines sandbox, built-in, MCP, community, and subagent tools
|
||||
- System prompt generated by `apply_prompt_template()` with skills, memory, and subagent instructions
|
||||
|
||||
**ThreadState** (`packages/harness/deerflow/agents/thread_state.py`):
|
||||
- Extends `AgentState` with: `sandbox`, `thread_data`, `title`, `artifacts`, `todos`, `uploaded_files`, `viewed_images`
|
||||
- Uses custom reducers: `merge_artifacts` (deduplicate), `merge_viewed_images` (merge/clear)
|
||||
|
||||
**Runtime Configuration** (via `config.configurable`):
|
||||
- `thinking_enabled` - Enable model's extended thinking
|
||||
- `model_name` - Select specific LLM model
|
||||
- `is_plan_mode` - Enable TodoList middleware
|
||||
- `subagent_enabled` - Enable task delegation tool
|
||||
|
||||
### Middleware Chain
|
||||
|
||||
Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`_build_middlewares`):
|
||||
|
||||
1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local `.deer-flow/threads/{thread_id}` directory
|
||||
2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
|
||||
3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
|
||||
4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption), including raw provider tool-call payloads preserved only in `additional_kwargs["tool_calls"]`
|
||||
5. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later middleware/tool stages run
|
||||
6. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
|
||||
7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
|
||||
8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
|
||||
9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
|
||||
10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
|
||||
11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
|
||||
12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
|
||||
13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
|
||||
14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
|
||||
15. **DeferredToolFilterMiddleware** - Hides deferred tool schemas from the bound model until tool search is enabled (optional)
|
||||
16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
|
||||
17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
|
||||
18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
|
||||
|
||||
### Configuration System
|
||||
|
||||
**Main Configuration** (`config.yaml`):
|
||||
|
||||
Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** directory.
|
||||
|
||||
**Config Versioning**: `config.example.yaml` has a `config_version` field. On startup, `AppConfig.from_file()` compares user version vs example version and emits a warning if outdated. Missing `config_version` = version 0. Run `make config-upgrade` to auto-merge missing fields. When changing the config schema, bump `config_version` in `config.example.yaml`.
|
||||
|
||||
**Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.
|
||||
|
||||
Configuration priority:
|
||||
1. Explicit `config_path` argument
|
||||
2. `DEER_FLOW_CONFIG_PATH` environment variable
|
||||
3. `config.yaml` in current directory (backend/)
|
||||
4. `config.yaml` in parent directory (project root - **recommended location**)
|
||||
|
||||
Config values starting with `$` are resolved as environment variables (e.g., `$OPENAI_API_KEY`).
|
||||
`ModelConfig` also declares `use_responses_api` and `output_version` so OpenAI `/v1/responses` can be enabled explicitly while still using `langchain_openai:ChatOpenAI`.
|
||||
|
||||
**Extensions Configuration** (`extensions_config.json`):
|
||||
|
||||
MCP servers and skills are configured together in `extensions_config.json` in project root:
|
||||
|
||||
Configuration priority:
|
||||
1. Explicit `config_path` argument
|
||||
2. `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable
|
||||
3. `extensions_config.json` in current directory (backend/)
|
||||
4. `extensions_config.json` in parent directory (project root - **recommended location**)
|
||||
|
||||
### Gateway API (`app/gateway/`)
|
||||
|
||||
FastAPI application on port 8001 with health check at `GET /health`.
|
||||
|
||||
**Routers**:
|
||||
|
||||
| Router | Endpoints |
|
||||
|--------|-----------|
|
||||
| **Models** (`/api/models`) | `GET /` - list models; `GET /{name}` - model details |
|
||||
| **MCP** (`/api/mcp`) | `GET /config` - get config; `PUT /config` - update config (saves to extensions_config.json) |
|
||||
| **Skills** (`/api/skills`) | `GET /` - list skills; `GET /{name}` - details; `PUT /{name}` - update enabled; `POST /install` - install from .skill archive (accepts standard optional frontmatter like `version`, `author`, `compatibility`) |
|
||||
| **Memory** (`/api/memory`) | `GET /` - memory data; `POST /reload` - force reload; `GET /config` - config; `GET /status` - config + data |
|
||||
| **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
|
||||
| **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
|
||||
| **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
|
||||
| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized before JSON parsing |
|
||||
|
||||
Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
|
||||
|
||||
### Sandbox System (`packages/harness/deerflow/sandbox/`)
|
||||
|
||||
**Interface**: Abstract `Sandbox` with `execute_command`, `read_file`, `write_file`, `list_dir`
|
||||
**Provider Pattern**: `SandboxProvider` with `acquire`, `get`, `release` lifecycle
|
||||
**Implementations**:
|
||||
- `LocalSandboxProvider` - Singleton local filesystem execution with path mappings
|
||||
- `AioSandboxProvider` (`packages/harness/deerflow/community/`) - Docker-based isolation
|
||||
|
||||
**Virtual Path System**:
|
||||
- Agent sees: `/mnt/user-data/{workspace,uploads,outputs}`, `/mnt/skills`
|
||||
- Physical: `backend/.deer-flow/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
|
||||
- Translation: `replace_virtual_path()` / `replace_virtual_paths_in_command()`
|
||||
- Detection: `is_local_sandbox()` checks `sandbox_id == "local"`
|
||||
|
||||
**Sandbox Tools** (in `packages/harness/deerflow/sandbox/tools.py`):
|
||||
- `bash` - Execute commands with path translation and error handling
|
||||
- `ls` - Directory listing (tree format, max 2 levels)
|
||||
- `read_file` - Read file contents with optional line range
|
||||
- `write_file` - Write/append to files, creates directories
|
||||
- `str_replace` - Substring replacement (single or all occurrences); same-path serialization is scoped to `(sandbox.id, path)` so isolated sandboxes do not contend on identical virtual paths inside one process
|
||||
|
||||
### Subagent System (`packages/harness/deerflow/subagents/`)
|
||||
|
||||
**Built-in Agents**: `general-purpose` (all tools except `task`) and `bash` (command specialist)
|
||||
**Execution**: Dual thread pool - `_scheduler_pool` (3 workers) + `_execution_pool` (3 workers)
|
||||
**Concurrency**: `MAX_CONCURRENT_SUBAGENTS = 3` enforced by `SubagentLimitMiddleware` (truncates excess tool calls in `after_model`), 15-minute timeout
|
||||
**Flow**: `task()` tool → `SubagentExecutor` → background thread → poll 5s → SSE events → result
|
||||
**Events**: `task_started`, `task_running`, `task_completed`/`task_failed`/`task_timed_out`
|
||||
|
||||
### Tool System (`packages/harness/deerflow/tools/`)
|
||||
|
||||
`get_available_tools(groups, include_mcp, model_name, subagent_enabled)` assembles:
|
||||
1. **Config-defined tools** - Resolved from `config.yaml` via `resolve_variable()`
|
||||
2. **MCP tools** - From enabled MCP servers (lazy initialized, cached with mtime invalidation)
|
||||
3. **Built-in tools**:
|
||||
- `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
|
||||
- `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
|
||||
- `view_image` - Read image as base64 (added only if model supports vision)
|
||||
4. **Subagent tool** (if enabled):
|
||||
- `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
|
||||
|
||||
**Community tools** (`packages/harness/deerflow/community/`):
|
||||
- `tavily/` - Web search (5 results default) and web fetch (4KB limit)
|
||||
- `jina_ai/` - Web fetch via Jina reader API with readability extraction
|
||||
- `firecrawl/` - Web scraping via Firecrawl API
|
||||
|
||||
**ACP agent tools**:
|
||||
- `invoke_acp_agent` - Invokes external ACP-compatible agents from `config.yaml`
|
||||
- ACP launchers must be real ACP adapters. The standard `codex` CLI is not ACP-compatible by itself; configure a wrapper such as `npx -y @zed-industries/codex-acp` or an installed `codex-acp` binary
|
||||
- Missing ACP executables now return an actionable error message instead of a raw `[Errno 2]`
|
||||
- Each ACP agent uses a per-thread workspace at `{base_dir}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
|
||||
- `image_search/` - Image search via DuckDuckGo
|
||||
|
||||
### MCP System (`packages/harness/deerflow/mcp/`)
|
||||
|
||||
- Uses `langchain-mcp-adapters` `MultiServerMCPClient` for multi-server management
|
||||
- **Lazy initialization**: Tools loaded on first use via `get_cached_mcp_tools()`
|
||||
- **Cache invalidation**: Detects config file changes via mtime comparison
|
||||
- **Transports**: stdio (command-based), SSE, HTTP
|
||||
- **OAuth (HTTP/SSE)**: Supports token endpoint flows (`client_credentials`, `refresh_token`) with automatic token refresh + Authorization header injection
|
||||
- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
|
||||
|
||||
### Skills System (`packages/harness/deerflow/skills/`)
|
||||
|
||||
- **Location**: `deer-flow/skills/{public,custom}/`
|
||||
- **Format**: Directory with `SKILL.md` (YAML frontmatter: name, description, license, allowed-tools)
|
||||
- **Loading**: `load_skills()` recursively scans `skills/{public,custom}` for `SKILL.md`, parses metadata, and reads enabled state from extensions_config.json
|
||||
- **Injection**: Enabled skills listed in agent system prompt with container paths
|
||||
- **Installation**: `POST /api/skills/install` extracts .skill ZIP archive to custom/ directory
|
||||
|
||||
### Model Factory (`packages/harness/deerflow/models/factory.py`)
|
||||
|
||||
- `create_chat_model(name, thinking_enabled)` instantiates LLM from config via reflection
|
||||
- Supports `thinking_enabled` flag with per-model `when_thinking_enabled` overrides
|
||||
- Supports vLLM-style thinking toggles via `when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking` for Qwen reasoning models, while normalizing legacy `thinking` configs for backward compatibility
|
||||
- Supports `supports_vision` flag for image understanding models
|
||||
- Config values starting with `$` resolved as environment variables
|
||||
- Missing provider modules surface actionable install hints from reflection resolvers (for example `uv add langchain-google-genai`)
|
||||
|
||||
### vLLM Provider (`packages/harness/deerflow/models/vllm_provider.py`)
|
||||
|
||||
- `VllmChatModel` subclasses `langchain_openai:ChatOpenAI` for vLLM 0.19.0 OpenAI-compatible endpoints
|
||||
- Preserves vLLM's non-standard assistant `reasoning` field on full responses, streaming deltas, and follow-up tool-call turns
|
||||
- Designed for configs that enable thinking through `extra_body.chat_template_kwargs.enable_thinking` on vLLM 0.19.0 Qwen reasoning models, while accepting the older `thinking` alias
|
||||
|
||||
### IM Channels System (`app/channels/`)
|
||||
|
||||
Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
|
||||
|
||||
**Architecture**: Channels communicate with the LangGraph Server through `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side.
|
||||
|
||||
**Components**:
|
||||
- `message_bus.py` - Async pub/sub hub (`InboundMessage` → queue → dispatcher; `OutboundMessage` → callbacks → channels)
|
||||
- `store.py` - JSON-file persistence mapping `channel_name:chat_id[:topic_id]` → `thread_id` (keys are `channel:chat` for root conversations and `channel:chat:topic` for threaded conversations)
|
||||
- `manager.py` - Core dispatcher: creates threads via `client.threads.create()`, routes commands, keeps Slack/Telegram on `client.runs.wait()`, and uses `client.runs.stream(["messages-tuple", "values"])` for Feishu incremental outbound updates
|
||||
- `base.py` - Abstract `Channel` base class (start/stop/send lifecycle)
|
||||
- `service.py` - Manages lifecycle of all configured channels from `config.yaml`
|
||||
- `slack.py` / `feishu.py` / `telegram.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place)
|
||||
|
||||
**Message Flow**:
|
||||
1. External platform -> Channel impl -> `MessageBus.publish_inbound()`
|
||||
2. `ChannelManager._dispatch_loop()` consumes from queue
|
||||
3. For chat: look up/create thread on LangGraph Server
|
||||
4. Feishu chat: `runs.stream()` → accumulate AI text → publish multiple outbound updates (`is_final=False`) → publish final outbound (`is_final=True`)
|
||||
5. Slack/Telegram chat: `runs.wait()` → extract final response → publish outbound
|
||||
6. Feishu channel sends one running reply card up front, then patches the same card for each outbound update (card JSON sets `config.update_multi=true` for Feishu's patch API requirement)
|
||||
7. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
|
||||
8. Outbound → channel callbacks → platform reply
|
||||
|
||||
**Configuration** (`config.yaml` -> `channels`):
|
||||
- `langgraph_url` - LangGraph Server URL (default: `http://localhost:2024`)
|
||||
- `gateway_url` - Gateway API URL for auxiliary commands (default: `http://localhost:8001`)
|
||||
- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://langgraph:2024` / `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
|
||||
- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token)
|
||||
|
||||
### Memory System (`packages/harness/deerflow/agents/memory/`)
|
||||
|
||||
**Components**:
|
||||
- `updater.py` - LLM-based memory updates with fact extraction, whitespace-normalized fact deduplication (trims leading/trailing whitespace before comparing), and atomic file I/O
|
||||
- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time)
|
||||
- `prompt.py` - Prompt templates for memory updates
|
||||
|
||||
**Data Structure** (stored in `backend/.deer-flow/memory.json`):
|
||||
- **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
|
||||
- **History**: `recentMonths`, `earlierContext`, `longTermBackground`
|
||||
- **Facts**: Discrete facts with `id`, `content`, `category` (preference/knowledge/context/behavior/goal), `confidence` (0-1), `createdAt`, `source`
|
||||
|
||||
**Workflow**:
|
||||
1. `MemoryMiddleware` filters messages (user inputs + final AI responses) and queues conversation
|
||||
2. Queue debounces (30s default), batches updates, deduplicates per-thread
|
||||
3. Background thread invokes LLM to extract context updates and facts
|
||||
4. Applies updates atomically (temp file + rename) with cache invalidation, skipping duplicate fact content before append
|
||||
5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
|
||||
|
||||
Focused regression coverage for the updater lives in `backend/tests/test_memory_updater.py`.
|
||||
|
||||
**Configuration** (`config.yaml` → `memory`):
|
||||
- `enabled` / `injection_enabled` - Master switches
|
||||
- `storage_path` - Path to memory.json
|
||||
- `debounce_seconds` - Wait time before processing (default: 30)
|
||||
- `model_name` - LLM for updates (null = default model)
|
||||
- `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
|
||||
- `max_injection_tokens` - Token limit for prompt injection (2000)
|
||||
|
||||
### Reflection System (`packages/harness/deerflow/reflection/`)
|
||||
|
||||
- `resolve_variable(path)` - Import module and return variable (e.g., `module.path:variable_name`)
|
||||
- `resolve_class(path, base_class)` - Import and validate class against base class
|
||||
|
||||
### Config Schema
|
||||
|
||||
**`config.yaml`** key sections:
|
||||
- `models[]` - LLM configs with `use` class path, `supports_thinking`, `supports_vision`, provider-specific fields
|
||||
- vLLM reasoning models should use `deerflow.models.vllm_provider:VllmChatModel`; for Qwen-style parsers prefer `when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking`, and DeerFlow will also normalize the older `thinking` alias
|
||||
- `tools[]` - Tool configs with `use` variable path and `group`
|
||||
- `tool_groups[]` - Logical groupings for tools
|
||||
- `sandbox.use` - Sandbox provider class path
|
||||
- `skills.path` / `skills.container_path` - Host and container paths to skills directory
|
||||
- `title` - Auto-title generation (enabled, max_words, max_chars, prompt_template)
|
||||
- `summarization` - Context summarization (enabled, trigger conditions, keep policy)
|
||||
- `subagents.enabled` - Master switch for subagent delegation
|
||||
- `memory` - Memory system (enabled, storage_path, debounce_seconds, model_name, max_facts, fact_confidence_threshold, injection_enabled, max_injection_tokens)
|
||||
|
||||
**`extensions_config.json`**:
|
||||
- `mcpServers` - Map of server name → config (enabled, type, command, args, env, url, headers, oauth, description)
|
||||
- `skills` - Map of skill name → state (enabled)
|
||||
|
||||
Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` methods.
|
||||
|
||||
### Embedded Client (`packages/harness/deerflow/client.py`)
|
||||
|
||||
`DeerFlowClient` provides direct in-process access to all DeerFlow capabilities without HTTP services. All return types align with the Gateway API response schemas, so consumer code works identically in HTTP and embedded modes.
|
||||
|
||||
**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
|
||||
|
||||
**Agent Conversation** (replaces LangGraph Server):
|
||||
- `chat(message, thread_id)` — synchronous, accumulates streaming deltas per message-id and returns the final AI text
|
||||
- `stream(message, thread_id)` — subscribes to LangGraph `stream_mode=["values", "messages", "custom"]` and yields `StreamEvent`:
|
||||
- `"values"` — full state snapshot (title, messages, artifacts); AI text already delivered via `messages` mode is **not** re-synthesized here to avoid duplicate deliveries
|
||||
- `"messages-tuple"` — per-chunk update: for AI text this is a **delta** (concat per `id` to rebuild the full message); tool calls and tool results are emitted once each
|
||||
- `"custom"` — forwarded from `StreamWriter`
|
||||
- `"end"` — stream finished (carries cumulative `usage` counted once per message id)
|
||||
- Agent created lazily via `create_agent()` + `_build_middlewares()`, same as `make_lead_agent`
|
||||
- Supports `checkpointer` parameter for state persistence across turns
|
||||
- `reset_agent()` forces agent recreation (e.g. after memory or skill changes)
|
||||
- See [docs/STREAMING.md](docs/STREAMING.md) for the full design: why Gateway and DeerFlowClient are parallel paths, LangGraph's `stream_mode` semantics, the per-id dedup invariants, and regression testing strategy
|
||||
|
||||
**Gateway Equivalent Methods** (replaces Gateway API):
|
||||
|
||||
| Category | Methods | Return format |
|
||||
|----------|---------|---------------|
|
||||
| Models | `list_models()`, `get_model(name)` | `{"models": [...]}`, `{name, display_name, ...}` |
|
||||
| MCP | `get_mcp_config()`, `update_mcp_config(servers)` | `{"mcp_servers": {...}}` |
|
||||
| Skills | `list_skills()`, `get_skill(name)`, `update_skill(name, enabled)`, `install_skill(path)` | `{"skills": [...]}` |
|
||||
| Memory | `get_memory()`, `reload_memory()`, `get_memory_config()`, `get_memory_status()` | dict |
|
||||
| Uploads | `upload_files(thread_id, files)`, `list_uploads(thread_id)`, `delete_upload(thread_id, filename)` | `{"success": true, "files": [...]}`, `{"files": [...], "count": N}` |
|
||||
| Artifacts | `get_artifact(thread_id, path)` → `(bytes, mime_type)` | tuple |
|
||||
|
||||
**Key difference from Gateway**: Upload accepts local `Path` objects instead of HTTP `UploadFile`, rejects directory paths before copying, and reuses a single worker when document conversion must run inside an active event loop. Artifact returns `(bytes, mime_type)` instead of HTTP Response. The new Gateway-only thread cleanup route deletes `.deer-flow/threads/{thread_id}` after LangGraph thread deletion; there is no matching `DeerFlowClient` method yet. `update_mcp_config()` and `update_skill()` automatically invalidate the cached agent.
|
||||
|
||||
**Tests**: `tests/test_client.py` (77 unit tests including `TestGatewayConformance`), `tests/test_client_live.py` (live integration tests, requires config.yaml)
|
||||
|
||||
**Gateway Conformance Tests** (`TestGatewayConformance`): Validate that every dict-returning client method conforms to the corresponding Gateway Pydantic response model. Each test parses the client output through the Gateway model — if Gateway adds a required field that the client doesn't provide, Pydantic raises `ValidationError` and CI catches the drift. Covers: `ModelsListResponse`, `ModelResponse`, `SkillsListResponse`, `SkillResponse`, `SkillInstallResponse`, `McpConfigResponse`, `UploadResponse`, `MemoryConfigResponse`, `MemoryStatusResponse`.
|
||||
|
||||
## Development Workflow
|
||||
|
||||
### Test-Driven Development (TDD) — MANDATORY
|
||||
|
||||
**Every new feature or bug fix MUST be accompanied by unit tests. No exceptions.**
|
||||
|
||||
- Write tests in `backend/tests/` following the existing naming convention `test_<feature>.py`
|
||||
- Run the full suite before and after your change: `make test`
|
||||
- Tests must pass before a feature is considered complete
|
||||
- For lightweight config/utility modules, prefer pure unit tests with no external dependencies
|
||||
- If a module causes circular import issues in tests, add a `sys.modules` mock in `tests/conftest.py` (see existing example for `deerflow.subagents.executor`)
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
make test
|
||||
|
||||
# Run a specific test file
|
||||
PYTHONPATH=. uv run pytest tests/test_<feature>.py -v
|
||||
```
|
||||
|
||||
### Running the Full Application
|
||||
|
||||
From the **project root** directory:
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
This starts all services and makes the application available at `http://localhost:2026`.
|
||||
|
||||
**All startup modes:**
|
||||
|
||||
| | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
|
||||
|---|---|---|---|---|
|
||||
| **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
|
||||
| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
|
||||
| **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
|
||||
| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
|
||||
|
||||
| Action | Local | Docker Dev | Docker Prod |
|
||||
|---|---|---|---|
|
||||
| **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
|
||||
| **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
|
||||
|
||||
Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
|
||||
|
||||
**Nginx routing**:
|
||||
- Standard mode: `/api/langgraph/*` → LangGraph Server (2024)
|
||||
- Gateway mode: `/api/langgraph/*` → Gateway embedded runtime (8001) (via envsubst)
|
||||
- `/api/*` (other) → Gateway API (8001)
|
||||
- `/` (non-API) → Frontend (3000)
|
||||
|
||||
### Running Backend Services Separately
|
||||
|
||||
From the **backend** directory:
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
Direct access (without nginx):
|
||||
- LangGraph: `http://localhost:2024`
|
||||
- Gateway: `http://localhost:8001`
|
||||
|
||||
### Frontend Configuration
|
||||
|
||||
The frontend uses environment variables to connect to backend services:
|
||||
- `NEXT_PUBLIC_LANGGRAPH_BASE_URL` - Defaults to `/api/langgraph` (through nginx)
|
||||
- `NEXT_PUBLIC_BACKEND_BASE_URL` - Defaults to empty string (through nginx)
|
||||
|
||||
When using `make dev` from root, the frontend automatically connects through nginx.
|
||||
|
||||
## Key Features
|
||||
|
||||
### File Upload
|
||||
|
||||
Multi-file upload with automatic document conversion:
|
||||
- Endpoint: `POST /api/threads/{thread_id}/uploads`
|
||||
- Supports: PDF, PPT, Excel, Word documents (converted via `markitdown`)
|
||||
- Rejects directory inputs before copying so uploads stay all-or-nothing
|
||||
- Reuses one conversion worker per request when called from an active event loop
|
||||
- Files stored in thread-isolated directories
|
||||
- Agent receives uploaded file list via `UploadsMiddleware`
|
||||
|
||||
See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.
|
||||
|
||||
### Plan Mode
|
||||
|
||||
TodoList middleware for complex multi-step tasks:
|
||||
- Controlled via runtime config: `config.configurable.is_plan_mode = True`
|
||||
- Provides `write_todos` tool for task tracking
|
||||
- One task in_progress at a time, real-time updates
|
||||
|
||||
See [docs/plan_mode_usage.md](docs/plan_mode_usage.md) for details.
|
||||
|
||||
### Context Summarization
|
||||
|
||||
Automatic conversation summarization when approaching token limits:
|
||||
- Configured in `config.yaml` under `summarization` key
|
||||
- Trigger types: tokens, messages, or fraction of max input
|
||||
- Keeps recent messages while summarizing older ones
|
||||
|
||||
See [docs/summarization.md](docs/summarization.md) for details.
|
||||
|
||||
### Vision Support
|
||||
|
||||
For models with `supports_vision: true`:
|
||||
- `ViewImageMiddleware` processes images in conversation
|
||||
- `view_image_tool` added to agent's toolset
|
||||
- Images automatically converted to base64 and injected into state
|
||||
|
||||
## Code Style
|
||||
|
||||
- Uses `ruff` for linting and formatting
|
||||
- Line length: 240 characters
|
||||
- Python 3.12+ with type hints
|
||||
- Double quotes, space indentation
|
||||
|
||||
## Documentation
|
||||
|
||||
See `docs/` directory for detailed documentation:
|
||||
- [CONFIGURATION.md](docs/CONFIGURATION.md) - Configuration options
|
||||
- [ARCHITECTURE.md](docs/ARCHITECTURE.md) - Architecture details
|
||||
- [API.md](docs/API.md) - API reference
|
||||
- [SETUP.md](docs/SETUP.md) - Setup guide
|
||||
- [FILE_UPLOAD.md](docs/FILE_UPLOAD.md) - File upload feature
|
||||
- [PATH_EXAMPLES.md](docs/PATH_EXAMPLES.md) - Path types and usage
|
||||
- [summarization.md](docs/summarization.md) - Context summarization
|
||||
- [plan_mode_usage.md](docs/plan_mode_usage.md) - Plan mode with TodoList
|
||||
@@ -1,426 +0,0 @@
|
||||
# Contributing to DeerFlow Backend
|
||||
|
||||
Thank you for your interest in contributing to DeerFlow! This document provides guidelines and instructions for contributing to the backend codebase.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Getting Started](#getting-started)
|
||||
- [Development Setup](#development-setup)
|
||||
- [Project Structure](#project-structure)
|
||||
- [Code Style](#code-style)
|
||||
- [Making Changes](#making-changes)
|
||||
- [Testing](#testing)
|
||||
- [Pull Request Process](#pull-request-process)
|
||||
- [Architecture Guidelines](#architecture-guidelines)
|
||||
|
||||
## Getting Started
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Python 3.12 or higher
|
||||
- [uv](https://docs.astral.sh/uv/) package manager
|
||||
- Git
|
||||
- Docker (optional, for Docker sandbox testing)
|
||||
|
||||
### Fork and Clone
|
||||
|
||||
1. Fork the repository on GitHub
|
||||
2. Clone your fork locally:
|
||||
```bash
|
||||
git clone https://github.com/YOUR_USERNAME/deer-flow.git
|
||||
cd deer-flow
|
||||
```
|
||||
|
||||
## Development Setup
|
||||
|
||||
### Install Dependencies
|
||||
|
||||
```bash
|
||||
# From project root
|
||||
cp config.example.yaml config.yaml
|
||||
|
||||
# Install backend dependencies
|
||||
cd backend
|
||||
make install
|
||||
```
|
||||
|
||||
### Configure Environment
|
||||
|
||||
Set up your API keys for testing:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="your-api-key"
|
||||
# Add other keys as needed
|
||||
```
|
||||
|
||||
### Run the Development Server
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
backend/src/
|
||||
├── agents/ # Agent system
|
||||
│ ├── lead_agent/ # Main agent implementation
|
||||
│ │ └── agent.py # Agent factory and creation
|
||||
│ ├── middlewares/ # Agent middlewares
|
||||
│ │ ├── thread_data_middleware.py
|
||||
│ │ ├── sandbox_middleware.py
|
||||
│ │ ├── title_middleware.py
|
||||
│ │ ├── uploads_middleware.py
|
||||
│ │ ├── view_image_middleware.py
|
||||
│ │ └── clarification_middleware.py
|
||||
│ └── thread_state.py # Thread state definition
|
||||
│
|
||||
├── gateway/ # FastAPI Gateway
|
||||
│ ├── app.py # FastAPI application
|
||||
│ └── routers/ # Route handlers
|
||||
│ ├── models.py # /api/models endpoints
|
||||
│ ├── mcp.py # /api/mcp endpoints
|
||||
│ ├── skills.py # /api/skills endpoints
|
||||
│ ├── artifacts.py # /api/threads/.../artifacts
|
||||
│ └── uploads.py # /api/threads/.../uploads
|
||||
│
|
||||
├── sandbox/ # Sandbox execution
|
||||
│ ├── __init__.py # Sandbox interface
|
||||
│ ├── local.py # Local sandbox provider
|
||||
│ └── tools.py # Sandbox tools (bash, file ops)
|
||||
│
|
||||
├── tools/ # Agent tools
|
||||
│ └── builtins/ # Built-in tools
|
||||
│ ├── present_file_tool.py
|
||||
│ ├── ask_clarification_tool.py
|
||||
│ └── view_image_tool.py
|
||||
│
|
||||
├── mcp/ # MCP integration
|
||||
│ └── manager.py # MCP server management
|
||||
│
|
||||
├── models/ # Model system
|
||||
│ └── factory.py # Model factory
|
||||
│
|
||||
├── skills/ # Skills system
|
||||
│ └── loader.py # Skills loader
|
||||
│
|
||||
├── config/ # Configuration
|
||||
│ ├── app_config.py # Main app config
|
||||
│ ├── extensions_config.py # Extensions config
|
||||
│ └── summarization_config.py
|
||||
│
|
||||
├── community/ # Community tools
|
||||
│ ├── tavily/ # Tavily web search
|
||||
│ ├── jina/ # Jina web fetch
|
||||
│ ├── firecrawl/ # Firecrawl scraping
|
||||
│ └── aio_sandbox/ # Docker sandbox
|
||||
│
|
||||
├── reflection/ # Dynamic loading
|
||||
│ └── __init__.py # Module resolution
|
||||
│
|
||||
└── utils/ # Utilities
|
||||
└── __init__.py
|
||||
```
|
||||
|
||||
## Code Style
|
||||
|
||||
### Linting and Formatting
|
||||
|
||||
We use `ruff` for both linting and formatting:
|
||||
|
||||
```bash
|
||||
# Check for issues
|
||||
make lint
|
||||
|
||||
# Auto-fix and format
|
||||
make format
|
||||
```
|
||||
|
||||
### Style Guidelines
|
||||
|
||||
- **Line length**: 240 characters maximum
|
||||
- **Python version**: 3.12+ features allowed
|
||||
- **Type hints**: Use type hints for function signatures
|
||||
- **Quotes**: Double quotes for strings
|
||||
- **Indentation**: 4 spaces (no tabs)
|
||||
- **Imports**: Group by standard library, third-party, local
|
||||
|
||||
### Docstrings
|
||||
|
||||
Use docstrings for public functions and classes:
|
||||
|
||||
```python
|
||||
def create_chat_model(name: str, thinking_enabled: bool = False) -> BaseChatModel:
|
||||
"""Create a chat model instance from configuration.
|
||||
|
||||
Args:
|
||||
name: The model name as defined in config.yaml
|
||||
thinking_enabled: Whether to enable extended thinking
|
||||
|
||||
Returns:
|
||||
A configured LangChain chat model instance
|
||||
|
||||
Raises:
|
||||
ValueError: If the model name is not found in configuration
|
||||
"""
|
||||
...
|
||||
```
|
||||
|
||||
## Making Changes
|
||||
|
||||
### Branch Naming
|
||||
|
||||
Use descriptive branch names:
|
||||
|
||||
- `feature/add-new-tool` - New features
|
||||
- `fix/sandbox-timeout` - Bug fixes
|
||||
- `docs/update-readme` - Documentation
|
||||
- `refactor/config-system` - Code refactoring
|
||||
|
||||
### Commit Messages
|
||||
|
||||
Write clear, concise commit messages:
|
||||
|
||||
```
|
||||
feat: add support for Claude 3.5 model
|
||||
|
||||
- Add model configuration in config.yaml
|
||||
- Update model factory to handle Claude-specific settings
|
||||
- Add tests for new model
|
||||
```
|
||||
|
||||
Prefix types:
|
||||
- `feat:` - New feature
|
||||
- `fix:` - Bug fix
|
||||
- `docs:` - Documentation
|
||||
- `refactor:` - Code refactoring
|
||||
- `test:` - Tests
|
||||
- `chore:` - Build/config changes
|
||||
|
||||
## Testing
|
||||
|
||||
### Running Tests
|
||||
|
||||
```bash
|
||||
uv run pytest
|
||||
```
|
||||
|
||||
### Writing Tests
|
||||
|
||||
Place tests in the `tests/` directory mirroring the source structure:
|
||||
|
||||
```
|
||||
tests/
|
||||
├── test_models/
|
||||
│ └── test_factory.py
|
||||
├── test_sandbox/
|
||||
│ └── test_local.py
|
||||
└── test_gateway/
|
||||
└── test_models_router.py
|
||||
```
|
||||
|
||||
Example test:
|
||||
|
||||
```python
|
||||
import pytest
|
||||
from deerflow.models.factory import create_chat_model
|
||||
|
||||
def test_create_chat_model_with_valid_name():
|
||||
"""Test that a valid model name creates a model instance."""
|
||||
model = create_chat_model("gpt-4")
|
||||
assert model is not None
|
||||
|
||||
def test_create_chat_model_with_invalid_name():
|
||||
"""Test that an invalid model name raises ValueError."""
|
||||
with pytest.raises(ValueError):
|
||||
create_chat_model("nonexistent-model")
|
||||
```
|
||||
|
||||
## Pull Request Process
|
||||
|
||||
### Before Submitting
|
||||
|
||||
1. **Ensure tests pass**: `uv run pytest`
|
||||
2. **Run linter**: `make lint`
|
||||
3. **Format code**: `make format`
|
||||
4. **Update documentation** if needed
|
||||
|
||||
### PR Description
|
||||
|
||||
Include in your PR description:
|
||||
|
||||
- **What**: Brief description of changes
|
||||
- **Why**: Motivation for the change
|
||||
- **How**: Implementation approach
|
||||
- **Testing**: How you tested the changes
|
||||
|
||||
### Review Process
|
||||
|
||||
1. Submit PR with clear description
|
||||
2. Address review feedback
|
||||
3. Ensure CI passes
|
||||
4. Maintainer will merge when approved
|
||||
|
||||
## Architecture Guidelines
|
||||
|
||||
### Adding New Tools
|
||||
|
||||
1. Create tool in `packages/harness/deerflow/tools/builtins/` or `packages/harness/deerflow/community/`:
|
||||
|
||||
```python
|
||||
# packages/harness/deerflow/tools/builtins/my_tool.py
|
||||
from langchain_core.tools import tool
|
||||
|
||||
@tool
|
||||
def my_tool(param: str) -> str:
|
||||
"""Tool description for the agent.
|
||||
|
||||
Args:
|
||||
param: Description of the parameter
|
||||
|
||||
Returns:
|
||||
Description of return value
|
||||
"""
|
||||
return f"Result: {param}"
|
||||
```
|
||||
|
||||
2. Register in `config.yaml`:
|
||||
|
||||
```yaml
|
||||
tools:
|
||||
- name: my_tool
|
||||
group: my_group
|
||||
use: deerflow.tools.builtins.my_tool:my_tool
|
||||
```
|
||||
|
||||
### Adding New Middleware
|
||||
|
||||
1. Create middleware in `packages/harness/deerflow/agents/middlewares/`:
|
||||
|
||||
```python
|
||||
# packages/harness/deerflow/agents/middlewares/my_middleware.py
|
||||
from langchain.agents.middleware import BaseMiddleware
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
|
||||
class MyMiddleware(BaseMiddleware):
|
||||
"""Middleware description."""
|
||||
|
||||
def transform_state(self, state: dict, config: RunnableConfig) -> dict:
|
||||
"""Transform the state before agent execution."""
|
||||
# Modify state as needed
|
||||
return state
|
||||
```
|
||||
|
||||
2. Register in `packages/harness/deerflow/agents/lead_agent/agent.py`:
|
||||
|
||||
```python
|
||||
middlewares = [
|
||||
ThreadDataMiddleware(),
|
||||
SandboxMiddleware(),
|
||||
MyMiddleware(), # Add your middleware
|
||||
TitleMiddleware(),
|
||||
ClarificationMiddleware(),
|
||||
]
|
||||
```
|
||||
|
||||
### Adding New API Endpoints
|
||||
|
||||
1. Create router in `app/gateway/routers/`:
|
||||
|
||||
```python
|
||||
# app/gateway/routers/my_router.py
|
||||
from fastapi import APIRouter
|
||||
|
||||
router = APIRouter(prefix="/my-endpoint", tags=["my-endpoint"])
|
||||
|
||||
@router.get("/")
|
||||
async def get_items():
|
||||
"""Get all items."""
|
||||
return {"items": []}
|
||||
|
||||
@router.post("/")
|
||||
async def create_item(data: dict):
|
||||
"""Create a new item."""
|
||||
return {"created": data}
|
||||
```
|
||||
|
||||
2. Register in `app/gateway/app.py`:
|
||||
|
||||
```python
|
||||
from app.gateway.routers import my_router
|
||||
|
||||
app.include_router(my_router.router)
|
||||
```
|
||||
|
||||
### Configuration Changes
|
||||
|
||||
When adding new configuration options:
|
||||
|
||||
1. Update `packages/harness/deerflow/config/app_config.py` with new fields
|
||||
2. Add default values in `config.example.yaml`
|
||||
3. Document in `docs/CONFIGURATION.md`
|
||||
|
||||
### MCP Server Integration
|
||||
|
||||
To add support for a new MCP server:
|
||||
|
||||
1. Add configuration in `extensions_config.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"my-server": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@my-org/mcp-server"],
|
||||
"description": "My MCP Server"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
2. Update `extensions_config.example.json` with the new server
|
||||
|
||||
### Skills Development
|
||||
|
||||
To create a new skill:
|
||||
|
||||
1. Create directory in `skills/public/` or `skills/custom/`:
|
||||
|
||||
```
|
||||
skills/public/my-skill/
|
||||
└── SKILL.md
|
||||
```
|
||||
|
||||
2. Write `SKILL.md` with YAML front matter:
|
||||
|
||||
```markdown
|
||||
---
|
||||
name: My Skill
|
||||
description: What this skill does
|
||||
license: MIT
|
||||
allowed-tools:
|
||||
- read_file
|
||||
- write_file
|
||||
- bash
|
||||
---
|
||||
|
||||
# My Skill
|
||||
|
||||
Instructions for the agent when this skill is enabled...
|
||||
```
|
||||
|
||||
## Questions?
|
||||
|
||||
If you have questions about contributing:
|
||||
|
||||
1. Check existing documentation in `docs/`
|
||||
2. Look for similar issues or PRs on GitHub
|
||||
3. Open a discussion or issue on GitHub
|
||||
|
||||
Thank you for contributing to DeerFlow!
|
||||
@@ -1,87 +0,0 @@
|
||||
# Backend Dockerfile — multi-stage build
|
||||
# Stage 1 (builder): compiles native Python extensions with build-essential
|
||||
# Stage 2 (dev): retains toolchain for dev containers (uv sync at startup)
|
||||
# Stage 3 (runtime): clean image without compiler toolchain for production
|
||||
|
||||
# UV source image (override for restricted networks that cannot reach ghcr.io)
|
||||
ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.7.20
|
||||
FROM ${UV_IMAGE} AS uv-source
|
||||
|
||||
# ── Stage 1: Builder ──────────────────────────────────────────────────────────
|
||||
FROM python:3.12-slim-bookworm AS builder
|
||||
|
||||
ARG NODE_MAJOR=22
|
||||
ARG APT_MIRROR
|
||||
ARG UV_INDEX_URL
|
||||
|
||||
# Optionally override apt mirror for restricted networks (e.g. APT_MIRROR=mirrors.aliyun.com)
|
||||
RUN if [ -n "${APT_MIRROR}" ]; then \
|
||||
sed -i "s|deb.debian.org|${APT_MIRROR}|g" /etc/apt/sources.list.d/debian.sources 2>/dev/null || true; \
|
||||
sed -i "s|deb.debian.org|${APT_MIRROR}|g" /etc/apt/sources.list 2>/dev/null || true; \
|
||||
fi
|
||||
|
||||
# Install build tools + Node.js (build-essential needed for native Python extensions)
|
||||
RUN apt-get update && apt-get install -y \
|
||||
curl \
|
||||
build-essential \
|
||||
gnupg \
|
||||
ca-certificates \
|
||||
&& mkdir -p /etc/apt/keyrings \
|
||||
&& curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
|
||||
&& echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_${NODE_MAJOR}.x nodistro main" > /etc/apt/sources.list.d/nodesource.list \
|
||||
&& apt-get update \
|
||||
&& apt-get install -y nodejs \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install uv (source image overridable via UV_IMAGE build arg)
|
||||
COPY --from=uv-source /uv /uvx /usr/local/bin/
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Copy backend source code
|
||||
COPY backend ./backend
|
||||
|
||||
# Install dependencies with cache mount
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync"
|
||||
|
||||
# ── Stage 2: Dev ──────────────────────────────────────────────────────────────
|
||||
# Retains compiler toolchain from builder so startup-time `uv sync` can build
|
||||
# source distributions in development containers.
|
||||
FROM builder AS dev
|
||||
|
||||
# Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
|
||||
COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
|
||||
|
||||
EXPOSE 8001 2024
|
||||
|
||||
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
|
||||
|
||||
# ── Stage 3: Runtime ──────────────────────────────────────────────────────────
|
||||
# Clean image without build-essential — reduces size (~200 MB) and attack surface.
|
||||
FROM python:3.12-slim-bookworm
|
||||
|
||||
# Copy Node.js runtime from builder (provides npx for MCP servers)
|
||||
COPY --from=builder /usr/bin/node /usr/bin/node
|
||||
COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
|
||||
RUN ln -s ../lib/node_modules/npm/bin/npm-cli.js /usr/bin/npm \
|
||||
&& ln -s ../lib/node_modules/npm/bin/npx-cli.js /usr/bin/npx
|
||||
|
||||
# Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
|
||||
COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
|
||||
|
||||
# Install uv (source image overridable via UV_IMAGE build arg)
|
||||
COPY --from=uv-source /uv /uvx /usr/local/bin/
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Copy backend with pre-built virtualenv from builder
|
||||
COPY --from=builder /app/backend ./backend
|
||||
|
||||
# Expose ports (gateway: 8001, langgraph: 2024)
|
||||
EXPOSE 8001 2024
|
||||
|
||||
# Default command (can be overridden in docker-compose)
|
||||
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run --no-sync uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
|
||||
@@ -1,18 +0,0 @@
|
||||
install:
|
||||
uv sync
|
||||
|
||||
dev:
|
||||
uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
|
||||
|
||||
gateway:
|
||||
PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
|
||||
|
||||
test:
|
||||
PYTHONPATH=. uv run pytest tests/ -v
|
||||
|
||||
lint:
|
||||
uvx ruff check .
|
||||
uvx ruff format --check .
|
||||
|
||||
format:
|
||||
uvx ruff check . --fix && uvx ruff format .
|
||||
@@ -1,418 +0,0 @@
|
||||
# DeerFlow Backend
|
||||
|
||||
DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent memory, and extensible tool integration. The backend enables AI agents to execute code, browse the web, manage files, delegate tasks to subagents, and retain context across conversations - all in isolated, per-thread environments.
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────┐
|
||||
│ Nginx (Port 2026) │
|
||||
│ Unified reverse proxy │
|
||||
└───────┬──────────────────┬───────────┘
|
||||
│ │
|
||||
/api/langgraph/* │ │ /api/* (other)
|
||||
▼ ▼
|
||||
┌────────────────────┐ ┌────────────────────────┐
|
||||
│ LangGraph Server │ │ Gateway API (8001) │
|
||||
│ (Port 2024) │ │ FastAPI REST │
|
||||
│ │ │ │
|
||||
│ ┌────────────────┐ │ │ Models, MCP, Skills, │
|
||||
│ │ Lead Agent │ │ │ Memory, Uploads, │
|
||||
│ │ ┌──────────┐ │ │ │ Artifacts │
|
||||
│ │ │Middleware│ │ │ └────────────────────────┘
|
||||
│ │ │ Chain │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ │ ┌──────────┐ │ │
|
||||
│ │ │ Tools │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ │ ┌──────────┐ │ │
|
||||
│ │ │Subagents │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ └────────────────┘ │
|
||||
└────────────────────┘
|
||||
```
|
||||
|
||||
**Request Routing** (via Nginx):
|
||||
- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
|
||||
- `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup
|
||||
- `/` (non-API) → Frontend - Next.js web interface
|
||||
|
||||
---
|
||||
|
||||
## Core Components
|
||||
|
||||
### Lead Agent
|
||||
|
||||
The single LangGraph agent (`lead_agent`) is the runtime entry point, created via `make_lead_agent(config)`. It combines:
|
||||
|
||||
- **Dynamic model selection** with thinking and vision support
|
||||
- **Middleware chain** for cross-cutting concerns (9 middlewares)
|
||||
- **Tool system** with sandbox, MCP, community, and built-in tools
|
||||
- **Subagent delegation** for parallel task execution
|
||||
- **System prompt** with skills injection, memory context, and working directory guidance
|
||||
|
||||
### Middleware Chain
|
||||
|
||||
Middlewares execute in strict order, each handling a specific concern:
|
||||
|
||||
| # | Middleware | Purpose |
|
||||
|---|-----------|---------|
|
||||
| 1 | **ThreadDataMiddleware** | Creates per-thread isolated directories (workspace, uploads, outputs) |
|
||||
| 2 | **UploadsMiddleware** | Injects newly uploaded files into conversation context |
|
||||
| 3 | **SandboxMiddleware** | Acquires sandbox environment for code execution |
|
||||
| 4 | **SummarizationMiddleware** | Reduces context when approaching token limits (optional) |
|
||||
| 5 | **TodoListMiddleware** | Tracks multi-step tasks in plan mode (optional) |
|
||||
| 6 | **TitleMiddleware** | Auto-generates conversation titles after first exchange |
|
||||
| 7 | **MemoryMiddleware** | Queues conversations for async memory extraction |
|
||||
| 8 | **ViewImageMiddleware** | Injects image data for vision-capable models (conditional) |
|
||||
| 9 | **ClarificationMiddleware** | Intercepts clarification requests and interrupts execution (must be last) |
|
||||
|
||||
### Sandbox System
|
||||
|
||||
Per-thread isolated execution with virtual path translation:
|
||||
|
||||
- **Abstract interface**: `execute_command`, `read_file`, `write_file`, `list_dir`
|
||||
- **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/)
|
||||
- **Virtual paths**: `/mnt/user-data/{workspace,uploads,outputs}` → thread-specific physical directories
|
||||
- **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory
|
||||
- **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths
|
||||
- **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match
|
||||
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
|
||||
|
||||
### Subagent System
|
||||
|
||||
Async task delegation with concurrent execution:
|
||||
|
||||
- **Built-in agents**: `general-purpose` (full toolset) and `bash` (command specialist, exposed only when shell access is available)
|
||||
- **Concurrency**: Max 3 subagents per turn, 15-minute timeout
|
||||
- **Execution**: Background thread pools with status tracking and SSE events
|
||||
- **Flow**: Agent calls `task()` tool → executor runs subagent in background → polls for completion → returns result
|
||||
|
||||
### Memory System
|
||||
|
||||
LLM-powered persistent context retention across conversations:
|
||||
|
||||
- **Automatic extraction**: Analyzes conversations for user context, facts, and preferences
|
||||
- **Structured storage**: User context (work, personal, top-of-mind), history, and confidence-scored facts
|
||||
- **Debounced updates**: Batches updates to minimize LLM calls (configurable wait time)
|
||||
- **System prompt injection**: Top facts + context injected into agent prompts
|
||||
- **Storage**: JSON file with mtime-based cache invalidation
|
||||
|
||||
### Tool Ecosystem
|
||||
|
||||
| Category | Tools |
|
||||
|----------|-------|
|
||||
| **Sandbox** | `bash`, `ls`, `read_file`, `write_file`, `str_replace` |
|
||||
| **Built-in** | `present_files`, `ask_clarification`, `view_image`, `task` (subagent) |
|
||||
| **Community** | Tavily (web search), Jina AI (web fetch), Firecrawl (scraping), DuckDuckGo (image search) |
|
||||
| **MCP** | Any Model Context Protocol server (stdio, SSE, HTTP transports) |
|
||||
| **Skills** | Domain-specific workflows injected via system prompt |
|
||||
|
||||
### Gateway API
|
||||
|
||||
FastAPI application providing REST endpoints for frontend integration:
|
||||
|
||||
| Route | Purpose |
|
||||
|-------|---------|
|
||||
| `GET /api/models` | List available LLM models |
|
||||
| `GET/PUT /api/mcp/config` | Manage MCP server configurations |
|
||||
| `GET/PUT /api/skills` | List and manage skills |
|
||||
| `POST /api/skills/install` | Install skill from `.skill` archive |
|
||||
| `GET /api/memory` | Retrieve memory data |
|
||||
| `POST /api/memory/reload` | Force memory reload |
|
||||
| `GET /api/memory/config` | Memory configuration |
|
||||
| `GET /api/memory/status` | Combined config + data |
|
||||
| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
|
||||
| `GET /api/threads/{id}/uploads/list` | List uploaded files |
|
||||
| `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
|
||||
| `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
|
||||
|
||||
### IM Channels
|
||||
|
||||
The IM bridge supports Feishu, Slack, and Telegram. Slack and Telegram still use the final `runs.wait()` response path, while Feishu now streams through `runs.stream(["messages-tuple", "values"])` and updates a single in-thread card in place.
|
||||
|
||||
For Feishu card updates, DeerFlow stores the running card's `message_id` per inbound message and patches that same card until the run finishes, preserving the existing `OK` / `DONE` reaction flow.
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Python 3.12+
|
||||
- [uv](https://docs.astral.sh/uv/) package manager
|
||||
- API keys for your chosen LLM provider
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
cd deer-flow
|
||||
|
||||
# Copy configuration files
|
||||
cp config.example.yaml config.yaml
|
||||
|
||||
# Install backend dependencies
|
||||
cd backend
|
||||
make install
|
||||
```
|
||||
|
||||
### Configuration
|
||||
|
||||
Edit `config.yaml` in the project root:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4o
|
||||
display_name: GPT-4o
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-4o
|
||||
api_key: $OPENAI_API_KEY
|
||||
supports_thinking: false
|
||||
supports_vision: true
|
||||
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
supports_vision: true
|
||||
```
|
||||
|
||||
Set your API keys:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="your-api-key-here"
|
||||
```
|
||||
|
||||
### Running
|
||||
|
||||
**Full Application** (from project root):
|
||||
|
||||
```bash
|
||||
make dev # Starts LangGraph + Gateway + Frontend + Nginx
|
||||
```
|
||||
|
||||
Access at: http://localhost:2026
|
||||
|
||||
**Backend Only** (from backend directory):
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
backend/
|
||||
├── src/
|
||||
│ ├── agents/ # Agent system
|
||||
│ │ ├── lead_agent/ # Main agent (factory, prompts)
|
||||
│ │ ├── middlewares/ # 9 middleware components
|
||||
│ │ ├── memory/ # Memory extraction & storage
|
||||
│ │ └── thread_state.py # ThreadState schema
|
||||
│ ├── gateway/ # FastAPI Gateway API
|
||||
│ │ ├── app.py # Application setup
|
||||
│ │ └── routers/ # 6 route modules
|
||||
│ ├── sandbox/ # Sandbox execution
|
||||
│ │ ├── local/ # Local filesystem provider
|
||||
│ │ ├── sandbox.py # Abstract interface
|
||||
│ │ ├── tools.py # bash, ls, read/write/str_replace
|
||||
│ │ └── middleware.py # Sandbox lifecycle
|
||||
│ ├── subagents/ # Subagent delegation
|
||||
│ │ ├── builtins/ # general-purpose, bash agents
|
||||
│ │ ├── executor.py # Background execution engine
|
||||
│ │ └── registry.py # Agent registry
|
||||
│ ├── tools/builtins/ # Built-in tools
|
||||
│ ├── mcp/ # MCP protocol integration
|
||||
│ ├── models/ # Model factory
|
||||
│ ├── skills/ # Skill discovery & loading
|
||||
│ ├── config/ # Configuration system
|
||||
│ ├── community/ # Community tools & providers
|
||||
│ ├── reflection/ # Dynamic module loading
|
||||
│ └── utils/ # Utilities
|
||||
├── docs/ # Documentation
|
||||
├── tests/ # Test suite
|
||||
├── langgraph.json # LangGraph server configuration
|
||||
├── pyproject.toml # Python dependencies
|
||||
├── Makefile # Development commands
|
||||
└── Dockerfile # Container build
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Main Configuration (`config.yaml`)
|
||||
|
||||
Place in project root. Config values starting with `$` resolve as environment variables.
|
||||
|
||||
Key sections:
|
||||
- `models` - LLM configurations with class paths, API keys, thinking/vision flags
|
||||
- `tools` - Tool definitions with module paths and groups
|
||||
- `tool_groups` - Logical tool groupings
|
||||
- `sandbox` - Execution environment provider
|
||||
- `skills` - Skills directory paths
|
||||
- `title` - Auto-title generation settings
|
||||
- `summarization` - Context summarization settings
|
||||
- `subagents` - Subagent system (enabled/disabled)
|
||||
- `memory` - Memory system settings (enabled, storage, debounce, facts limits)
|
||||
|
||||
Provider note:
|
||||
- `models[*].use` references provider classes by module path (for example `langchain_openai:ChatOpenAI`).
|
||||
- If a provider module is missing, DeerFlow now returns an actionable error with install guidance (for example `uv add langchain-google-genai`).
|
||||
|
||||
### Extensions Configuration (`extensions_config.json`)
|
||||
|
||||
MCP servers and skill states in a single file:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"}
|
||||
},
|
||||
"secure-http": {
|
||||
"enabled": true,
|
||||
"type": "http",
|
||||
"url": "https://api.example.com/mcp",
|
||||
"oauth": {
|
||||
"enabled": true,
|
||||
"token_url": "https://auth.example.com/oauth/token",
|
||||
"grant_type": "client_credentials",
|
||||
"client_id": "$MCP_OAUTH_CLIENT_ID",
|
||||
"client_secret": "$MCP_OAUTH_CLIENT_SECRET"
|
||||
}
|
||||
}
|
||||
},
|
||||
"skills": {
|
||||
"pdf-processing": {"enabled": true}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Environment Variables
|
||||
|
||||
- `DEER_FLOW_CONFIG_PATH` - Override config.yaml location
|
||||
- `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Override extensions_config.json location
|
||||
- Model API keys: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `DEEPSEEK_API_KEY`, etc.
|
||||
- Tool API keys: `TAVILY_API_KEY`, `GITHUB_TOKEN`, etc.
|
||||
|
||||
### LangSmith Tracing
|
||||
|
||||
DeerFlow has built-in [LangSmith](https://smith.langchain.com) integration for observability. When enabled, all LLM calls, agent runs, tool executions, and middleware processing are traced and visible in the LangSmith dashboard.
|
||||
|
||||
**Setup:**
|
||||
|
||||
1. Sign up at [smith.langchain.com](https://smith.langchain.com) and create a project.
|
||||
2. Add the following to your `.env` file in the project root:
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
|
||||
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
|
||||
LANGSMITH_PROJECT=xxx
|
||||
```
|
||||
|
||||
**Legacy variables:** The `LANGCHAIN_TRACING_V2`, `LANGCHAIN_API_KEY`, `LANGCHAIN_PROJECT`, and `LANGCHAIN_ENDPOINT` variables are also supported for backward compatibility. `LANGSMITH_*` variables take precedence when both are set.
|
||||
|
||||
### Langfuse Tracing
|
||||
|
||||
DeerFlow also supports [Langfuse](https://langfuse.com) observability for LangChain-compatible runs.
|
||||
|
||||
Add the following to your `.env` file:
|
||||
|
||||
```bash
|
||||
LANGFUSE_TRACING=true
|
||||
LANGFUSE_PUBLIC_KEY=pk-lf-xxxxxxxxxxxxxxxx
|
||||
LANGFUSE_SECRET_KEY=sk-lf-xxxxxxxxxxxxxxxx
|
||||
LANGFUSE_BASE_URL=https://cloud.langfuse.com
|
||||
```
|
||||
|
||||
If you are using a self-hosted Langfuse deployment, set `LANGFUSE_BASE_URL` to your Langfuse host.
|
||||
|
||||
### Dual Provider Behavior
|
||||
|
||||
If both LangSmith and Langfuse are enabled, DeerFlow initializes and attaches both callbacks so the same run data is reported to both systems.
|
||||
|
||||
If a provider is explicitly enabled but required credentials are missing, or the provider callback cannot be initialized, DeerFlow raises an error when tracing is initialized during model creation instead of silently disabling tracing.
|
||||
|
||||
**Docker:** In `docker-compose.yaml`, tracing is disabled by default (`LANGSMITH_TRACING=false`). Set `LANGSMITH_TRACING=true` and/or `LANGFUSE_TRACING=true` in your `.env`, together with the required credentials, to enable tracing in containerized deployments.
|
||||
|
||||
---
|
||||
|
||||
## Development
|
||||
|
||||
### Commands
|
||||
|
||||
```bash
|
||||
make install # Install dependencies
|
||||
make dev # Run LangGraph server (port 2024)
|
||||
make gateway # Run Gateway API (port 8001)
|
||||
make lint # Run linter (ruff)
|
||||
make format # Format code (ruff)
|
||||
```
|
||||
|
||||
### Code Style
|
||||
|
||||
- **Linter/Formatter**: `ruff`
|
||||
- **Line length**: 240 characters
|
||||
- **Python**: 3.12+ with type hints
|
||||
- **Quotes**: Double quotes
|
||||
- **Indentation**: 4 spaces
|
||||
|
||||
### Testing
|
||||
|
||||
```bash
|
||||
uv run pytest
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Technology Stack
|
||||
|
||||
- **LangGraph** (1.0.6+) - Agent framework and multi-agent orchestration
|
||||
- **LangChain** (1.2.3+) - LLM abstractions and tool system
|
||||
- **FastAPI** (0.115.0+) - Gateway REST API
|
||||
- **langchain-mcp-adapters** - Model Context Protocol support
|
||||
- **agent-sandbox** - Sandboxed code execution
|
||||
- **markitdown** - Multi-format document conversion
|
||||
- **tavily-python** / **firecrawl-py** - Web search and scraping
|
||||
|
||||
---
|
||||
|
||||
## Documentation
|
||||
|
||||
- [Configuration Guide](docs/CONFIGURATION.md)
|
||||
- [Architecture Details](docs/ARCHITECTURE.md)
|
||||
- [API Reference](docs/API.md)
|
||||
- [File Upload](docs/FILE_UPLOAD.md)
|
||||
- [Path Examples](docs/PATH_EXAMPLES.md)
|
||||
- [Context Summarization](docs/summarization.md)
|
||||
- [Plan Mode](docs/plan_mode_usage.md)
|
||||
- [Setup Guide](docs/SETUP.md)
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
See the [LICENSE](../LICENSE) file in the project root.
|
||||
|
||||
## Contributing
|
||||
|
||||
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.
|
||||
@@ -1,16 +0,0 @@
|
||||
"""IM Channel integration for DeerFlow.
|
||||
|
||||
Provides a pluggable channel system that connects external messaging platforms
|
||||
(Feishu/Lark, Slack, Telegram) to the DeerFlow agent via the ChannelManager,
|
||||
which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
|
||||
"""
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessage, MessageBus, OutboundMessage
|
||||
|
||||
__all__ = [
|
||||
"Channel",
|
||||
"InboundMessage",
|
||||
"MessageBus",
|
||||
"OutboundMessage",
|
||||
]
|
||||
@@ -1,126 +0,0 @@
|
||||
"""Abstract base class for IM channels."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Channel(ABC):
|
||||
"""Base class for all IM channel implementations.
|
||||
|
||||
Each channel connects to an external messaging platform and:
|
||||
1. Receives messages, wraps them as InboundMessage, publishes to the bus.
|
||||
2. Subscribes to outbound messages and sends replies back to the platform.
|
||||
|
||||
Subclasses must implement ``start``, ``stop``, and ``send``.
|
||||
"""
|
||||
|
||||
def __init__(self, name: str, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
self.name = name
|
||||
self.bus = bus
|
||||
self.config = config
|
||||
self._running = False
|
||||
|
||||
@property
|
||||
def is_running(self) -> bool:
|
||||
return self._running
|
||||
|
||||
# -- lifecycle ---------------------------------------------------------
|
||||
|
||||
@abstractmethod
|
||||
async def start(self) -> None:
|
||||
"""Start listening for messages from the external platform."""
|
||||
|
||||
@abstractmethod
|
||||
async def stop(self) -> None:
|
||||
"""Gracefully stop the channel."""
|
||||
|
||||
# -- outbound ----------------------------------------------------------
|
||||
|
||||
@abstractmethod
|
||||
async def send(self, msg: OutboundMessage) -> None:
|
||||
"""Send a message back to the external platform.
|
||||
|
||||
The implementation should use ``msg.chat_id`` and ``msg.thread_ts``
|
||||
to route the reply to the correct conversation/thread.
|
||||
"""
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
"""Upload a single file attachment to the platform.
|
||||
|
||||
Returns True if the upload succeeded, False otherwise.
|
||||
Default implementation returns False (no file upload support).
|
||||
"""
|
||||
return False
|
||||
|
||||
# -- helpers -----------------------------------------------------------
|
||||
|
||||
def _make_inbound(
|
||||
self,
|
||||
chat_id: str,
|
||||
user_id: str,
|
||||
text: str,
|
||||
*,
|
||||
msg_type: InboundMessageType = InboundMessageType.CHAT,
|
||||
thread_ts: str | None = None,
|
||||
files: list[dict[str, Any]] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> InboundMessage:
|
||||
"""Convenience factory for creating InboundMessage instances."""
|
||||
return InboundMessage(
|
||||
channel_name=self.name,
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_ts,
|
||||
files=files or [],
|
||||
metadata=metadata or {},
|
||||
)
|
||||
|
||||
async def _on_outbound(self, msg: OutboundMessage) -> None:
|
||||
"""Outbound callback registered with the bus.
|
||||
|
||||
Only forwards messages targeted at this channel.
|
||||
Sends the text message first, then uploads any file attachments.
|
||||
File uploads are skipped entirely when the text send fails to avoid
|
||||
partial deliveries (files without accompanying text).
|
||||
"""
|
||||
if msg.channel_name == self.name:
|
||||
try:
|
||||
await self.send(msg)
|
||||
except Exception:
|
||||
logger.exception("Failed to send outbound message on channel %s", self.name)
|
||||
return # Do not attempt file uploads when the text message failed
|
||||
|
||||
for attachment in msg.attachments:
|
||||
try:
|
||||
success = await self.send_file(msg, attachment)
|
||||
if not success:
|
||||
logger.warning("[%s] file upload skipped for %s", self.name, attachment.filename)
|
||||
except Exception:
|
||||
logger.exception("[%s] failed to upload file %s", self.name, attachment.filename)
|
||||
|
||||
async def receive_file(self, msg: InboundMessage, thread_id: str) -> InboundMessage:
|
||||
"""
|
||||
Optionally process and materialize inbound file attachments for this channel.
|
||||
|
||||
By default, this method does nothing and simply returns the original message.
|
||||
Subclasses (e.g. FeishuChannel) may override this to download files (images, documents, etc)
|
||||
referenced in msg.files, save them to the sandbox, and update msg.text to include
|
||||
the sandbox file paths for downstream model consumption.
|
||||
|
||||
Args:
|
||||
msg: The inbound message, possibly containing file metadata in msg.files.
|
||||
thread_id: The resolved DeerFlow thread ID for sandbox path context.
|
||||
|
||||
Returns:
|
||||
The (possibly modified) InboundMessage, with text and/or files updated as needed.
|
||||
"""
|
||||
return msg
|
||||
@@ -1,20 +0,0 @@
|
||||
"""Shared command definitions used by all channel implementations.
|
||||
|
||||
Keeping the authoritative command set in one place ensures that channel
|
||||
parsers (e.g. Feishu) and the ChannelManager dispatcher stay in sync
|
||||
automatically — adding or removing a command here is the single edit
|
||||
required.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
KNOWN_CHANNEL_COMMANDS: frozenset[str] = frozenset(
|
||||
{
|
||||
"/bootstrap",
|
||||
"/new",
|
||||
"/status",
|
||||
"/models",
|
||||
"/memory",
|
||||
"/help",
|
||||
}
|
||||
)
|
||||
@@ -1,273 +0,0 @@
|
||||
"""Discord channel integration using discord.py."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DISCORD_MAX_MESSAGE_LEN = 2000
|
||||
|
||||
|
||||
class DiscordChannel(Channel):
|
||||
"""Discord bot channel.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.discord``):
|
||||
- ``bot_token``: Discord Bot token.
|
||||
- ``allowed_guilds``: (optional) List of allowed Discord guild IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="discord", bus=bus, config=config)
|
||||
self._bot_token = str(config.get("bot_token", "")).strip()
|
||||
self._allowed_guilds: set[int] = set()
|
||||
for guild_id in config.get("allowed_guilds", []):
|
||||
try:
|
||||
self._allowed_guilds.add(int(guild_id))
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
|
||||
self._client = None
|
||||
self._thread: threading.Thread | None = None
|
||||
self._discord_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._discord_module = None
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
import discord
|
||||
except ImportError:
|
||||
logger.error("discord.py is not installed. Install it with: uv add discord.py")
|
||||
return
|
||||
|
||||
if not self._bot_token:
|
||||
logger.error("Discord channel requires bot_token")
|
||||
return
|
||||
|
||||
intents = discord.Intents.default()
|
||||
intents.messages = True
|
||||
intents.guilds = True
|
||||
intents.message_content = True
|
||||
|
||||
client = discord.Client(
|
||||
intents=intents,
|
||||
allowed_mentions=discord.AllowedMentions.none(),
|
||||
)
|
||||
self._client = client
|
||||
self._discord_module = discord
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
|
||||
@client.event
|
||||
async def on_message(message) -> None:
|
||||
await self._on_message(message)
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
self._thread = threading.Thread(target=self._run_client, daemon=True)
|
||||
self._thread.start()
|
||||
logger.info("Discord channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
|
||||
if self._client and self._discord_loop and self._discord_loop.is_running():
|
||||
close_future = asyncio.run_coroutine_threadsafe(self._client.close(), self._discord_loop)
|
||||
try:
|
||||
await asyncio.wait_for(asyncio.wrap_future(close_future), timeout=10)
|
||||
except TimeoutError:
|
||||
logger.warning("[Discord] client close timed out after 10s")
|
||||
except Exception:
|
||||
logger.exception("[Discord] error while closing client")
|
||||
|
||||
if self._thread:
|
||||
self._thread.join(timeout=10)
|
||||
self._thread = None
|
||||
|
||||
self._client = None
|
||||
self._discord_loop = None
|
||||
self._discord_module = None
|
||||
logger.info("Discord channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage) -> None:
|
||||
target = await self._resolve_target(msg)
|
||||
if target is None:
|
||||
logger.error("[Discord] target not found for chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
|
||||
return
|
||||
|
||||
text = msg.text or ""
|
||||
for chunk in self._split_text(text):
|
||||
send_future = asyncio.run_coroutine_threadsafe(target.send(chunk), self._discord_loop)
|
||||
await asyncio.wrap_future(send_future)
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
target = await self._resolve_target(msg)
|
||||
if target is None:
|
||||
logger.error("[Discord] target not found for file upload chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
|
||||
return False
|
||||
|
||||
if self._discord_module is None:
|
||||
return False
|
||||
|
||||
try:
|
||||
fp = open(str(attachment.actual_path), "rb") # noqa: SIM115
|
||||
file = self._discord_module.File(fp, filename=attachment.filename)
|
||||
send_future = asyncio.run_coroutine_threadsafe(target.send(file=file), self._discord_loop)
|
||||
await asyncio.wrap_future(send_future)
|
||||
logger.info("[Discord] file uploaded: %s", attachment.filename)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to upload file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _on_message(self, message) -> None:
|
||||
if not self._running or not self._client:
|
||||
return
|
||||
|
||||
if message.author.bot:
|
||||
return
|
||||
|
||||
if self._client.user and message.author.id == self._client.user.id:
|
||||
return
|
||||
|
||||
guild = message.guild
|
||||
if self._allowed_guilds:
|
||||
if guild is None or guild.id not in self._allowed_guilds:
|
||||
return
|
||||
|
||||
text = (message.content or "").strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
if self._discord_module is None:
|
||||
return
|
||||
|
||||
if isinstance(message.channel, self._discord_module.Thread):
|
||||
chat_id = str(message.channel.parent_id or message.channel.id)
|
||||
thread_id = str(message.channel.id)
|
||||
else:
|
||||
thread = await self._create_thread(message)
|
||||
if thread is None:
|
||||
return
|
||||
chat_id = str(message.channel.id)
|
||||
thread_id = str(thread.id)
|
||||
|
||||
msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=str(message.author.id),
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_id,
|
||||
metadata={
|
||||
"guild_id": str(guild.id) if guild else None,
|
||||
"channel_id": str(message.channel.id),
|
||||
"message_id": str(message.id),
|
||||
},
|
||||
)
|
||||
inbound.topic_id = thread_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
future = asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._main_loop)
|
||||
future.add_done_callback(lambda f: logger.exception("[Discord] publish_inbound failed", exc_info=f.exception()) if f.exception() else None)
|
||||
|
||||
def _run_client(self) -> None:
|
||||
self._discord_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self._discord_loop)
|
||||
try:
|
||||
self._discord_loop.run_until_complete(self._client.start(self._bot_token))
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Discord client error")
|
||||
finally:
|
||||
try:
|
||||
if self._client and not self._client.is_closed():
|
||||
self._discord_loop.run_until_complete(self._client.close())
|
||||
except Exception:
|
||||
logger.exception("Error during Discord shutdown")
|
||||
|
||||
async def _create_thread(self, message):
|
||||
try:
|
||||
thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
|
||||
return await message.create_thread(name=thread_name)
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to create thread for message=%s (threads may be disabled or missing permissions)", message.id)
|
||||
try:
|
||||
await message.channel.send("Could not create a thread for your message. Please check that threads are enabled in this channel.")
|
||||
except Exception:
|
||||
pass
|
||||
return None
|
||||
|
||||
async def _resolve_target(self, msg: OutboundMessage):
|
||||
if not self._client or not self._discord_loop:
|
||||
return None
|
||||
|
||||
target_ids: list[str] = []
|
||||
if msg.thread_ts:
|
||||
target_ids.append(msg.thread_ts)
|
||||
if msg.chat_id and msg.chat_id not in target_ids:
|
||||
target_ids.append(msg.chat_id)
|
||||
|
||||
for raw_id in target_ids:
|
||||
target = await self._get_channel_or_thread(raw_id)
|
||||
if target is not None:
|
||||
return target
|
||||
return None
|
||||
|
||||
async def _get_channel_or_thread(self, raw_id: str):
|
||||
if not self._client or not self._discord_loop:
|
||||
return None
|
||||
|
||||
try:
|
||||
target_id = int(raw_id)
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
|
||||
get_future = asyncio.run_coroutine_threadsafe(self._fetch_channel(target_id), self._discord_loop)
|
||||
try:
|
||||
return await asyncio.wrap_future(get_future)
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to resolve target id=%s", raw_id)
|
||||
return None
|
||||
|
||||
async def _fetch_channel(self, target_id: int):
|
||||
if not self._client:
|
||||
return None
|
||||
|
||||
channel = self._client.get_channel(target_id)
|
||||
if channel is not None:
|
||||
return channel
|
||||
|
||||
try:
|
||||
return await self._client.fetch_channel(target_id)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _split_text(text: str) -> list[str]:
|
||||
if not text:
|
||||
return [""]
|
||||
|
||||
chunks: list[str] = []
|
||||
remaining = text
|
||||
while len(remaining) > _DISCORD_MAX_MESSAGE_LEN:
|
||||
split_at = remaining.rfind("\n", 0, _DISCORD_MAX_MESSAGE_LEN)
|
||||
if split_at <= 0:
|
||||
split_at = _DISCORD_MAX_MESSAGE_LEN
|
||||
chunks.append(remaining[:split_at])
|
||||
remaining = remaining[split_at:].lstrip("\n")
|
||||
|
||||
if remaining:
|
||||
chunks.append(remaining)
|
||||
|
||||
return chunks
|
||||
@@ -1,692 +0,0 @@
|
||||
"""Feishu/Lark channel — connects to Feishu via WebSocket (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import threading
|
||||
from typing import Any, Literal
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
|
||||
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _is_feishu_command(text: str) -> bool:
|
||||
if not text.startswith("/"):
|
||||
return False
|
||||
return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
|
||||
|
||||
|
||||
class FeishuChannel(Channel):
|
||||
"""Feishu/Lark IM channel using the ``lark-oapi`` WebSocket client.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.feishu``):
|
||||
- ``app_id``: Feishu app ID.
|
||||
- ``app_secret``: Feishu app secret.
|
||||
- ``verification_token``: (optional) Event verification token.
|
||||
|
||||
The channel uses WebSocket long-connection mode so no public IP is required.
|
||||
|
||||
Message flow:
|
||||
1. User sends a message → bot adds "OK" emoji reaction
|
||||
2. Bot replies in thread: "Working on it......"
|
||||
3. Agent processes the message and returns a result
|
||||
4. Bot replies in thread with the result
|
||||
5. Bot adds "DONE" emoji reaction to the original message
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="feishu", bus=bus, config=config)
|
||||
self._thread: threading.Thread | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._api_client = None
|
||||
self._CreateMessageReactionRequest = None
|
||||
self._CreateMessageReactionRequestBody = None
|
||||
self._Emoji = None
|
||||
self._PatchMessageRequest = None
|
||||
self._PatchMessageRequestBody = None
|
||||
self._background_tasks: set[asyncio.Task] = set()
|
||||
self._running_card_ids: dict[str, str] = {}
|
||||
self._running_card_tasks: dict[str, asyncio.Task] = {}
|
||||
self._CreateFileRequest = None
|
||||
self._CreateFileRequestBody = None
|
||||
self._CreateImageRequest = None
|
||||
self._CreateImageRequestBody = None
|
||||
self._GetMessageResourceRequest = None
|
||||
self._thread_lock = threading.Lock()
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
import lark_oapi as lark
|
||||
from lark_oapi.api.im.v1 import (
|
||||
CreateFileRequest,
|
||||
CreateFileRequestBody,
|
||||
CreateImageRequest,
|
||||
CreateImageRequestBody,
|
||||
CreateMessageReactionRequest,
|
||||
CreateMessageReactionRequestBody,
|
||||
CreateMessageRequest,
|
||||
CreateMessageRequestBody,
|
||||
Emoji,
|
||||
GetMessageResourceRequest,
|
||||
PatchMessageRequest,
|
||||
PatchMessageRequestBody,
|
||||
ReplyMessageRequest,
|
||||
ReplyMessageRequestBody,
|
||||
)
|
||||
except ImportError:
|
||||
logger.error("lark-oapi is not installed. Install it with: uv add lark-oapi")
|
||||
return
|
||||
|
||||
self._lark = lark
|
||||
self._CreateMessageRequest = CreateMessageRequest
|
||||
self._CreateMessageRequestBody = CreateMessageRequestBody
|
||||
self._ReplyMessageRequest = ReplyMessageRequest
|
||||
self._ReplyMessageRequestBody = ReplyMessageRequestBody
|
||||
self._CreateMessageReactionRequest = CreateMessageReactionRequest
|
||||
self._CreateMessageReactionRequestBody = CreateMessageReactionRequestBody
|
||||
self._Emoji = Emoji
|
||||
self._PatchMessageRequest = PatchMessageRequest
|
||||
self._PatchMessageRequestBody = PatchMessageRequestBody
|
||||
self._CreateFileRequest = CreateFileRequest
|
||||
self._CreateFileRequestBody = CreateFileRequestBody
|
||||
self._CreateImageRequest = CreateImageRequest
|
||||
self._CreateImageRequestBody = CreateImageRequestBody
|
||||
self._GetMessageResourceRequest = GetMessageResourceRequest
|
||||
|
||||
app_id = self.config.get("app_id", "")
|
||||
app_secret = self.config.get("app_secret", "")
|
||||
domain = self.config.get("domain", "https://open.feishu.cn")
|
||||
|
||||
if not app_id or not app_secret:
|
||||
logger.error("Feishu channel requires app_id and app_secret")
|
||||
return
|
||||
|
||||
self._api_client = lark.Client.builder().app_id(app_id).app_secret(app_secret).domain(domain).build()
|
||||
logger.info("[Feishu] using domain: %s", domain)
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Both ws.Client construction and start() must happen in a dedicated
|
||||
# thread with its own event loop. lark-oapi caches the running loop
|
||||
# at construction time and later calls loop.run_until_complete(),
|
||||
# which conflicts with an already-running uvloop.
|
||||
self._thread = threading.Thread(
|
||||
target=self._run_ws,
|
||||
args=(app_id, app_secret, domain),
|
||||
daemon=True,
|
||||
)
|
||||
self._thread.start()
|
||||
logger.info("Feishu channel started")
|
||||
|
||||
def _run_ws(self, app_id: str, app_secret: str, domain: str) -> None:
|
||||
"""Construct and run the lark WS client in a thread with a fresh event loop.
|
||||
|
||||
The lark-oapi SDK captures a module-level event loop at import time
|
||||
(``lark_oapi.ws.client.loop``). When uvicorn uses uvloop, that
|
||||
captured loop is the *main* thread's uvloop — which is already
|
||||
running, so ``loop.run_until_complete()`` inside ``Client.start()``
|
||||
raises ``RuntimeError``.
|
||||
|
||||
We work around this by creating a plain asyncio event loop for this
|
||||
thread and patching the SDK's module-level reference before calling
|
||||
``start()``.
|
||||
"""
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
import lark_oapi as lark
|
||||
import lark_oapi.ws.client as _ws_client_mod
|
||||
|
||||
# Replace the SDK's module-level loop so Client.start() uses
|
||||
# this thread's (non-running) event loop instead of the main
|
||||
# thread's uvloop.
|
||||
_ws_client_mod.loop = loop
|
||||
|
||||
event_handler = lark.EventDispatcherHandler.builder("", "").register_p2_im_message_receive_v1(self._on_message).build()
|
||||
ws_client = lark.ws.Client(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
event_handler=event_handler,
|
||||
log_level=lark.LogLevel.INFO,
|
||||
domain=domain,
|
||||
)
|
||||
ws_client.start()
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Feishu WebSocket error")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
for task in list(self._background_tasks):
|
||||
task.cancel()
|
||||
self._background_tasks.clear()
|
||||
for task in list(self._running_card_tasks.values()):
|
||||
task.cancel()
|
||||
self._running_card_tasks.clear()
|
||||
if self._thread:
|
||||
self._thread.join(timeout=5)
|
||||
self._thread = None
|
||||
logger.info("Feishu channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._api_client:
|
||||
logger.warning("[Feishu] send called but no api_client available")
|
||||
return
|
||||
|
||||
logger.info(
|
||||
"[Feishu] sending reply: chat_id=%s, thread_ts=%s, text_len=%d",
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
len(msg.text),
|
||||
)
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._send_card_message(msg)
|
||||
return # success
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Feishu] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Feishu] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Feishu send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._api_client:
|
||||
return False
|
||||
|
||||
# Check size limits (image: 10MB, file: 30MB)
|
||||
if attachment.is_image and attachment.size > 10 * 1024 * 1024:
|
||||
logger.warning("[Feishu] image too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
if not attachment.is_image and attachment.size > 30 * 1024 * 1024:
|
||||
logger.warning("[Feishu] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
|
||||
try:
|
||||
if attachment.is_image:
|
||||
file_key = await self._upload_image(attachment.actual_path)
|
||||
msg_type = "image"
|
||||
content = json.dumps({"image_key": file_key})
|
||||
else:
|
||||
file_key = await self._upload_file(attachment.actual_path, attachment.filename)
|
||||
msg_type = "file"
|
||||
content = json.dumps({"file_key": file_key})
|
||||
|
||||
if msg.thread_ts:
|
||||
request = self._ReplyMessageRequest.builder().message_id(msg.thread_ts).request_body(self._ReplyMessageRequestBody.builder().msg_type(msg_type).content(content).reply_in_thread(True).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.reply, request)
|
||||
else:
|
||||
request = self._CreateMessageRequest.builder().receive_id_type("chat_id").request_body(self._CreateMessageRequestBody.builder().receive_id(msg.chat_id).msg_type(msg_type).content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.create, request)
|
||||
|
||||
logger.info("[Feishu] file sent: %s (type=%s)", attachment.filename, msg_type)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to upload/send file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _upload_image(self, path) -> str:
|
||||
"""Upload an image to Feishu and return the image_key."""
|
||||
with open(str(path), "rb") as f:
|
||||
request = self._CreateImageRequest.builder().request_body(self._CreateImageRequestBody.builder().image_type("message").image(f).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.image.create, request)
|
||||
if not response.success():
|
||||
raise RuntimeError(f"Feishu image upload failed: code={response.code}, msg={response.msg}")
|
||||
return response.data.image_key
|
||||
|
||||
async def _upload_file(self, path, filename: str) -> str:
|
||||
"""Upload a file to Feishu and return the file_key."""
|
||||
suffix = path.suffix.lower() if hasattr(path, "suffix") else ""
|
||||
if suffix in (".xls", ".xlsx", ".csv"):
|
||||
file_type = "xls"
|
||||
elif suffix in (".ppt", ".pptx"):
|
||||
file_type = "ppt"
|
||||
elif suffix == ".pdf":
|
||||
file_type = "pdf"
|
||||
elif suffix in (".doc", ".docx"):
|
||||
file_type = "doc"
|
||||
else:
|
||||
file_type = "stream"
|
||||
|
||||
with open(str(path), "rb") as f:
|
||||
request = self._CreateFileRequest.builder().request_body(self._CreateFileRequestBody.builder().file_type(file_type).file_name(filename).file(f).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.file.create, request)
|
||||
if not response.success():
|
||||
raise RuntimeError(f"Feishu file upload failed: code={response.code}, msg={response.msg}")
|
||||
return response.data.file_key
|
||||
|
||||
async def receive_file(self, msg: InboundMessage, thread_id: str) -> InboundMessage:
|
||||
"""Download a Feishu file into the thread uploads directory.
|
||||
|
||||
Returns the sandbox virtual path when the image is persisted successfully.
|
||||
"""
|
||||
if not msg.thread_ts:
|
||||
logger.warning("[Feishu] received file message without thread_ts, cannot associate with conversation: %s", msg)
|
||||
return msg
|
||||
files = msg.files
|
||||
if not files:
|
||||
logger.warning("[Feishu] received message with no files: %s", msg)
|
||||
return msg
|
||||
text = msg.text
|
||||
for file in files:
|
||||
if file.get("image_key"):
|
||||
virtual_path = await self._receive_single_file(msg.thread_ts, file["image_key"], "image", thread_id)
|
||||
text = text.replace("[image]", virtual_path, 1)
|
||||
elif file.get("file_key"):
|
||||
virtual_path = await self._receive_single_file(msg.thread_ts, file["file_key"], "file", thread_id)
|
||||
text = text.replace("[file]", virtual_path, 1)
|
||||
msg.text = text
|
||||
return msg
|
||||
|
||||
async def _receive_single_file(self, message_id: str, file_key: str, type: Literal["image", "file"], thread_id: str) -> str:
|
||||
request = self._GetMessageResourceRequest.builder().message_id(message_id).file_key(file_key).type(type).build()
|
||||
|
||||
def inner():
|
||||
return self._api_client.im.v1.message_resource.get(request)
|
||||
|
||||
try:
|
||||
response = await asyncio.to_thread(inner)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] resource get request failed for resource_key=%s type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
if not response.success():
|
||||
logger.warning(
|
||||
"[Feishu] resource get failed: resource_key=%s, type=%s, code=%s, msg=%s, log_id=%s ",
|
||||
file_key,
|
||||
type,
|
||||
response.code,
|
||||
response.msg,
|
||||
response.get_log_id(),
|
||||
)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
image_stream = getattr(response, "file", None)
|
||||
if image_stream is None:
|
||||
logger.warning("[Feishu] resource get returned no file stream: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
try:
|
||||
content: bytes = await asyncio.to_thread(image_stream.read)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to read resource stream: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
if not content:
|
||||
logger.warning("[Feishu] empty resource content: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
paths = get_paths()
|
||||
paths.ensure_thread_dirs(thread_id)
|
||||
uploads_dir = paths.sandbox_uploads_dir(thread_id).resolve()
|
||||
|
||||
ext = "png" if type == "image" else "bin"
|
||||
raw_filename = getattr(response, "file_name", "") or f"feishu_{file_key[-12:]}.{ext}"
|
||||
|
||||
# Sanitize filename: preserve extension, replace path chars in name part
|
||||
if "." in raw_filename:
|
||||
name_part, ext = raw_filename.rsplit(".", 1)
|
||||
name_part = re.sub(r"[./\\]", "_", name_part)
|
||||
filename = f"{name_part}.{ext}"
|
||||
else:
|
||||
filename = re.sub(r"[./\\]", "_", raw_filename)
|
||||
resolved_target = uploads_dir / filename
|
||||
|
||||
def down_load():
|
||||
# use thread_lock to avoid filename conflicts when writing
|
||||
with self._thread_lock:
|
||||
resolved_target.write_bytes(content)
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(down_load)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to persist downloaded resource: %s, type=%s", resolved_target, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"
|
||||
|
||||
try:
|
||||
sandbox_provider = get_sandbox_provider()
|
||||
sandbox_id = sandbox_provider.acquire(thread_id)
|
||||
if sandbox_id != "local":
|
||||
sandbox = sandbox_provider.get(sandbox_id)
|
||||
if sandbox is None:
|
||||
logger.warning("[Feishu] sandbox not found for thread_id=%s", thread_id)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
sandbox.update_file(virtual_path, content)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to sync resource into non-local sandbox: %s", virtual_path)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
logger.info("[Feishu] downloaded resource mapped: file_key=%s -> %s", file_key, virtual_path)
|
||||
return virtual_path
|
||||
|
||||
# -- message formatting ------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _build_card_content(text: str) -> str:
|
||||
"""Build a Feishu interactive card with markdown content.
|
||||
|
||||
Feishu's interactive card format natively renders markdown, including
|
||||
headers, bold/italic, code blocks, lists, and links.
|
||||
"""
|
||||
card = {
|
||||
"config": {"wide_screen_mode": True, "update_multi": True},
|
||||
"elements": [{"tag": "markdown", "content": text}],
|
||||
}
|
||||
return json.dumps(card)
|
||||
|
||||
# -- reaction helpers --------------------------------------------------
|
||||
|
||||
async def _add_reaction(self, message_id: str, emoji_type: str = "THUMBSUP") -> None:
|
||||
"""Add an emoji reaction to a message."""
|
||||
if not self._api_client or not self._CreateMessageReactionRequest:
|
||||
return
|
||||
try:
|
||||
request = self._CreateMessageReactionRequest.builder().message_id(message_id).request_body(self._CreateMessageReactionRequestBody.builder().reaction_type(self._Emoji.builder().emoji_type(emoji_type).build()).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message_reaction.create, request)
|
||||
logger.info("[Feishu] reaction '%s' added to message %s", emoji_type, message_id)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to add reaction '%s' to message %s", emoji_type, message_id)
|
||||
|
||||
async def _reply_card(self, message_id: str, text: str) -> str | None:
|
||||
"""Reply with an interactive card and return the created card message ID."""
|
||||
if not self._api_client:
|
||||
return None
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._ReplyMessageRequest.builder().message_id(message_id).request_body(self._ReplyMessageRequestBody.builder().msg_type("interactive").content(content).reply_in_thread(True).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.message.reply, request)
|
||||
response_data = getattr(response, "data", None)
|
||||
return getattr(response_data, "message_id", None)
|
||||
|
||||
async def _create_card(self, chat_id: str, text: str) -> None:
|
||||
"""Create a new card message in the target chat."""
|
||||
if not self._api_client:
|
||||
return
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._CreateMessageRequest.builder().receive_id_type("chat_id").request_body(self._CreateMessageRequestBody.builder().receive_id(chat_id).msg_type("interactive").content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.create, request)
|
||||
|
||||
async def _update_card(self, message_id: str, text: str) -> None:
|
||||
"""Patch an existing card message in place."""
|
||||
if not self._api_client or not self._PatchMessageRequest:
|
||||
return
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._PatchMessageRequest.builder().message_id(message_id).request_body(self._PatchMessageRequestBody.builder().content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.patch, request)
|
||||
|
||||
def _track_background_task(self, task: asyncio.Task, *, name: str, msg_id: str) -> None:
|
||||
"""Keep a strong reference to fire-and-forget tasks and surface errors."""
|
||||
self._background_tasks.add(task)
|
||||
task.add_done_callback(lambda done_task, task_name=name, mid=msg_id: self._finalize_background_task(done_task, task_name, mid))
|
||||
|
||||
def _finalize_background_task(self, task: asyncio.Task, name: str, msg_id: str) -> None:
|
||||
self._background_tasks.discard(task)
|
||||
self._log_task_error(task, name, msg_id)
|
||||
|
||||
async def _create_running_card(self, source_message_id: str, text: str) -> str | None:
|
||||
"""Create the running card and cache its message ID when available."""
|
||||
running_card_id = await self._reply_card(source_message_id, text)
|
||||
if running_card_id:
|
||||
self._running_card_ids[source_message_id] = running_card_id
|
||||
logger.info("[Feishu] running card created: source=%s card=%s", source_message_id, running_card_id)
|
||||
else:
|
||||
logger.warning("[Feishu] running card creation returned no message_id for source=%s, subsequent updates will fall back to new replies", source_message_id)
|
||||
return running_card_id
|
||||
|
||||
def _ensure_running_card_started(self, source_message_id: str, text: str = "Working on it...") -> asyncio.Task | None:
|
||||
"""Start running-card creation once per source message."""
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
if running_card_id:
|
||||
return None
|
||||
|
||||
running_card_task = self._running_card_tasks.get(source_message_id)
|
||||
if running_card_task:
|
||||
return running_card_task
|
||||
|
||||
running_card_task = asyncio.create_task(self._create_running_card(source_message_id, text))
|
||||
self._running_card_tasks[source_message_id] = running_card_task
|
||||
running_card_task.add_done_callback(lambda done_task, mid=source_message_id: self._finalize_running_card_task(mid, done_task))
|
||||
return running_card_task
|
||||
|
||||
def _finalize_running_card_task(self, source_message_id: str, task: asyncio.Task) -> None:
|
||||
if self._running_card_tasks.get(source_message_id) is task:
|
||||
self._running_card_tasks.pop(source_message_id, None)
|
||||
self._log_task_error(task, "create_running_card", source_message_id)
|
||||
|
||||
async def _ensure_running_card(self, source_message_id: str, text: str = "Working on it...") -> str | None:
|
||||
"""Ensure the in-thread running card exists and track its message ID."""
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
if running_card_id:
|
||||
return running_card_id
|
||||
|
||||
running_card_task = self._ensure_running_card_started(source_message_id, text)
|
||||
if running_card_task is None:
|
||||
return self._running_card_ids.get(source_message_id)
|
||||
return await running_card_task
|
||||
|
||||
async def _send_running_reply(self, message_id: str) -> None:
|
||||
"""Reply to a message in-thread with a running card."""
|
||||
try:
|
||||
await self._ensure_running_card(message_id)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to send running reply for message %s", message_id)
|
||||
|
||||
async def _send_card_message(self, msg: OutboundMessage) -> None:
|
||||
"""Send or update the Feishu card tied to the current request."""
|
||||
source_message_id = msg.thread_ts
|
||||
if source_message_id:
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
awaited_running_card_task = False
|
||||
|
||||
if not running_card_id:
|
||||
running_card_task = self._running_card_tasks.get(source_message_id)
|
||||
if running_card_task:
|
||||
awaited_running_card_task = True
|
||||
running_card_id = await running_card_task
|
||||
|
||||
if running_card_id:
|
||||
try:
|
||||
await self._update_card(running_card_id, msg.text)
|
||||
except Exception:
|
||||
if not msg.is_final:
|
||||
raise
|
||||
logger.exception(
|
||||
"[Feishu] failed to patch running card %s, falling back to final reply",
|
||||
running_card_id,
|
||||
)
|
||||
await self._reply_card(source_message_id, msg.text)
|
||||
else:
|
||||
logger.info("[Feishu] running card updated: source=%s card=%s", source_message_id, running_card_id)
|
||||
elif msg.is_final:
|
||||
await self._reply_card(source_message_id, msg.text)
|
||||
elif awaited_running_card_task:
|
||||
logger.warning(
|
||||
"[Feishu] running card task finished without message_id for source=%s, skipping duplicate non-final creation",
|
||||
source_message_id,
|
||||
)
|
||||
else:
|
||||
await self._ensure_running_card(source_message_id, msg.text)
|
||||
|
||||
if msg.is_final:
|
||||
self._running_card_ids.pop(source_message_id, None)
|
||||
await self._add_reaction(source_message_id, "DONE")
|
||||
return
|
||||
|
||||
await self._create_card(msg.chat_id, msg.text)
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _log_future_error(fut, name: str, msg_id: str) -> None:
|
||||
"""Callback for run_coroutine_threadsafe futures to surface errors."""
|
||||
try:
|
||||
exc = fut.exception()
|
||||
if exc:
|
||||
logger.error("[Feishu] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def _log_task_error(task: asyncio.Task, name: str, msg_id: str) -> None:
|
||||
"""Callback for background asyncio tasks to surface errors."""
|
||||
try:
|
||||
exc = task.exception()
|
||||
if exc:
|
||||
logger.error("[Feishu] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except asyncio.CancelledError:
|
||||
logger.info("[Feishu] %s cancelled for msg_id=%s", name, msg_id)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
async def _prepare_inbound(self, msg_id: str, inbound) -> None:
|
||||
"""Kick off Feishu side effects without delaying inbound dispatch."""
|
||||
reaction_task = asyncio.create_task(self._add_reaction(msg_id, "OK"))
|
||||
self._track_background_task(reaction_task, name="add_reaction", msg_id=msg_id)
|
||||
self._ensure_running_card_started(msg_id)
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
def _on_message(self, event) -> None:
|
||||
"""Called by lark-oapi when a message is received (runs in lark thread)."""
|
||||
try:
|
||||
logger.info("[Feishu] raw event received: type=%s", type(event).__name__)
|
||||
message = event.event.message
|
||||
chat_id = message.chat_id
|
||||
msg_id = message.message_id
|
||||
sender_id = event.event.sender.sender_id.open_id
|
||||
|
||||
# root_id is set when the message is a reply within a Feishu thread.
|
||||
# Use it as topic_id so all replies share the same DeerFlow thread.
|
||||
root_id = getattr(message, "root_id", None) or None
|
||||
|
||||
# Parse message content
|
||||
content = json.loads(message.content)
|
||||
|
||||
# files_list store the any-file-key in feishu messages, which can be used to download the file content later
|
||||
# In Feishu channel, image_keys are independent of file_keys.
|
||||
# The file_key includes files, videos, and audio, but does not include stickers.
|
||||
files_list = []
|
||||
|
||||
if "text" in content:
|
||||
# Handle plain text messages
|
||||
text = content["text"]
|
||||
elif "file_key" in content:
|
||||
file_key = content.get("file_key")
|
||||
if isinstance(file_key, str) and file_key:
|
||||
files_list.append({"file_key": file_key})
|
||||
text = "[file]"
|
||||
else:
|
||||
text = ""
|
||||
elif "image_key" in content:
|
||||
image_key = content.get("image_key")
|
||||
if isinstance(image_key, str) and image_key:
|
||||
files_list.append({"image_key": image_key})
|
||||
text = "[image]"
|
||||
else:
|
||||
text = ""
|
||||
elif "content" in content and isinstance(content["content"], list):
|
||||
# Handle rich-text messages with a top-level "content" list (e.g., topic groups/posts)
|
||||
text_paragraphs: list[str] = []
|
||||
for paragraph in content["content"]:
|
||||
if isinstance(paragraph, list):
|
||||
paragraph_text_parts: list[str] = []
|
||||
for element in paragraph:
|
||||
if isinstance(element, dict):
|
||||
# Include both normal text and @ mentions
|
||||
if element.get("tag") in ("text", "at"):
|
||||
text_value = element.get("text", "")
|
||||
if text_value:
|
||||
paragraph_text_parts.append(text_value)
|
||||
elif element.get("tag") == "img":
|
||||
image_key = element.get("image_key")
|
||||
if isinstance(image_key, str) and image_key:
|
||||
files_list.append({"image_key": image_key})
|
||||
paragraph_text_parts.append("[image]")
|
||||
elif element.get("tag") in ("file", "media"):
|
||||
file_key = element.get("file_key")
|
||||
if isinstance(file_key, str) and file_key:
|
||||
files_list.append({"file_key": file_key})
|
||||
paragraph_text_parts.append("[file]")
|
||||
if paragraph_text_parts:
|
||||
# Join text segments within a paragraph with spaces to avoid "helloworld"
|
||||
text_paragraphs.append(" ".join(paragraph_text_parts))
|
||||
|
||||
# Join paragraphs with blank lines to preserve paragraph boundaries
|
||||
text = "\n\n".join(text_paragraphs)
|
||||
else:
|
||||
text = ""
|
||||
text = text.strip()
|
||||
|
||||
logger.info(
|
||||
"[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, sender=%s, text=%r",
|
||||
chat_id,
|
||||
msg_id,
|
||||
root_id,
|
||||
sender_id,
|
||||
text[:100] if text else "",
|
||||
)
|
||||
|
||||
if not (text or files_list):
|
||||
logger.info("[Feishu] empty text, ignoring message")
|
||||
return
|
||||
|
||||
# Only treat known slash commands as commands; absolute paths and
|
||||
# other slash-prefixed text should be handled as normal chat.
|
||||
if _is_feishu_command(text):
|
||||
msg_type = InboundMessageType.COMMAND
|
||||
else:
|
||||
msg_type = InboundMessageType.CHAT
|
||||
|
||||
# topic_id: use root_id for replies (same topic), msg_id for new messages (new topic)
|
||||
topic_id = root_id or msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=sender_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=msg_id,
|
||||
files=files_list,
|
||||
metadata={"message_id": msg_id, "root_id": root_id},
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
# Schedule on the async event loop
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
logger.info("[Feishu] publishing inbound message to bus (type=%s, msg_id=%s)", msg_type.value, msg_id)
|
||||
fut = asyncio.run_coroutine_threadsafe(self._prepare_inbound(msg_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f, mid=msg_id: self._log_future_error(f, "prepare_inbound", mid))
|
||||
else:
|
||||
logger.warning("[Feishu] main loop not running, cannot publish inbound message")
|
||||
except Exception:
|
||||
logger.exception("[Feishu] error processing message")
|
||||
@@ -1,960 +0,0 @@
|
||||
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import mimetypes
|
||||
import re
|
||||
import time
|
||||
from collections.abc import Awaitable, Callable, Mapping
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
from langgraph_sdk.errors import ConflictError
|
||||
|
||||
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
from app.channels.store import ChannelStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_LANGGRAPH_URL = "http://localhost:2024"
|
||||
DEFAULT_GATEWAY_URL = "http://localhost:8001"
|
||||
DEFAULT_ASSISTANT_ID = "lead_agent"
|
||||
CUSTOM_AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
|
||||
|
||||
DEFAULT_RUN_CONFIG: dict[str, Any] = {"recursion_limit": 100}
|
||||
DEFAULT_RUN_CONTEXT: dict[str, Any] = {
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": False,
|
||||
"subagent_enabled": False,
|
||||
}
|
||||
STREAM_UPDATE_MIN_INTERVAL_SECONDS = 0.35
|
||||
THREAD_BUSY_MESSAGE = "This conversation is already processing another request. Please wait for it to finish and try again."
|
||||
|
||||
CHANNEL_CAPABILITIES = {
|
||||
"discord": {"supports_streaming": False},
|
||||
"feishu": {"supports_streaming": True},
|
||||
"slack": {"supports_streaming": False},
|
||||
"telegram": {"supports_streaming": False},
|
||||
"wechat": {"supports_streaming": False},
|
||||
"wecom": {"supports_streaming": True},
|
||||
}
|
||||
|
||||
InboundFileReader = Callable[[dict[str, Any], httpx.AsyncClient], Awaitable[bytes | None]]
|
||||
|
||||
|
||||
INBOUND_FILE_READERS: dict[str, InboundFileReader] = {}
|
||||
|
||||
|
||||
def register_inbound_file_reader(channel_name: str, reader: InboundFileReader) -> None:
|
||||
INBOUND_FILE_READERS[channel_name] = reader
|
||||
|
||||
|
||||
async def _read_http_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
url = file_info.get("url")
|
||||
if not isinstance(url, str) or not url:
|
||||
return None
|
||||
|
||||
resp = await client.get(url)
|
||||
resp.raise_for_status()
|
||||
return resp.content
|
||||
|
||||
|
||||
async def _read_wecom_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
data = await _read_http_inbound_file(file_info, client)
|
||||
if data is None:
|
||||
return None
|
||||
|
||||
aeskey = file_info.get("aeskey") if isinstance(file_info.get("aeskey"), str) else None
|
||||
if not aeskey:
|
||||
return data
|
||||
|
||||
try:
|
||||
from aibot.crypto_utils import decrypt_file
|
||||
except Exception:
|
||||
logger.exception("[Manager] failed to import WeCom decrypt_file")
|
||||
return None
|
||||
|
||||
return decrypt_file(data, aeskey)
|
||||
|
||||
|
||||
async def _read_wechat_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
raw_path = file_info.get("path")
|
||||
if isinstance(raw_path, str) and raw_path.strip():
|
||||
try:
|
||||
return await asyncio.to_thread(Path(raw_path).read_bytes)
|
||||
except OSError:
|
||||
logger.exception("[Manager] failed to read WeChat inbound file from local path: %s", raw_path)
|
||||
return None
|
||||
|
||||
full_url = file_info.get("full_url")
|
||||
if isinstance(full_url, str) and full_url.strip():
|
||||
return await _read_http_inbound_file({"url": full_url}, client)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
register_inbound_file_reader("wecom", _read_wecom_inbound_file)
|
||||
register_inbound_file_reader("wechat", _read_wechat_inbound_file)
|
||||
|
||||
|
||||
class InvalidChannelSessionConfigError(ValueError):
|
||||
"""Raised when IM channel session overrides contain invalid agent config."""
|
||||
|
||||
|
||||
def _is_thread_busy_error(exc: BaseException | None) -> bool:
|
||||
if exc is None:
|
||||
return False
|
||||
if isinstance(exc, ConflictError):
|
||||
return True
|
||||
return "already running a task" in str(exc)
|
||||
|
||||
|
||||
def _as_dict(value: Any) -> dict[str, Any]:
|
||||
return dict(value) if isinstance(value, Mapping) else {}
|
||||
|
||||
|
||||
def _merge_dicts(*layers: Any) -> dict[str, Any]:
|
||||
merged: dict[str, Any] = {}
|
||||
for layer in layers:
|
||||
if isinstance(layer, Mapping):
|
||||
merged.update(layer)
|
||||
return merged
|
||||
|
||||
|
||||
def _normalize_custom_agent_name(raw_value: str) -> str:
|
||||
"""Normalize legacy channel assistant IDs into valid custom agent names."""
|
||||
normalized = raw_value.strip().lower().replace("_", "-")
|
||||
if not normalized:
|
||||
raise InvalidChannelSessionConfigError("Channel session assistant_id is empty. Use 'lead_agent' or a valid custom agent name.")
|
||||
if not CUSTOM_AGENT_NAME_PATTERN.fullmatch(normalized):
|
||||
raise InvalidChannelSessionConfigError(f"Invalid channel session assistant_id {raw_value!r}. Use 'lead_agent' or a custom agent name containing only letters, digits, and hyphens.")
|
||||
return normalized
|
||||
|
||||
|
||||
def _extract_response_text(result: dict | list) -> str:
|
||||
"""Extract the last AI message text from a LangGraph runs.wait result.
|
||||
|
||||
``runs.wait`` returns the final state dict which contains a ``messages``
|
||||
list. Each message is a dict with at least ``type`` and ``content``.
|
||||
|
||||
Handles special cases:
|
||||
- Regular AI text responses
|
||||
- Clarification interrupts (``ask_clarification`` tool messages)
|
||||
- AI messages with tool_calls but no text content
|
||||
"""
|
||||
if isinstance(result, list):
|
||||
messages = result
|
||||
elif isinstance(result, dict):
|
||||
messages = result.get("messages", [])
|
||||
else:
|
||||
return ""
|
||||
|
||||
# Walk backwards to find usable response text, but stop at the last
|
||||
# human message to avoid returning text from a previous turn.
|
||||
for msg in reversed(messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
|
||||
msg_type = msg.get("type")
|
||||
|
||||
# Stop at the last human message — anything before it is a previous turn
|
||||
if msg_type == "human":
|
||||
break
|
||||
|
||||
# Check for tool messages from ask_clarification (interrupt case)
|
||||
if msg_type == "tool" and msg.get("name") == "ask_clarification":
|
||||
content = msg.get("content", "")
|
||||
if isinstance(content, str) and content:
|
||||
return content
|
||||
|
||||
# Regular AI message with text content
|
||||
if msg_type == "ai":
|
||||
content = msg.get("content", "")
|
||||
if isinstance(content, str) and content:
|
||||
return content
|
||||
# content can be a list of content blocks
|
||||
if isinstance(content, list):
|
||||
parts = []
|
||||
for block in content:
|
||||
if isinstance(block, dict) and block.get("type") == "text":
|
||||
parts.append(block.get("text", ""))
|
||||
elif isinstance(block, str):
|
||||
parts.append(block)
|
||||
text = "".join(parts)
|
||||
if text:
|
||||
return text
|
||||
return ""
|
||||
|
||||
|
||||
def _extract_text_content(content: Any) -> str:
|
||||
"""Extract text from a streaming payload content field."""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts: list[str] = []
|
||||
for block in content:
|
||||
if isinstance(block, str):
|
||||
parts.append(block)
|
||||
elif isinstance(block, Mapping):
|
||||
text = block.get("text")
|
||||
if isinstance(text, str):
|
||||
parts.append(text)
|
||||
else:
|
||||
nested = block.get("content")
|
||||
if isinstance(nested, str):
|
||||
parts.append(nested)
|
||||
return "".join(parts)
|
||||
if isinstance(content, Mapping):
|
||||
for key in ("text", "content"):
|
||||
value = content.get(key)
|
||||
if isinstance(value, str):
|
||||
return value
|
||||
return ""
|
||||
|
||||
|
||||
def _merge_stream_text(existing: str, chunk: str) -> str:
|
||||
"""Merge either delta text or cumulative text into a single snapshot."""
|
||||
if not chunk:
|
||||
return existing
|
||||
if not existing or chunk == existing:
|
||||
return chunk or existing
|
||||
if chunk.startswith(existing):
|
||||
return chunk
|
||||
if existing.endswith(chunk):
|
||||
return existing
|
||||
return existing + chunk
|
||||
|
||||
|
||||
def _extract_stream_message_id(payload: Any, metadata: Any) -> str | None:
|
||||
"""Best-effort extraction of the streamed AI message identifier."""
|
||||
candidates = [payload, metadata]
|
||||
if isinstance(payload, Mapping):
|
||||
candidates.append(payload.get("kwargs"))
|
||||
|
||||
for candidate in candidates:
|
||||
if not isinstance(candidate, Mapping):
|
||||
continue
|
||||
for key in ("id", "message_id"):
|
||||
value = candidate.get(key)
|
||||
if isinstance(value, str) and value:
|
||||
return value
|
||||
return None
|
||||
|
||||
|
||||
def _accumulate_stream_text(
|
||||
buffers: dict[str, str],
|
||||
current_message_id: str | None,
|
||||
event_data: Any,
|
||||
) -> tuple[str | None, str | None]:
|
||||
"""Convert a ``messages-tuple`` event into the latest displayable AI text."""
|
||||
payload = event_data
|
||||
metadata: Any = None
|
||||
if isinstance(event_data, (list, tuple)):
|
||||
if event_data:
|
||||
payload = event_data[0]
|
||||
if len(event_data) > 1:
|
||||
metadata = event_data[1]
|
||||
|
||||
if isinstance(payload, str):
|
||||
message_id = current_message_id or "__default__"
|
||||
buffers[message_id] = _merge_stream_text(buffers.get(message_id, ""), payload)
|
||||
return buffers[message_id], message_id
|
||||
|
||||
if not isinstance(payload, Mapping):
|
||||
return None, current_message_id
|
||||
|
||||
payload_type = str(payload.get("type", "")).lower()
|
||||
if "tool" in payload_type:
|
||||
return None, current_message_id
|
||||
|
||||
text = _extract_text_content(payload.get("content"))
|
||||
if not text and isinstance(payload.get("kwargs"), Mapping):
|
||||
text = _extract_text_content(payload["kwargs"].get("content"))
|
||||
if not text:
|
||||
return None, current_message_id
|
||||
|
||||
message_id = _extract_stream_message_id(payload, metadata) or current_message_id or "__default__"
|
||||
buffers[message_id] = _merge_stream_text(buffers.get(message_id, ""), text)
|
||||
return buffers[message_id], message_id
|
||||
|
||||
|
||||
def _extract_artifacts(result: dict | list) -> list[str]:
|
||||
"""Extract artifact paths from the last AI response cycle only.
|
||||
|
||||
Instead of reading the full accumulated ``artifacts`` state (which contains
|
||||
all artifacts ever produced in the thread), this inspects the messages after
|
||||
the last human message and collects file paths from ``present_files`` tool
|
||||
calls. This ensures only newly-produced artifacts are returned.
|
||||
"""
|
||||
if isinstance(result, list):
|
||||
messages = result
|
||||
elif isinstance(result, dict):
|
||||
messages = result.get("messages", [])
|
||||
else:
|
||||
return []
|
||||
|
||||
artifacts: list[str] = []
|
||||
for msg in reversed(messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
# Stop at the last human message — anything before it is a previous turn
|
||||
if msg.get("type") == "human":
|
||||
break
|
||||
# Look for AI messages with present_files tool calls
|
||||
if msg.get("type") == "ai":
|
||||
for tc in msg.get("tool_calls", []):
|
||||
if isinstance(tc, dict) and tc.get("name") == "present_files":
|
||||
args = tc.get("args", {})
|
||||
paths = args.get("filepaths", [])
|
||||
if isinstance(paths, list):
|
||||
artifacts.extend(p for p in paths if isinstance(p, str))
|
||||
return artifacts
|
||||
|
||||
|
||||
def _format_artifact_text(artifacts: list[str]) -> str:
|
||||
"""Format artifact paths into a human-readable text block listing filenames."""
|
||||
import posixpath
|
||||
|
||||
filenames = [posixpath.basename(p) for p in artifacts]
|
||||
if len(filenames) == 1:
|
||||
return f"Created File: 📎 {filenames[0]}"
|
||||
return "Created Files: 📎 " + "、".join(filenames)
|
||||
|
||||
|
||||
_OUTPUTS_VIRTUAL_PREFIX = "/mnt/user-data/outputs/"
|
||||
|
||||
|
||||
def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedAttachment]:
|
||||
"""Resolve virtual artifact paths to host filesystem paths with metadata.
|
||||
|
||||
Only paths under ``/mnt/user-data/outputs/`` are accepted; any other
|
||||
virtual path is rejected with a warning to prevent exfiltrating uploads
|
||||
or workspace files via IM channels.
|
||||
|
||||
Skips artifacts that cannot be resolved (missing files, invalid paths)
|
||||
and logs warnings for them.
|
||||
"""
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
attachments: list[ResolvedAttachment] = []
|
||||
paths = get_paths()
|
||||
outputs_dir = paths.sandbox_outputs_dir(thread_id).resolve()
|
||||
for virtual_path in artifacts:
|
||||
# Security: only allow files from the agent outputs directory
|
||||
if not virtual_path.startswith(_OUTPUTS_VIRTUAL_PREFIX):
|
||||
logger.warning("[Manager] rejected non-outputs artifact path: %s", virtual_path)
|
||||
continue
|
||||
try:
|
||||
actual = paths.resolve_virtual_path(thread_id, virtual_path)
|
||||
# Verify the resolved path is actually under the outputs directory
|
||||
# (guards against path-traversal even after prefix check)
|
||||
try:
|
||||
actual.resolve().relative_to(outputs_dir)
|
||||
except ValueError:
|
||||
logger.warning("[Manager] artifact path escapes outputs dir: %s -> %s", virtual_path, actual)
|
||||
continue
|
||||
if not actual.is_file():
|
||||
logger.warning("[Manager] artifact not found on disk: %s -> %s", virtual_path, actual)
|
||||
continue
|
||||
mime, _ = mimetypes.guess_type(str(actual))
|
||||
mime = mime or "application/octet-stream"
|
||||
attachments.append(
|
||||
ResolvedAttachment(
|
||||
virtual_path=virtual_path,
|
||||
actual_path=actual,
|
||||
filename=actual.name,
|
||||
mime_type=mime,
|
||||
size=actual.stat().st_size,
|
||||
is_image=mime.startswith("image/"),
|
||||
)
|
||||
)
|
||||
except (ValueError, OSError) as exc:
|
||||
logger.warning("[Manager] failed to resolve artifact %s: %s", virtual_path, exc)
|
||||
return attachments
|
||||
|
||||
|
||||
def _prepare_artifact_delivery(
|
||||
thread_id: str,
|
||||
response_text: str,
|
||||
artifacts: list[str],
|
||||
) -> tuple[str, list[ResolvedAttachment]]:
|
||||
"""Resolve attachments and append filename fallbacks to the text response."""
|
||||
attachments: list[ResolvedAttachment] = []
|
||||
if not artifacts:
|
||||
return response_text, attachments
|
||||
|
||||
attachments = _resolve_attachments(thread_id, artifacts)
|
||||
resolved_virtuals = {attachment.virtual_path for attachment in attachments}
|
||||
unresolved = [path for path in artifacts if path not in resolved_virtuals]
|
||||
|
||||
if unresolved:
|
||||
artifact_text = _format_artifact_text(unresolved)
|
||||
response_text = (response_text + "\n\n" + artifact_text) if response_text else artifact_text
|
||||
|
||||
# Always include resolved attachment filenames as a text fallback so files
|
||||
# remain discoverable even when the upload is skipped or fails.
|
||||
if attachments:
|
||||
resolved_text = _format_artifact_text([attachment.virtual_path for attachment in attachments])
|
||||
response_text = (response_text + "\n\n" + resolved_text) if response_text else resolved_text
|
||||
|
||||
return response_text, attachments
|
||||
|
||||
|
||||
async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dict[str, Any]]:
|
||||
if not msg.files:
|
||||
return []
|
||||
|
||||
from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
|
||||
|
||||
uploads_dir = ensure_uploads_dir(thread_id)
|
||||
seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
|
||||
|
||||
created: list[dict[str, Any]] = []
|
||||
file_reader = INBOUND_FILE_READERS.get(msg.channel_name, _read_http_inbound_file)
|
||||
async with httpx.AsyncClient(timeout=httpx.Timeout(20.0)) as client:
|
||||
for idx, f in enumerate(msg.files):
|
||||
if not isinstance(f, dict):
|
||||
continue
|
||||
|
||||
ftype = f.get("type") if isinstance(f.get("type"), str) else "file"
|
||||
filename = f.get("filename") if isinstance(f.get("filename"), str) else ""
|
||||
|
||||
try:
|
||||
data = await file_reader(f, client)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"[Manager] failed to read inbound file: channel=%s, file=%s",
|
||||
msg.channel_name,
|
||||
f.get("url") or filename or idx,
|
||||
)
|
||||
continue
|
||||
|
||||
if data is None:
|
||||
logger.warning(
|
||||
"[Manager] inbound file reader returned no data: channel=%s, file=%s",
|
||||
msg.channel_name,
|
||||
f.get("url") or filename or idx,
|
||||
)
|
||||
continue
|
||||
|
||||
if not filename:
|
||||
ext = ".bin"
|
||||
if ftype == "image":
|
||||
ext = ".png"
|
||||
filename = f"{msg.thread_ts or 'msg'}_{idx}{ext}"
|
||||
|
||||
try:
|
||||
safe_name = claim_unique_filename(normalize_filename(filename), seen_names)
|
||||
except ValueError:
|
||||
logger.warning(
|
||||
"[Manager] skipping inbound file with unsafe filename: channel=%s, file=%r",
|
||||
msg.channel_name,
|
||||
filename,
|
||||
)
|
||||
continue
|
||||
|
||||
dest = uploads_dir / safe_name
|
||||
try:
|
||||
dest.write_bytes(data)
|
||||
except Exception:
|
||||
logger.exception("[Manager] failed to write inbound file: %s", dest)
|
||||
continue
|
||||
|
||||
created.append(
|
||||
{
|
||||
"filename": safe_name,
|
||||
"size": len(data),
|
||||
"path": f"/mnt/user-data/uploads/{safe_name}",
|
||||
"is_image": ftype == "image",
|
||||
}
|
||||
)
|
||||
|
||||
return created
|
||||
|
||||
|
||||
def _format_uploaded_files_block(files: list[dict[str, Any]]) -> str:
|
||||
lines = [
|
||||
"<uploaded_files>",
|
||||
"The following files were uploaded in this message:",
|
||||
"",
|
||||
]
|
||||
if not files:
|
||||
lines.append("(empty)")
|
||||
else:
|
||||
for f in files:
|
||||
filename = f.get("filename", "")
|
||||
size = int(f.get("size") or 0)
|
||||
size_kb = size / 1024 if size else 0
|
||||
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
|
||||
path = f.get("path", "")
|
||||
is_image = bool(f.get("is_image"))
|
||||
file_kind = "image" if is_image else "file"
|
||||
lines.append(f"- {filename} ({size_str})")
|
||||
lines.append(f" Type: {file_kind}")
|
||||
lines.append(f" Path: {path}")
|
||||
lines.append("")
|
||||
lines.append("Use `read_file` for text-based files and documents.")
|
||||
lines.append("Use `view_image` for image files (jpg, jpeg, png, webp) so the model can inspect the image content.")
|
||||
lines.append("</uploaded_files>")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
class ChannelManager:
|
||||
"""Core dispatcher that bridges IM channels to the DeerFlow agent.
|
||||
|
||||
It reads from the MessageBus inbound queue, creates/reuses threads on
|
||||
the LangGraph Server, sends messages via ``runs.wait``, and publishes
|
||||
outbound responses back through the bus.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
bus: MessageBus,
|
||||
store: ChannelStore,
|
||||
*,
|
||||
max_concurrency: int = 5,
|
||||
langgraph_url: str = DEFAULT_LANGGRAPH_URL,
|
||||
gateway_url: str = DEFAULT_GATEWAY_URL,
|
||||
assistant_id: str = DEFAULT_ASSISTANT_ID,
|
||||
default_session: dict[str, Any] | None = None,
|
||||
channel_sessions: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
self.bus = bus
|
||||
self.store = store
|
||||
self._max_concurrency = max_concurrency
|
||||
self._langgraph_url = langgraph_url
|
||||
self._gateway_url = gateway_url
|
||||
self._assistant_id = assistant_id
|
||||
self._default_session = _as_dict(default_session)
|
||||
self._channel_sessions = dict(channel_sessions or {})
|
||||
self._client = None # lazy init — langgraph_sdk async client
|
||||
self._semaphore: asyncio.Semaphore | None = None
|
||||
self._running = False
|
||||
self._task: asyncio.Task | None = None
|
||||
|
||||
@staticmethod
|
||||
def _channel_supports_streaming(channel_name: str) -> bool:
|
||||
return CHANNEL_CAPABILITIES.get(channel_name, {}).get("supports_streaming", False)
|
||||
|
||||
def _resolve_session_layer(self, msg: InboundMessage) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
channel_layer = _as_dict(self._channel_sessions.get(msg.channel_name))
|
||||
users_layer = _as_dict(channel_layer.get("users"))
|
||||
user_layer = _as_dict(users_layer.get(msg.user_id))
|
||||
return channel_layer, user_layer
|
||||
|
||||
def _resolve_run_params(self, msg: InboundMessage, thread_id: str) -> tuple[str, dict[str, Any], dict[str, Any]]:
|
||||
channel_layer, user_layer = self._resolve_session_layer(msg)
|
||||
|
||||
assistant_id = user_layer.get("assistant_id") or channel_layer.get("assistant_id") or self._default_session.get("assistant_id") or self._assistant_id
|
||||
if not isinstance(assistant_id, str) or not assistant_id.strip():
|
||||
assistant_id = self._assistant_id
|
||||
|
||||
run_config = _merge_dicts(
|
||||
DEFAULT_RUN_CONFIG,
|
||||
self._default_session.get("config"),
|
||||
channel_layer.get("config"),
|
||||
user_layer.get("config"),
|
||||
)
|
||||
|
||||
run_context = _merge_dicts(
|
||||
DEFAULT_RUN_CONTEXT,
|
||||
self._default_session.get("context"),
|
||||
channel_layer.get("context"),
|
||||
user_layer.get("context"),
|
||||
{"thread_id": thread_id},
|
||||
)
|
||||
|
||||
# Custom agents are implemented as lead_agent + agent_name context.
|
||||
# Keep backward compatibility for channel configs that set
|
||||
# assistant_id: <custom-agent-name> by routing through lead_agent.
|
||||
if assistant_id != DEFAULT_ASSISTANT_ID:
|
||||
run_context.setdefault("agent_name", _normalize_custom_agent_name(assistant_id))
|
||||
assistant_id = DEFAULT_ASSISTANT_ID
|
||||
|
||||
return assistant_id, run_config, run_context
|
||||
|
||||
# -- LangGraph SDK client (lazy) ----------------------------------------
|
||||
|
||||
def _get_client(self):
|
||||
"""Return the ``langgraph_sdk`` async client, creating it on first use."""
|
||||
if self._client is None:
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
self._client = get_client(url=self._langgraph_url)
|
||||
return self._client
|
||||
|
||||
# -- lifecycle ---------------------------------------------------------
|
||||
|
||||
async def start(self) -> None:
|
||||
"""Start the dispatch loop."""
|
||||
if self._running:
|
||||
return
|
||||
self._running = True
|
||||
self._semaphore = asyncio.Semaphore(self._max_concurrency)
|
||||
self._task = asyncio.create_task(self._dispatch_loop())
|
||||
logger.info("ChannelManager started (max_concurrency=%d)", self._max_concurrency)
|
||||
|
||||
async def stop(self) -> None:
|
||||
"""Stop the dispatch loop."""
|
||||
self._running = False
|
||||
if self._task:
|
||||
self._task.cancel()
|
||||
try:
|
||||
await self._task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
self._task = None
|
||||
logger.info("ChannelManager stopped")
|
||||
|
||||
# -- dispatch loop -----------------------------------------------------
|
||||
|
||||
async def _dispatch_loop(self) -> None:
|
||||
logger.info("[Manager] dispatch loop started, waiting for inbound messages")
|
||||
while self._running:
|
||||
try:
|
||||
msg = await asyncio.wait_for(self.bus.get_inbound(), timeout=1.0)
|
||||
except TimeoutError:
|
||||
continue
|
||||
except asyncio.CancelledError:
|
||||
break
|
||||
|
||||
logger.info(
|
||||
"[Manager] received inbound: channel=%s, chat_id=%s, type=%s, text=%r",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
msg.msg_type.value,
|
||||
msg.text[:100] if msg.text else "",
|
||||
)
|
||||
task = asyncio.create_task(self._handle_message(msg))
|
||||
task.add_done_callback(self._log_task_error)
|
||||
|
||||
@staticmethod
|
||||
def _log_task_error(task: asyncio.Task) -> None:
|
||||
"""Surface unhandled exceptions from background tasks."""
|
||||
if task.cancelled():
|
||||
return
|
||||
exc = task.exception()
|
||||
if exc:
|
||||
logger.error("[Manager] unhandled error in message task: %s", exc, exc_info=exc)
|
||||
|
||||
async def _handle_message(self, msg: InboundMessage) -> None:
|
||||
async with self._semaphore:
|
||||
try:
|
||||
if msg.msg_type == InboundMessageType.COMMAND:
|
||||
await self._handle_command(msg)
|
||||
else:
|
||||
await self._handle_chat(msg)
|
||||
except InvalidChannelSessionConfigError as exc:
|
||||
logger.warning(
|
||||
"Invalid channel session config for %s (chat=%s): %s",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
exc,
|
||||
)
|
||||
await self._send_error(msg, str(exc))
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"Error handling message from %s (chat=%s)",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
)
|
||||
await self._send_error(msg, "An internal error occurred. Please try again.")
|
||||
|
||||
# -- chat handling -----------------------------------------------------
|
||||
|
||||
async def _create_thread(self, client, msg: InboundMessage) -> str:
|
||||
"""Create a new thread on the LangGraph Server and store the mapping."""
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
self.store.set_thread_id(
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
thread_id,
|
||||
topic_id=msg.topic_id,
|
||||
user_id=msg.user_id,
|
||||
)
|
||||
logger.info("[Manager] new thread created on LangGraph Server: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
|
||||
return thread_id
|
||||
|
||||
async def _handle_chat(self, msg: InboundMessage, extra_context: dict[str, Any] | None = None) -> None:
|
||||
client = self._get_client()
|
||||
|
||||
# Look up existing DeerFlow thread.
|
||||
# topic_id may be None (e.g. Telegram private chats) — the store
|
||||
# handles this by using the "channel:chat_id" key without a topic suffix.
|
||||
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
|
||||
if thread_id:
|
||||
logger.info("[Manager] reusing thread: thread_id=%s for topic_id=%s", thread_id, msg.topic_id)
|
||||
|
||||
# No existing thread found — create a new one
|
||||
if thread_id is None:
|
||||
thread_id = await self._create_thread(client, msg)
|
||||
|
||||
assistant_id, run_config, run_context = self._resolve_run_params(msg, thread_id)
|
||||
|
||||
# If the inbound message contains file attachments, let the channel
|
||||
# materialize (download) them and update msg.text to include sandbox file paths.
|
||||
# This enables downstream models to access user-uploaded files by path.
|
||||
# Channels that do not support file download will simply return the original message.
|
||||
if msg.files:
|
||||
from .service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
channel = service.get_channel(msg.channel_name) if service else None
|
||||
logger.info("[Manager] preparing receive file context for %d attachments", len(msg.files))
|
||||
msg = await channel.receive_file(msg, thread_id) if channel else msg
|
||||
if extra_context:
|
||||
run_context.update(extra_context)
|
||||
|
||||
uploaded = await _ingest_inbound_files(thread_id, msg)
|
||||
if uploaded:
|
||||
msg.text = f"{_format_uploaded_files_block(uploaded)}\n\n{msg.text}".strip()
|
||||
|
||||
if self._channel_supports_streaming(msg.channel_name):
|
||||
await self._handle_streaming_chat(
|
||||
client,
|
||||
msg,
|
||||
thread_id,
|
||||
assistant_id,
|
||||
run_config,
|
||||
run_context,
|
||||
)
|
||||
return
|
||||
|
||||
logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "human", "content": msg.text}]},
|
||||
config=run_config,
|
||||
context=run_context,
|
||||
)
|
||||
|
||||
response_text = _extract_response_text(result)
|
||||
artifacts = _extract_artifacts(result)
|
||||
|
||||
logger.info(
|
||||
"[Manager] agent response received: thread_id=%s, response_len=%d, artifacts=%d",
|
||||
thread_id,
|
||||
len(response_text) if response_text else 0,
|
||||
len(artifacts),
|
||||
)
|
||||
|
||||
response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
|
||||
|
||||
if not response_text:
|
||||
if attachments:
|
||||
response_text = _format_artifact_text([a.virtual_path for a in attachments])
|
||||
else:
|
||||
response_text = "(No response from agent)"
|
||||
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=response_text,
|
||||
artifacts=artifacts,
|
||||
attachments=attachments,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
logger.info("[Manager] publishing outbound message to bus: channel=%s, chat_id=%s", msg.channel_name, msg.chat_id)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
|
||||
async def _handle_streaming_chat(
|
||||
self,
|
||||
client,
|
||||
msg: InboundMessage,
|
||||
thread_id: str,
|
||||
assistant_id: str,
|
||||
run_config: dict[str, Any],
|
||||
run_context: dict[str, Any],
|
||||
) -> None:
|
||||
logger.info("[Manager] invoking runs.stream(thread_id=%s, text=%r)", thread_id, msg.text[:100])
|
||||
|
||||
last_values: dict[str, Any] | list | None = None
|
||||
streamed_buffers: dict[str, str] = {}
|
||||
current_message_id: str | None = None
|
||||
latest_text = ""
|
||||
last_published_text = ""
|
||||
last_publish_at = 0.0
|
||||
stream_error: BaseException | None = None
|
||||
|
||||
try:
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "human", "content": msg.text}]},
|
||||
config=run_config,
|
||||
context=run_context,
|
||||
stream_mode=["messages-tuple", "values"],
|
||||
multitask_strategy="reject",
|
||||
):
|
||||
event = getattr(chunk, "event", "")
|
||||
data = getattr(chunk, "data", None)
|
||||
|
||||
if event == "messages-tuple":
|
||||
accumulated_text, current_message_id = _accumulate_stream_text(streamed_buffers, current_message_id, data)
|
||||
if accumulated_text:
|
||||
latest_text = accumulated_text
|
||||
elif event == "values" and isinstance(data, (dict, list)):
|
||||
last_values = data
|
||||
snapshot_text = _extract_response_text(data)
|
||||
if snapshot_text:
|
||||
latest_text = snapshot_text
|
||||
|
||||
if not latest_text or latest_text == last_published_text:
|
||||
continue
|
||||
|
||||
now = time.monotonic()
|
||||
if last_published_text and now - last_publish_at < STREAM_UPDATE_MIN_INTERVAL_SECONDS:
|
||||
continue
|
||||
|
||||
await self.bus.publish_outbound(
|
||||
OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=latest_text,
|
||||
is_final=False,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
)
|
||||
last_published_text = latest_text
|
||||
last_publish_at = now
|
||||
except Exception as exc:
|
||||
stream_error = exc
|
||||
if _is_thread_busy_error(exc):
|
||||
logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
|
||||
else:
|
||||
logger.exception("[Manager] streaming error: thread_id=%s", thread_id)
|
||||
finally:
|
||||
result = last_values if last_values is not None else {"messages": [{"type": "ai", "content": latest_text}]}
|
||||
response_text = _extract_response_text(result)
|
||||
artifacts = _extract_artifacts(result)
|
||||
response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
|
||||
|
||||
if not response_text:
|
||||
if attachments:
|
||||
response_text = _format_artifact_text([attachment.virtual_path for attachment in attachments])
|
||||
elif stream_error:
|
||||
if _is_thread_busy_error(stream_error):
|
||||
response_text = THREAD_BUSY_MESSAGE
|
||||
else:
|
||||
response_text = "An error occurred while processing your request. Please try again."
|
||||
else:
|
||||
response_text = latest_text or "(No response from agent)"
|
||||
|
||||
logger.info(
|
||||
"[Manager] streaming response completed: thread_id=%s, response_len=%d, artifacts=%d, error=%s",
|
||||
thread_id,
|
||||
len(response_text),
|
||||
len(artifacts),
|
||||
stream_error,
|
||||
)
|
||||
await self.bus.publish_outbound(
|
||||
OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=response_text,
|
||||
artifacts=artifacts,
|
||||
attachments=attachments,
|
||||
is_final=True,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
)
|
||||
|
||||
# -- command handling --------------------------------------------------
|
||||
|
||||
async def _handle_command(self, msg: InboundMessage) -> None:
|
||||
text = msg.text.strip()
|
||||
parts = text.split(maxsplit=1)
|
||||
command = parts[0].lower().lstrip("/")
|
||||
|
||||
if command == "bootstrap":
|
||||
from dataclasses import replace as _dc_replace
|
||||
|
||||
chat_text = parts[1] if len(parts) > 1 else "Initialize workspace"
|
||||
chat_msg = _dc_replace(msg, text=chat_text, msg_type=InboundMessageType.CHAT)
|
||||
await self._handle_chat(chat_msg, extra_context={"is_bootstrap": True})
|
||||
return
|
||||
|
||||
if command == "new":
|
||||
# Create a new thread on the LangGraph Server
|
||||
client = self._get_client()
|
||||
thread = await client.threads.create()
|
||||
new_thread_id = thread["thread_id"]
|
||||
self.store.set_thread_id(
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
new_thread_id,
|
||||
topic_id=msg.topic_id,
|
||||
user_id=msg.user_id,
|
||||
)
|
||||
reply = "New conversation started."
|
||||
elif command == "status":
|
||||
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
|
||||
reply = f"Active thread: {thread_id}" if thread_id else "No active conversation."
|
||||
elif command == "models":
|
||||
reply = await self._fetch_gateway("/api/models", "models")
|
||||
elif command == "memory":
|
||||
reply = await self._fetch_gateway("/api/memory", "memory")
|
||||
elif command == "help":
|
||||
reply = (
|
||||
"Available commands:\n"
|
||||
"/bootstrap — Start a bootstrap session (enables agent setup)\n"
|
||||
"/new — Start a new conversation\n"
|
||||
"/status — Show current thread info\n"
|
||||
"/models — List available models\n"
|
||||
"/memory — Show memory status\n"
|
||||
"/help — Show this help"
|
||||
)
|
||||
else:
|
||||
available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
|
||||
reply = f"Unknown command: /{command}. Available commands: {available}"
|
||||
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
|
||||
text=reply,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
|
||||
async def _fetch_gateway(self, path: str, kind: str) -> str:
|
||||
"""Fetch data from the Gateway API for command responses."""
|
||||
import httpx
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient() as http:
|
||||
resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch %s from gateway", kind)
|
||||
return f"Failed to fetch {kind} information."
|
||||
|
||||
if kind == "models":
|
||||
names = [m["name"] for m in data.get("models", [])]
|
||||
return ("Available models:\n" + "\n".join(f"• {n}" for n in names)) if names else "No models configured."
|
||||
elif kind == "memory":
|
||||
facts = data.get("facts", [])
|
||||
return f"Memory contains {len(facts)} fact(s)."
|
||||
return str(data)
|
||||
|
||||
# -- error helper ------------------------------------------------------
|
||||
|
||||
async def _send_error(self, msg: InboundMessage, error_text: str) -> None:
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
|
||||
text=error_text,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
@@ -1,173 +0,0 @@
|
||||
"""MessageBus — async pub/sub hub that decouples channels from the agent dispatcher."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
from collections.abc import Callable, Coroutine
|
||||
from dataclasses import dataclass, field
|
||||
from enum import StrEnum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Message types
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class InboundMessageType(StrEnum):
|
||||
"""Types of messages arriving from IM channels."""
|
||||
|
||||
CHAT = "chat"
|
||||
COMMAND = "command"
|
||||
|
||||
|
||||
@dataclass
|
||||
class InboundMessage:
|
||||
"""A message arriving from an IM channel toward the agent dispatcher.
|
||||
|
||||
Attributes:
|
||||
channel_name: Name of the source channel (e.g. "feishu", "slack").
|
||||
chat_id: Platform-specific chat/conversation identifier.
|
||||
user_id: Platform-specific user identifier.
|
||||
text: The message text.
|
||||
msg_type: Whether this is a regular chat message or a command.
|
||||
thread_ts: Optional platform thread identifier (for threaded replies).
|
||||
topic_id: Conversation topic identifier used to map to a DeerFlow thread.
|
||||
Messages sharing the same ``topic_id`` within a ``chat_id`` will
|
||||
reuse the same DeerFlow thread. When ``None``, each message
|
||||
creates a new thread (one-shot Q&A).
|
||||
files: Optional list of file attachments (platform-specific dicts).
|
||||
metadata: Arbitrary extra data from the channel.
|
||||
created_at: Unix timestamp when the message was created.
|
||||
"""
|
||||
|
||||
channel_name: str
|
||||
chat_id: str
|
||||
user_id: str
|
||||
text: str
|
||||
msg_type: InboundMessageType = InboundMessageType.CHAT
|
||||
thread_ts: str | None = None
|
||||
topic_id: str | None = None
|
||||
files: list[dict[str, Any]] = field(default_factory=list)
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
created_at: float = field(default_factory=time.time)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ResolvedAttachment:
|
||||
"""A file attachment resolved to a host filesystem path, ready for upload.
|
||||
|
||||
Attributes:
|
||||
virtual_path: Original virtual path (e.g. /mnt/user-data/outputs/report.pdf).
|
||||
actual_path: Resolved host filesystem path.
|
||||
filename: Basename of the file.
|
||||
mime_type: MIME type (e.g. "application/pdf").
|
||||
size: File size in bytes.
|
||||
is_image: True for image/* MIME types (platforms may handle images differently).
|
||||
"""
|
||||
|
||||
virtual_path: str
|
||||
actual_path: Path
|
||||
filename: str
|
||||
mime_type: str
|
||||
size: int
|
||||
is_image: bool
|
||||
|
||||
|
||||
@dataclass
|
||||
class OutboundMessage:
|
||||
"""A message from the agent dispatcher back to a channel.
|
||||
|
||||
Attributes:
|
||||
channel_name: Target channel name (used for routing).
|
||||
chat_id: Target chat/conversation identifier.
|
||||
thread_id: DeerFlow thread ID that produced this response.
|
||||
text: The response text.
|
||||
artifacts: List of artifact paths produced by the agent.
|
||||
is_final: Whether this is the final message in the response stream.
|
||||
thread_ts: Optional platform thread identifier for threaded replies.
|
||||
metadata: Arbitrary extra data.
|
||||
created_at: Unix timestamp.
|
||||
"""
|
||||
|
||||
channel_name: str
|
||||
chat_id: str
|
||||
thread_id: str
|
||||
text: str
|
||||
artifacts: list[str] = field(default_factory=list)
|
||||
attachments: list[ResolvedAttachment] = field(default_factory=list)
|
||||
is_final: bool = True
|
||||
thread_ts: str | None = None
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
created_at: float = field(default_factory=time.time)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# MessageBus
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
OutboundCallback = Callable[[OutboundMessage], Coroutine[Any, Any, None]]
|
||||
|
||||
|
||||
class MessageBus:
|
||||
"""Async pub/sub hub connecting channels and the agent dispatcher.
|
||||
|
||||
Channels publish inbound messages; the dispatcher consumes them.
|
||||
The dispatcher publishes outbound messages; channels receive them
|
||||
via registered callbacks.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._inbound_queue: asyncio.Queue[InboundMessage] = asyncio.Queue()
|
||||
self._outbound_listeners: list[OutboundCallback] = []
|
||||
|
||||
# -- inbound -----------------------------------------------------------
|
||||
|
||||
async def publish_inbound(self, msg: InboundMessage) -> None:
|
||||
"""Enqueue an inbound message from a channel."""
|
||||
await self._inbound_queue.put(msg)
|
||||
logger.info(
|
||||
"[Bus] inbound enqueued: channel=%s, chat_id=%s, type=%s, queue_size=%d",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
msg.msg_type.value,
|
||||
self._inbound_queue.qsize(),
|
||||
)
|
||||
|
||||
async def get_inbound(self) -> InboundMessage:
|
||||
"""Block until the next inbound message is available."""
|
||||
return await self._inbound_queue.get()
|
||||
|
||||
@property
|
||||
def inbound_queue(self) -> asyncio.Queue[InboundMessage]:
|
||||
return self._inbound_queue
|
||||
|
||||
# -- outbound ----------------------------------------------------------
|
||||
|
||||
def subscribe_outbound(self, callback: OutboundCallback) -> None:
|
||||
"""Register an async callback for outbound messages."""
|
||||
self._outbound_listeners.append(callback)
|
||||
|
||||
def unsubscribe_outbound(self, callback: OutboundCallback) -> None:
|
||||
"""Remove a previously registered outbound callback."""
|
||||
self._outbound_listeners = [cb for cb in self._outbound_listeners if cb is not callback]
|
||||
|
||||
async def publish_outbound(self, msg: OutboundMessage) -> None:
|
||||
"""Dispatch an outbound message to all registered listeners."""
|
||||
logger.info(
|
||||
"[Bus] outbound dispatching: channel=%s, chat_id=%s, listeners=%d, text_len=%d",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
len(self._outbound_listeners),
|
||||
len(msg.text),
|
||||
)
|
||||
for callback in self._outbound_listeners:
|
||||
try:
|
||||
await callback(msg)
|
||||
except Exception:
|
||||
logger.exception("Error in outbound callback for channel=%s", msg.channel_name)
|
||||
@@ -1,200 +0,0 @@
|
||||
"""ChannelService — manages the lifecycle of all IM channels."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
|
||||
from app.channels.message_bus import MessageBus
|
||||
from app.channels.store import ChannelStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Channel name → import path for lazy loading
|
||||
_CHANNEL_REGISTRY: dict[str, str] = {
|
||||
"discord": "app.channels.discord:DiscordChannel",
|
||||
"feishu": "app.channels.feishu:FeishuChannel",
|
||||
"slack": "app.channels.slack:SlackChannel",
|
||||
"telegram": "app.channels.telegram:TelegramChannel",
|
||||
"wechat": "app.channels.wechat:WechatChannel",
|
||||
"wecom": "app.channels.wecom:WeComChannel",
|
||||
}
|
||||
|
||||
_CHANNELS_LANGGRAPH_URL_ENV = "DEER_FLOW_CHANNELS_LANGGRAPH_URL"
|
||||
_CHANNELS_GATEWAY_URL_ENV = "DEER_FLOW_CHANNELS_GATEWAY_URL"
|
||||
|
||||
|
||||
def _resolve_service_url(config: dict[str, Any], config_key: str, env_key: str, default: str) -> str:
|
||||
value = config.pop(config_key, None)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value
|
||||
env_value = os.getenv(env_key, "").strip()
|
||||
if env_value:
|
||||
return env_value
|
||||
return default
|
||||
|
||||
|
||||
class ChannelService:
|
||||
"""Manages the lifecycle of all configured IM channels.
|
||||
|
||||
Reads configuration from ``config.yaml`` under the ``channels`` key,
|
||||
instantiates enabled channels, and starts the ChannelManager dispatcher.
|
||||
"""
|
||||
|
||||
def __init__(self, channels_config: dict[str, Any] | None = None) -> None:
|
||||
self.bus = MessageBus()
|
||||
self.store = ChannelStore()
|
||||
config = dict(channels_config or {})
|
||||
langgraph_url = _resolve_service_url(config, "langgraph_url", _CHANNELS_LANGGRAPH_URL_ENV, DEFAULT_LANGGRAPH_URL)
|
||||
gateway_url = _resolve_service_url(config, "gateway_url", _CHANNELS_GATEWAY_URL_ENV, DEFAULT_GATEWAY_URL)
|
||||
default_session = config.pop("session", None)
|
||||
channel_sessions = {name: channel_config.get("session") for name, channel_config in config.items() if isinstance(channel_config, dict)}
|
||||
self.manager = ChannelManager(
|
||||
bus=self.bus,
|
||||
store=self.store,
|
||||
langgraph_url=langgraph_url,
|
||||
gateway_url=gateway_url,
|
||||
default_session=default_session if isinstance(default_session, dict) else None,
|
||||
channel_sessions=channel_sessions,
|
||||
)
|
||||
self._channels: dict[str, Any] = {} # name -> Channel instance
|
||||
self._config = config
|
||||
self._running = False
|
||||
|
||||
@classmethod
|
||||
def from_app_config(cls) -> ChannelService:
|
||||
"""Create a ChannelService from the application config."""
|
||||
from deerflow.config.app_config import get_app_config
|
||||
|
||||
config = get_app_config()
|
||||
channels_config = {}
|
||||
# extra fields are allowed by AppConfig (extra="allow")
|
||||
extra = config.model_extra or {}
|
||||
if "channels" in extra:
|
||||
channels_config = extra["channels"]
|
||||
return cls(channels_config=channels_config)
|
||||
|
||||
async def start(self) -> None:
|
||||
"""Start the manager and all enabled channels."""
|
||||
if self._running:
|
||||
return
|
||||
|
||||
await self.manager.start()
|
||||
|
||||
for name, channel_config in self._config.items():
|
||||
if not isinstance(channel_config, dict):
|
||||
continue
|
||||
if not channel_config.get("enabled", False):
|
||||
logger.info("Channel %s is disabled, skipping", name)
|
||||
continue
|
||||
|
||||
await self._start_channel(name, channel_config)
|
||||
|
||||
self._running = True
|
||||
logger.info("ChannelService started with channels: %s", list(self._channels.keys()))
|
||||
|
||||
async def stop(self) -> None:
|
||||
"""Stop all channels and the manager."""
|
||||
for name, channel in list(self._channels.items()):
|
||||
try:
|
||||
await channel.stop()
|
||||
logger.info("Channel %s stopped", name)
|
||||
except Exception:
|
||||
logger.exception("Error stopping channel %s", name)
|
||||
self._channels.clear()
|
||||
|
||||
await self.manager.stop()
|
||||
self._running = False
|
||||
logger.info("ChannelService stopped")
|
||||
|
||||
async def restart_channel(self, name: str) -> bool:
|
||||
"""Restart a specific channel. Returns True if successful."""
|
||||
if name in self._channels:
|
||||
try:
|
||||
await self._channels[name].stop()
|
||||
except Exception:
|
||||
logger.exception("Error stopping channel %s for restart", name)
|
||||
del self._channels[name]
|
||||
|
||||
config = self._config.get(name)
|
||||
if not config or not isinstance(config, dict):
|
||||
logger.warning("No config for channel %s", name)
|
||||
return False
|
||||
|
||||
return await self._start_channel(name, config)
|
||||
|
||||
async def _start_channel(self, name: str, config: dict[str, Any]) -> bool:
|
||||
"""Instantiate and start a single channel."""
|
||||
import_path = _CHANNEL_REGISTRY.get(name)
|
||||
if not import_path:
|
||||
logger.warning("Unknown channel type: %s", name)
|
||||
return False
|
||||
|
||||
try:
|
||||
from deerflow.reflection import resolve_class
|
||||
|
||||
channel_cls = resolve_class(import_path, base_class=None)
|
||||
except Exception:
|
||||
logger.exception("Failed to import channel class for %s", name)
|
||||
return False
|
||||
|
||||
try:
|
||||
channel = channel_cls(bus=self.bus, config=config)
|
||||
await channel.start()
|
||||
self._channels[name] = channel
|
||||
logger.info("Channel %s started", name)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("Failed to start channel %s", name)
|
||||
return False
|
||||
|
||||
def get_status(self) -> dict[str, Any]:
|
||||
"""Return status information for all channels."""
|
||||
channels_status = {}
|
||||
for name in _CHANNEL_REGISTRY:
|
||||
config = self._config.get(name, {})
|
||||
enabled = isinstance(config, dict) and config.get("enabled", False)
|
||||
running = name in self._channels and self._channels[name].is_running
|
||||
channels_status[name] = {
|
||||
"enabled": enabled,
|
||||
"running": running,
|
||||
}
|
||||
return {
|
||||
"service_running": self._running,
|
||||
"channels": channels_status,
|
||||
}
|
||||
|
||||
def get_channel(self, name: str) -> Channel | None:
|
||||
"""Return a running channel instance by name when available."""
|
||||
return self._channels.get(name)
|
||||
|
||||
|
||||
# -- singleton access -------------------------------------------------------
|
||||
|
||||
_channel_service: ChannelService | None = None
|
||||
|
||||
|
||||
def get_channel_service() -> ChannelService | None:
|
||||
"""Get the singleton ChannelService instance (if started)."""
|
||||
return _channel_service
|
||||
|
||||
|
||||
async def start_channel_service() -> ChannelService:
|
||||
"""Create and start the global ChannelService from app config."""
|
||||
global _channel_service
|
||||
if _channel_service is not None:
|
||||
return _channel_service
|
||||
_channel_service = ChannelService.from_app_config()
|
||||
await _channel_service.start()
|
||||
return _channel_service
|
||||
|
||||
|
||||
async def stop_channel_service() -> None:
|
||||
"""Stop the global ChannelService."""
|
||||
global _channel_service
|
||||
if _channel_service is not None:
|
||||
await _channel_service.stop()
|
||||
_channel_service = None
|
||||
@@ -1,246 +0,0 @@
|
||||
"""Slack channel — connects via Socket Mode (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from markdown_to_mrkdwn import SlackMarkdownConverter
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_slack_md_converter = SlackMarkdownConverter()
|
||||
|
||||
|
||||
class SlackChannel(Channel):
|
||||
"""Slack IM channel using Socket Mode (WebSocket, no public IP).
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.slack``):
|
||||
- ``bot_token``: Slack Bot User OAuth Token (xoxb-...).
|
||||
- ``app_token``: Slack App-Level Token (xapp-...) for Socket Mode.
|
||||
- ``allowed_users``: (optional) List of allowed Slack user IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="slack", bus=bus, config=config)
|
||||
self._socket_client = None
|
||||
self._web_client = None
|
||||
self._loop: asyncio.AbstractEventLoop | None = None
|
||||
self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
from slack_sdk import WebClient
|
||||
from slack_sdk.socket_mode import SocketModeClient
|
||||
from slack_sdk.socket_mode.response import SocketModeResponse
|
||||
except ImportError:
|
||||
logger.error("slack-sdk is not installed. Install it with: uv add slack-sdk")
|
||||
return
|
||||
|
||||
self._SocketModeResponse = SocketModeResponse
|
||||
|
||||
bot_token = self.config.get("bot_token", "")
|
||||
app_token = self.config.get("app_token", "")
|
||||
|
||||
if not bot_token or not app_token:
|
||||
logger.error("Slack channel requires bot_token and app_token")
|
||||
return
|
||||
|
||||
self._web_client = WebClient(token=bot_token)
|
||||
self._socket_client = SocketModeClient(
|
||||
app_token=app_token,
|
||||
web_client=self._web_client,
|
||||
)
|
||||
self._loop = asyncio.get_event_loop()
|
||||
|
||||
self._socket_client.socket_mode_request_listeners.append(self._on_socket_event)
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Start socket mode in background thread
|
||||
asyncio.get_event_loop().run_in_executor(None, self._socket_client.connect)
|
||||
logger.info("Slack channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._socket_client:
|
||||
self._socket_client.close()
|
||||
self._socket_client = None
|
||||
logger.info("Slack channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._web_client:
|
||||
return
|
||||
|
||||
kwargs: dict[str, Any] = {
|
||||
"channel": msg.chat_id,
|
||||
"text": _slack_md_converter.convert(msg.text),
|
||||
}
|
||||
if msg.thread_ts:
|
||||
kwargs["thread_ts"] = msg.thread_ts
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await asyncio.to_thread(self._web_client.chat_postMessage, **kwargs)
|
||||
# Add a completion reaction to the thread root
|
||||
if msg.thread_ts:
|
||||
await asyncio.to_thread(
|
||||
self._add_reaction,
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
"white_check_mark",
|
||||
)
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Slack] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Slack] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
# Add failure reaction on error
|
||||
if msg.thread_ts:
|
||||
try:
|
||||
await asyncio.to_thread(
|
||||
self._add_reaction,
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
"x",
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Slack send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._web_client:
|
||||
return False
|
||||
|
||||
try:
|
||||
kwargs: dict[str, Any] = {
|
||||
"channel": msg.chat_id,
|
||||
"file": str(attachment.actual_path),
|
||||
"filename": attachment.filename,
|
||||
"title": attachment.filename,
|
||||
}
|
||||
if msg.thread_ts:
|
||||
kwargs["thread_ts"] = msg.thread_ts
|
||||
|
||||
await asyncio.to_thread(self._web_client.files_upload_v2, **kwargs)
|
||||
logger.info("[Slack] file uploaded: %s to channel=%s", attachment.filename, msg.chat_id)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Slack] failed to upload file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
|
||||
def _add_reaction(self, channel_id: str, timestamp: str, emoji: str) -> None:
|
||||
"""Add an emoji reaction to a message (best-effort, non-blocking)."""
|
||||
if not self._web_client:
|
||||
return
|
||||
try:
|
||||
self._web_client.reactions_add(
|
||||
channel=channel_id,
|
||||
timestamp=timestamp,
|
||||
name=emoji,
|
||||
)
|
||||
except Exception as exc:
|
||||
if "already_reacted" not in str(exc):
|
||||
logger.warning("[Slack] failed to add reaction %s: %s", emoji, exc)
|
||||
|
||||
def _send_running_reply(self, channel_id: str, thread_ts: str) -> None:
|
||||
"""Send a 'Working on it......' reply in the thread (called from SDK thread)."""
|
||||
if not self._web_client:
|
||||
return
|
||||
try:
|
||||
self._web_client.chat_postMessage(
|
||||
channel=channel_id,
|
||||
text=":hourglass_flowing_sand: Working on it...",
|
||||
thread_ts=thread_ts,
|
||||
)
|
||||
logger.info("[Slack] 'Working on it...' reply sent in channel=%s, thread_ts=%s", channel_id, thread_ts)
|
||||
except Exception:
|
||||
logger.exception("[Slack] failed to send running reply in channel=%s", channel_id)
|
||||
|
||||
def _on_socket_event(self, client, req) -> None:
|
||||
"""Called by slack-sdk for each Socket Mode event."""
|
||||
try:
|
||||
# Acknowledge the event
|
||||
response = self._SocketModeResponse(envelope_id=req.envelope_id)
|
||||
client.send_socket_mode_response(response)
|
||||
|
||||
event_type = req.type
|
||||
if event_type != "events_api":
|
||||
return
|
||||
|
||||
event = req.payload.get("event", {})
|
||||
etype = event.get("type", "")
|
||||
|
||||
# Handle message events (DM or @mention)
|
||||
if etype in ("message", "app_mention"):
|
||||
self._handle_message_event(event)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error processing Slack event")
|
||||
|
||||
def _handle_message_event(self, event: dict) -> None:
|
||||
# Ignore bot messages
|
||||
if event.get("bot_id") or event.get("subtype"):
|
||||
return
|
||||
|
||||
user_id = event.get("user", "")
|
||||
|
||||
# Check allowed users
|
||||
if self._allowed_users and user_id not in self._allowed_users:
|
||||
logger.debug("Ignoring message from non-allowed user: %s", user_id)
|
||||
return
|
||||
|
||||
text = event.get("text", "").strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
channel_id = event.get("channel", "")
|
||||
thread_ts = event.get("thread_ts") or event.get("ts", "")
|
||||
|
||||
if text.startswith("/"):
|
||||
msg_type = InboundMessageType.COMMAND
|
||||
else:
|
||||
msg_type = InboundMessageType.CHAT
|
||||
|
||||
# topic_id: use thread_ts as the topic identifier.
|
||||
# For threaded messages, thread_ts is the root message ts (shared topic).
|
||||
# For non-threaded messages, thread_ts is the message's own ts (new topic).
|
||||
inbound = self._make_inbound(
|
||||
chat_id=channel_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_ts,
|
||||
)
|
||||
inbound.topic_id = thread_ts
|
||||
|
||||
if self._loop and self._loop.is_running():
|
||||
# Acknowledge with an eyes reaction
|
||||
self._add_reaction(channel_id, event.get("ts", thread_ts), "eyes")
|
||||
# Send "running" reply first (fire-and-forget from SDK thread)
|
||||
self._send_running_reply(channel_id, thread_ts)
|
||||
asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._loop)
|
||||
@@ -1,153 +0,0 @@
|
||||
"""ChannelStore — persists IM chat-to-DeerFlow thread mappings."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ChannelStore:
|
||||
"""JSON-file-backed store that maps IM conversations to DeerFlow threads.
|
||||
|
||||
Data layout (on disk)::
|
||||
|
||||
{
|
||||
"<channel_name>:<chat_id>": {
|
||||
"thread_id": "<uuid>",
|
||||
"user_id": "<platform_user>",
|
||||
"created_at": 1700000000.0,
|
||||
"updated_at": 1700000000.0
|
||||
},
|
||||
...
|
||||
}
|
||||
|
||||
The store is intentionally simple — a single JSON file that is atomically
|
||||
rewritten on every mutation. For production workloads with high concurrency,
|
||||
this can be swapped for a proper database backend.
|
||||
"""
|
||||
|
||||
def __init__(self, path: str | Path | None = None) -> None:
|
||||
if path is None:
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
path = Path(get_paths().base_dir) / "channels" / "store.json"
|
||||
self._path = Path(path)
|
||||
self._path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self._data: dict[str, dict[str, Any]] = self._load()
|
||||
self._lock = threading.Lock()
|
||||
|
||||
# -- persistence -------------------------------------------------------
|
||||
|
||||
def _load(self) -> dict[str, dict[str, Any]]:
|
||||
if self._path.exists():
|
||||
try:
|
||||
return json.loads(self._path.read_text(encoding="utf-8"))
|
||||
except (json.JSONDecodeError, OSError):
|
||||
logger.warning("Corrupt channel store at %s, starting fresh", self._path)
|
||||
return {}
|
||||
|
||||
def _save(self) -> None:
|
||||
fd = tempfile.NamedTemporaryFile(
|
||||
mode="w",
|
||||
dir=self._path.parent,
|
||||
suffix=".tmp",
|
||||
delete=False,
|
||||
)
|
||||
try:
|
||||
json.dump(self._data, fd, indent=2)
|
||||
fd.close()
|
||||
Path(fd.name).replace(self._path)
|
||||
except BaseException:
|
||||
fd.close()
|
||||
Path(fd.name).unlink(missing_ok=True)
|
||||
raise
|
||||
|
||||
# -- key helpers -------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _key(channel_name: str, chat_id: str, topic_id: str | None = None) -> str:
|
||||
if topic_id:
|
||||
return f"{channel_name}:{chat_id}:{topic_id}"
|
||||
return f"{channel_name}:{chat_id}"
|
||||
|
||||
# -- public API --------------------------------------------------------
|
||||
|
||||
def get_thread_id(self, channel_name: str, chat_id: str, topic_id: str | None = None) -> str | None:
|
||||
"""Look up the DeerFlow thread_id for a given IM conversation/topic."""
|
||||
entry = self._data.get(self._key(channel_name, chat_id, topic_id))
|
||||
return entry["thread_id"] if entry else None
|
||||
|
||||
def set_thread_id(
|
||||
self,
|
||||
channel_name: str,
|
||||
chat_id: str,
|
||||
thread_id: str,
|
||||
*,
|
||||
topic_id: str | None = None,
|
||||
user_id: str = "",
|
||||
) -> None:
|
||||
"""Create or update the mapping for an IM conversation/topic."""
|
||||
with self._lock:
|
||||
key = self._key(channel_name, chat_id, topic_id)
|
||||
now = time.time()
|
||||
existing = self._data.get(key)
|
||||
self._data[key] = {
|
||||
"thread_id": thread_id,
|
||||
"user_id": user_id,
|
||||
"created_at": existing["created_at"] if existing else now,
|
||||
"updated_at": now,
|
||||
}
|
||||
self._save()
|
||||
|
||||
def remove(self, channel_name: str, chat_id: str, topic_id: str | None = None) -> bool:
|
||||
"""Remove a mapping.
|
||||
|
||||
If ``topic_id`` is provided, only that specific conversation/topic mapping is removed.
|
||||
If ``topic_id`` is omitted, all mappings whose key starts with
|
||||
``"<channel_name>:<chat_id>"`` (including topic-specific ones) are removed.
|
||||
|
||||
Returns True if at least one mapping was removed.
|
||||
"""
|
||||
with self._lock:
|
||||
# Remove a specific conversation/topic mapping.
|
||||
if topic_id is not None:
|
||||
key = self._key(channel_name, chat_id, topic_id)
|
||||
if key in self._data:
|
||||
del self._data[key]
|
||||
self._save()
|
||||
return True
|
||||
return False
|
||||
|
||||
# Remove all mappings for this channel/chat_id (base and any topic-specific keys).
|
||||
prefix = self._key(channel_name, chat_id)
|
||||
keys_to_delete = [k for k in self._data if k == prefix or k.startswith(prefix + ":")]
|
||||
if not keys_to_delete:
|
||||
return False
|
||||
|
||||
for k in keys_to_delete:
|
||||
del self._data[k]
|
||||
self._save()
|
||||
return True
|
||||
|
||||
def list_entries(self, channel_name: str | None = None) -> list[dict[str, Any]]:
|
||||
"""List all stored mappings, optionally filtered by channel."""
|
||||
results = []
|
||||
for key, entry in self._data.items():
|
||||
parts = key.split(":", 2)
|
||||
ch = parts[0]
|
||||
chat = parts[1] if len(parts) > 1 else ""
|
||||
topic = parts[2] if len(parts) > 2 else None
|
||||
if channel_name and ch != channel_name:
|
||||
continue
|
||||
item: dict[str, Any] = {"channel_name": ch, "chat_id": chat, **entry}
|
||||
if topic is not None:
|
||||
item["topic_id"] = topic
|
||||
results.append(item)
|
||||
return results
|
||||
@@ -1,317 +0,0 @@
|
||||
"""Telegram channel — connects via long-polling (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TelegramChannel(Channel):
|
||||
"""Telegram bot channel using long-polling.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.telegram``):
|
||||
- ``bot_token``: Telegram Bot API token (from @BotFather).
|
||||
- ``allowed_users``: (optional) List of allowed Telegram user IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="telegram", bus=bus, config=config)
|
||||
self._application = None
|
||||
self._thread: threading.Thread | None = None
|
||||
self._tg_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._allowed_users: set[int] = set()
|
||||
for uid in config.get("allowed_users", []):
|
||||
try:
|
||||
self._allowed_users.add(int(uid))
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
# chat_id -> last sent message_id for threaded replies
|
||||
self._last_bot_message: dict[str, int] = {}
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
from telegram.ext import ApplicationBuilder, CommandHandler, MessageHandler, filters
|
||||
except ImportError:
|
||||
logger.error("python-telegram-bot is not installed. Install it with: uv add python-telegram-bot")
|
||||
return
|
||||
|
||||
bot_token = self.config.get("bot_token", "")
|
||||
if not bot_token:
|
||||
logger.error("Telegram channel requires bot_token")
|
||||
return
|
||||
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Build the application
|
||||
app = ApplicationBuilder().token(bot_token).build()
|
||||
|
||||
# Command handlers
|
||||
app.add_handler(CommandHandler("start", self._cmd_start))
|
||||
app.add_handler(CommandHandler("new", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("status", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("models", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("memory", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("help", self._cmd_generic))
|
||||
|
||||
# General message handler
|
||||
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self._on_text))
|
||||
|
||||
self._application = app
|
||||
|
||||
# Run polling in a dedicated thread with its own event loop
|
||||
self._thread = threading.Thread(target=self._run_polling, daemon=True)
|
||||
self._thread.start()
|
||||
logger.info("Telegram channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._tg_loop and self._tg_loop.is_running():
|
||||
self._tg_loop.call_soon_threadsafe(self._tg_loop.stop)
|
||||
if self._thread:
|
||||
self._thread.join(timeout=10)
|
||||
self._thread = None
|
||||
self._application = None
|
||||
logger.info("Telegram channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._application:
|
||||
return
|
||||
|
||||
try:
|
||||
chat_id = int(msg.chat_id)
|
||||
except (ValueError, TypeError):
|
||||
logger.error("Invalid Telegram chat_id: %s", msg.chat_id)
|
||||
return
|
||||
|
||||
kwargs: dict[str, Any] = {"chat_id": chat_id, "text": msg.text}
|
||||
|
||||
# Reply to the last bot message in this chat for threading
|
||||
reply_to = self._last_bot_message.get(msg.chat_id)
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
|
||||
bot = self._application.bot
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
sent = await bot.send_message(**kwargs)
|
||||
self._last_bot_message[msg.chat_id] = sent.message_id
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Telegram] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Telegram] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Telegram send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._application:
|
||||
return False
|
||||
|
||||
try:
|
||||
chat_id = int(msg.chat_id)
|
||||
except (ValueError, TypeError):
|
||||
logger.error("[Telegram] Invalid chat_id: %s", msg.chat_id)
|
||||
return False
|
||||
|
||||
# Telegram limits: 10MB for photos, 50MB for documents
|
||||
if attachment.size > 50 * 1024 * 1024:
|
||||
logger.warning("[Telegram] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
|
||||
bot = self._application.bot
|
||||
reply_to = self._last_bot_message.get(msg.chat_id)
|
||||
|
||||
try:
|
||||
if attachment.is_image and attachment.size <= 10 * 1024 * 1024:
|
||||
with open(attachment.actual_path, "rb") as f:
|
||||
kwargs: dict[str, Any] = {"chat_id": chat_id, "photo": f}
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
sent = await bot.send_photo(**kwargs)
|
||||
else:
|
||||
from telegram import InputFile
|
||||
|
||||
with open(attachment.actual_path, "rb") as f:
|
||||
input_file = InputFile(f, filename=attachment.filename)
|
||||
kwargs = {"chat_id": chat_id, "document": input_file}
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
sent = await bot.send_document(**kwargs)
|
||||
|
||||
self._last_bot_message[msg.chat_id] = sent.message_id
|
||||
logger.info("[Telegram] file sent: %s to chat=%s", attachment.filename, msg.chat_id)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Telegram] failed to send file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
# -- helpers -----------------------------------------------------------
|
||||
|
||||
async def _send_running_reply(self, chat_id: str, reply_to_message_id: int) -> None:
|
||||
"""Send a 'Working on it...' reply to the user's message."""
|
||||
if not self._application:
|
||||
return
|
||||
try:
|
||||
bot = self._application.bot
|
||||
await bot.send_message(
|
||||
chat_id=int(chat_id),
|
||||
text="Working on it...",
|
||||
reply_to_message_id=reply_to_message_id,
|
||||
)
|
||||
logger.info("[Telegram] 'Working on it...' reply sent in chat=%s", chat_id)
|
||||
except Exception:
|
||||
logger.exception("[Telegram] failed to send running reply in chat=%s", chat_id)
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
@staticmethod
|
||||
def _log_future_error(fut, name: str, msg_id: str):
|
||||
try:
|
||||
exc = fut.exception()
|
||||
if exc:
|
||||
logger.error("[Telegram] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except Exception:
|
||||
logger.exception("[Telegram] Failed to inspect future for %s (msg_id=%s)", name, msg_id)
|
||||
|
||||
def _run_polling(self) -> None:
|
||||
"""Run telegram polling in a dedicated thread."""
|
||||
self._tg_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self._tg_loop)
|
||||
try:
|
||||
# Cannot use run_polling() because it calls add_signal_handler(),
|
||||
# which only works in the main thread. Instead, manually
|
||||
# initialize the application and start the updater.
|
||||
self._tg_loop.run_until_complete(self._application.initialize())
|
||||
self._tg_loop.run_until_complete(self._application.start())
|
||||
self._tg_loop.run_until_complete(self._application.updater.start_polling())
|
||||
self._tg_loop.run_forever()
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Telegram polling error")
|
||||
finally:
|
||||
# Graceful shutdown
|
||||
try:
|
||||
if self._application.updater.running:
|
||||
self._tg_loop.run_until_complete(self._application.updater.stop())
|
||||
self._tg_loop.run_until_complete(self._application.stop())
|
||||
self._tg_loop.run_until_complete(self._application.shutdown())
|
||||
except Exception:
|
||||
logger.exception("Error during Telegram shutdown")
|
||||
|
||||
def _check_user(self, user_id: int) -> bool:
|
||||
if not self._allowed_users:
|
||||
return True
|
||||
return user_id in self._allowed_users
|
||||
|
||||
async def _cmd_start(self, update, context) -> None:
|
||||
"""Handle /start command."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
await update.message.reply_text("Welcome to DeerFlow! Send me a message to start a conversation.\nType /help for available commands.")
|
||||
|
||||
async def _process_incoming_with_reply(self, chat_id: str, msg_id: int, inbound: InboundMessage) -> None:
|
||||
await self._send_running_reply(chat_id, msg_id)
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
async def _cmd_generic(self, update, context) -> None:
|
||||
"""Forward slash commands to the channel manager."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
|
||||
text = update.message.text
|
||||
chat_id = str(update.effective_chat.id)
|
||||
user_id = str(update.effective_user.id)
|
||||
msg_id = str(update.message.message_id)
|
||||
|
||||
# Use the same topic_id logic as _on_text so that commands
|
||||
# like /new target the correct thread mapping.
|
||||
if update.effective_chat.type == "private":
|
||||
topic_id = None
|
||||
else:
|
||||
reply_to = update.message.reply_to_message
|
||||
if reply_to:
|
||||
topic_id = str(reply_to.message_id)
|
||||
else:
|
||||
topic_id = msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=InboundMessageType.COMMAND,
|
||||
thread_ts=msg_id,
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
fut = asyncio.run_coroutine_threadsafe(self._process_incoming_with_reply(chat_id, update.message.message_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f: self._log_future_error(f, "process_incoming_with_reply", update.message.message_id))
|
||||
else:
|
||||
logger.warning("[Telegram] Main loop not running. Cannot publish inbound message.")
|
||||
|
||||
async def _on_text(self, update, context) -> None:
|
||||
"""Handle regular text messages."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
|
||||
text = update.message.text.strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
chat_id = str(update.effective_chat.id)
|
||||
user_id = str(update.effective_user.id)
|
||||
msg_id = str(update.message.message_id)
|
||||
|
||||
# topic_id determines which DeerFlow thread the message maps to.
|
||||
# In private chats, use None so that all messages share a single
|
||||
# thread (the store key becomes "channel:chat_id").
|
||||
# In group chats, use the reply-to message id or the current
|
||||
# message id to keep separate conversation threads.
|
||||
if update.effective_chat.type == "private":
|
||||
topic_id = None
|
||||
else:
|
||||
reply_to = update.message.reply_to_message
|
||||
if reply_to:
|
||||
topic_id = str(reply_to.message_id)
|
||||
else:
|
||||
topic_id = msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=InboundMessageType.CHAT,
|
||||
thread_ts=msg_id,
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
fut = asyncio.run_coroutine_threadsafe(self._process_incoming_with_reply(chat_id, update.message.message_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f: self._log_future_error(f, "process_incoming_with_reply", update.message.message_id))
|
||||
else:
|
||||
logger.warning("[Telegram] Main loop not running. Cannot publish inbound message.")
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,394 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import hashlib
|
||||
import logging
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import Any, cast
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import (
|
||||
InboundMessageType,
|
||||
MessageBus,
|
||||
OutboundMessage,
|
||||
ResolvedAttachment,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class WeComChannel(Channel):
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="wecom", bus=bus, config=config)
|
||||
self._bot_id: str | None = None
|
||||
self._bot_secret: str | None = None
|
||||
self._ws_client = None
|
||||
self._ws_task: asyncio.Task | None = None
|
||||
self._ws_frames: dict[str, dict[str, Any]] = {}
|
||||
self._ws_stream_ids: dict[str, str] = {}
|
||||
self._working_message = "Working on it..."
|
||||
|
||||
def _clear_ws_context(self, thread_ts: str | None) -> None:
|
||||
if not thread_ts:
|
||||
return
|
||||
self._ws_frames.pop(thread_ts, None)
|
||||
self._ws_stream_ids.pop(thread_ts, None)
|
||||
|
||||
async def _send_ws_upload_command(self, req_id: str, body: dict[str, Any], cmd: str) -> dict[str, Any]:
|
||||
if not self._ws_client:
|
||||
raise RuntimeError("WeCom WebSocket client is not available")
|
||||
|
||||
ws_manager = getattr(self._ws_client, "_ws_manager", None)
|
||||
send_reply = getattr(ws_manager, "send_reply", None)
|
||||
if not callable(send_reply):
|
||||
raise RuntimeError("Installed wecom-aibot-python-sdk does not expose the WebSocket media upload API expected by DeerFlow. Use wecom-aibot-python-sdk==0.1.6 or update the adapter.")
|
||||
|
||||
send_reply_async = cast(Callable[[str, dict[str, Any], str], Awaitable[dict[str, Any]]], send_reply)
|
||||
return await send_reply_async(req_id, body, cmd)
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
bot_id = self.config.get("bot_id")
|
||||
bot_secret = self.config.get("bot_secret")
|
||||
working_message = self.config.get("working_message")
|
||||
|
||||
self._bot_id = bot_id if isinstance(bot_id, str) and bot_id else None
|
||||
self._bot_secret = bot_secret if isinstance(bot_secret, str) and bot_secret else None
|
||||
self._working_message = working_message if isinstance(working_message, str) and working_message else "Working on it..."
|
||||
|
||||
if not self._bot_id or not self._bot_secret:
|
||||
logger.error("WeCom channel requires bot_id and bot_secret")
|
||||
return
|
||||
|
||||
try:
|
||||
from aibot import WSClient, WSClientOptions
|
||||
except ImportError:
|
||||
logger.error("wecom-aibot-python-sdk is not installed. Install it with: uv add wecom-aibot-python-sdk")
|
||||
return
|
||||
else:
|
||||
self._ws_client = WSClient(WSClientOptions(bot_id=self._bot_id, secret=self._bot_secret, logger=logger))
|
||||
self._ws_client.on("message.text", self._on_ws_text)
|
||||
self._ws_client.on("message.mixed", self._on_ws_mixed)
|
||||
self._ws_client.on("message.image", self._on_ws_image)
|
||||
self._ws_client.on("message.file", self._on_ws_file)
|
||||
self._ws_task = asyncio.create_task(self._ws_client.connect())
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
logger.info("WeCom channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._ws_task:
|
||||
try:
|
||||
self._ws_task.cancel()
|
||||
except Exception:
|
||||
pass
|
||||
self._ws_task = None
|
||||
if self._ws_client:
|
||||
try:
|
||||
self._ws_client.disconnect()
|
||||
except Exception:
|
||||
pass
|
||||
self._ws_client = None
|
||||
self._ws_frames.clear()
|
||||
self._ws_stream_ids.clear()
|
||||
logger.info("WeCom channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if self._ws_client:
|
||||
await self._send_ws(msg, _max_retries=_max_retries)
|
||||
return
|
||||
logger.warning("[WeCom] send called but WebSocket client is not available")
|
||||
|
||||
async def _on_outbound(self, msg: OutboundMessage) -> None:
|
||||
if msg.channel_name != self.name:
|
||||
return
|
||||
|
||||
try:
|
||||
await self.send(msg)
|
||||
except Exception:
|
||||
logger.exception("Failed to send outbound message on channel %s", self.name)
|
||||
if msg.is_final:
|
||||
self._clear_ws_context(msg.thread_ts)
|
||||
return
|
||||
|
||||
for attachment in msg.attachments:
|
||||
try:
|
||||
success = await self.send_file(msg, attachment)
|
||||
if not success:
|
||||
logger.warning("[%s] file upload skipped for %s", self.name, attachment.filename)
|
||||
except Exception:
|
||||
logger.exception("[%s] failed to upload file %s", self.name, attachment.filename)
|
||||
|
||||
if msg.is_final:
|
||||
self._clear_ws_context(msg.thread_ts)
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not msg.is_final:
|
||||
return True
|
||||
if not self._ws_client:
|
||||
return False
|
||||
if not msg.thread_ts:
|
||||
return False
|
||||
frame = self._ws_frames.get(msg.thread_ts)
|
||||
if not frame:
|
||||
return False
|
||||
|
||||
media_type = "image" if attachment.is_image else "file"
|
||||
size_limit = 2 * 1024 * 1024 if attachment.is_image else 20 * 1024 * 1024
|
||||
if attachment.size > size_limit:
|
||||
logger.warning(
|
||||
"[WeCom] %s too large (%d bytes), skipping: %s",
|
||||
media_type,
|
||||
attachment.size,
|
||||
attachment.filename,
|
||||
)
|
||||
return False
|
||||
|
||||
try:
|
||||
media_id = await self._upload_media_ws(
|
||||
media_type=media_type,
|
||||
filename=attachment.filename,
|
||||
path=str(attachment.actual_path),
|
||||
size=attachment.size,
|
||||
)
|
||||
if not media_id:
|
||||
return False
|
||||
|
||||
body = {media_type: {"media_id": media_id}, "msgtype": media_type}
|
||||
await self._ws_client.reply(frame, body)
|
||||
logger.debug("[WeCom] %s sent via ws: %s", media_type, attachment.filename)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[WeCom] failed to upload/send file via ws: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _on_ws_text(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
text = ((body.get("text") or {}).get("content") or "").strip()
|
||||
quote = body.get("quote", {}).get("text", {}).get("content", "").strip()
|
||||
if not text and not quote:
|
||||
return
|
||||
await self._publish_ws_inbound(frame, text + (f"\nQuote message: {quote}" if quote else ""))
|
||||
|
||||
async def _on_ws_mixed(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
mixed = body.get("mixed") or {}
|
||||
items = mixed.get("msg_item") or []
|
||||
parts: list[str] = []
|
||||
files: list[dict[str, Any]] = []
|
||||
for item in items:
|
||||
item_type = (item or {}).get("msgtype")
|
||||
if item_type == "text":
|
||||
content = (((item or {}).get("text") or {}).get("content") or "").strip()
|
||||
if content:
|
||||
parts.append(content)
|
||||
elif item_type in ("image", "file"):
|
||||
payload = (item or {}).get(item_type) or {}
|
||||
url = payload.get("url")
|
||||
aeskey = payload.get("aeskey")
|
||||
if isinstance(url, str) and url:
|
||||
files.append(
|
||||
{
|
||||
"type": item_type,
|
||||
"url": url,
|
||||
"aeskey": (aeskey if isinstance(aeskey, str) and aeskey else None),
|
||||
}
|
||||
)
|
||||
text = "\n\n".join(parts).strip()
|
||||
if not text and not files:
|
||||
return
|
||||
if not text:
|
||||
text = "（receive image/file）"
|
||||
await self._publish_ws_inbound(frame, text, files=files)
|
||||
|
||||
async def _on_ws_image(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
image = body.get("image") or {}
|
||||
url = image.get("url")
|
||||
aeskey = image.get("aeskey")
|
||||
if not isinstance(url, str) or not url:
|
||||
return
|
||||
await self._publish_ws_inbound(
|
||||
frame,
|
||||
"（receive image ）",
|
||||
files=[
|
||||
{
|
||||
"type": "image",
|
||||
"url": url,
|
||||
"aeskey": aeskey if isinstance(aeskey, str) and aeskey else None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
async def _on_ws_file(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
file_obj = body.get("file") or {}
|
||||
url = file_obj.get("url")
|
||||
aeskey = file_obj.get("aeskey")
|
||||
if not isinstance(url, str) or not url:
|
||||
return
|
||||
await self._publish_ws_inbound(
|
||||
frame,
|
||||
"（receive file）",
|
||||
files=[
|
||||
{
|
||||
"type": "file",
|
||||
"url": url,
|
||||
"aeskey": aeskey if isinstance(aeskey, str) and aeskey else None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
async def _publish_ws_inbound(
|
||||
self,
|
||||
frame: dict[str, Any],
|
||||
text: str,
|
||||
*,
|
||||
files: list[dict[str, Any]] | None = None,
|
||||
) -> None:
|
||||
if not self._ws_client:
|
||||
return
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
return
|
||||
|
||||
body = frame.get("body", {}) or {}
|
||||
msg_id = body.get("msgid")
|
||||
if not msg_id:
|
||||
return
|
||||
|
||||
user_id = (body.get("from") or {}).get("userid")
|
||||
|
||||
inbound_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
|
||||
inbound = self._make_inbound(
|
||||
chat_id=user_id, # keep user's conversation in memory
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=inbound_type,
|
||||
thread_ts=msg_id,
|
||||
files=files or [],
|
||||
metadata={"aibotid": body.get("aibotid"), "chattype": body.get("chattype")},
|
||||
)
|
||||
inbound.topic_id = user_id # keep the same thread
|
||||
|
||||
stream_id = generate_req_id("stream")
|
||||
self._ws_frames[msg_id] = frame
|
||||
self._ws_stream_ids[msg_id] = stream_id
|
||||
|
||||
try:
|
||||
await self._ws_client.reply_stream(frame, stream_id, self._working_message, False)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
async def _send_ws(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._ws_client:
|
||||
return
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
generate_req_id = None
|
||||
|
||||
if msg.thread_ts and msg.thread_ts in self._ws_frames:
|
||||
frame = self._ws_frames[msg.thread_ts]
|
||||
stream_id = self._ws_stream_ids.get(msg.thread_ts)
|
||||
if not stream_id and generate_req_id:
|
||||
stream_id = generate_req_id("stream")
|
||||
self._ws_stream_ids[msg.thread_ts] = stream_id
|
||||
if not stream_id:
|
||||
return
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._ws_client.reply_stream(frame, stream_id, msg.text, bool(msg.is_final))
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
await asyncio.sleep(2**attempt)
|
||||
if last_exc:
|
||||
raise last_exc
|
||||
|
||||
body = {"msgtype": "markdown", "markdown": {"content": msg.text}}
|
||||
last_exc = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._ws_client.send_message(msg.chat_id, body)
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
await asyncio.sleep(2**attempt)
|
||||
if last_exc:
|
||||
raise last_exc
|
||||
|
||||
async def _upload_media_ws(
|
||||
self,
|
||||
*,
|
||||
media_type: str,
|
||||
filename: str,
|
||||
path: str,
|
||||
size: int,
|
||||
) -> str | None:
|
||||
if not self._ws_client:
|
||||
return None
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
chunk_size = 512 * 1024
|
||||
total_chunks = (size + chunk_size - 1) // chunk_size
|
||||
if total_chunks < 1 or total_chunks > 100:
|
||||
logger.warning("[WeCom] invalid total_chunks=%d for %s", total_chunks, filename)
|
||||
return None
|
||||
|
||||
md5_hasher = hashlib.md5()
|
||||
with open(path, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(1024 * 1024), b""):
|
||||
md5_hasher.update(chunk)
|
||||
md5 = md5_hasher.hexdigest()
|
||||
|
||||
init_req_id = generate_req_id("aibot_upload_media_init")
|
||||
init_body = {
|
||||
"type": media_type,
|
||||
"filename": filename,
|
||||
"total_size": int(size),
|
||||
"total_chunks": int(total_chunks),
|
||||
"md5": md5,
|
||||
}
|
||||
init_ack = await self._send_ws_upload_command(init_req_id, init_body, "aibot_upload_media_init")
|
||||
upload_id = (init_ack.get("body") or {}).get("upload_id")
|
||||
if not upload_id:
|
||||
logger.warning("[WeCom] upload init returned no upload_id: %s", init_ack)
|
||||
return None
|
||||
|
||||
with open(path, "rb") as f:
|
||||
for idx in range(total_chunks):
|
||||
data = f.read(chunk_size)
|
||||
if not data:
|
||||
break
|
||||
chunk_req_id = generate_req_id("aibot_upload_media_chunk")
|
||||
chunk_body = {
|
||||
"upload_id": upload_id,
|
||||
"chunk_index": int(idx),
|
||||
"base64_data": base64.b64encode(data).decode("utf-8"),
|
||||
}
|
||||
await self._send_ws_upload_command(chunk_req_id, chunk_body, "aibot_upload_media_chunk")
|
||||
|
||||
finish_req_id = generate_req_id("aibot_upload_media_finish")
|
||||
finish_ack = await self._send_ws_upload_command(finish_req_id, {"upload_id": upload_id}, "aibot_upload_media_finish")
|
||||
media_id = (finish_ack.get("body") or {}).get("media_id")
|
||||
if not media_id:
|
||||
logger.warning("[WeCom] upload finish returned no media_id: %s", finish_ack)
|
||||
return None
|
||||
return media_id
|
||||
@@ -1,4 +0,0 @@
|
||||
from .app import app, create_app
|
||||
from .config import GatewayConfig, get_gateway_config
|
||||
|
||||
__all__ = ["app", "create_app", "GatewayConfig", "get_gateway_config"]
|
||||
@@ -1,221 +0,0 @@
|
||||
import logging
|
||||
from collections.abc import AsyncGenerator
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI
|
||||
|
||||
from app.gateway.config import get_gateway_config
|
||||
from app.gateway.deps import langgraph_runtime
|
||||
from app.gateway.routers import (
|
||||
agents,
|
||||
artifacts,
|
||||
assistants_compat,
|
||||
channels,
|
||||
mcp,
|
||||
memory,
|
||||
models,
|
||||
runs,
|
||||
skills,
|
||||
suggestions,
|
||||
thread_runs,
|
||||
threads,
|
||||
uploads,
|
||||
)
|
||||
from deerflow.config.app_config import get_app_config
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
"""Application lifespan handler."""
|
||||
|
||||
# Load config and check necessary environment variables at startup
|
||||
try:
|
||||
get_app_config()
|
||||
logger.info("Configuration loaded successfully")
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to load configuration during gateway startup: {e}"
|
||||
logger.exception(error_msg)
|
||||
raise RuntimeError(error_msg) from e
|
||||
config = get_gateway_config()
|
||||
logger.info(f"Starting API Gateway on {config.host}:{config.port}")
|
||||
|
||||
# Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
|
||||
async with langgraph_runtime(app):
|
||||
logger.info("LangGraph runtime initialised")
|
||||
|
||||
# Start IM channel service if any channels are configured
|
||||
try:
|
||||
from app.channels.service import start_channel_service
|
||||
|
||||
channel_service = await start_channel_service()
|
||||
logger.info("Channel service started: %s", channel_service.get_status())
|
||||
except Exception:
|
||||
logger.exception("No IM channels configured or channel service failed to start")
|
||||
|
||||
yield
|
||||
|
||||
# Stop channel service on shutdown
|
||||
try:
|
||||
from app.channels.service import stop_channel_service
|
||||
|
||||
await stop_channel_service()
|
||||
except Exception:
|
||||
logger.exception("Failed to stop channel service")
|
||||
|
||||
logger.info("Shutting down API Gateway")
|
||||
|
||||
|
||||
def create_app() -> FastAPI:
|
||||
"""Create and configure the FastAPI application.
|
||||
|
||||
Returns:
|
||||
Configured FastAPI application instance.
|
||||
"""
|
||||
|
||||
app = FastAPI(
|
||||
title="DeerFlow API Gateway",
|
||||
description="""
|
||||
## DeerFlow API Gateway
|
||||
|
||||
API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execution capabilities.
|
||||
|
||||
### Features
|
||||
|
||||
- **Models Management**: Query and retrieve available AI models
|
||||
- **MCP Configuration**: Manage Model Context Protocol (MCP) server configurations
|
||||
- **Memory Management**: Access and manage global memory data for personalized conversations
|
||||
- **Skills Management**: Query and manage skills and their enabled status
|
||||
- **Artifacts**: Access thread artifacts and generated files
|
||||
- **Health Monitoring**: System health check endpoints
|
||||
|
||||
### Architecture
|
||||
|
||||
LangGraph requests are handled by nginx reverse proxy.
|
||||
This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
|
||||
""",
|
||||
version="0.1.0",
|
||||
lifespan=lifespan,
|
||||
docs_url="/docs",
|
||||
redoc_url="/redoc",
|
||||
openapi_url="/openapi.json",
|
||||
openapi_tags=[
|
||||
{
|
||||
"name": "models",
|
||||
"description": "Operations for querying available AI models and their configurations",
|
||||
},
|
||||
{
|
||||
"name": "mcp",
|
||||
"description": "Manage Model Context Protocol (MCP) server configurations",
|
||||
},
|
||||
{
|
||||
"name": "memory",
|
||||
"description": "Access and manage global memory data for personalized conversations",
|
||||
},
|
||||
{
|
||||
"name": "skills",
|
||||
"description": "Manage skills and their configurations",
|
||||
},
|
||||
{
|
||||
"name": "artifacts",
|
||||
"description": "Access and download thread artifacts and generated files",
|
||||
},
|
||||
{
|
||||
"name": "uploads",
|
||||
"description": "Upload and manage user files for threads",
|
||||
},
|
||||
{
|
||||
"name": "threads",
|
||||
"description": "Manage DeerFlow thread-local filesystem data",
|
||||
},
|
||||
{
|
||||
"name": "agents",
|
||||
"description": "Create and manage custom agents with per-agent config and prompts",
|
||||
},
|
||||
{
|
||||
"name": "suggestions",
|
||||
"description": "Generate follow-up question suggestions for conversations",
|
||||
},
|
||||
{
|
||||
"name": "channels",
|
||||
"description": "Manage IM channel integrations (Feishu, Slack, Telegram)",
|
||||
},
|
||||
{
|
||||
"name": "assistants-compat",
|
||||
"description": "LangGraph Platform-compatible assistants API (stub)",
|
||||
},
|
||||
{
|
||||
"name": "runs",
|
||||
"description": "LangGraph Platform-compatible runs lifecycle (create, stream, cancel)",
|
||||
},
|
||||
{
|
||||
"name": "health",
|
||||
"description": "Health check and system status endpoints",
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
# CORS is handled by nginx - no need for FastAPI middleware
|
||||
|
||||
# Include routers
|
||||
# Models API is mounted at /api/models
|
||||
app.include_router(models.router)
|
||||
|
||||
# MCP API is mounted at /api/mcp
|
||||
app.include_router(mcp.router)
|
||||
|
||||
# Memory API is mounted at /api/memory
|
||||
app.include_router(memory.router)
|
||||
|
||||
# Skills API is mounted at /api/skills
|
||||
app.include_router(skills.router)
|
||||
|
||||
# Artifacts API is mounted at /api/threads/{thread_id}/artifacts
|
||||
app.include_router(artifacts.router)
|
||||
|
||||
# Uploads API is mounted at /api/threads/{thread_id}/uploads
|
||||
app.include_router(uploads.router)
|
||||
|
||||
# Thread cleanup API is mounted at /api/threads/{thread_id}
|
||||
app.include_router(threads.router)
|
||||
|
||||
# Agents API is mounted at /api/agents
|
||||
app.include_router(agents.router)
|
||||
|
||||
# Suggestions API is mounted at /api/threads/{thread_id}/suggestions
|
||||
app.include_router(suggestions.router)
|
||||
|
||||
# Channels API is mounted at /api/channels
|
||||
app.include_router(channels.router)
|
||||
|
||||
# Assistants compatibility API (LangGraph Platform stub)
|
||||
app.include_router(assistants_compat.router)
|
||||
|
||||
# Thread Runs API (LangGraph Platform-compatible runs lifecycle)
|
||||
app.include_router(thread_runs.router)
|
||||
|
||||
# Stateless Runs API (stream/wait without a pre-existing thread)
|
||||
app.include_router(runs.router)
|
||||
|
||||
@app.get("/health", tags=["health"])
|
||||
async def health_check() -> dict:
|
||||
"""Health check endpoint.
|
||||
|
||||
Returns:
|
||||
Service health status information.
|
||||
"""
|
||||
return {"status": "healthy", "service": "deer-flow-gateway"}
|
||||
|
||||
return app
|
||||
|
||||
|
||||
# Create app instance for uvicorn
|
||||
app = create_app()
|
||||
@@ -1,27 +0,0 @@
|
||||
import os
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class GatewayConfig(BaseModel):
|
||||
"""Configuration for the API Gateway."""
|
||||
|
||||
host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
|
||||
port: int = Field(default=8001, description="Port to bind the gateway server")
|
||||
cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
|
||||
|
||||
|
||||
_gateway_config: GatewayConfig | None = None
|
||||
|
||||
|
||||
def get_gateway_config() -> GatewayConfig:
|
||||
"""Get gateway config, loading from environment if available."""
|
||||
global _gateway_config
|
||||
if _gateway_config is None:
|
||||
cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
|
||||
_gateway_config = GatewayConfig(
|
||||
host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
|
||||
port=int(os.getenv("GATEWAY_PORT", "8001")),
|
||||
cors_origins=cors_origins_str.split(","),
|
||||
)
|
||||
return _gateway_config
|
||||
@@ -1,70 +0,0 @@
|
||||
"""Centralized accessors for singleton objects stored on ``app.state``.
|
||||
|
||||
**Getters** (used by routers): raise 503 when a required dependency is
|
||||
missing, except ``get_store`` which returns ``None``.
|
||||
|
||||
Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import AsyncGenerator
|
||||
from contextlib import AsyncExitStack, asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Request
|
||||
|
||||
from deerflow.runtime import RunManager, StreamBridge
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
"""Bootstrap and tear down all LangGraph runtime singletons.
|
||||
|
||||
Usage in ``app.py``::
|
||||
|
||||
async with langgraph_runtime(app):
|
||||
yield
|
||||
"""
|
||||
from deerflow.agents.checkpointer.async_provider import make_checkpointer
|
||||
from deerflow.runtime import make_store, make_stream_bridge
|
||||
|
||||
async with AsyncExitStack() as stack:
|
||||
app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
|
||||
app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
|
||||
app.state.store = await stack.enter_async_context(make_store())
|
||||
app.state.run_manager = RunManager()
|
||||
yield
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Getters – called by routers per-request
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def get_stream_bridge(request: Request) -> StreamBridge:
|
||||
"""Return the global :class:`StreamBridge`, or 503."""
|
||||
bridge = getattr(request.app.state, "stream_bridge", None)
|
||||
if bridge is None:
|
||||
raise HTTPException(status_code=503, detail="Stream bridge not available")
|
||||
return bridge
|
||||
|
||||
|
||||
def get_run_manager(request: Request) -> RunManager:
|
||||
"""Return the global :class:`RunManager`, or 503."""
|
||||
mgr = getattr(request.app.state, "run_manager", None)
|
||||
if mgr is None:
|
||||
raise HTTPException(status_code=503, detail="Run manager not available")
|
||||
return mgr
|
||||
|
||||
|
||||
def get_checkpointer(request: Request):
|
||||
"""Return the global checkpointer, or 503."""
|
||||
cp = getattr(request.app.state, "checkpointer", None)
|
||||
if cp is None:
|
||||
raise HTTPException(status_code=503, detail="Checkpointer not available")
|
||||
return cp
|
||||
|
||||
|
||||
def get_store(request: Request):
|
||||
"""Return the global store (may be ``None`` if not configured)."""
|
||||
return getattr(request.app.state, "store", None)
|
||||
@@ -1,28 +0,0 @@
|
||||
"""Shared path resolution for thread virtual paths (e.g. mnt/user-data/outputs/...)."""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import HTTPException
|
||||
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
|
||||
def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
|
||||
"""Resolve a virtual path to the actual filesystem path under thread user-data.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
virtual_path: The virtual path as seen inside the sandbox
|
||||
(e.g., /mnt/user-data/outputs/file.txt).
|
||||
|
||||
Returns:
|
||||
The resolved filesystem path.
|
||||
|
||||
Raises:
|
||||
HTTPException: If the path is invalid or outside allowed directories.
|
||||
"""
|
||||
try:
|
||||
return get_paths().resolve_virtual_path(thread_id, virtual_path)
|
||||
except ValueError as e:
|
||||
status = 403 if "traversal" in str(e) else 400
|
||||
raise HTTPException(status_code=status, detail=str(e))
|
||||
@@ -1,3 +0,0 @@
|
||||
from . import artifacts, assistants_compat, mcp, models, skills, suggestions, thread_runs, threads, uploads
|
||||
|
||||
__all__ = ["artifacts", "assistants_compat", "mcp", "models", "skills", "suggestions", "threads", "thread_runs", "uploads"]
|
||||
@@ -1,404 +0,0 @@
|
||||
"""CRUD API for custom agents."""
|
||||
|
||||
import logging
|
||||
import re
|
||||
import shutil
|
||||
|
||||
import yaml
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config.agents_api_config import get_agents_api_config
|
||||
from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api", tags=["agents"])
|
||||
|
||||
AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
|
||||
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
"""Response model for a custom agent."""
|
||||
|
||||
name: str = Field(..., description="Agent name (hyphen-case)")
|
||||
description: str = Field(default="", description="Agent description")
|
||||
model: str | None = Field(default=None, description="Optional model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
|
||||
soul: str | None = Field(default=None, description="SOUL.md content")
|
||||
|
||||
|
||||
class AgentsListResponse(BaseModel):
|
||||
"""Response model for listing all custom agents."""
|
||||
|
||||
agents: list[AgentResponse]
|
||||
|
||||
|
||||
class AgentCreateRequest(BaseModel):
|
||||
"""Request body for creating a custom agent."""
|
||||
|
||||
name: str = Field(..., description="Agent name (must match ^[A-Za-z0-9-]+$, stored as lowercase)")
|
||||
description: str = Field(default="", description="Agent description")
|
||||
model: str | None = Field(default=None, description="Optional model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
|
||||
soul: str = Field(default="", description="SOUL.md content — agent personality and behavioral guardrails")
|
||||
|
||||
|
||||
class AgentUpdateRequest(BaseModel):
|
||||
"""Request body for updating a custom agent."""
|
||||
|
||||
description: str | None = Field(default=None, description="Updated description")
|
||||
model: str | None = Field(default=None, description="Updated model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Updated tool group whitelist")
|
||||
soul: str | None = Field(default=None, description="Updated SOUL.md content")
|
||||
|
||||
|
||||
def _validate_agent_name(name: str) -> None:
|
||||
"""Validate agent name against allowed pattern.
|
||||
|
||||
Args:
|
||||
name: The agent name to validate.
|
||||
|
||||
Raises:
|
||||
HTTPException: 422 if the name is invalid.
|
||||
"""
|
||||
if not AGENT_NAME_PATTERN.match(name):
|
||||
raise HTTPException(
|
||||
status_code=422,
|
||||
detail=f"Invalid agent name '{name}'. Must match ^[A-Za-z0-9-]+$ (letters, digits, and hyphens only).",
|
||||
)
|
||||
|
||||
|
||||
def _normalize_agent_name(name: str) -> str:
|
||||
"""Normalize agent name to lowercase for filesystem storage."""
|
||||
return name.lower()
|
||||
|
||||
|
||||
def _require_agents_api_enabled() -> None:
|
||||
"""Reject access unless the custom-agent management API is explicitly enabled."""
|
||||
if not get_agents_api_config().enabled:
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail=("Custom-agent management API is disabled. Set agents_api.enabled=true to expose agent and user-profile routes over HTTP."),
|
||||
)
|
||||
|
||||
|
||||
def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
|
||||
"""Convert AgentConfig to AgentResponse."""
|
||||
soul: str | None = None
|
||||
if include_soul:
|
||||
soul = load_agent_soul(agent_cfg.name) or ""
|
||||
|
||||
return AgentResponse(
|
||||
name=agent_cfg.name,
|
||||
description=agent_cfg.description,
|
||||
model=agent_cfg.model,
|
||||
tool_groups=agent_cfg.tool_groups,
|
||||
soul=soul,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents",
|
||||
response_model=AgentsListResponse,
|
||||
summary="List Custom Agents",
|
||||
description="List all custom agents available in the agents directory, including their soul content.",
|
||||
)
|
||||
async def list_agents() -> AgentsListResponse:
|
||||
"""List all custom agents.
|
||||
|
||||
Returns:
|
||||
List of all custom agents with their metadata and soul content.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
|
||||
try:
|
||||
agents = list_custom_agents()
|
||||
return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to list agents: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list agents: {str(e)}")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents/check",
|
||||
summary="Check Agent Name",
|
||||
description="Validate an agent name and check if it is available (case-insensitive).",
|
||||
)
|
||||
async def check_agent_name(name: str) -> dict:
|
||||
"""Check whether an agent name is valid and not yet taken.
|
||||
|
||||
Args:
|
||||
name: The agent name to check.
|
||||
|
||||
Returns:
|
||||
``{"available": true/false, "name": "<normalized>"}``
|
||||
|
||||
Raises:
|
||||
HTTPException: 422 if the name is invalid.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
_validate_agent_name(name)
|
||||
normalized = _normalize_agent_name(name)
|
||||
available = not get_paths().agent_dir(normalized).exists()
|
||||
return {"available": available, "name": normalized}
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents/{name}",
|
||||
response_model=AgentResponse,
|
||||
summary="Get Custom Agent",
|
||||
description="Retrieve details and SOUL.md content for a specific custom agent.",
|
||||
)
|
||||
async def get_agent(name: str) -> AgentResponse:
|
||||
"""Get a specific custom agent by name.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
|
||||
Returns:
|
||||
Agent details including SOUL.md content.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
try:
|
||||
agent_cfg = load_agent_config(name)
|
||||
return _agent_config_to_response(agent_cfg, include_soul=True)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get agent: {str(e)}")
|
||||
|
||||
|
||||
@router.post(
|
||||
"/agents",
|
||||
response_model=AgentResponse,
|
||||
status_code=201,
|
||||
summary="Create Custom Agent",
|
||||
description="Create a new custom agent with its config and SOUL.md.",
|
||||
)
|
||||
async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
|
||||
"""Create a new custom agent.
|
||||
|
||||
Args:
|
||||
request: The agent creation request.
|
||||
|
||||
Returns:
|
||||
The created agent details.
|
||||
|
||||
Raises:
|
||||
HTTPException: 409 if agent already exists, 422 if name is invalid.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
_validate_agent_name(request.name)
|
||||
normalized_name = _normalize_agent_name(request.name)
|
||||
|
||||
agent_dir = get_paths().agent_dir(normalized_name)
|
||||
|
||||
if agent_dir.exists():
|
||||
raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
|
||||
|
||||
try:
|
||||
agent_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Write config.yaml
|
||||
config_data: dict = {"name": normalized_name}
|
||||
if request.description:
|
||||
config_data["description"] = request.description
|
||||
if request.model is not None:
|
||||
config_data["model"] = request.model
|
||||
if request.tool_groups is not None:
|
||||
config_data["tool_groups"] = request.tool_groups
|
||||
|
||||
config_file = agent_dir / "config.yaml"
|
||||
with open(config_file, "w", encoding="utf-8") as f:
|
||||
yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
|
||||
|
||||
# Write SOUL.md
|
||||
soul_file = agent_dir / "SOUL.md"
|
||||
soul_file.write_text(request.soul, encoding="utf-8")
|
||||
|
||||
logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
|
||||
|
||||
agent_cfg = load_agent_config(normalized_name)
|
||||
return _agent_config_to_response(agent_cfg, include_soul=True)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
# Clean up on failure
|
||||
if agent_dir.exists():
|
||||
shutil.rmtree(agent_dir)
|
||||
logger.error(f"Failed to create agent '{request.name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to create agent: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/agents/{name}",
|
||||
response_model=AgentResponse,
|
||||
summary="Update Custom Agent",
|
||||
description="Update an existing custom agent's config and/or SOUL.md.",
|
||||
)
|
||||
async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
|
||||
"""Update an existing custom agent.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
request: The update request (all fields optional).
|
||||
|
||||
Returns:
|
||||
The updated agent details.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
try:
|
||||
agent_cfg = load_agent_config(name)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
|
||||
agent_dir = get_paths().agent_dir(name)
|
||||
|
||||
try:
|
||||
# Update config if any config fields changed
|
||||
config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])
|
||||
|
||||
if config_changed:
|
||||
updated: dict = {
|
||||
"name": agent_cfg.name,
|
||||
"description": request.description if request.description is not None else agent_cfg.description,
|
||||
}
|
||||
new_model = request.model if request.model is not None else agent_cfg.model
|
||||
if new_model is not None:
|
||||
updated["model"] = new_model
|
||||
|
||||
new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
|
||||
if new_tool_groups is not None:
|
||||
updated["tool_groups"] = new_tool_groups
|
||||
|
||||
config_file = agent_dir / "config.yaml"
|
||||
with open(config_file, "w", encoding="utf-8") as f:
|
||||
yaml.dump(updated, f, default_flow_style=False, allow_unicode=True)
|
||||
|
||||
# Update SOUL.md if provided
|
||||
if request.soul is not None:
|
||||
soul_path = agent_dir / "SOUL.md"
|
||||
soul_path.write_text(request.soul, encoding="utf-8")
|
||||
|
||||
logger.info(f"Updated agent '{name}'")
|
||||
|
||||
refreshed_cfg = load_agent_config(name)
|
||||
return _agent_config_to_response(refreshed_cfg, include_soul=True)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update agent: {str(e)}")
|
||||
|
||||
|
||||
class UserProfileResponse(BaseModel):
|
||||
"""Response model for the global user profile (USER.md)."""
|
||||
|
||||
content: str | None = Field(default=None, description="USER.md content, or null if not yet created")
|
||||
|
||||
|
||||
class UserProfileUpdateRequest(BaseModel):
|
||||
"""Request body for setting the global user profile."""
|
||||
|
||||
content: str = Field(default="", description="USER.md content — describes the user's background and preferences")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/user-profile",
|
||||
response_model=UserProfileResponse,
|
||||
summary="Get User Profile",
|
||||
description="Read the global USER.md file that is injected into all custom agents.",
|
||||
)
|
||||
async def get_user_profile() -> UserProfileResponse:
|
||||
"""Return the current USER.md content.
|
||||
|
||||
Returns:
|
||||
UserProfileResponse with content=None if USER.md does not exist yet.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
|
||||
try:
|
||||
user_md_path = get_paths().user_md_file
|
||||
if not user_md_path.exists():
|
||||
return UserProfileResponse(content=None)
|
||||
raw = user_md_path.read_text(encoding="utf-8").strip()
|
||||
return UserProfileResponse(content=raw or None)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to read user profile: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to read user profile: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/user-profile",
|
||||
response_model=UserProfileResponse,
|
||||
summary="Update User Profile",
|
||||
description="Write the global USER.md file that is injected into all custom agents.",
|
||||
)
|
||||
async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileResponse:
|
||||
"""Create or overwrite the global USER.md.
|
||||
|
||||
Args:
|
||||
request: The update request with the new USER.md content.
|
||||
|
||||
Returns:
|
||||
UserProfileResponse with the saved content.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
|
||||
try:
|
||||
paths = get_paths()
|
||||
paths.base_dir.mkdir(parents=True, exist_ok=True)
|
||||
paths.user_md_file.write_text(request.content, encoding="utf-8")
|
||||
logger.info(f"Updated USER.md at {paths.user_md_file}")
|
||||
return UserProfileResponse(content=request.content or None)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update user profile: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update user profile: {str(e)}")
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/agents/{name}",
|
||||
status_code=204,
|
||||
summary="Delete Custom Agent",
|
||||
description="Delete a custom agent and all its files (config, SOUL.md, memory).",
|
||||
)
|
||||
async def delete_agent(name: str) -> None:
|
||||
"""Delete a custom agent.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_require_agents_api_enabled()
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
agent_dir = get_paths().agent_dir(name)
|
||||
|
||||
if not agent_dir.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
|
||||
try:
|
||||
shutil.rmtree(agent_dir)
|
||||
logger.info(f"Deleted agent '{name}' from {agent_dir}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete agent: {str(e)}")
|
||||
@@ -1,181 +0,0 @@
|
||||
import logging
|
||||
import mimetypes
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
from urllib.parse import quote
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from fastapi.responses import FileResponse, PlainTextResponse, Response
|
||||
|
||||
from app.gateway.path_utils import resolve_thread_virtual_path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["artifacts"])
|
||||
|
||||
ACTIVE_CONTENT_MIME_TYPES = {
|
||||
"text/html",
|
||||
"application/xhtml+xml",
|
||||
"image/svg+xml",
|
||||
}
|
||||
|
||||
|
||||
def _build_content_disposition(disposition_type: str, filename: str) -> str:
|
||||
"""Build an RFC 5987 encoded Content-Disposition header value."""
|
||||
return f"{disposition_type}; filename*=UTF-8''{quote(filename)}"
|
||||
|
||||
|
||||
def _build_attachment_headers(filename: str, extra_headers: dict[str, str] | None = None) -> dict[str, str]:
|
||||
headers = {"Content-Disposition": _build_content_disposition("attachment", filename)}
|
||||
if extra_headers:
|
||||
headers.update(extra_headers)
|
||||
return headers
|
||||
|
||||
|
||||
def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
|
||||
"""Check if file is text by examining content for null bytes."""
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
chunk = f.read(sample_size)
|
||||
# Text files shouldn't contain null bytes
|
||||
return b"\x00" not in chunk
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
|
||||
"""Extract a file from a .skill ZIP archive.
|
||||
|
||||
Args:
|
||||
zip_path: Path to the .skill file (ZIP archive).
|
||||
internal_path: Path to the file inside the archive (e.g., "SKILL.md").
|
||||
|
||||
Returns:
|
||||
The file content as bytes, or None if not found.
|
||||
"""
|
||||
if not zipfile.is_zipfile(zip_path):
|
||||
return None
|
||||
|
||||
try:
|
||||
with zipfile.ZipFile(zip_path, "r") as zip_ref:
|
||||
# List all files in the archive
|
||||
namelist = zip_ref.namelist()
|
||||
|
||||
# Try direct path first
|
||||
if internal_path in namelist:
|
||||
return zip_ref.read(internal_path)
|
||||
|
||||
# Try with any top-level directory prefix (e.g., "skill-name/SKILL.md")
|
||||
for name in namelist:
|
||||
if name.endswith("/" + internal_path) or name == internal_path:
|
||||
return zip_ref.read(name)
|
||||
|
||||
# Not found
|
||||
return None
|
||||
except (zipfile.BadZipFile, KeyError):
|
||||
return None
|
||||
|
||||
|
||||
@router.get(
|
||||
"/threads/{thread_id}/artifacts/{path:path}",
|
||||
summary="Get Artifact File",
|
||||
description="Retrieve an artifact file generated by the AI agent. Text and binary files can be viewed inline, while active web content is always downloaded.",
|
||||
)
|
||||
async def get_artifact(thread_id: str, path: str, request: Request, download: bool = False) -> Response:
|
||||
"""Get an artifact file by its path.
|
||||
|
||||
The endpoint automatically detects file types and returns appropriate content types.
|
||||
Use the `download` query parameter to force file download for non-active content.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
path: The artifact path with virtual prefix (e.g., mnt/user-data/outputs/file.txt).
|
||||
request: FastAPI request object (automatically injected).
|
||||
|
||||
Returns:
|
||||
The file content as a FileResponse with appropriate content type:
|
||||
- Active content (HTML/XHTML/SVG): Served as download attachment
|
||||
- Text files: Plain text with proper MIME type
|
||||
- Binary files: Inline display with download option
|
||||
|
||||
Raises:
|
||||
HTTPException:
|
||||
- 400 if path is invalid or not a file
|
||||
- 403 if access denied (path traversal detected)
|
||||
- 404 if file not found
|
||||
|
||||
Query Parameters:
|
||||
download (bool): If true, forces attachment download for file types that are
|
||||
otherwise returned inline or as plain text. Active HTML/XHTML/SVG content
|
||||
is always downloaded regardless of this flag.
|
||||
|
||||
Example:
|
||||
- Get text file inline: `/api/threads/abc123/artifacts/mnt/user-data/outputs/notes.txt`
|
||||
- Download file: `/api/threads/abc123/artifacts/mnt/user-data/outputs/data.csv?download=true`
|
||||
- Active web content such as `.html`, `.xhtml`, and `.svg` artifacts is always downloaded
|
||||
"""
|
||||
# Check if this is a request for a file inside a .skill archive (e.g., xxx.skill/SKILL.md)
|
||||
if ".skill/" in path:
|
||||
# Split the path at ".skill/" to get the ZIP file path and internal path
|
||||
skill_marker = ".skill/"
|
||||
marker_pos = path.find(skill_marker)
|
||||
skill_file_path = path[: marker_pos + len(".skill")] # e.g., "mnt/user-data/outputs/my-skill.skill"
|
||||
internal_path = path[marker_pos + len(skill_marker) :] # e.g., "SKILL.md"
|
||||
|
||||
actual_skill_path = resolve_thread_virtual_path(thread_id, skill_file_path)
|
||||
|
||||
if not actual_skill_path.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Skill file not found: {skill_file_path}")
|
||||
|
||||
if not actual_skill_path.is_file():
|
||||
raise HTTPException(status_code=400, detail=f"Path is not a file: {skill_file_path}")
|
||||
|
||||
# Extract the file from the .skill archive
|
||||
content = _extract_file_from_skill_archive(actual_skill_path, internal_path)
|
||||
if content is None:
|
||||
raise HTTPException(status_code=404, detail=f"File '{internal_path}' not found in skill archive")
|
||||
|
||||
# Determine MIME type based on the internal file
|
||||
mime_type, _ = mimetypes.guess_type(internal_path)
|
||||
# Add cache headers to avoid repeated ZIP extraction (cache for 5 minutes)
|
||||
cache_headers = {"Cache-Control": "private, max-age=300"}
|
||||
download_name = Path(internal_path).name or actual_skill_path.stem
|
||||
if download or mime_type in ACTIVE_CONTENT_MIME_TYPES:
|
||||
return Response(content=content, media_type=mime_type or "application/octet-stream", headers=_build_attachment_headers(download_name, cache_headers))
|
||||
|
||||
if mime_type and mime_type.startswith("text/"):
|
||||
return PlainTextResponse(content=content.decode("utf-8"), media_type=mime_type, headers=cache_headers)
|
||||
|
||||
# Default to plain text for unknown types that look like text
|
||||
try:
|
||||
return PlainTextResponse(content=content.decode("utf-8"), media_type="text/plain", headers=cache_headers)
|
||||
except UnicodeDecodeError:
|
||||
return Response(content=content, media_type=mime_type or "application/octet-stream", headers=cache_headers)
|
||||
|
||||
actual_path = resolve_thread_virtual_path(thread_id, path)
|
||||
|
||||
logger.info(f"Resolving artifact path: thread_id={thread_id}, requested_path={path}, actual_path={actual_path}")
|
||||
|
||||
if not actual_path.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Artifact not found: {path}")
|
||||
|
||||
if not actual_path.is_file():
|
||||
raise HTTPException(status_code=400, detail=f"Path is not a file: {path}")
|
||||
|
||||
mime_type, _ = mimetypes.guess_type(actual_path)
|
||||
|
||||
if download:
|
||||
return FileResponse(path=actual_path, filename=actual_path.name, media_type=mime_type, headers=_build_attachment_headers(actual_path.name))
|
||||
|
||||
# Always force download for active content types to prevent script execution
|
||||
# in the application origin when users open generated artifacts.
|
||||
if mime_type in ACTIVE_CONTENT_MIME_TYPES:
|
||||
return FileResponse(path=actual_path, filename=actual_path.name, media_type=mime_type, headers=_build_attachment_headers(actual_path.name))
|
||||
|
||||
if mime_type and mime_type.startswith("text/"):
|
||||
return PlainTextResponse(content=actual_path.read_text(encoding="utf-8"), media_type=mime_type)
|
||||
|
||||
if is_text_file_by_content(actual_path):
|
||||
return PlainTextResponse(content=actual_path.read_text(encoding="utf-8"), media_type=mime_type)
|
||||
|
||||
return Response(content=actual_path.read_bytes(), media_type=mime_type, headers={"Content-Disposition": _build_content_disposition("inline", actual_path.name)})
|
||||
@@ -1,149 +0,0 @@
|
||||
"""Assistants compatibility endpoints.
|
||||
|
||||
Provides LangGraph Platform-compatible assistants API backed by the
|
||||
``langgraph.json`` graph registry and ``config.yaml`` agent definitions.
|
||||
|
||||
This is a minimal stub that satisfies the ``useStream`` React hook's
|
||||
initialization requirements (``assistants.search()`` and ``assistants.get()``).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/assistants", tags=["assistants-compat"])
|
||||
|
||||
|
||||
class AssistantResponse(BaseModel):
|
||||
assistant_id: str
|
||||
graph_id: str
|
||||
name: str
|
||||
config: dict[str, Any] = Field(default_factory=dict)
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
description: str | None = None
|
||||
created_at: str = ""
|
||||
updated_at: str = ""
|
||||
version: int = 1
|
||||
|
||||
|
||||
class AssistantSearchRequest(BaseModel):
|
||||
graph_id: str | None = None
|
||||
name: str | None = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
limit: int = 10
|
||||
offset: int = 0
|
||||
|
||||
|
||||
def _get_default_assistant() -> AssistantResponse:
|
||||
"""Return the default lead_agent assistant."""
|
||||
now = datetime.now(UTC).isoformat()
|
||||
return AssistantResponse(
|
||||
assistant_id="lead_agent",
|
||||
graph_id="lead_agent",
|
||||
name="lead_agent",
|
||||
config={},
|
||||
metadata={"created_by": "system"},
|
||||
description="DeerFlow lead agent",
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
version=1,
|
||||
)
|
||||
|
||||
|
||||
def _list_assistants() -> list[AssistantResponse]:
|
||||
"""List all available assistants from config."""
|
||||
assistants = [_get_default_assistant()]
|
||||
|
||||
# Also include custom agents from config.yaml agents directory
|
||||
try:
|
||||
from deerflow.config.agents_config import list_custom_agents
|
||||
|
||||
for agent_cfg in list_custom_agents():
|
||||
now = datetime.now(UTC).isoformat()
|
||||
assistants.append(
|
||||
AssistantResponse(
|
||||
assistant_id=agent_cfg.name,
|
||||
graph_id="lead_agent", # All agents use the same graph
|
||||
name=agent_cfg.name,
|
||||
config={},
|
||||
metadata={"created_by": "user"},
|
||||
description=agent_cfg.description or "",
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
version=1,
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
logger.debug("Could not load custom agents for assistants list")
|
||||
|
||||
return assistants
|
||||
|
||||
|
||||
@router.post("/search", response_model=list[AssistantResponse])
|
||||
async def search_assistants(body: AssistantSearchRequest | None = None) -> list[AssistantResponse]:
|
||||
"""Search assistants.
|
||||
|
||||
Returns all registered assistants (lead_agent + custom agents from config).
|
||||
"""
|
||||
assistants = _list_assistants()
|
||||
|
||||
if body and body.graph_id:
|
||||
assistants = [a for a in assistants if a.graph_id == body.graph_id]
|
||||
if body and body.name:
|
||||
assistants = [a for a in assistants if body.name.lower() in a.name.lower()]
|
||||
|
||||
offset = body.offset if body else 0
|
||||
limit = body.limit if body else 10
|
||||
return assistants[offset : offset + limit]
|
||||
|
||||
|
||||
@router.get("/{assistant_id}", response_model=AssistantResponse)
|
||||
async def get_assistant_compat(assistant_id: str) -> AssistantResponse:
|
||||
"""Get an assistant by ID."""
|
||||
for a in _list_assistants():
|
||||
if a.assistant_id == assistant_id:
|
||||
return a
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
|
||||
@router.get("/{assistant_id}/graph")
|
||||
async def get_assistant_graph(assistant_id: str) -> dict:
|
||||
"""Get the graph structure for an assistant.
|
||||
|
||||
Returns a minimal graph description. Full graph introspection is
|
||||
not supported in the Gateway — this stub satisfies SDK validation.
|
||||
"""
|
||||
found = any(a.assistant_id == assistant_id for a in _list_assistants())
|
||||
if not found:
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
return {
|
||||
"graph_id": "lead_agent",
|
||||
"nodes": [],
|
||||
"edges": [],
|
||||
}
|
||||
|
||||
|
||||
@router.get("/{assistant_id}/schemas")
|
||||
async def get_assistant_schemas(assistant_id: str) -> dict:
|
||||
"""Get JSON schemas for an assistant's input/output/state.
|
||||
|
||||
Returns empty schemas — full introspection not supported in Gateway.
|
||||
"""
|
||||
found = any(a.assistant_id == assistant_id for a in _list_assistants())
|
||||
if not found:
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
return {
|
||||
"graph_id": "lead_agent",
|
||||
"input_schema": {},
|
||||
"output_schema": {},
|
||||
"state_schema": {},
|
||||
"config_schema": {},
|
||||
}
|
||||
@@ -1,52 +0,0 @@
|
||||
"""Gateway router for IM channel management."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api/channels", tags=["channels"])
|
||||
|
||||
|
||||
class ChannelStatusResponse(BaseModel):
|
||||
service_running: bool
|
||||
channels: dict[str, dict]
|
||||
|
||||
|
||||
class ChannelRestartResponse(BaseModel):
|
||||
success: bool
|
||||
message: str
|
||||
|
||||
|
||||
@router.get("/", response_model=ChannelStatusResponse)
|
||||
async def get_channels_status() -> ChannelStatusResponse:
|
||||
"""Get the status of all IM channels."""
|
||||
from app.channels.service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
if service is None:
|
||||
return ChannelStatusResponse(service_running=False, channels={})
|
||||
status = service.get_status()
|
||||
return ChannelStatusResponse(**status)
|
||||
|
||||
|
||||
@router.post("/{name}/restart", response_model=ChannelRestartResponse)
|
||||
async def restart_channel(name: str) -> ChannelRestartResponse:
|
||||
"""Restart a specific IM channel."""
|
||||
from app.channels.service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
if service is None:
|
||||
raise HTTPException(status_code=503, detail="Channel service is not running")
|
||||
|
||||
success = await service.restart_channel(name)
|
||||
if success:
|
||||
logger.info("Channel %s restarted successfully", name)
|
||||
return ChannelRestartResponse(success=True, message=f"Channel {name} restarted successfully")
|
||||
else:
|
||||
logger.warning("Failed to restart channel %s", name)
|
||||
return ChannelRestartResponse(success=False, message=f"Failed to restart channel {name}")
|
||||
@@ -1,169 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Literal
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api", tags=["mcp"])
|
||||
|
||||
|
||||
class McpOAuthConfigResponse(BaseModel):
|
||||
"""OAuth configuration for an MCP server."""
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
|
||||
token_url: str = Field(default="", description="OAuth token endpoint URL")
|
||||
grant_type: Literal["client_credentials", "refresh_token"] = Field(default="client_credentials", description="OAuth grant type")
|
||||
client_id: str | None = Field(default=None, description="OAuth client ID")
|
||||
client_secret: str | None = Field(default=None, description="OAuth client secret")
|
||||
refresh_token: str | None = Field(default=None, description="OAuth refresh token")
|
||||
scope: str | None = Field(default=None, description="OAuth scope")
|
||||
audience: str | None = Field(default=None, description="OAuth audience")
|
||||
token_field: str = Field(default="access_token", description="Token response field containing access token")
|
||||
token_type_field: str = Field(default="token_type", description="Token response field containing token type")
|
||||
expires_in_field: str = Field(default="expires_in", description="Token response field containing expires-in seconds")
|
||||
default_token_type: str = Field(default="Bearer", description="Default token type when response omits token_type")
|
||||
refresh_skew_seconds: int = Field(default=60, description="Refresh this many seconds before expiry")
|
||||
extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
|
||||
|
||||
|
||||
class McpServerConfigResponse(BaseModel):
|
||||
"""Response model for MCP server configuration."""
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
|
||||
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
|
||||
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
|
||||
args: list[str] = Field(default_factory=list, description="Arguments to pass to the command (for stdio type)")
|
||||
env: dict[str, str] = Field(default_factory=dict, description="Environment variables for the MCP server")
|
||||
url: str | None = Field(default=None, description="URL of the MCP server (for sse or http type)")
|
||||
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
|
||||
oauth: McpOAuthConfigResponse | None = Field(default=None, description="OAuth configuration for MCP HTTP/SSE servers")
|
||||
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
|
||||
|
||||
|
||||
class McpConfigResponse(BaseModel):
|
||||
"""Response model for MCP configuration."""
|
||||
|
||||
mcp_servers: dict[str, McpServerConfigResponse] = Field(
|
||||
default_factory=dict,
|
||||
description="Map of MCP server name to configuration",
|
||||
)
|
||||
|
||||
|
||||
class McpConfigUpdateRequest(BaseModel):
|
||||
"""Request model for updating MCP configuration."""
|
||||
|
||||
mcp_servers: dict[str, McpServerConfigResponse] = Field(
|
||||
...,
|
||||
description="Map of MCP server name to configuration",
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/mcp/config",
|
||||
response_model=McpConfigResponse,
|
||||
summary="Get MCP Configuration",
|
||||
description="Retrieve the current Model Context Protocol (MCP) server configurations.",
|
||||
)
|
||||
async def get_mcp_configuration() -> McpConfigResponse:
|
||||
"""Get the current MCP configuration.
|
||||
|
||||
Returns:
|
||||
The current MCP configuration with all servers.
|
||||
|
||||
Example:
|
||||
```json
|
||||
{
|
||||
"mcp_servers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "ghp_xxx"},
|
||||
"description": "GitHub MCP server for repository operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_extensions_config()
|
||||
|
||||
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
|
||||
|
||||
|
||||
@router.put(
|
||||
"/mcp/config",
|
||||
response_model=McpConfigResponse,
|
||||
summary="Update MCP Configuration",
|
||||
description="Update Model Context Protocol (MCP) server configurations and save to file.",
|
||||
)
|
||||
async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
|
||||
"""Update the MCP configuration.
|
||||
|
||||
This will:
|
||||
1. Save the new configuration to the mcp_config.json file
|
||||
2. Reload the configuration cache
|
||||
3. Reset MCP tools cache to trigger reinitialization
|
||||
|
||||
Args:
|
||||
request: The new MCP configuration to save.
|
||||
|
||||
Returns:
|
||||
The updated MCP configuration.
|
||||
|
||||
Raises:
|
||||
HTTPException: 500 if the configuration file cannot be written.
|
||||
|
||||
Example Request:
|
||||
```json
|
||||
{
|
||||
"mcp_servers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"},
|
||||
"description": "GitHub MCP server for repository operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
"""
|
||||
try:
|
||||
# Get the current config path (or determine where to save it)
|
||||
config_path = ExtensionsConfig.resolve_config_path()
|
||||
|
||||
# If no config file exists, create one in the parent directory (project root)
|
||||
if config_path is None:
|
||||
config_path = Path.cwd().parent / "extensions_config.json"
|
||||
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
|
||||
|
||||
# Load current config to preserve skills configuration
|
||||
current_config = get_extensions_config()
|
||||
|
||||
# Convert request to dict format for JSON serialization
|
||||
config_data = {
|
||||
"mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
|
||||
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
|
||||
}
|
||||
|
||||
# Write the configuration to file
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
json.dump(config_data, f, indent=2)
|
||||
|
||||
logger.info(f"MCP configuration updated and saved to: {config_path}")
|
||||
|
||||
# NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
|
||||
# will detect config file changes via mtime and reinitialize MCP tools automatically
|
||||
|
||||
# Reload the configuration and update the global cache
|
||||
reloaded_config = reload_extensions_config()
|
||||
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update MCP configuration: {str(e)}")
|
||||
@@ -1,353 +0,0 @@
|
||||
"""Memory API router for retrieving and managing global memory data."""
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.agents.memory.updater import (
|
||||
clear_memory_data,
|
||||
create_memory_fact,
|
||||
delete_memory_fact,
|
||||
get_memory_data,
|
||||
import_memory_data,
|
||||
reload_memory_data,
|
||||
update_memory_fact,
|
||||
)
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["memory"])
|
||||
|
||||
|
||||
class ContextSection(BaseModel):
|
||||
"""Model for context sections (user and history)."""
|
||||
|
||||
summary: str = Field(default="", description="Summary content")
|
||||
updatedAt: str = Field(default="", description="Last update timestamp")
|
||||
|
||||
|
||||
class UserContext(BaseModel):
|
||||
"""Model for user context."""
|
||||
|
||||
workContext: ContextSection = Field(default_factory=ContextSection)
|
||||
personalContext: ContextSection = Field(default_factory=ContextSection)
|
||||
topOfMind: ContextSection = Field(default_factory=ContextSection)
|
||||
|
||||
|
||||
class HistoryContext(BaseModel):
|
||||
"""Model for history context."""
|
||||
|
||||
recentMonths: ContextSection = Field(default_factory=ContextSection)
|
||||
earlierContext: ContextSection = Field(default_factory=ContextSection)
|
||||
longTermBackground: ContextSection = Field(default_factory=ContextSection)
|
||||
|
||||
|
||||
class Fact(BaseModel):
|
||||
"""Model for a memory fact."""
|
||||
|
||||
id: str = Field(..., description="Unique identifier for the fact")
|
||||
content: str = Field(..., description="Fact content")
|
||||
category: str = Field(default="context", description="Fact category")
|
||||
confidence: float = Field(default=0.5, description="Confidence score (0-1)")
|
||||
createdAt: str = Field(default="", description="Creation timestamp")
|
||||
source: str = Field(default="unknown", description="Source thread ID")
|
||||
sourceError: str | None = Field(default=None, description="Optional description of the prior mistake or wrong approach")
|
||||
|
||||
|
||||
class MemoryResponse(BaseModel):
|
||||
"""Response model for memory data."""
|
||||
|
||||
version: str = Field(default="1.0", description="Memory schema version")
|
||||
lastUpdated: str = Field(default="", description="Last update timestamp")
|
||||
user: UserContext = Field(default_factory=UserContext)
|
||||
history: HistoryContext = Field(default_factory=HistoryContext)
|
||||
facts: list[Fact] = Field(default_factory=list)
|
||||
|
||||
|
||||
def _map_memory_fact_value_error(exc: ValueError) -> HTTPException:
|
||||
"""Convert updater validation errors into stable API responses."""
|
||||
if exc.args and exc.args[0] == "confidence":
|
||||
detail = "Invalid confidence value; must be between 0 and 1."
|
||||
else:
|
||||
detail = "Memory fact content cannot be empty."
|
||||
return HTTPException(status_code=400, detail=detail)
|
||||
|
||||
|
||||
class FactCreateRequest(BaseModel):
|
||||
"""Request model for creating a memory fact."""
|
||||
|
||||
content: str = Field(..., min_length=1, description="Fact content")
|
||||
category: str = Field(default="context", description="Fact category")
|
||||
confidence: float = Field(default=0.5, ge=0.0, le=1.0, description="Confidence score (0-1)")
|
||||
|
||||
|
||||
class FactPatchRequest(BaseModel):
|
||||
"""PATCH request model that preserves existing values for omitted fields."""
|
||||
|
||||
content: str | None = Field(default=None, min_length=1, description="Fact content")
|
||||
category: str | None = Field(default=None, description="Fact category")
|
||||
confidence: float | None = Field(default=None, ge=0.0, le=1.0, description="Confidence score (0-1)")
|
||||
|
||||
|
||||
class MemoryConfigResponse(BaseModel):
|
||||
"""Response model for memory configuration."""
|
||||
|
||||
enabled: bool = Field(..., description="Whether memory is enabled")
|
||||
storage_path: str = Field(..., description="Path to memory storage file")
|
||||
debounce_seconds: int = Field(..., description="Debounce time for memory updates")
|
||||
max_facts: int = Field(..., description="Maximum number of facts to store")
|
||||
fact_confidence_threshold: float = Field(..., description="Minimum confidence threshold for facts")
|
||||
injection_enabled: bool = Field(..., description="Whether memory injection is enabled")
|
||||
max_injection_tokens: int = Field(..., description="Maximum tokens for memory injection")
|
||||
|
||||
|
||||
class MemoryStatusResponse(BaseModel):
|
||||
"""Response model for memory status."""
|
||||
|
||||
config: MemoryConfigResponse
|
||||
data: MemoryResponse
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Get Memory Data",
|
||||
description="Retrieve the current global memory data including user context, history, and facts.",
|
||||
)
|
||||
async def get_memory() -> MemoryResponse:
|
||||
"""Get the current global memory data.
|
||||
|
||||
Returns:
|
||||
The current memory data with user context, history, and facts.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"version": "1.0",
|
||||
"lastUpdated": "2024-01-15T10:30:00Z",
|
||||
"user": {
|
||||
"workContext": {"summary": "Working on DeerFlow project", "updatedAt": "..."},
|
||||
"personalContext": {"summary": "Prefers concise responses", "updatedAt": "..."},
|
||||
"topOfMind": {"summary": "Building memory API", "updatedAt": "..."}
|
||||
},
|
||||
"history": {
|
||||
"recentMonths": {"summary": "Recent development activities", "updatedAt": "..."},
|
||||
"earlierContext": {"summary": "", "updatedAt": ""},
|
||||
"longTermBackground": {"summary": "", "updatedAt": ""}
|
||||
},
|
||||
"facts": [
|
||||
{
|
||||
"id": "fact_abc123",
|
||||
"content": "User prefers TypeScript over JavaScript",
|
||||
"category": "preference",
|
||||
"confidence": 0.9,
|
||||
"createdAt": "2024-01-15T10:30:00Z",
|
||||
"source": "thread_xyz"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
"""
|
||||
memory_data = get_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/reload",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Reload Memory Data",
|
||||
description="Reload memory data from the storage file, refreshing the in-memory cache.",
|
||||
)
|
||||
async def reload_memory() -> MemoryResponse:
|
||||
"""Reload memory data from file.
|
||||
|
||||
This forces a reload of the memory data from the storage file,
|
||||
useful when the file has been modified externally.
|
||||
|
||||
Returns:
|
||||
The reloaded memory data.
|
||||
"""
|
||||
memory_data = reload_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/memory",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Clear All Memory Data",
|
||||
description="Delete all saved memory data and reset the memory structure to an empty state.",
|
||||
)
|
||||
async def clear_memory() -> MemoryResponse:
|
||||
"""Clear all persisted memory data."""
|
||||
try:
|
||||
memory_data = clear_memory_data()
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/facts",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Create Memory Fact",
|
||||
description="Create a single saved memory fact manually.",
|
||||
)
|
||||
async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
|
||||
"""Create a single fact manually."""
|
||||
try:
|
||||
memory_data = create_memory_fact(
|
||||
content=request.content,
|
||||
category=request.category,
|
||||
confidence=request.confidence,
|
||||
)
|
||||
except ValueError as exc:
|
||||
raise _map_memory_fact_value_error(exc) from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to create memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/memory/facts/{fact_id}",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Delete Memory Fact",
|
||||
description="Delete a single saved memory fact by its fact id.",
|
||||
)
|
||||
async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
|
||||
"""Delete a single fact from memory by fact id."""
|
||||
try:
|
||||
memory_data = delete_memory_fact(fact_id)
|
||||
except KeyError as exc:
|
||||
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to delete memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.patch(
|
||||
"/memory/facts/{fact_id}",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Patch Memory Fact",
|
||||
description="Partially update a single saved memory fact by its fact id while preserving omitted fields.",
|
||||
)
|
||||
async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -> MemoryResponse:
|
||||
"""Partially update a single fact manually."""
|
||||
try:
|
||||
memory_data = update_memory_fact(
|
||||
fact_id=fact_id,
|
||||
content=request.content,
|
||||
category=request.category,
|
||||
confidence=request.confidence,
|
||||
)
|
||||
except ValueError as exc:
|
||||
raise _map_memory_fact_value_error(exc) from exc
|
||||
except KeyError as exc:
|
||||
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to update memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/export",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Export Memory Data",
|
||||
description="Export the current global memory data as JSON for backup or transfer.",
|
||||
)
|
||||
async def export_memory() -> MemoryResponse:
|
||||
"""Export the current memory data."""
|
||||
memory_data = get_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/import",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Import Memory Data",
|
||||
description="Import and overwrite the current global memory data from a JSON payload.",
|
||||
)
|
||||
async def import_memory(request: MemoryResponse) -> MemoryResponse:
|
||||
"""Import and persist memory data."""
|
||||
try:
|
||||
memory_data = import_memory_data(request.model_dump())
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/config",
|
||||
response_model=MemoryConfigResponse,
|
||||
summary="Get Memory Configuration",
|
||||
description="Retrieve the current memory system configuration.",
|
||||
)
|
||||
async def get_memory_config_endpoint() -> MemoryConfigResponse:
|
||||
"""Get the memory system configuration.
|
||||
|
||||
Returns:
|
||||
The current memory configuration settings.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"enabled": true,
|
||||
"storage_path": ".deer-flow/memory.json",
|
||||
"debounce_seconds": 30,
|
||||
"max_facts": 100,
|
||||
"fact_confidence_threshold": 0.7,
|
||||
"injection_enabled": true,
|
||||
"max_injection_tokens": 2000
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_memory_config()
|
||||
return MemoryConfigResponse(
|
||||
enabled=config.enabled,
|
||||
storage_path=config.storage_path,
|
||||
debounce_seconds=config.debounce_seconds,
|
||||
max_facts=config.max_facts,
|
||||
fact_confidence_threshold=config.fact_confidence_threshold,
|
||||
injection_enabled=config.injection_enabled,
|
||||
max_injection_tokens=config.max_injection_tokens,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/status",
|
||||
response_model=MemoryStatusResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Get Memory Status",
|
||||
description="Retrieve both memory configuration and current data in a single request.",
|
||||
)
|
||||
async def get_memory_status() -> MemoryStatusResponse:
|
||||
"""Get the memory system status including configuration and data.
|
||||
|
||||
Returns:
|
||||
Combined memory configuration and current data.
|
||||
"""
|
||||
config = get_memory_config()
|
||||
memory_data = get_memory_data()
|
||||
|
||||
return MemoryStatusResponse(
|
||||
config=MemoryConfigResponse(
|
||||
enabled=config.enabled,
|
||||
storage_path=config.storage_path,
|
||||
debounce_seconds=config.debounce_seconds,
|
||||
max_facts=config.max_facts,
|
||||
fact_confidence_threshold=config.fact_confidence_threshold,
|
||||
injection_enabled=config.injection_enabled,
|
||||
max_injection_tokens=config.max_injection_tokens,
|
||||
),
|
||||
data=MemoryResponse(**memory_data),
|
||||
)
|
||||
@@ -1,133 +0,0 @@
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["models"])
|
||||
|
||||
|
||||
class ModelResponse(BaseModel):
|
||||
"""Response model for model information."""
|
||||
|
||||
name: str = Field(..., description="Unique identifier for the model")
|
||||
model: str = Field(..., description="Actual provider model identifier")
|
||||
display_name: str | None = Field(None, description="Human-readable name")
|
||||
description: str | None = Field(None, description="Model description")
|
||||
supports_thinking: bool = Field(default=False, description="Whether model supports thinking mode")
|
||||
supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")
|
||||
|
||||
|
||||
class TokenUsageResponse(BaseModel):
|
||||
"""Token usage display configuration."""
|
||||
|
||||
enabled: bool = Field(default=False, description="Whether token usage display is enabled")
|
||||
|
||||
|
||||
class ModelsListResponse(BaseModel):
|
||||
"""Response model for listing all models."""
|
||||
|
||||
models: list[ModelResponse]
|
||||
token_usage: TokenUsageResponse
|
||||
|
||||
|
||||
@router.get(
|
||||
"/models",
|
||||
response_model=ModelsListResponse,
|
||||
summary="List All Models",
|
||||
description="Retrieve a list of all available AI models configured in the system.",
|
||||
)
|
||||
async def list_models() -> ModelsListResponse:
|
||||
"""List all available models from configuration.
|
||||
|
||||
Returns model information suitable for frontend display,
|
||||
excluding sensitive fields like API keys and internal configuration.
|
||||
|
||||
Returns:
|
||||
A list of all configured models with their metadata and token usage display settings.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"models": [
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"model": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"description": "OpenAI GPT-4 model",
|
||||
"supports_thinking": false,
|
||||
"supports_reasoning_effort": false
|
||||
},
|
||||
{
|
||||
"name": "claude-3-opus",
|
||||
"model": "claude-3-opus",
|
||||
"display_name": "Claude 3 Opus",
|
||||
"description": "Anthropic Claude 3 Opus model",
|
||||
"supports_thinking": true,
|
||||
"supports_reasoning_effort": false
|
||||
}
|
||||
],
|
||||
"token_usage": {
|
||||
"enabled": true
|
||||
}
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_app_config()
|
||||
models = [
|
||||
ModelResponse(
|
||||
name=model.name,
|
||||
model=model.model,
|
||||
display_name=model.display_name,
|
||||
description=model.description,
|
||||
supports_thinking=model.supports_thinking,
|
||||
supports_reasoning_effort=model.supports_reasoning_effort,
|
||||
)
|
||||
for model in config.models
|
||||
]
|
||||
return ModelsListResponse(
|
||||
models=models,
|
||||
token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/models/{model_name}",
|
||||
response_model=ModelResponse,
|
||||
summary="Get Model Details",
|
||||
description="Retrieve detailed information about a specific AI model by its name.",
|
||||
)
|
||||
async def get_model(model_name: str) -> ModelResponse:
|
||||
"""Get a specific model by name.
|
||||
|
||||
Args:
|
||||
model_name: The unique name of the model to retrieve.
|
||||
|
||||
Returns:
|
||||
Model information if found.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if model not found.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"description": "OpenAI GPT-4 model",
|
||||
"supports_thinking": false
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_app_config()
|
||||
model = config.get_model_config(model_name)
|
||||
if model is None:
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
|
||||
|
||||
return ModelResponse(
|
||||
name=model.name,
|
||||
model=model.model,
|
||||
display_name=model.display_name,
|
||||
description=model.description,
|
||||
supports_thinking=model.supports_thinking,
|
||||
supports_reasoning_effort=model.supports_reasoning_effort,
|
||||
)
|
||||
@@ -1,87 +0,0 @@
|
||||
"""Stateless runs endpoints -- stream and wait without a pre-existing thread.
|
||||
|
||||
These endpoints auto-create a temporary thread when no ``thread_id`` is
|
||||
supplied in the request body. When a ``thread_id`` **is** provided, it
|
||||
is reused so that conversation history is preserved across calls.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import uuid
|
||||
|
||||
from fastapi import APIRouter, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
|
||||
from app.gateway.routers.thread_runs import RunCreateRequest
|
||||
from app.gateway.services import sse_consumer, start_run
|
||||
from deerflow.runtime import serialize_channel_values
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/runs", tags=["runs"])
|
||||
|
||||
|
||||
def _resolve_thread_id(body: RunCreateRequest) -> str:
|
||||
"""Return the thread_id from the request body, or generate a new one."""
|
||||
thread_id = (body.config or {}).get("configurable", {}).get("thread_id")
|
||||
if thread_id:
|
||||
return str(thread_id)
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
@router.post("/stream")
|
||||
async def stateless_stream(body: RunCreateRequest, request: Request) -> StreamingResponse:
|
||||
"""Create a run and stream events via SSE.
|
||||
|
||||
If ``config.configurable.thread_id`` is provided, the run is created
|
||||
on the given thread so that conversation history is preserved.
|
||||
Otherwise a new temporary thread is created.
|
||||
"""
|
||||
thread_id = _resolve_thread_id(body)
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
"Content-Location": f"/api/threads/{thread_id}/runs/{record.run_id}",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/wait", response_model=dict)
|
||||
async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
|
||||
"""Create a run and block until completion.
|
||||
|
||||
If ``config.configurable.thread_id`` is provided, the run is created
|
||||
on the given thread so that conversation history is preserved.
|
||||
Otherwise a new temporary thread is created.
|
||||
"""
|
||||
thread_id = _resolve_thread_id(body)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
if record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
checkpointer = get_checkpointer(request)
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
if checkpoint_tuple is not None:
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
return serialize_channel_values(channel_values)
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch final state for run %s", record.run_id)
|
||||
|
||||
return {"status": record.status.value, "error": record.error}
|
||||
@@ -1,362 +0,0 @@
|
||||
import errno
|
||||
import json
|
||||
import logging
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.path_utils import resolve_thread_virtual_path
|
||||
from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
|
||||
from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
|
||||
from deerflow.skills import Skill, load_skills
|
||||
from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
|
||||
from deerflow.skills.manager import (
|
||||
append_history,
|
||||
atomic_write,
|
||||
custom_skill_exists,
|
||||
ensure_custom_skill_is_editable,
|
||||
get_custom_skill_dir,
|
||||
get_custom_skill_file,
|
||||
get_skill_history_file,
|
||||
read_custom_skill_content,
|
||||
read_history,
|
||||
validate_skill_markdown_content,
|
||||
)
|
||||
from deerflow.skills.security_scanner import scan_skill_content
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["skills"])
|
||||
|
||||
|
||||
class SkillResponse(BaseModel):
|
||||
"""Response model for skill information."""
|
||||
|
||||
name: str = Field(..., description="Name of the skill")
|
||||
description: str = Field(..., description="Description of what the skill does")
|
||||
license: str | None = Field(None, description="License information")
|
||||
category: str = Field(..., description="Category of the skill (public or custom)")
|
||||
enabled: bool = Field(default=True, description="Whether this skill is enabled")
|
||||
|
||||
|
||||
class SkillsListResponse(BaseModel):
|
||||
"""Response model for listing all skills."""
|
||||
|
||||
skills: list[SkillResponse]
|
||||
|
||||
|
||||
class SkillUpdateRequest(BaseModel):
|
||||
"""Request model for updating a skill."""
|
||||
|
||||
enabled: bool = Field(..., description="Whether to enable or disable the skill")
|
||||
|
||||
|
||||
class SkillInstallRequest(BaseModel):
|
||||
"""Request model for installing a skill from a .skill file."""
|
||||
|
||||
thread_id: str = Field(..., description="The thread ID where the .skill file is located")
|
||||
path: str = Field(..., description="Virtual path to the .skill file (e.g., mnt/user-data/outputs/my-skill.skill)")
|
||||
|
||||
|
||||
class SkillInstallResponse(BaseModel):
|
||||
"""Response model for skill installation."""
|
||||
|
||||
success: bool = Field(..., description="Whether the installation was successful")
|
||||
skill_name: str = Field(..., description="Name of the installed skill")
|
||||
message: str = Field(..., description="Installation result message")
|
||||
|
||||
|
||||
class CustomSkillContentResponse(SkillResponse):
|
||||
content: str = Field(..., description="Raw SKILL.md content")
|
||||
|
||||
|
||||
class CustomSkillUpdateRequest(BaseModel):
|
||||
content: str = Field(..., description="Replacement SKILL.md content")
|
||||
|
||||
|
||||
class CustomSkillHistoryResponse(BaseModel):
|
||||
history: list[dict]
|
||||
|
||||
|
||||
class SkillRollbackRequest(BaseModel):
|
||||
history_index: int = Field(default=-1, description="History entry index to restore from, defaulting to the latest change.")
|
||||
|
||||
|
||||
def _skill_to_response(skill: Skill) -> SkillResponse:
|
||||
"""Convert a Skill object to a SkillResponse."""
|
||||
return SkillResponse(
|
||||
name=skill.name,
|
||||
description=skill.description,
|
||||
license=skill.license,
|
||||
category=skill.category,
|
||||
enabled=skill.enabled,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/skills",
|
||||
response_model=SkillsListResponse,
|
||||
summary="List All Skills",
|
||||
description="Retrieve a list of all available skills from both public and custom directories.",
|
||||
)
|
||||
async def list_skills() -> SkillsListResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load skills: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to load skills: {str(e)}")
|
||||
|
||||
|
||||
@router.post(
|
||||
"/skills/install",
|
||||
response_model=SkillInstallResponse,
|
||||
summary="Install Skill",
|
||||
description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
|
||||
)
|
||||
async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
|
||||
try:
|
||||
skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
|
||||
result = install_skill_from_archive(skill_file_path)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return SkillInstallResponse(**result)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except SkillAlreadyExistsError as e:
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to install skill: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to install skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
|
||||
async def list_custom_skills() -> SkillsListResponse:
|
||||
try:
|
||||
skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
|
||||
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
|
||||
except Exception as e:
|
||||
logger.error("Failed to list custom skills: %s", e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list custom skills: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
|
||||
async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error("Failed to get custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
|
||||
async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
|
||||
try:
|
||||
ensure_custom_skill_is_editable(skill_name)
|
||||
validate_skill_markdown_content(skill_name, request.content)
|
||||
scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
|
||||
if scan.decision == "block":
|
||||
raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
|
||||
skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
|
||||
prev_content = skill_file.read_text(encoding="utf-8")
|
||||
atomic_write(skill_file, request.content)
|
||||
append_history(
|
||||
skill_name,
|
||||
{
|
||||
"action": "human_edit",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": prev_content,
|
||||
"new_content": request.content,
|
||||
"scanner": {"decision": scan.decision, "reason": scan.reason},
|
||||
},
|
||||
)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return await get_custom_skill(skill_name)
|
||||
except HTTPException:
|
||||
raise
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to update custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
|
||||
async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
|
||||
try:
|
||||
ensure_custom_skill_is_editable(skill_name)
|
||||
skill_dir = get_custom_skill_dir(skill_name)
|
||||
prev_content = read_custom_skill_content(skill_name)
|
||||
try:
|
||||
append_history(
|
||||
skill_name,
|
||||
{
|
||||
"action": "human_delete",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": prev_content,
|
||||
"new_content": None,
|
||||
"scanner": {"decision": "allow", "reason": "Deletion requested."},
|
||||
},
|
||||
)
|
||||
except OSError as e:
|
||||
if not isinstance(e, PermissionError) and e.errno not in {errno.EACCES, errno.EPERM, errno.EROFS}:
|
||||
raise
|
||||
logger.warning("Skipping delete history write for custom skill %s due to readonly/permission failure; continuing with skill directory removal: %s", skill_name, e)
|
||||
shutil.rmtree(skill_dir)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return {"success": True}
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to delete custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
|
||||
async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
|
||||
try:
|
||||
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
return CustomSkillHistoryResponse(history=read_history(skill_name))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error("Failed to read history for %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to read history: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
|
||||
async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
|
||||
try:
|
||||
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
history = read_history(skill_name)
|
||||
if not history:
|
||||
raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
|
||||
record = history[request.history_index]
|
||||
target_content = record.get("prev_content")
|
||||
if target_content is None:
|
||||
raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
|
||||
validate_skill_markdown_content(skill_name, target_content)
|
||||
scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
|
||||
skill_file = get_custom_skill_file(skill_name)
|
||||
current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
|
||||
history_entry = {
|
||||
"action": "rollback",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": current_content,
|
||||
"new_content": target_content,
|
||||
"rollback_from_ts": record.get("ts"),
|
||||
"scanner": {"decision": scan.decision, "reason": scan.reason},
|
||||
}
|
||||
if scan.decision == "block":
|
||||
append_history(skill_name, history_entry)
|
||||
raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
|
||||
atomic_write(skill_file, target_content)
|
||||
append_history(skill_name, history_entry)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return await get_custom_skill(skill_name)
|
||||
except HTTPException:
|
||||
raise
|
||||
except IndexError:
|
||||
raise HTTPException(status_code=400, detail="history_index is out of range")
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to roll back custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to roll back custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/skills/{skill_name}",
|
||||
response_model=SkillResponse,
|
||||
summary="Get Skill Details",
|
||||
description="Retrieve detailed information about a specific skill by its name.",
|
||||
)
|
||||
async def get_skill(skill_name: str) -> SkillResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
|
||||
|
||||
return _skill_to_response(skill)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get skill {skill_name}: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get skill: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/skills/{skill_name}",
|
||||
response_model=SkillResponse,
|
||||
summary="Update Skill",
|
||||
description="Update a skill's enabled status by modifying the extensions_config.json file.",
|
||||
)
|
||||
async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
|
||||
|
||||
config_path = ExtensionsConfig.resolve_config_path()
|
||||
if config_path is None:
|
||||
config_path = Path.cwd().parent / "extensions_config.json"
|
||||
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
|
||||
|
||||
extensions_config = get_extensions_config()
|
||||
extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)
|
||||
|
||||
config_data = {
|
||||
"mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
|
||||
"skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
|
||||
}
|
||||
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
json.dump(config_data, f, indent=2)
|
||||
|
||||
logger.info(f"Skills configuration updated and saved to: {config_path}")
|
||||
reload_extensions_config()
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
|
||||
skills = load_skills(enabled_only=False)
|
||||
updated_skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if updated_skill is None:
|
||||
raise HTTPException(status_code=500, detail=f"Failed to reload skill '{skill_name}' after update")
|
||||
|
||||
logger.info(f"Skill '{skill_name}' enabled status updated to {request.enabled}")
|
||||
return _skill_to_response(updated_skill)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update skill {skill_name}: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update skill: {str(e)}")
|
||||
@@ -1,132 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["suggestions"])
|
||||
|
||||
|
||||
class SuggestionMessage(BaseModel):
|
||||
role: str = Field(..., description="Message role: user|assistant")
|
||||
content: str = Field(..., description="Message content as plain text")
|
||||
|
||||
|
||||
class SuggestionsRequest(BaseModel):
|
||||
messages: list[SuggestionMessage] = Field(..., description="Recent conversation messages")
|
||||
n: int = Field(default=3, ge=1, le=5, description="Number of suggestions to generate")
|
||||
model_name: str | None = Field(default=None, description="Optional model override")
|
||||
|
||||
|
||||
class SuggestionsResponse(BaseModel):
|
||||
suggestions: list[str] = Field(default_factory=list, description="Suggested follow-up questions")
|
||||
|
||||
|
||||
def _strip_markdown_code_fence(text: str) -> str:
|
||||
stripped = text.strip()
|
||||
if not stripped.startswith("```"):
|
||||
return stripped
|
||||
lines = stripped.splitlines()
|
||||
if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
|
||||
return "\n".join(lines[1:-1]).strip()
|
||||
return stripped
|
||||
|
||||
|
||||
def _parse_json_string_list(text: str) -> list[str] | None:
|
||||
candidate = _strip_markdown_code_fence(text)
|
||||
start = candidate.find("[")
|
||||
end = candidate.rfind("]")
|
||||
if start == -1 or end == -1 or end <= start:
|
||||
return None
|
||||
candidate = candidate[start : end + 1]
|
||||
try:
|
||||
data = json.loads(candidate)
|
||||
except Exception:
|
||||
return None
|
||||
if not isinstance(data, list):
|
||||
return None
|
||||
out: list[str] = []
|
||||
for item in data:
|
||||
if not isinstance(item, str):
|
||||
continue
|
||||
s = item.strip()
|
||||
if not s:
|
||||
continue
|
||||
out.append(s)
|
||||
return out
|
||||
|
||||
|
||||
def _extract_response_text(content: object) -> str:
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts: list[str] = []
|
||||
for block in content:
|
||||
if isinstance(block, str):
|
||||
parts.append(block)
|
||||
elif isinstance(block, dict) and block.get("type") in {"text", "output_text"}:
|
||||
text = block.get("text")
|
||||
if isinstance(text, str):
|
||||
parts.append(text)
|
||||
return "\n".join(parts) if parts else ""
|
||||
if content is None:
|
||||
return ""
|
||||
return str(content)
|
||||
|
||||
|
||||
def _format_conversation(messages: list[SuggestionMessage]) -> str:
|
||||
parts: list[str] = []
|
||||
for m in messages:
|
||||
role = m.role.strip().lower()
|
||||
if role in ("user", "human"):
|
||||
parts.append(f"User: {m.content.strip()}")
|
||||
elif role in ("assistant", "ai"):
|
||||
parts.append(f"Assistant: {m.content.strip()}")
|
||||
else:
|
||||
parts.append(f"{m.role}: {m.content.strip()}")
|
||||
return "\n".join(parts).strip()
|
||||
|
||||
|
||||
@router.post(
|
||||
"/threads/{thread_id}/suggestions",
|
||||
response_model=SuggestionsResponse,
|
||||
summary="Generate Follow-up Questions",
|
||||
description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
|
||||
)
|
||||
async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> SuggestionsResponse:
|
||||
if not request.messages:
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
|
||||
n = request.n
|
||||
conversation = _format_conversation(request.messages)
|
||||
if not conversation:
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
|
||||
system_instruction = (
|
||||
"You are generating follow-up questions to help the user continue the conversation.\n"
|
||||
f"Based on the conversation below, produce EXACTLY {n} short questions the user might ask next.\n"
|
||||
"Requirements:\n"
|
||||
"- Questions must be relevant to the preceding conversation.\n"
|
||||
"- Questions must be written in the same language as the user.\n"
|
||||
"- Keep each question concise (ideally <= 20 words / <= 40 Chinese characters).\n"
|
||||
"- Do NOT include numbering, markdown, or any extra text.\n"
|
||||
"- Output MUST be a JSON array of strings only.\n"
|
||||
)
|
||||
user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
|
||||
|
||||
try:
|
||||
model = create_chat_model(name=request.model_name, thinking_enabled=False)
|
||||
response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
|
||||
raw = _extract_response_text(response.content)
|
||||
suggestions = _parse_json_string_list(raw) or []
|
||||
cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]
|
||||
cleaned = cleaned[:n]
|
||||
return SuggestionsResponse(suggestions=cleaned)
|
||||
except Exception as exc:
|
||||
logger.exception("Failed to generate suggestions: thread_id=%s err=%s", thread_id, exc)
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
@@ -1,267 +0,0 @@
|
||||
"""Runs endpoints — create, stream, wait, cancel.
|
||||
|
||||
Implements the LangGraph Platform runs API on top of
|
||||
:class:`deerflow.agents.runs.RunManager` and
|
||||
:class:`deerflow.agents.stream_bridge.StreamBridge`.
|
||||
|
||||
SSE format is aligned with the LangGraph Platform protocol so that
|
||||
the ``useStream`` React hook from ``@langchain/langgraph-sdk/react``
|
||||
works without modification.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any, Literal
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Query, Request
|
||||
from fastapi.responses import Response, StreamingResponse
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
|
||||
from app.gateway.services import sse_consumer, start_run
|
||||
from deerflow.runtime import RunRecord, serialize_channel_values
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/threads", tags=["runs"])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Request / response models
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class RunCreateRequest(BaseModel):
|
||||
assistant_id: str | None = Field(default=None, description="Agent / assistant to use")
|
||||
input: dict[str, Any] | None = Field(default=None, description="Graph input (e.g. {messages: [...]})")
|
||||
command: dict[str, Any] | None = Field(default=None, description="LangGraph Command")
|
||||
metadata: dict[str, Any] | None = Field(default=None, description="Run metadata")
|
||||
config: dict[str, Any] | None = Field(default=None, description="RunnableConfig overrides")
|
||||
context: dict[str, Any] | None = Field(default=None, description="DeerFlow context overrides (model_name, thinking_enabled, etc.)")
|
||||
webhook: str | None = Field(default=None, description="Completion callback URL")
|
||||
checkpoint_id: str | None = Field(default=None, description="Resume from checkpoint")
|
||||
checkpoint: dict[str, Any] | None = Field(default=None, description="Full checkpoint object")
|
||||
interrupt_before: list[str] | Literal["*"] | None = Field(default=None, description="Nodes to interrupt before")
|
||||
interrupt_after: list[str] | Literal["*"] | None = Field(default=None, description="Nodes to interrupt after")
|
||||
stream_mode: list[str] | str | None = Field(default=None, description="Stream mode(s)")
|
||||
stream_subgraphs: bool = Field(default=False, description="Include subgraph events")
|
||||
stream_resumable: bool | None = Field(default=None, description="SSE resumable mode")
|
||||
on_disconnect: Literal["cancel", "continue"] = Field(default="cancel", description="Behaviour on SSE disconnect")
|
||||
on_completion: Literal["delete", "keep"] = Field(default="keep", description="Delete temp thread on completion")
|
||||
multitask_strategy: Literal["reject", "rollback", "interrupt", "enqueue"] = Field(default="reject", description="Concurrency strategy")
|
||||
after_seconds: float | None = Field(default=None, description="Delayed execution")
|
||||
if_not_exists: Literal["reject", "create"] = Field(default="create", description="Thread creation policy")
|
||||
feedback_keys: list[str] | None = Field(default=None, description="LangSmith feedback keys")
|
||||
|
||||
|
||||
class RunResponse(BaseModel):
|
||||
run_id: str
|
||||
thread_id: str
|
||||
assistant_id: str | None = None
|
||||
status: str
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
kwargs: dict[str, Any] = Field(default_factory=dict)
|
||||
multitask_strategy: str = "reject"
|
||||
created_at: str = ""
|
||||
updated_at: str = ""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _record_to_response(record: RunRecord) -> RunResponse:
|
||||
return RunResponse(
|
||||
run_id=record.run_id,
|
||||
thread_id=record.thread_id,
|
||||
assistant_id=record.assistant_id,
|
||||
status=record.status.value,
|
||||
metadata=record.metadata,
|
||||
kwargs=record.kwargs,
|
||||
multitask_strategy=record.multitask_strategy,
|
||||
created_at=record.created_at,
|
||||
updated_at=record.updated_at,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Endpoints
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs", response_model=RunResponse)
|
||||
async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -> RunResponse:
|
||||
"""Create a background run (returns immediately)."""
|
||||
record = await start_run(body, thread_id, request)
|
||||
return _record_to_response(record)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/stream")
|
||||
async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -> StreamingResponse:
|
||||
"""Create a run and stream events via SSE.
|
||||
|
||||
The response includes a ``Content-Location`` header with the run's
|
||||
resource URL, matching the LangGraph Platform protocol. The
|
||||
``useStream`` React hook uses this to extract run metadata.
|
||||
"""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
# LangGraph Platform includes run metadata in this header.
|
||||
# The SDK uses a greedy regex to extract the run id from this path,
|
||||
# so it must point at the canonical run resource without extra suffixes.
|
||||
"Content-Location": f"/api/threads/{thread_id}/runs/{record.run_id}",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/wait", response_model=dict)
|
||||
async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
|
||||
"""Create a run and block until it completes, returning the final state."""
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
if record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
checkpointer = get_checkpointer(request)
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
if checkpoint_tuple is not None:
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
return serialize_channel_values(channel_values)
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch final state for run %s", record.run_id)
|
||||
|
||||
return {"status": record.status.value, "error": record.error}
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs", response_model=list[RunResponse])
|
||||
async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
|
||||
"""List all runs for a thread."""
|
||||
run_mgr = get_run_manager(request)
|
||||
records = await run_mgr.list_by_thread(thread_id)
|
||||
return [_record_to_response(r) for r in records]
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs/{run_id}", response_model=RunResponse)
|
||||
async def get_run(thread_id: str, run_id: str, request: Request) -> RunResponse:
|
||||
"""Get details of a specific run."""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
return _record_to_response(record)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/{run_id}/cancel")
|
||||
async def cancel_run(
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
request: Request,
|
||||
wait: bool = Query(default=False, description="Block until run completes after cancel"),
|
||||
action: Literal["interrupt", "rollback"] = Query(default="interrupt", description="Cancel action"),
|
||||
) -> Response:
|
||||
"""Cancel a running or pending run.
|
||||
|
||||
- action=interrupt: Stop execution, keep current checkpoint (can be resumed)
|
||||
- action=rollback: Stop execution, revert to pre-run checkpoint state
|
||||
- wait=true: Block until the run fully stops, return 204
|
||||
- wait=false: Return immediately with 202
|
||||
"""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
cancelled = await run_mgr.cancel(run_id, action=action)
|
||||
if not cancelled:
|
||||
raise HTTPException(
|
||||
status_code=409,
|
||||
detail=f"Run {run_id} is not cancellable (status: {record.status.value})",
|
||||
)
|
||||
|
||||
if wait and record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
return Response(status_code=204)
|
||||
|
||||
return Response(status_code=202)
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs/{run_id}/join")
|
||||
async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingResponse:
|
||||
"""Join an existing run's SSE stream."""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
|
||||
async def stream_existing_run(
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
request: Request,
|
||||
action: Literal["interrupt", "rollback"] | None = Query(default=None, description="Cancel action"),
|
||||
wait: int = Query(default=0, description="Block until cancelled (1) or return immediately (0)"),
|
||||
):
|
||||
"""Join an existing run's SSE stream (GET), or cancel-then-stream (POST).
|
||||
|
||||
The LangGraph SDK's ``joinStream`` and ``useStream`` stop button both use
|
||||
``POST`` to this endpoint. When ``action=interrupt`` or ``action=rollback``
|
||||
is present the run is cancelled first; the response then streams any
|
||||
remaining buffered events so the client observes a clean shutdown.
|
||||
"""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
# Cancel if an action was requested (stop-button / interrupt flow)
|
||||
if action is not None:
|
||||
cancelled = await run_mgr.cancel(run_id, action=action)
|
||||
if cancelled and wait and record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except (asyncio.CancelledError, Exception):
|
||||
pass
|
||||
return Response(status_code=204)
|
||||
|
||||
bridge = get_stream_bridge(request)
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
@@ -1,682 +0,0 @@
|
||||
"""Thread CRUD, state, and history endpoints.
|
||||
|
||||
Combines the existing thread-local filesystem cleanup with LangGraph
|
||||
Platform-compatible thread management backed by the checkpointer.
|
||||
|
||||
Channel values returned in state responses are serialized through
|
||||
:func:`deerflow.runtime.serialization.serialize_channel_values` to
|
||||
ensure LangChain message objects are converted to JSON-safe dicts
|
||||
matching the LangGraph Platform wire format expected by the
|
||||
``useStream`` React hook.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_store
|
||||
from deerflow.config.paths import Paths, get_paths
|
||||
from deerflow.runtime import serialize_channel_values
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Store namespace
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
THREADS_NS: tuple[str, ...] = ("threads",)
|
||||
"""Namespace used by the Store for thread metadata records."""
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/threads", tags=["threads"])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Response / request models
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ThreadDeleteResponse(BaseModel):
|
||||
"""Response model for thread cleanup."""
|
||||
|
||||
success: bool
|
||||
message: str
|
||||
|
||||
|
||||
class ThreadResponse(BaseModel):
|
||||
"""Response model for a single thread."""
|
||||
|
||||
thread_id: str = Field(description="Unique thread identifier")
|
||||
status: str = Field(default="idle", description="Thread status: idle, busy, interrupted, error")
|
||||
created_at: str = Field(default="", description="ISO timestamp")
|
||||
updated_at: str = Field(default="", description="ISO timestamp")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Thread metadata")
|
||||
values: dict[str, Any] = Field(default_factory=dict, description="Current state channel values")
|
||||
interrupts: dict[str, Any] = Field(default_factory=dict, description="Pending interrupts")
|
||||
|
||||
|
||||
class ThreadCreateRequest(BaseModel):
|
||||
"""Request body for creating a thread."""
|
||||
|
||||
thread_id: str | None = Field(default=None, description="Optional thread ID (auto-generated if omitted)")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Initial metadata")
|
||||
|
||||
|
||||
class ThreadSearchRequest(BaseModel):
|
||||
"""Request body for searching threads."""
|
||||
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata filter (exact match)")
|
||||
limit: int = Field(default=100, ge=1, le=1000, description="Maximum results")
|
||||
offset: int = Field(default=0, ge=0, description="Pagination offset")
|
||||
status: str | None = Field(default=None, description="Filter by thread status")
|
||||
|
||||
|
||||
class ThreadStateResponse(BaseModel):
|
||||
"""Response model for thread state."""
|
||||
|
||||
values: dict[str, Any] = Field(default_factory=dict, description="Current channel values")
|
||||
next: list[str] = Field(default_factory=list, description="Next tasks to execute")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Checkpoint metadata")
|
||||
checkpoint: dict[str, Any] = Field(default_factory=dict, description="Checkpoint info")
|
||||
checkpoint_id: str | None = Field(default=None, description="Current checkpoint ID")
|
||||
parent_checkpoint_id: str | None = Field(default=None, description="Parent checkpoint ID")
|
||||
created_at: str | None = Field(default=None, description="Checkpoint timestamp")
|
||||
tasks: list[dict[str, Any]] = Field(default_factory=list, description="Interrupted task details")
|
||||
|
||||
|
||||
class ThreadPatchRequest(BaseModel):
|
||||
"""Request body for patching thread metadata."""
|
||||
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata to merge")
|
||||
|
||||
|
||||
class ThreadStateUpdateRequest(BaseModel):
|
||||
"""Request body for updating thread state (human-in-the-loop resume)."""
|
||||
|
||||
values: dict[str, Any] | None = Field(default=None, description="Channel values to merge")
|
||||
checkpoint_id: str | None = Field(default=None, description="Checkpoint to branch from")
|
||||
checkpoint: dict[str, Any] | None = Field(default=None, description="Full checkpoint object")
|
||||
as_node: str | None = Field(default=None, description="Node identity for the update")
|
||||
|
||||
|
||||
class HistoryEntry(BaseModel):
|
||||
"""Single checkpoint history entry."""
|
||||
|
||||
checkpoint_id: str
|
||||
parent_checkpoint_id: str | None = None
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
values: dict[str, Any] = Field(default_factory=dict)
|
||||
created_at: str | None = None
|
||||
next: list[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class ThreadHistoryRequest(BaseModel):
|
||||
"""Request body for checkpoint history."""
|
||||
|
||||
limit: int = Field(default=10, ge=1, le=100, description="Maximum entries")
|
||||
before: str | None = Field(default=None, description="Cursor for pagination")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _delete_thread_data(thread_id: str, paths: Paths | None = None) -> ThreadDeleteResponse:
|
||||
"""Delete local persisted filesystem data for a thread."""
|
||||
path_manager = paths or get_paths()
|
||||
try:
|
||||
path_manager.delete_thread_dir(thread_id)
|
||||
except ValueError as exc:
|
||||
raise HTTPException(status_code=422, detail=str(exc)) from exc
|
||||
except FileNotFoundError:
|
||||
# Not critical — thread data may not exist on disk
|
||||
logger.debug("No local thread data to delete for %s", thread_id)
|
||||
return ThreadDeleteResponse(success=True, message=f"No local data for {thread_id}")
|
||||
except Exception as exc:
|
||||
logger.exception("Failed to delete thread data for %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to delete local thread data.") from exc
|
||||
|
||||
logger.info("Deleted local thread data for %s", thread_id)
|
||||
return ThreadDeleteResponse(success=True, message=f"Deleted local thread data for {thread_id}")
|
||||
|
||||
|
||||
async def _store_get(store, thread_id: str) -> dict | None:
|
||||
"""Fetch a thread record from the Store; returns ``None`` if absent."""
|
||||
item = await store.aget(THREADS_NS, thread_id)
|
||||
return item.value if item is not None else None
|
||||
|
||||
|
||||
async def _store_put(store, record: dict) -> None:
|
||||
"""Write a thread record to the Store."""
|
||||
await store.aput(THREADS_NS, record["thread_id"], record)
|
||||
|
||||
|
||||
async def _store_upsert(store, thread_id: str, *, metadata: dict | None = None, values: dict | None = None) -> None:
|
||||
"""Create or refresh a thread record in the Store.
|
||||
|
||||
On creation the record is written with ``status="idle"``. On update only
|
||||
``updated_at`` (and optionally ``metadata`` / ``values``) are changed so
|
||||
that existing fields are preserved.
|
||||
|
||||
``values`` carries the agent-state snapshot exposed to the frontend
|
||||
(currently just ``{"title": "..."}``).
|
||||
"""
|
||||
now = time.time()
|
||||
existing = await _store_get(store, thread_id)
|
||||
if existing is None:
|
||||
await _store_put(
|
||||
store,
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
"metadata": metadata or {},
|
||||
"values": values or {},
|
||||
},
|
||||
)
|
||||
else:
|
||||
val = dict(existing)
|
||||
val["updated_at"] = now
|
||||
if metadata:
|
||||
val.setdefault("metadata", {}).update(metadata)
|
||||
if values:
|
||||
val.setdefault("values", {}).update(values)
|
||||
await _store_put(store, val)
|
||||
|
||||
|
||||
def _derive_thread_status(checkpoint_tuple) -> str:
|
||||
"""Derive thread status from checkpoint metadata."""
|
||||
if checkpoint_tuple is None:
|
||||
return "idle"
|
||||
pending_writes = getattr(checkpoint_tuple, "pending_writes", None) or []
|
||||
|
||||
# Check for error in pending writes
|
||||
for pw in pending_writes:
|
||||
if len(pw) >= 2 and pw[1] == "__error__":
|
||||
return "error"
|
||||
|
||||
# Check for pending next tasks (indicates interrupt)
|
||||
tasks = getattr(checkpoint_tuple, "tasks", None)
|
||||
if tasks:
|
||||
return "interrupted"
|
||||
|
||||
return "idle"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Endpoints
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@router.delete("/{thread_id}", response_model=ThreadDeleteResponse)
|
||||
async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteResponse:
|
||||
"""Delete local persisted filesystem data for a thread.
|
||||
|
||||
Cleans DeerFlow-managed thread directories, removes checkpoint data,
|
||||
and removes the thread record from the Store.
|
||||
"""
|
||||
# Clean local filesystem
|
||||
response = _delete_thread_data(thread_id)
|
||||
|
||||
# Remove from Store (best-effort)
|
||||
store = get_store(request)
|
||||
if store is not None:
|
||||
try:
|
||||
await store.adelete(THREADS_NS, thread_id)
|
||||
except Exception:
|
||||
logger.debug("Could not delete store record for thread %s (not critical)", thread_id)
|
||||
|
||||
# Remove checkpoints (best-effort)
|
||||
checkpointer = getattr(request.app.state, "checkpointer", None)
|
||||
if checkpointer is not None:
|
||||
try:
|
||||
if hasattr(checkpointer, "adelete_thread"):
|
||||
await checkpointer.adelete_thread(thread_id)
|
||||
except Exception:
|
||||
logger.debug("Could not delete checkpoints for thread %s (not critical)", thread_id)
|
||||
|
||||
return response
|
||||
|
||||
|
||||
@router.post("", response_model=ThreadResponse)
|
||||
async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadResponse:
|
||||
"""Create a new thread.
|
||||
|
||||
The thread record is written to the Store (for fast listing) and an
|
||||
empty checkpoint is written to the checkpointer (for state reads).
|
||||
Idempotent: returns the existing record when ``thread_id`` already exists.
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
thread_id = body.thread_id or str(uuid.uuid4())
|
||||
now = time.time()
|
||||
|
||||
# Idempotency: return existing record from Store when already present
|
||||
if store is not None:
|
||||
existing_record = await _store_get(store, thread_id)
|
||||
if existing_record is not None:
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=existing_record.get("status", "idle"),
|
||||
created_at=str(existing_record.get("created_at", "")),
|
||||
updated_at=str(existing_record.get("updated_at", "")),
|
||||
metadata=existing_record.get("metadata", {}),
|
||||
)
|
||||
|
||||
# Write thread record to Store
|
||||
if store is not None:
|
||||
try:
|
||||
await _store_put(
|
||||
store,
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
"metadata": body.metadata,
|
||||
},
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Failed to write thread %s to store", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to create thread")
|
||||
|
||||
# Write an empty checkpoint so state endpoints work immediately
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
from langgraph.checkpoint.base import empty_checkpoint
|
||||
|
||||
ckpt_metadata = {
|
||||
"step": -1,
|
||||
"source": "input",
|
||||
"writes": None,
|
||||
"parents": {},
|
||||
**body.metadata,
|
||||
"created_at": now,
|
||||
}
|
||||
await checkpointer.aput(config, empty_checkpoint(), ckpt_metadata, {})
|
||||
except Exception:
|
||||
logger.exception("Failed to create checkpoint for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to create thread")
|
||||
|
||||
logger.info("Thread created: %s", thread_id)
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status="idle",
|
||||
created_at=str(now),
|
||||
updated_at=str(now),
|
||||
metadata=body.metadata,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/search", response_model=list[ThreadResponse])
|
||||
async def search_threads(body: ThreadSearchRequest, request: Request) -> list[ThreadResponse]:
|
||||
"""Search and list threads.
|
||||
|
||||
Two-phase approach:
|
||||
|
||||
**Phase 1 — Store (fast path, O(threads))**: returns threads that were
|
||||
created or run through this Gateway. Store records are tiny metadata
|
||||
dicts so fetching all of them at once is cheap.
|
||||
|
||||
**Phase 2 — Checkpointer supplement (lazy migration)**: threads that
|
||||
were created directly by LangGraph Server (and therefore absent from the
|
||||
Store) are discovered here by iterating the shared checkpointer. Any
|
||||
newly found thread is immediately written to the Store so that the next
|
||||
search skips Phase 2 for that thread — the Store converges to a full
|
||||
index over time without a one-shot migration job.
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 1: Store
|
||||
# -----------------------------------------------------------------------
|
||||
merged: dict[str, ThreadResponse] = {}
|
||||
|
||||
if store is not None:
|
||||
try:
|
||||
items = await store.asearch(THREADS_NS, limit=10_000)
|
||||
except Exception:
|
||||
logger.warning("Store search failed — falling back to checkpointer only", exc_info=True)
|
||||
items = []
|
||||
|
||||
for item in items:
|
||||
val = item.value
|
||||
merged[val["thread_id"]] = ThreadResponse(
|
||||
thread_id=val["thread_id"],
|
||||
status=val.get("status", "idle"),
|
||||
created_at=str(val.get("created_at", "")),
|
||||
updated_at=str(val.get("updated_at", "")),
|
||||
metadata=val.get("metadata", {}),
|
||||
values=val.get("values", {}),
|
||||
)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 2: Checkpointer supplement
|
||||
# Discovers threads not yet in the Store (e.g. created by LangGraph
|
||||
# Server) and lazily migrates them so future searches skip this phase.
|
||||
# -----------------------------------------------------------------------
|
||||
try:
|
||||
async for checkpoint_tuple in checkpointer.alist(None):
|
||||
cfg = getattr(checkpoint_tuple, "config", {})
|
||||
thread_id = cfg.get("configurable", {}).get("thread_id")
|
||||
if not thread_id or thread_id in merged:
|
||||
continue
|
||||
|
||||
# Skip sub-graph checkpoints (checkpoint_ns is non-empty for those)
|
||||
if cfg.get("configurable", {}).get("checkpoint_ns", ""):
|
||||
continue
|
||||
|
||||
ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
# Strip LangGraph internal keys from the user-visible metadata dict
|
||||
user_meta = {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")}
|
||||
|
||||
# Extract state values (title) from the checkpoint's channel_values
|
||||
checkpoint_data = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint_data.get("channel_values", {})
|
||||
ckpt_values = {}
|
||||
if title := channel_values.get("title"):
|
||||
ckpt_values["title"] = title
|
||||
|
||||
thread_resp = ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=_derive_thread_status(checkpoint_tuple),
|
||||
created_at=str(ckpt_meta.get("created_at", "")),
|
||||
updated_at=str(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
|
||||
metadata=user_meta,
|
||||
values=ckpt_values,
|
||||
)
|
||||
merged[thread_id] = thread_resp
|
||||
|
||||
# Lazy migration — write to Store so the next search finds it there
|
||||
if store is not None:
|
||||
try:
|
||||
await _store_upsert(store, thread_id, metadata=user_meta, values=ckpt_values or None)
|
||||
except Exception:
|
||||
logger.debug("Failed to migrate thread %s to store (non-fatal)", thread_id)
|
||||
except Exception:
|
||||
logger.exception("Checkpointer scan failed during thread search")
|
||||
# Don't raise — return whatever was collected from Store + partial scan
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 3: Filter → sort → paginate
|
||||
# -----------------------------------------------------------------------
|
||||
results = list(merged.values())
|
||||
|
||||
if body.metadata:
|
||||
results = [r for r in results if all(r.metadata.get(k) == v for k, v in body.metadata.items())]
|
||||
|
||||
if body.status:
|
||||
results = [r for r in results if r.status == body.status]
|
||||
|
||||
results.sort(key=lambda r: r.updated_at, reverse=True)
|
||||
return results[body.offset : body.offset + body.limit]
|
||||
|
||||
|
||||
@router.patch("/{thread_id}", response_model=ThreadResponse)
|
||||
async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Request) -> ThreadResponse:
|
||||
"""Merge metadata into a thread record."""
|
||||
store = get_store(request)
|
||||
if store is None:
|
||||
raise HTTPException(status_code=503, detail="Store not available")
|
||||
|
||||
record = await _store_get(store, thread_id)
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
now = time.time()
|
||||
updated = dict(record)
|
||||
updated.setdefault("metadata", {}).update(body.metadata)
|
||||
updated["updated_at"] = now
|
||||
|
||||
try:
|
||||
await _store_put(store, updated)
|
||||
except Exception:
|
||||
logger.exception("Failed to patch thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to update thread")
|
||||
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=updated.get("status", "idle"),
|
||||
created_at=str(updated.get("created_at", "")),
|
||||
updated_at=str(now),
|
||||
metadata=updated.get("metadata", {}),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/{thread_id}", response_model=ThreadResponse)
|
||||
async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
|
||||
"""Get thread info.
|
||||
|
||||
Reads metadata from the Store and derives the accurate execution
|
||||
status from the checkpointer. Falls back to the checkpointer alone
|
||||
for threads that pre-date Store adoption (backward compat).
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
record: dict | None = None
|
||||
if store is not None:
|
||||
record = await _store_get(store, thread_id)
|
||||
|
||||
# Derive accurate status from the checkpointer
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get checkpoint for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread")
|
||||
|
||||
if record is None and checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
# If the thread exists in the checkpointer but not the store (e.g. legacy
|
||||
# data), synthesize a minimal store record from the checkpoint metadata.
|
||||
if record is None and checkpoint_tuple is not None:
|
||||
ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
record = {
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": ckpt_meta.get("created_at", ""),
|
||||
"updated_at": ckpt_meta.get("updated_at", ckpt_meta.get("created_at", "")),
|
||||
"metadata": {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")},
|
||||
}
|
||||
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
status = _derive_thread_status(checkpoint_tuple) if checkpoint_tuple is not None else record.get("status", "idle")
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {} if checkpoint_tuple is not None else {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=status,
|
||||
created_at=str(record.get("created_at", "")),
|
||||
updated_at=str(record.get("updated_at", "")),
|
||||
metadata=record.get("metadata", {}),
|
||||
values=serialize_channel_values(channel_values),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/{thread_id}/state", response_model=ThreadStateResponse)
|
||||
async def get_thread_state(thread_id: str, request: Request) -> ThreadStateResponse:
|
||||
"""Get the latest state snapshot for a thread.
|
||||
|
||||
Channel values are serialized to ensure LangChain message objects
|
||||
are converted to JSON-safe dicts.
|
||||
"""
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread state")
|
||||
|
||||
if checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
metadata = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
checkpoint_id = None
|
||||
ckpt_config = getattr(checkpoint_tuple, "config", {})
|
||||
if ckpt_config:
|
||||
checkpoint_id = ckpt_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
parent_config = getattr(checkpoint_tuple, "parent_config", None)
|
||||
parent_checkpoint_id = None
|
||||
if parent_config:
|
||||
parent_checkpoint_id = parent_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
tasks_raw = getattr(checkpoint_tuple, "tasks", []) or []
|
||||
next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
|
||||
tasks = [{"id": getattr(t, "id", ""), "name": getattr(t, "name", "")} for t in tasks_raw]
|
||||
|
||||
return ThreadStateResponse(
|
||||
values=serialize_channel_values(channel_values),
|
||||
next=next_tasks,
|
||||
metadata=metadata,
|
||||
checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
|
||||
checkpoint_id=checkpoint_id,
|
||||
parent_checkpoint_id=parent_checkpoint_id,
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
tasks=tasks,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/state", response_model=ThreadStateResponse)
|
||||
async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, request: Request) -> ThreadStateResponse:
|
||||
"""Update thread state (e.g. for human-in-the-loop resume or title rename).
|
||||
|
||||
Writes a new checkpoint that merges *body.values* into the latest
|
||||
channel values, then syncs any updated ``title`` field back to the Store
|
||||
so that ``/threads/search`` reflects the change immediately.
|
||||
"""
|
||||
checkpointer = get_checkpointer(request)
|
||||
store = get_store(request)
|
||||
|
||||
# checkpoint_ns must be present in the config for aput — default to ""
|
||||
# (the root graph namespace). checkpoint_id is optional; omitting it
|
||||
# fetches the latest checkpoint for the thread.
|
||||
read_config: dict[str, Any] = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"checkpoint_ns": "",
|
||||
}
|
||||
}
|
||||
if body.checkpoint_id:
|
||||
read_config["configurable"]["checkpoint_id"] = body.checkpoint_id
|
||||
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(read_config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread state")
|
||||
|
||||
if checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
# Work on mutable copies so we don't accidentally mutate cached objects.
|
||||
checkpoint: dict[str, Any] = dict(getattr(checkpoint_tuple, "checkpoint", {}) or {})
|
||||
metadata: dict[str, Any] = dict(getattr(checkpoint_tuple, "metadata", {}) or {})
|
||||
channel_values: dict[str, Any] = dict(checkpoint.get("channel_values", {}))
|
||||
|
||||
if body.values:
|
||||
channel_values.update(body.values)
|
||||
|
||||
checkpoint["channel_values"] = channel_values
|
||||
metadata["updated_at"] = time.time()
|
||||
|
||||
if body.as_node:
|
||||
metadata["source"] = "update"
|
||||
metadata["step"] = metadata.get("step", 0) + 1
|
||||
metadata["writes"] = {body.as_node: body.values}
|
||||
|
||||
# aput requires checkpoint_ns in the config — use the same config used for the
|
||||
# read (which always includes checkpoint_ns=""). Do NOT include checkpoint_id
|
||||
# so that aput generates a fresh checkpoint ID for the new snapshot.
|
||||
write_config: dict[str, Any] = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"checkpoint_ns": "",
|
||||
}
|
||||
}
|
||||
try:
|
||||
new_config = await checkpointer.aput(write_config, checkpoint, metadata, {})
|
||||
except Exception:
|
||||
logger.exception("Failed to update state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to update thread state")
|
||||
|
||||
new_checkpoint_id: str | None = None
|
||||
if isinstance(new_config, dict):
|
||||
new_checkpoint_id = new_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
# Sync title changes to the Store so /threads/search reflects them immediately.
|
||||
if store is not None and body.values and "title" in body.values:
|
||||
try:
|
||||
await _store_upsert(store, thread_id, values={"title": body.values["title"]})
|
||||
except Exception:
|
||||
logger.debug("Failed to sync title to store for thread %s (non-fatal)", thread_id)
|
||||
|
||||
return ThreadStateResponse(
|
||||
values=serialize_channel_values(channel_values),
|
||||
next=[],
|
||||
metadata=metadata,
|
||||
checkpoint_id=new_checkpoint_id,
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/history", response_model=list[HistoryEntry])
|
||||
async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request: Request) -> list[HistoryEntry]:
|
||||
"""Get checkpoint history for a thread."""
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
config: dict[str, Any] = {"configurable": {"thread_id": thread_id}}
|
||||
if body.before:
|
||||
config["configurable"]["checkpoint_id"] = body.before
|
||||
|
||||
entries: list[HistoryEntry] = []
|
||||
try:
|
||||
async for checkpoint_tuple in checkpointer.alist(config, limit=body.limit):
|
||||
ckpt_config = getattr(checkpoint_tuple, "config", {})
|
||||
parent_config = getattr(checkpoint_tuple, "parent_config", None)
|
||||
metadata = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
|
||||
checkpoint_id = ckpt_config.get("configurable", {}).get("checkpoint_id", "")
|
||||
parent_id = None
|
||||
if parent_config:
|
||||
parent_id = parent_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
# Derive next tasks
|
||||
tasks_raw = getattr(checkpoint_tuple, "tasks", []) or []
|
||||
next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
|
||||
|
||||
entries.append(
|
||||
HistoryEntry(
|
||||
checkpoint_id=checkpoint_id,
|
||||
parent_checkpoint_id=parent_id,
|
||||
metadata=metadata,
|
||||
values=serialize_channel_values(channel_values),
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
next=next_tasks,
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Failed to get history for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread history")
|
||||
|
||||
return entries
|
||||
@@ -1,201 +0,0 @@
|
||||
"""Upload router for handling file uploads."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import stat
|
||||
|
||||
from fastapi import APIRouter, File, HTTPException, UploadFile
|
||||
from pydantic import BaseModel
|
||||
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.config.paths import get_paths
|
||||
from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
|
||||
from deerflow.uploads.manager import (
|
||||
PathTraversalError,
|
||||
delete_file_safe,
|
||||
enrich_file_listing,
|
||||
ensure_uploads_dir,
|
||||
get_uploads_dir,
|
||||
list_files_in_dir,
|
||||
normalize_filename,
|
||||
upload_artifact_url,
|
||||
upload_virtual_path,
|
||||
)
|
||||
from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
|
||||
|
||||
|
||||
class UploadResponse(BaseModel):
|
||||
"""Response model for file upload."""
|
||||
|
||||
success: bool
|
||||
files: list[dict[str, str]]
|
||||
message: str
|
||||
|
||||
|
||||
def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
|
||||
"""Ensure uploaded files remain writable when mounted into non-local sandboxes.
|
||||
|
||||
In AIO sandbox mode, the gateway writes the authoritative host-side file
|
||||
first, then the sandbox runtime may rewrite the same mounted path. Granting
|
||||
world-writable access here prevents permission mismatches between the
|
||||
gateway user and the sandbox runtime user.
|
||||
"""
|
||||
file_stat = os.lstat(file_path)
|
||||
if stat.S_ISLNK(file_stat.st_mode):
|
||||
logger.warning("Skipping sandbox chmod for symlinked upload path: %s", file_path)
|
||||
return
|
||||
|
||||
writable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IWUSR | stat.S_IWGRP | stat.S_IWOTH
|
||||
chmod_kwargs = {"follow_symlinks": False} if os.chmod in os.supports_follow_symlinks else {}
|
||||
os.chmod(file_path, writable_mode, **chmod_kwargs)
|
||||
|
||||
|
||||
def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
|
||||
return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
|
||||
|
||||
|
||||
def _get_uploads_config_value(key: str, default: object) -> object:
|
||||
"""Read a value from the uploads config, supporting dict and attribute access."""
|
||||
cfg = get_app_config()
|
||||
uploads_cfg = getattr(cfg, "uploads", None)
|
||||
if isinstance(uploads_cfg, dict):
|
||||
return uploads_cfg.get(key, default)
|
||||
return getattr(uploads_cfg, key, default)
|
||||
|
||||
|
||||
def _auto_convert_documents_enabled() -> bool:
|
||||
"""Return whether automatic host-side document conversion is enabled.
|
||||
|
||||
The secure default is disabled unless an operator explicitly opts in via
|
||||
uploads.auto_convert_documents in config.yaml.
|
||||
"""
|
||||
try:
|
||||
raw = _get_uploads_config_value("auto_convert_documents", False)
|
||||
if isinstance(raw, str):
|
||||
return raw.strip().lower() in {"1", "true", "yes", "on"}
|
||||
return bool(raw)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
@router.post("", response_model=UploadResponse)
|
||||
async def upload_files(
|
||||
thread_id: str,
|
||||
files: list[UploadFile] = File(...),
|
||||
) -> UploadResponse:
|
||||
"""Upload multiple files to a thread's uploads directory."""
|
||||
if not files:
|
||||
raise HTTPException(status_code=400, detail="No files provided")
|
||||
|
||||
try:
|
||||
uploads_dir = ensure_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
|
||||
uploaded_files = []
|
||||
|
||||
sandbox_provider = get_sandbox_provider()
|
||||
sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
|
||||
sandbox = None
|
||||
if sync_to_sandbox:
|
||||
sandbox_id = sandbox_provider.acquire(thread_id)
|
||||
sandbox = sandbox_provider.get(sandbox_id)
|
||||
auto_convert_documents = _auto_convert_documents_enabled()
|
||||
|
||||
for file in files:
|
||||
if not file.filename:
|
||||
continue
|
||||
|
||||
try:
|
||||
safe_filename = normalize_filename(file.filename)
|
||||
except ValueError:
|
||||
logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
|
||||
continue
|
||||
|
||||
try:
|
||||
content = await file.read()
|
||||
file_path = uploads_dir / safe_filename
|
||||
file_path.write_bytes(content)
|
||||
|
||||
virtual_path = upload_virtual_path(safe_filename)
|
||||
|
||||
if sync_to_sandbox and sandbox is not None:
|
||||
_make_file_sandbox_writable(file_path)
|
||||
sandbox.update_file(virtual_path, content)
|
||||
|
||||
file_info = {
|
||||
"filename": safe_filename,
|
||||
"size": str(len(content)),
|
||||
"path": str(sandbox_uploads / safe_filename),
|
||||
"virtual_path": virtual_path,
|
||||
"artifact_url": upload_artifact_url(thread_id, safe_filename),
|
||||
}
|
||||
|
||||
logger.info(f"Saved file: {safe_filename} ({len(content)} bytes) to {file_info['path']}")
|
||||
|
||||
file_ext = file_path.suffix.lower()
|
||||
if auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
|
||||
md_path = await convert_file_to_markdown(file_path)
|
||||
if md_path:
|
||||
md_virtual_path = upload_virtual_path(md_path.name)
|
||||
|
||||
if sync_to_sandbox and sandbox is not None:
|
||||
_make_file_sandbox_writable(md_path)
|
||||
sandbox.update_file(md_virtual_path, md_path.read_bytes())
|
||||
|
||||
file_info["markdown_file"] = md_path.name
|
||||
file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
|
||||
file_info["markdown_virtual_path"] = md_virtual_path
|
||||
file_info["markdown_artifact_url"] = upload_artifact_url(thread_id, md_path.name)
|
||||
|
||||
uploaded_files.append(file_info)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to upload {file.filename}: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
|
||||
|
||||
return UploadResponse(
|
||||
success=True,
|
||||
files=uploaded_files,
|
||||
message=f"Successfully uploaded {len(uploaded_files)} file(s)",
|
||||
)
|
||||
|
||||
|
||||
@router.get("/list", response_model=dict)
|
||||
async def list_uploaded_files(thread_id: str) -> dict:
|
||||
"""List all files in a thread's uploads directory."""
|
||||
try:
|
||||
uploads_dir = get_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
result = list_files_in_dir(uploads_dir)
|
||||
enrich_file_listing(result, thread_id)
|
||||
|
||||
# Gateway additionally includes the sandbox-relative path.
|
||||
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
|
||||
for f in result["files"]:
|
||||
f["path"] = str(sandbox_uploads / f["filename"])
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@router.delete("/{filename}")
|
||||
async def delete_uploaded_file(thread_id: str, filename: str) -> dict:
|
||||
"""Delete a file from a thread's uploads directory."""
|
||||
try:
|
||||
uploads_dir = get_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
try:
|
||||
return delete_file_safe(uploads_dir, filename, convertible_extensions=CONVERTIBLE_EXTENSIONS)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"File not found: {filename}")
|
||||
except PathTraversalError:
|
||||
raise HTTPException(status_code=400, detail="Invalid path")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete {filename}: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete {filename}: {str(e)}")
|
||||
@@ -1,369 +0,0 @@
|
||||
"""Run lifecycle service layer.
|
||||
|
||||
Centralizes the business logic for creating runs, formatting SSE
|
||||
frames, and consuming stream bridge events. Router modules
|
||||
(``thread_runs``, ``runs``) are thin HTTP handlers that delegate here.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from fastapi import HTTPException, Request
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_store, get_stream_bridge
|
||||
from deerflow.runtime import (
|
||||
END_SENTINEL,
|
||||
HEARTBEAT_SENTINEL,
|
||||
ConflictError,
|
||||
DisconnectMode,
|
||||
RunManager,
|
||||
RunRecord,
|
||||
RunStatus,
|
||||
StreamBridge,
|
||||
UnsupportedStrategyError,
|
||||
run_agent,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SSE formatting
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def format_sse(event: str, data: Any, *, event_id: str | None = None) -> str:
|
||||
"""Format a single SSE frame.
|
||||
|
||||
Field order: ``event:`` -> ``data:`` -> ``id:`` (optional) -> blank line.
|
||||
This matches the LangGraph Platform wire format consumed by the
|
||||
``useStream`` React hook and the Python ``langgraph-sdk`` SSE decoder.
|
||||
"""
|
||||
payload = json.dumps(data, default=str, ensure_ascii=False)
|
||||
parts = [f"event: {event}", f"data: {payload}"]
|
||||
if event_id:
|
||||
parts.append(f"id: {event_id}")
|
||||
parts.append("")
|
||||
parts.append("")
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Input / config helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def normalize_stream_modes(raw: list[str] | str | None) -> list[str]:
|
||||
"""Normalize the stream_mode parameter to a list.
|
||||
|
||||
Default matches what ``useStream`` expects: values + messages-tuple.
|
||||
"""
|
||||
if raw is None:
|
||||
return ["values"]
|
||||
if isinstance(raw, str):
|
||||
return [raw]
|
||||
return raw if raw else ["values"]
|
||||
|
||||
|
||||
def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
|
||||
"""Convert LangGraph Platform input format to LangChain state dict."""
|
||||
if raw_input is None:
|
||||
return {}
|
||||
messages = raw_input.get("messages")
|
||||
if messages and isinstance(messages, list):
|
||||
converted = []
|
||||
for msg in messages:
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get("role", msg.get("type", "user"))
|
||||
content = msg.get("content", "")
|
||||
if role in ("user", "human"):
|
||||
converted.append(HumanMessage(content=content))
|
||||
else:
|
||||
# TODO: handle other message types (system, ai, tool)
|
||||
converted.append(HumanMessage(content=content))
|
||||
else:
|
||||
converted.append(msg)
|
||||
return {**raw_input, "messages": converted}
|
||||
return raw_input
|
||||
|
||||
|
||||
_DEFAULT_ASSISTANT_ID = "lead_agent"
|
||||
|
||||
|
||||
def resolve_agent_factory(assistant_id: str | None):
|
||||
"""Resolve the agent factory callable from config.
|
||||
|
||||
Custom agents are implemented as ``lead_agent`` + an ``agent_name``
|
||||
injected into ``configurable`` — see :func:`build_run_config`. All
|
||||
``assistant_id`` values therefore map to the same factory; the routing
|
||||
happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
|
||||
"""
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
|
||||
return make_lead_agent
|
||||
|
||||
|
||||
def build_run_config(
|
||||
thread_id: str,
|
||||
request_config: dict[str, Any] | None,
|
||||
metadata: dict[str, Any] | None,
|
||||
*,
|
||||
assistant_id: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Build a RunnableConfig dict for the agent.
|
||||
|
||||
When *assistant_id* refers to a custom agent (anything other than
|
||||
``"lead_agent"`` / ``None``), the name is forwarded as
|
||||
``configurable["agent_name"]``. ``make_lead_agent`` reads this key to
|
||||
load the matching ``agents/<name>/SOUL.md`` and per-agent config —
|
||||
without it the agent silently runs as the default lead agent.
|
||||
|
||||
This mirrors the channel manager's ``_resolve_run_params`` logic so that
|
||||
the LangGraph Platform-compatible HTTP API and the IM channel path behave
|
||||
identically.
|
||||
"""
|
||||
config: dict[str, Any] = {"recursion_limit": 100}
|
||||
if request_config:
|
||||
# LangGraph >= 0.6.0 introduced ``context`` as the preferred way to
|
||||
# pass thread-level data and rejects requests that include both
|
||||
# ``configurable`` and ``context``. If the caller already sends
|
||||
# ``context``, honour it and skip our own ``configurable`` dict.
|
||||
if "context" in request_config:
|
||||
if "configurable" in request_config:
|
||||
logger.warning(
|
||||
"build_run_config: client sent both 'context' and 'configurable'; preferring 'context' (LangGraph >= 0.6.0). thread_id=%s, caller_configurable keys=%s",
|
||||
thread_id,
|
||||
list(request_config.get("configurable", {}).keys()),
|
||||
)
|
||||
config["context"] = request_config["context"]
|
||||
else:
|
||||
configurable = {"thread_id": thread_id}
|
||||
configurable.update(request_config.get("configurable", {}))
|
||||
config["configurable"] = configurable
|
||||
for k, v in request_config.items():
|
||||
if k not in ("configurable", "context"):
|
||||
config[k] = v
|
||||
else:
|
||||
config["configurable"] = {"thread_id": thread_id}
|
||||
|
||||
# Inject custom agent name when the caller specified a non-default assistant.
|
||||
# Honour an explicit configurable["agent_name"] in the request if already set.
|
||||
if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
|
||||
if "agent_name" not in config["configurable"]:
|
||||
normalized = assistant_id.strip().lower().replace("_", "-")
|
||||
if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
|
||||
raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
|
||||
config["configurable"]["agent_name"] = normalized
|
||||
if metadata:
|
||||
config.setdefault("metadata", {}).update(metadata)
|
||||
return config
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Run lifecycle
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _upsert_thread_in_store(store, thread_id: str, metadata: dict | None) -> None:
|
||||
"""Create or refresh the thread record in the Store.
|
||||
|
||||
Called from :func:`start_run` so that threads created via the stateless
|
||||
``/runs/stream`` endpoint (which never calls ``POST /threads``) still
|
||||
appear in ``/threads/search`` results.
|
||||
"""
|
||||
# Deferred import to avoid circular import with the threads router module.
|
||||
from app.gateway.routers.threads import _store_upsert
|
||||
|
||||
try:
|
||||
await _store_upsert(store, thread_id, metadata=metadata)
|
||||
except Exception:
|
||||
logger.warning("Failed to upsert thread %s in store (non-fatal)", thread_id)
|
||||
|
||||
|
||||
async def _sync_thread_title_after_run(
|
||||
run_task: asyncio.Task,
|
||||
thread_id: str,
|
||||
checkpointer: Any,
|
||||
store: Any,
|
||||
) -> None:
|
||||
"""Wait for *run_task* to finish, then persist the generated title to the Store.
|
||||
|
||||
TitleMiddleware writes the generated title to the LangGraph agent state
|
||||
(checkpointer) but the Gateway's Store record is not updated automatically.
|
||||
This coroutine closes that gap by reading the final checkpoint after the
|
||||
run completes and syncing ``values.title`` into the Store record so that
|
||||
subsequent ``/threads/search`` responses include the correct title.
|
||||
|
||||
Runs as a fire-and-forget :func:`asyncio.create_task`; failures are
|
||||
logged at DEBUG level and never propagate.
|
||||
"""
|
||||
# Wait for the background run task to complete (any outcome).
|
||||
# asyncio.wait does not propagate task exceptions — it just returns
|
||||
# when the task is done, cancelled, or failed.
|
||||
await asyncio.wait({run_task})
|
||||
|
||||
# Deferred import to avoid circular import with the threads router module.
|
||||
from app.gateway.routers.threads import _store_get, _store_put
|
||||
|
||||
try:
|
||||
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
ckpt_tuple = await checkpointer.aget_tuple(ckpt_config)
|
||||
if ckpt_tuple is None:
|
||||
return
|
||||
|
||||
channel_values = ckpt_tuple.checkpoint.get("channel_values", {})
|
||||
title = channel_values.get("title")
|
||||
if not title:
|
||||
return
|
||||
|
||||
existing = await _store_get(store, thread_id)
|
||||
if existing is None:
|
||||
return
|
||||
|
||||
updated = dict(existing)
|
||||
updated.setdefault("values", {})["title"] = title
|
||||
updated["updated_at"] = time.time()
|
||||
await _store_put(store, updated)
|
||||
logger.debug("Synced title %r for thread %s", title, thread_id)
|
||||
except Exception:
|
||||
logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id, exc_info=True)
|
||||
|
||||
|
||||
async def start_run(
|
||||
body: Any,
|
||||
thread_id: str,
|
||||
request: Request,
|
||||
) -> RunRecord:
|
||||
"""Create a RunRecord and launch the background agent task.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
body : RunCreateRequest
|
||||
The validated request body (typed as Any to avoid circular import
|
||||
with the router module that defines the Pydantic model).
|
||||
thread_id : str
|
||||
Target thread.
|
||||
request : Request
|
||||
FastAPI request — used to retrieve singletons from ``app.state``.
|
||||
"""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
store = get_store(request)
|
||||
|
||||
disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
|
||||
|
||||
try:
|
||||
record = await run_mgr.create_or_reject(
|
||||
thread_id,
|
||||
body.assistant_id,
|
||||
on_disconnect=disconnect,
|
||||
metadata=body.metadata or {},
|
||||
kwargs={"input": body.input, "config": body.config},
|
||||
multitask_strategy=body.multitask_strategy,
|
||||
)
|
||||
except ConflictError as exc:
|
||||
raise HTTPException(status_code=409, detail=str(exc)) from exc
|
||||
except UnsupportedStrategyError as exc:
|
||||
raise HTTPException(status_code=501, detail=str(exc)) from exc
|
||||
|
||||
# Ensure the thread is visible in /threads/search, even for threads that
|
||||
# were never explicitly created via POST /threads (e.g. stateless runs).
|
||||
store = get_store(request)
|
||||
if store is not None:
|
||||
await _upsert_thread_in_store(store, thread_id, body.metadata)
|
||||
|
||||
agent_factory = resolve_agent_factory(body.assistant_id)
|
||||
graph_input = normalize_input(body.input)
|
||||
config = build_run_config(thread_id, body.config, body.metadata, assistant_id=body.assistant_id)
|
||||
|
||||
# Merge DeerFlow-specific context overrides into configurable.
|
||||
# The ``context`` field is a custom extension for the langgraph-compat layer
|
||||
# that carries agent configuration (model_name, thinking_enabled, etc.).
|
||||
# Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
|
||||
context = getattr(body, "context", None)
|
||||
if context:
|
||||
_CONTEXT_CONFIGURABLE_KEYS = {
|
||||
"model_name",
|
||||
"mode",
|
||||
"thinking_enabled",
|
||||
"reasoning_effort",
|
||||
"is_plan_mode",
|
||||
"subagent_enabled",
|
||||
"max_concurrent_subagents",
|
||||
"agent_name",
|
||||
"is_bootstrap",
|
||||
}
|
||||
configurable = config.setdefault("configurable", {})
|
||||
for key in _CONTEXT_CONFIGURABLE_KEYS:
|
||||
if key in context:
|
||||
configurable.setdefault(key, context[key])
|
||||
|
||||
stream_modes = normalize_stream_modes(body.stream_mode)
|
||||
|
||||
task = asyncio.create_task(
|
||||
run_agent(
|
||||
bridge,
|
||||
run_mgr,
|
||||
record,
|
||||
checkpointer=checkpointer,
|
||||
store=store,
|
||||
agent_factory=agent_factory,
|
||||
graph_input=graph_input,
|
||||
config=config,
|
||||
stream_modes=stream_modes,
|
||||
stream_subgraphs=body.stream_subgraphs,
|
||||
interrupt_before=body.interrupt_before,
|
||||
interrupt_after=body.interrupt_after,
|
||||
)
|
||||
)
|
||||
record.task = task
|
||||
|
||||
# After the run completes, sync the title generated by TitleMiddleware from
|
||||
# the checkpointer into the Store record so that /threads/search returns the
|
||||
# correct title instead of an empty values dict.
|
||||
if store is not None:
|
||||
asyncio.create_task(_sync_thread_title_after_run(task, thread_id, checkpointer, store))
|
||||
|
||||
return record
|
||||
|
||||
|
||||
async def sse_consumer(
|
||||
bridge: StreamBridge,
|
||||
record: RunRecord,
|
||||
request: Request,
|
||||
run_mgr: RunManager,
|
||||
):
|
||||
"""Async generator that yields SSE frames from the bridge.
|
||||
|
||||
The ``finally`` block implements ``on_disconnect`` semantics:
|
||||
- ``cancel``: abort the background task on client disconnect.
|
||||
- ``continue``: let the task run; events are discarded.
|
||||
"""
|
||||
last_event_id = request.headers.get("Last-Event-ID")
|
||||
try:
|
||||
async for entry in bridge.subscribe(record.run_id, last_event_id=last_event_id):
|
||||
if await request.is_disconnected():
|
||||
break
|
||||
|
||||
if entry is HEARTBEAT_SENTINEL:
|
||||
yield ": heartbeat\n\n"
|
||||
continue
|
||||
|
||||
if entry is END_SENTINEL:
|
||||
yield format_sse("end", None, event_id=entry.id or None)
|
||||
return
|
||||
|
||||
yield format_sse(entry.event, entry.data, event_id=entry.id or None)
|
||||
|
||||
finally:
|
||||
if record.status in (RunStatus.pending, RunStatus.running):
|
||||
if record.on_disconnect == DisconnectMode.cancel:
|
||||
await run_mgr.cancel(record.run_id)
|
||||
@@ -1,91 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Debug script for lead_agent.
|
||||
Run this file directly in VS Code with breakpoints.
|
||||
|
||||
Requirements:
|
||||
Run with `uv run` from the backend/ directory so that the uv workspace
|
||||
resolves deerflow-harness and app packages correctly:
|
||||
|
||||
cd backend && PYTHONPATH=. uv run python debug.py
|
||||
|
||||
Usage:
|
||||
1. Set breakpoints in agent.py or other files
|
||||
2. Press F5 or use "Run and Debug" panel
|
||||
3. Input messages in the terminal to interact with the agent
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from deerflow.agents import make_lead_agent
|
||||
|
||||
load_dotenv()
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
# Initialize MCP tools at startup
|
||||
try:
|
||||
from deerflow.mcp import initialize_mcp_tools
|
||||
|
||||
await initialize_mcp_tools()
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to initialize MCP tools: {e}")
|
||||
|
||||
# Create agent with default config
|
||||
config = {
|
||||
"configurable": {
|
||||
"thread_id": "debug-thread-001",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": True,
|
||||
# Uncomment to use a specific model
|
||||
"model_name": "kimi-k2.5",
|
||||
}
|
||||
}
|
||||
|
||||
agent = make_lead_agent(config)
|
||||
|
||||
print("=" * 50)
|
||||
print("Lead Agent Debug Mode")
|
||||
print("Type 'quit' or 'exit' to stop")
|
||||
print("=" * 50)
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\nYou: ").strip()
|
||||
if not user_input:
|
||||
continue
|
||||
if user_input.lower() in ("quit", "exit"):
|
||||
print("Goodbye!")
|
||||
break
|
||||
|
||||
# Invoke the agent
|
||||
state = {"messages": [HumanMessage(content=user_input)]}
|
||||
result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
|
||||
|
||||
# Print the response
|
||||
if result.get("messages"):
|
||||
last_message = result["messages"][-1]
|
||||
print(f"\nAgent: {last_message.content}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\nInterrupted. Goodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"\nError: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,655 +0,0 @@
|
||||
# API Reference
|
||||
|
||||
This document provides a complete reference for the DeerFlow backend APIs.
|
||||
|
||||
## Overview
|
||||
|
||||
DeerFlow backend exposes two sets of APIs:
|
||||
|
||||
1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
|
||||
2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)
|
||||
|
||||
All APIs are accessed through the Nginx reverse proxy at port 2026.
|
||||
|
||||
## LangGraph API
|
||||
|
||||
Base URL: `/api/langgraph`
|
||||
|
||||
The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
|
||||
|
||||
### Threads
|
||||
|
||||
#### Create Thread
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"metadata": {}
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"thread_id": "abc123",
|
||||
"created_at": "2024-01-15T10:30:00Z",
|
||||
"metadata": {}
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Thread State
|
||||
|
||||
```http
|
||||
GET /api/langgraph/threads/{thread_id}/state
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"values": {
|
||||
"messages": [...],
|
||||
"sandbox": {...},
|
||||
"artifacts": [...],
|
||||
"thread_data": {...},
|
||||
"title": "Conversation Title"
|
||||
},
|
||||
"next": [],
|
||||
"config": {...}
|
||||
}
|
||||
```
|
||||
|
||||
### Runs
|
||||
|
||||
#### Create Run
|
||||
|
||||
Execute the agent with input.
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads/{thread_id}/runs
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hello, can you help me?"
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {
|
||||
"recursion_limit": 100,
|
||||
"configurable": {
|
||||
"model_name": "gpt-4",
|
||||
"thinking_enabled": false,
|
||||
"is_plan_mode": false
|
||||
}
|
||||
},
|
||||
"stream_mode": ["values", "messages-tuple", "custom"]
|
||||
}
|
||||
```
|
||||
|
||||
**Stream Mode Compatibility:**
|
||||
- Use: `values`, `messages-tuple`, `custom`, `updates`, `events`, `debug`, `tasks`, `checkpoints`
|
||||
- Do not use: `tools` (deprecated/invalid in current `langgraph-api` and will trigger schema validation errors)
|
||||
|
||||
**Recursion Limit:**
|
||||
|
||||
`config.recursion_limit` caps the number of graph steps LangGraph will execute
|
||||
in a single run. The `/api/langgraph/*` endpoints go straight to the LangGraph
|
||||
server and therefore inherit LangGraph's native default of **25**, which is
|
||||
too low for plan-mode or subagent-heavy runs — the agent typically errors out
|
||||
with `GraphRecursionError` after the first round of subagent results comes
|
||||
back, before the lead agent can synthesize the final answer.
|
||||
|
||||
DeerFlow's own Gateway and IM-channel paths mitigate this by defaulting to
|
||||
`100` in `build_run_config` (see `backend/app/gateway/services.py`), but
|
||||
clients calling the LangGraph API directly must set `recursion_limit`
|
||||
explicitly in the request body. `100` matches the Gateway default and is a
|
||||
safe starting point; increase it if you run deeply nested subagent graphs.
|
||||
|
||||
**Configurable Options:**
|
||||
- `model_name` (string): Override the default model
|
||||
- `thinking_enabled` (boolean): Enable extended thinking for supported models
|
||||
- `is_plan_mode` (boolean): Enable TodoList middleware for task tracking
|
||||
|
||||
**Response:** Server-Sent Events (SSE) stream
|
||||
|
||||
```
|
||||
event: values
|
||||
data: {"messages": [...], "title": "..."}
|
||||
|
||||
event: messages
|
||||
data: {"content": "Hello! I'd be happy to help.", "role": "assistant"}
|
||||
|
||||
event: end
|
||||
data: {}
|
||||
```
|
||||
|
||||
#### Get Run History
|
||||
|
||||
```http
|
||||
GET /api/langgraph/threads/{thread_id}/runs
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"runs": [
|
||||
{
|
||||
"run_id": "run123",
|
||||
"status": "success",
|
||||
"created_at": "2024-01-15T10:30:00Z"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Stream Run
|
||||
|
||||
Stream responses in real-time.
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads/{thread_id}/runs/stream
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
Same request body as Create Run. Returns SSE stream.
|
||||
|
||||
---
|
||||
|
||||
## Gateway API
|
||||
|
||||
Base URL: `/api`
|
||||
|
||||
### Models
|
||||
|
||||
#### List Models
|
||||
|
||||
Get all available LLM models from configuration.
|
||||
|
||||
```http
|
||||
GET /api/models
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"models": [
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
},
|
||||
{
|
||||
"name": "claude-3-opus",
|
||||
"display_name": "Claude 3 Opus",
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
},
|
||||
{
|
||||
"name": "deepseek-v3",
|
||||
"display_name": "DeepSeek V3",
|
||||
"supports_thinking": true,
|
||||
"supports_vision": false
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Model Details
|
||||
|
||||
```http
|
||||
GET /api/models/{model_name}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"model": "gpt-4",
|
||||
"max_tokens": 4096,
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
}
|
||||
```
|
||||
|
||||
### MCP Configuration
|
||||
|
||||
#### Get MCP Config
|
||||
|
||||
Get current MCP server configurations.
|
||||
|
||||
```http
|
||||
GET /api/mcp/config
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {
|
||||
"GITHUB_TOKEN": "***"
|
||||
},
|
||||
"description": "GitHub operations"
|
||||
},
|
||||
"filesystem": {
|
||||
"enabled": false,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-filesystem"],
|
||||
"description": "File system access"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Update MCP Config
|
||||
|
||||
Update MCP server configurations.
|
||||
|
||||
```http
|
||||
PUT /api/mcp/config
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {
|
||||
"GITHUB_TOKEN": "$GITHUB_TOKEN"
|
||||
},
|
||||
"description": "GitHub operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "MCP configuration updated"
|
||||
}
|
||||
```
|
||||
|
||||
### Skills
|
||||
|
||||
#### List Skills
|
||||
|
||||
Get all available skills.
|
||||
|
||||
```http
|
||||
GET /api/skills
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"skills": [
|
||||
{
|
||||
"name": "pdf-processing",
|
||||
"display_name": "PDF Processing",
|
||||
"description": "Handle PDF documents efficiently",
|
||||
"enabled": true,
|
||||
"license": "MIT",
|
||||
"path": "public/pdf-processing"
|
||||
},
|
||||
{
|
||||
"name": "frontend-design",
|
||||
"display_name": "Frontend Design",
|
||||
"description": "Design and build frontend interfaces",
|
||||
"enabled": false,
|
||||
"license": "MIT",
|
||||
"path": "public/frontend-design"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Skill Details
|
||||
|
||||
```http
|
||||
GET /api/skills/{skill_name}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"name": "pdf-processing",
|
||||
"display_name": "PDF Processing",
|
||||
"description": "Handle PDF documents efficiently",
|
||||
"enabled": true,
|
||||
"license": "MIT",
|
||||
"path": "public/pdf-processing",
|
||||
"allowed_tools": ["read_file", "write_file", "bash"],
|
||||
"content": "# PDF Processing\n\nInstructions for the agent..."
|
||||
}
|
||||
```
|
||||
|
||||
#### Enable Skill
|
||||
|
||||
```http
|
||||
POST /api/skills/{skill_name}/enable
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'pdf-processing' enabled"
|
||||
}
|
||||
```
|
||||
|
||||
#### Disable Skill
|
||||
|
||||
```http
|
||||
POST /api/skills/{skill_name}/disable
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'pdf-processing' disabled"
|
||||
}
|
||||
```
|
||||
|
||||
#### Install Skill
|
||||
|
||||
Install a skill from a `.skill` file.
|
||||
|
||||
```http
|
||||
POST /api/skills/install
|
||||
Content-Type: multipart/form-data
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
- `file`: The `.skill` file to install
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'my-skill' installed successfully",
|
||||
"skill": {
|
||||
"name": "my-skill",
|
||||
"display_name": "My Skill",
|
||||
"path": "custom/my-skill"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### File Uploads
|
||||
|
||||
#### Upload Files
|
||||
|
||||
Upload one or more files to a thread.
|
||||
|
||||
```http
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
Content-Type: multipart/form-data
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
- `files`: One or more files to upload
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"markdown_file": "document.md",
|
||||
"markdown_path": ".deer-flow/threads/abc123/user-data/uploads/document.md",
|
||||
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
|
||||
"markdown_artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.md"
|
||||
}
|
||||
],
|
||||
"message": "Successfully uploaded 1 file(s)"
|
||||
}
|
||||
```
|
||||
|
||||
**Supported Document Formats** (auto-converted to Markdown):
|
||||
- PDF (`.pdf`)
|
||||
- PowerPoint (`.ppt`, `.pptx`)
|
||||
- Excel (`.xls`, `.xlsx`)
|
||||
- Word (`.doc`, `.docx`)
|
||||
|
||||
#### List Uploaded Files
|
||||
|
||||
```http
|
||||
GET /api/threads/{thread_id}/uploads/list
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"extension": ".pdf",
|
||||
"modified": 1705997600.0
|
||||
}
|
||||
],
|
||||
"count": 1
|
||||
}
|
||||
```
|
||||
|
||||
#### Delete File
|
||||
|
||||
```http
|
||||
DELETE /api/threads/{thread_id}/uploads/{filename}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted document.pdf"
|
||||
}
|
||||
```
|
||||
|
||||
### Thread Cleanup
|
||||
|
||||
Remove DeerFlow-managed local thread files under `.deer-flow/threads/{thread_id}` after the LangGraph thread itself has been deleted.
|
||||
|
||||
```http
|
||||
DELETE /api/threads/{thread_id}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted local thread data for abc123"
|
||||
}
|
||||
```
|
||||
|
||||
**Error behavior:**
|
||||
- `422` for invalid thread IDs
|
||||
- `500` returns a generic `{"detail": "Failed to delete local thread data."}` response while full exception details stay in server logs
|
||||
|
||||
### Artifacts
|
||||
|
||||
#### Get Artifact
|
||||
|
||||
Download or view an artifact generated by the agent.
|
||||
|
||||
```http
|
||||
GET /api/threads/{thread_id}/artifacts/{path}
|
||||
```
|
||||
|
||||
**Path Examples:**
|
||||
- `/api/threads/abc123/artifacts/mnt/user-data/outputs/result.txt`
|
||||
- `/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf`
|
||||
|
||||
**Query Parameters:**
|
||||
- `download` (boolean): If `true`, force download with Content-Disposition header
|
||||
|
||||
**Response:** File content with appropriate Content-Type
|
||||
|
||||
---
|
||||
|
||||
## Error Responses
|
||||
|
||||
All APIs return errors in a consistent format:
|
||||
|
||||
```json
|
||||
{
|
||||
"detail": "Error message describing what went wrong"
|
||||
}
|
||||
```
|
||||
|
||||
**HTTP Status Codes:**
|
||||
- `400` - Bad Request: Invalid input
|
||||
- `404` - Not Found: Resource not found
|
||||
- `422` - Validation Error: Request validation failed
|
||||
- `500` - Internal Server Error: Server-side error
|
||||
|
||||
---
|
||||
|
||||
## Authentication
|
||||
|
||||
Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
|
||||
|
||||
Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
|
||||
|
||||
For production deployments, it is recommended to:
|
||||
1. Use Nginx for basic auth or OAuth integration
|
||||
2. Deploy behind a VPN or private network
|
||||
3. Implement custom authentication middleware
|
||||
|
||||
---
|
||||
|
||||
## Rate Limiting
|
||||
|
||||
No rate limiting is implemented by default. For production deployments, configure rate limiting in Nginx:
|
||||
|
||||
```nginx
|
||||
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
|
||||
|
||||
location /api/ {
|
||||
limit_req zone=api burst=20 nodelay;
|
||||
proxy_pass http://backend;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## WebSocket Support
|
||||
|
||||
The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
|
||||
|
||||
```
|
||||
ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## SDK Usage
|
||||
|
||||
### Python (LangGraph SDK)
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url="http://localhost:2026/api/langgraph")
|
||||
|
||||
# Create thread
|
||||
thread = await client.threads.create()
|
||||
|
||||
# Run agent
|
||||
async for event in client.runs.stream(
|
||||
thread["thread_id"],
|
||||
"lead_agent",
|
||||
input={"messages": [{"role": "user", "content": "Hello"}]},
|
||||
config={"configurable": {"model_name": "gpt-4"}},
|
||||
stream_mode=["values", "messages-tuple", "custom"],
|
||||
):
|
||||
print(event)
|
||||
```
|
||||
|
||||
### JavaScript/TypeScript
|
||||
|
||||
```typescript
|
||||
// Using fetch for Gateway API
|
||||
const response = await fetch('/api/models');
|
||||
const data = await response.json();
|
||||
console.log(data.models);
|
||||
|
||||
// Using EventSource for streaming
|
||||
const eventSource = new EventSource(
|
||||
`/api/langgraph/threads/${threadId}/runs/stream`
|
||||
);
|
||||
eventSource.onmessage = (event) => {
|
||||
console.log(JSON.parse(event.data));
|
||||
};
|
||||
```
|
||||
|
||||
### cURL Examples
|
||||
|
||||
```bash
|
||||
# List models
|
||||
curl http://localhost:2026/api/models
|
||||
|
||||
# Get MCP config
|
||||
curl http://localhost:2026/api/mcp/config
|
||||
|
||||
# Upload file
|
||||
curl -X POST http://localhost:2026/api/threads/abc123/uploads \
|
||||
-F "files=@document.pdf"
|
||||
|
||||
# Enable skill
|
||||
curl -X POST http://localhost:2026/api/skills/pdf-processing/enable
|
||||
|
||||
# Create thread and run agent
|
||||
curl -X POST http://localhost:2026/api/langgraph/threads \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{}'
|
||||
|
||||
curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": {"messages": [{"role": "user", "content": "Hello"}]},
|
||||
"config": {
|
||||
"recursion_limit": 100,
|
||||
"configurable": {"model_name": "gpt-4"}
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
> The `/api/langgraph/*` endpoints bypass DeerFlow's Gateway and inherit
|
||||
> LangGraph's native `recursion_limit` default of 25, which is too low for
|
||||
> plan-mode or subagent runs. Set `config.recursion_limit` explicitly — see
|
||||
> the [Create Run](#create-run) section for details.
|
||||
@@ -1,238 +0,0 @@
|
||||
# Apple Container Support
|
||||
|
||||
DeerFlow now supports Apple Container as the preferred container runtime on macOS, with automatic fallback to Docker.
|
||||
|
||||
## Overview
|
||||
|
||||
Starting with this version, DeerFlow automatically detects and uses Apple Container on macOS when available, falling back to Docker when:
|
||||
- Apple Container is not installed
|
||||
- Running on non-macOS platforms
|
||||
|
||||
This provides better performance on Apple Silicon Macs while maintaining compatibility across all platforms.
|
||||
|
||||
## Benefits
|
||||
|
||||
### On Apple Silicon Macs with Apple Container:
|
||||
- **Better Performance**: Native ARM64 execution without Rosetta 2 translation
|
||||
- **Lower Resource Usage**: Lighter weight than Docker Desktop
|
||||
- **Native Integration**: Uses macOS Virtualization.framework
|
||||
|
||||
### Fallback to Docker:
|
||||
- Full backward compatibility
|
||||
- Works on all platforms (macOS, Linux, Windows)
|
||||
- No configuration changes needed
|
||||
|
||||
## Requirements
|
||||
|
||||
### For Apple Container (macOS only):
|
||||
- macOS 15.0 or later
|
||||
- Apple Silicon (M1/M2/M3/M4)
|
||||
- Apple Container CLI installed
|
||||
|
||||
### Installation:
|
||||
```bash
|
||||
# Download from GitHub releases
|
||||
# https://github.com/apple/container/releases
|
||||
|
||||
# Verify installation
|
||||
container --version
|
||||
|
||||
# Start the service
|
||||
container system start
|
||||
```
|
||||
|
||||
### For Docker (all platforms):
|
||||
- Docker Desktop or Docker Engine
|
||||
|
||||
## How It Works
|
||||
|
||||
### Automatic Detection
|
||||
|
||||
The `AioSandboxProvider` automatically detects the available container runtime:
|
||||
|
||||
1. On macOS: Try `container --version`
|
||||
- Success → Use Apple Container
|
||||
- Failure → Fall back to Docker
|
||||
|
||||
2. On other platforms: Use Docker directly
|
||||
|
||||
### Runtime Differences
|
||||
|
||||
Both runtimes use nearly identical command syntax:
|
||||
|
||||
**Container Startup:**
|
||||
```bash
|
||||
# Apple Container
|
||||
container run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
|
||||
|
||||
# Docker
|
||||
docker run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
|
||||
```
|
||||
|
||||
**Container Cleanup:**
|
||||
```bash
|
||||
# Apple Container (with --rm flag)
|
||||
container stop <id> # Auto-removes due to --rm
|
||||
|
||||
# Docker (with --rm flag)
|
||||
docker stop <id> # Auto-removes due to --rm
|
||||
```
|
||||
|
||||
### Implementation Details
|
||||
|
||||
The implementation is in `backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py`:
|
||||
|
||||
- `_detect_container_runtime()`: Detects available runtime at startup
|
||||
- `_start_container()`: Uses detected runtime, skips Docker-specific options for Apple Container
|
||||
- `_stop_container()`: Uses appropriate stop command for the runtime
|
||||
|
||||
## Configuration
|
||||
|
||||
No configuration changes are needed! The system works automatically.
|
||||
|
||||
However, you can verify the runtime in use by checking the logs:
|
||||
|
||||
```
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Detected Apple Container: container version 0.1.0
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using container: ...
|
||||
```
|
||||
|
||||
Or for Docker:
|
||||
```
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Apple Container not available, falling back to Docker
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using docker: ...
|
||||
```
|
||||
|
||||
## Container Images
|
||||
|
||||
Both runtimes use OCI-compatible images. The default image works with both:
|
||||
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
image: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest # Default image
|
||||
```
|
||||
|
||||
Make sure your images are available for the appropriate architecture:
|
||||
- ARM64 for Apple Container on Apple Silicon
|
||||
- AMD64 for Docker on Intel Macs
|
||||
- Multi-arch images work on both
|
||||
|
||||
### Pre-pulling Images (Recommended)
|
||||
|
||||
**Important**: Container images are typically large (500MB+) and are pulled on first use, which can cause a long wait time without clear feedback.
|
||||
|
||||
**Best Practice**: Pre-pull the image during setup:
|
||||
|
||||
```bash
|
||||
# From project root
|
||||
make setup-sandbox
|
||||
```
|
||||
|
||||
This command will:
|
||||
1. Read the configured image from `config.yaml` (or use default)
|
||||
2. Detect available runtime (Apple Container or Docker)
|
||||
3. Pull the image with progress indication
|
||||
4. Verify the image is ready for use
|
||||
|
||||
**Manual pre-pull**:
|
||||
|
||||
```bash
|
||||
# Using Apple Container
|
||||
container image pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
|
||||
|
||||
# Using Docker
|
||||
docker pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
|
||||
```
|
||||
|
||||
If you skip pre-pulling, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
|
||||
|
||||
## Cleanup Scripts
|
||||
|
||||
The project includes a unified cleanup script that handles both runtimes:
|
||||
|
||||
**Script:** `scripts/cleanup-containers.sh`
|
||||
|
||||
**Usage:**
|
||||
```bash
|
||||
# Clean up all DeerFlow sandbox containers
|
||||
./scripts/cleanup-containers.sh deer-flow-sandbox
|
||||
|
||||
# Custom prefix
|
||||
./scripts/cleanup-containers.sh my-prefix
|
||||
```
|
||||
|
||||
**Makefile Integration:**
|
||||
|
||||
All cleanup commands in `Makefile` automatically handle both runtimes:
|
||||
```bash
|
||||
make stop # Stops all services and cleans up containers
|
||||
make clean # Full cleanup including logs
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
Test the container runtime detection:
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
python test_container_runtime.py
|
||||
```
|
||||
|
||||
This will:
|
||||
1. Detect the available runtime
|
||||
2. Optionally start a test container
|
||||
3. Verify connectivity
|
||||
4. Clean up
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Apple Container not detected on macOS
|
||||
|
||||
1. Check if installed:
|
||||
```bash
|
||||
which container
|
||||
container --version
|
||||
```
|
||||
|
||||
2. Check if service is running:
|
||||
```bash
|
||||
container system start
|
||||
```
|
||||
|
||||
3. Check logs for detection:
|
||||
```bash
|
||||
# Look for detection message in application logs
|
||||
grep "container runtime" logs/*.log
|
||||
```
|
||||
|
||||
### Containers not cleaning up
|
||||
|
||||
1. Manually check running containers:
|
||||
```bash
|
||||
# Apple Container
|
||||
container list
|
||||
|
||||
# Docker
|
||||
docker ps
|
||||
```
|
||||
|
||||
2. Run cleanup script manually:
|
||||
```bash
|
||||
./scripts/cleanup-containers.sh deer-flow-sandbox
|
||||
```
|
||||
|
||||
### Performance issues
|
||||
|
||||
- Apple Container should be faster on Apple Silicon
|
||||
- If experiencing issues, you can force Docker by temporarily renaming the `container` command:
|
||||
```bash
|
||||
# Temporary workaround - not recommended for permanent use
|
||||
sudo mv /opt/homebrew/bin/container /opt/homebrew/bin/container.bak
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
- [Apple Container GitHub](https://github.com/apple/container)
|
||||
- [Apple Container Documentation](https://github.com/apple/container/blob/main/docs/)
|
||||
- [OCI Image Spec](https://github.com/opencontainers/image-spec)
|
||||
@@ -1,484 +0,0 @@
|
||||
# Architecture Overview
|
||||
|
||||
This document provides a comprehensive overview of the DeerFlow backend architecture.
|
||||
|
||||
## System Architecture
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Client (Browser) │
|
||||
└─────────────────────────────────┬────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Nginx (Port 2026) │
|
||||
│ Unified Reverse Proxy Entry Point │
|
||||
│ ┌────────────────────────────────────────────────────────────────────┐ │
|
||||
│ │ /api/langgraph/* → LangGraph Server (2024) │ │
|
||||
│ │ /api/* → Gateway API (8001) │ │
|
||||
│ │ /* → Frontend (3000) │ │
|
||||
│ └────────────────────────────────────────────────────────────────────┘ │
|
||||
└─────────────────────────────────┬────────────────────────────────────────┘
|
||||
│
|
||||
┌───────────────────────┼───────────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
|
||||
│ LangGraph Server │ │ Gateway API │ │ Frontend │
|
||||
│ (Port 2024) │ │ (Port 8001) │ │ (Port 3000) │
|
||||
│ │ │ │ │ │
|
||||
│ - Agent Runtime │ │ - Models API │ │ - Next.js App │
|
||||
│ - Thread Mgmt │ │ - MCP Config │ │ - React UI │
|
||||
│ - SSE Streaming │ │ - Skills Mgmt │ │ - Chat Interface │
|
||||
│ - Checkpointing │ │ - File Uploads │ │ │
|
||||
│ │ │ - Thread Cleanup │ │ │
|
||||
│ │ │ - Artifacts │ │ │
|
||||
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
|
||||
│ │
|
||||
│ ┌─────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Shared Configuration │
|
||||
│ ┌─────────────────────────┐ ┌────────────────────────────────────────┐ │
|
||||
│ │ config.yaml │ │ extensions_config.json │ │
|
||||
│ │ - Models │ │ - MCP Servers │ │
|
||||
│ │ - Tools │ │ - Skills State │ │
|
||||
│ │ - Sandbox │ │ │ │
|
||||
│ │ - Summarization │ │ │ │
|
||||
│ └─────────────────────────┘ └────────────────────────────────────────┘ │
|
||||
└──────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Component Details
|
||||
|
||||
### LangGraph Server
|
||||
|
||||
The LangGraph server is the core agent runtime, built on LangGraph for robust multi-agent workflow orchestration.
|
||||
|
||||
**Entry Point**: `packages/harness/deerflow/agents/lead_agent/agent.py:make_lead_agent`
|
||||
|
||||
**Key Responsibilities**:
|
||||
- Agent creation and configuration
|
||||
- Thread state management
|
||||
- Middleware chain execution
|
||||
- Tool execution orchestration
|
||||
- SSE streaming for real-time responses
|
||||
|
||||
**Configuration**: `langgraph.json`
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"type": "agent",
|
||||
"path": "deerflow.agents:make_lead_agent"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Gateway API
|
||||
|
||||
FastAPI application providing REST endpoints for non-agent operations.
|
||||
|
||||
**Entry Point**: `app/gateway/app.py`
|
||||
|
||||
**Routers**:
|
||||
- `models.py` - `/api/models` - Model listing and details
|
||||
- `mcp.py` - `/api/mcp` - MCP server configuration
|
||||
- `skills.py` - `/api/skills` - Skills management
|
||||
- `uploads.py` - `/api/threads/{id}/uploads` - File upload
|
||||
- `threads.py` - `/api/threads/{id}` - Local DeerFlow thread data cleanup after LangGraph deletion
|
||||
- `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
|
||||
- `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation
|
||||
|
||||
The web conversation delete flow is now split across both backend surfaces: LangGraph handles `DELETE /api/langgraph/threads/{thread_id}` for thread state, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
|
||||
|
||||
### Agent Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ make_lead_agent(config) │
|
||||
└────────────────────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Middleware Chain │
|
||||
│ ┌──────────────────────────────────────────────────────────────────┐ │
|
||||
│ │ 1. ThreadDataMiddleware - Initialize workspace/uploads/outputs │ │
|
||||
│ │ 2. UploadsMiddleware - Process uploaded files │ │
|
||||
│ │ 3. SandboxMiddleware - Acquire sandbox environment │ │
|
||||
│ │ 4. SummarizationMiddleware - Context reduction (if enabled) │ │
|
||||
│ │ 5. TitleMiddleware - Auto-generate titles │ │
|
||||
│ │ 6. TodoListMiddleware - Task tracking (if plan_mode) │ │
|
||||
│ │ 7. ViewImageMiddleware - Vision model support │ │
|
||||
│ │ 8. ClarificationMiddleware - Handle clarifications │ │
|
||||
│ └──────────────────────────────────────────────────────────────────┘ │
|
||||
└────────────────────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Agent Core │
|
||||
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────────┐ │
|
||||
│ │ Model │ │ Tools │ │ System Prompt │ │
|
||||
│ │ (from factory) │ │ (configured + │ │ (with skills) │ │
|
||||
│ │ │ │ MCP + builtin) │ │ │ │
|
||||
│ └──────────────────┘ └──────────────────┘ └──────────────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Thread State
|
||||
|
||||
The `ThreadState` extends LangGraph's `AgentState` with additional fields:
|
||||
|
||||
```python
|
||||
class ThreadState(AgentState):
|
||||
# Core state from AgentState
|
||||
messages: list[BaseMessage]
|
||||
|
||||
# DeerFlow extensions
|
||||
sandbox: dict # Sandbox environment info
|
||||
artifacts: list[str] # Generated file paths
|
||||
thread_data: dict # {workspace, uploads, outputs} paths
|
||||
title: str | None # Auto-generated conversation title
|
||||
todos: list[dict] # Task tracking (plan mode)
|
||||
viewed_images: dict # Vision model image data
|
||||
```
|
||||
|
||||
### Sandbox System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Sandbox Architecture │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌─────────────────────────┐
|
||||
│ SandboxProvider │ (Abstract)
|
||||
│ - acquire() │
|
||||
│ - get() │
|
||||
│ - release() │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────────┐ ┌─────────────────────────┐
|
||||
│ LocalSandboxProvider │ │ AioSandboxProvider │
|
||||
│ (packages/harness/deerflow/sandbox/local.py) │ │ (packages/harness/deerflow/community/) │
|
||||
│ │ │ │
|
||||
│ - Singleton instance │ │ - Docker-based │
|
||||
│ - Direct execution │ │ - Isolated containers │
|
||||
│ - Development use │ │ - Production use │
|
||||
└─────────────────────────┘ └─────────────────────────┘
|
||||
|
||||
┌─────────────────────────┐
|
||||
│ Sandbox │ (Abstract)
|
||||
│ - execute_command() │
|
||||
│ - read_file() │
|
||||
│ - write_file() │
|
||||
│ - list_dir() │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
**Virtual Path Mapping**:
|
||||
|
||||
| Virtual Path | Physical Path |
|
||||
|-------------|---------------|
|
||||
| `/mnt/user-data/workspace` | `backend/.deer-flow/threads/{thread_id}/user-data/workspace` |
|
||||
| `/mnt/user-data/uploads` | `backend/.deer-flow/threads/{thread_id}/user-data/uploads` |
|
||||
| `/mnt/user-data/outputs` | `backend/.deer-flow/threads/{thread_id}/user-data/outputs` |
|
||||
| `/mnt/skills` | `deer-flow/skills/` |
|
||||
|
||||
### Tool System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Tool Sources │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
|
||||
│ Built-in Tools │ │ Configured Tools │ │ MCP Tools │
|
||||
│ (packages/harness/deerflow/tools/) │ │ (config.yaml) │ │ (extensions.json) │
|
||||
├─────────────────────┤ ├─────────────────────┤ ├─────────────────────┤
|
||||
│ - present_file │ │ - web_search │ │ - github │
|
||||
│ - ask_clarification │ │ - web_fetch │ │ - filesystem │
|
||||
│ - view_image │ │ - bash │ │ - postgres │
|
||||
│ │ │ - read_file │ │ - brave-search │
|
||||
│ │ │ - write_file │ │ - puppeteer │
|
||||
│ │ │ - str_replace │ │ - ... │
|
||||
│ │ │ - ls │ │ │
|
||||
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
|
||||
│ │ │
|
||||
└───────────────────────┴───────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ get_available_tools() │
|
||||
│ (packages/harness/deerflow/tools/__init__) │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
### Model Factory
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Model Factory │
|
||||
│ (packages/harness/deerflow/models/factory.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
config.yaml:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ models: │
|
||||
│ - name: gpt-4 │
|
||||
│ display_name: GPT-4 │
|
||||
│ use: langchain_openai:ChatOpenAI │
|
||||
│ model: gpt-4 │
|
||||
│ api_key: $OPENAI_API_KEY │
|
||||
│ max_tokens: 4096 │
|
||||
│ supports_thinking: false │
|
||||
│ supports_vision: true │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ create_chat_model() │
|
||||
│ - name: str │
|
||||
│ - thinking_enabled │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ resolve_class() │
|
||||
│ (reflection system) │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ BaseChatModel │
|
||||
│ (LangChain instance) │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
**Supported Providers**:
|
||||
- OpenAI (`langchain_openai:ChatOpenAI`)
|
||||
- Anthropic (`langchain_anthropic:ChatAnthropic`)
|
||||
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
|
||||
- Custom via LangChain integrations
|
||||
|
||||
### MCP Integration
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ MCP Integration │
|
||||
│ (packages/harness/deerflow/mcp/manager.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
extensions_config.json:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ { │
|
||||
│ "mcpServers": { │
|
||||
│ "github": { │
|
||||
│ "enabled": true, │
|
||||
│ "type": "stdio", │
|
||||
│ "command": "npx", │
|
||||
│ "args": ["-y", "@modelcontextprotocol/server-github"], │
|
||||
│ "env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"} │
|
||||
│ } │
|
||||
│ } │
|
||||
│ } │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ MultiServerMCPClient │
|
||||
│ (langchain-mcp-adapters)│
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌───────────┐ ┌───────────┐ ┌───────────┐
|
||||
│ stdio │ │ SSE │ │ HTTP │
|
||||
│ transport │ │ transport │ │ transport │
|
||||
└───────────┘ └───────────┘ └───────────┘
|
||||
```
|
||||
|
||||
### Skills System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Skills System │
|
||||
│ (packages/harness/deerflow/skills/loader.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
Directory Structure:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ skills/ │
|
||||
│ ├── public/ # Public skills (committed) │
|
||||
│ │ ├── pdf-processing/ │
|
||||
│ │ │ └── SKILL.md │
|
||||
│ │ ├── frontend-design/ │
|
||||
│ │ │ └── SKILL.md │
|
||||
│ │ └── ... │
|
||||
│ └── custom/ # Custom skills (gitignored) │
|
||||
│ └── user-installed/ │
|
||||
│ └── SKILL.md │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
SKILL.md Format:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ --- │
|
||||
│ name: PDF Processing │
|
||||
│ description: Handle PDF documents efficiently │
|
||||
│ license: MIT │
|
||||
│ allowed-tools: │
|
||||
│ - read_file │
|
||||
│ - write_file │
|
||||
│ - bash │
|
||||
│ --- │
|
||||
│ │
|
||||
│ # Skill Instructions │
|
||||
│ Content injected into system prompt... │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Request Flow
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Request Flow Example │
|
||||
│ User sends message to agent │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
1. Client → Nginx
|
||||
POST /api/langgraph/threads/{thread_id}/runs
|
||||
{"input": {"messages": [{"role": "user", "content": "Hello"}]}}
|
||||
|
||||
2. Nginx → LangGraph Server (2024)
|
||||
Proxied to LangGraph server
|
||||
|
||||
3. LangGraph Server
|
||||
a. Load/create thread state
|
||||
b. Execute middleware chain:
|
||||
- ThreadDataMiddleware: Set up paths
|
||||
- UploadsMiddleware: Inject file list
|
||||
- SandboxMiddleware: Acquire sandbox
|
||||
- SummarizationMiddleware: Check token limits
|
||||
- TitleMiddleware: Generate title if needed
|
||||
- TodoListMiddleware: Load todos (if plan mode)
|
||||
- ViewImageMiddleware: Process images
|
||||
- ClarificationMiddleware: Check for clarifications
|
||||
|
||||
c. Execute agent:
|
||||
- Model processes messages
|
||||
- May call tools (bash, web_search, etc.)
|
||||
- Tools execute via sandbox
|
||||
- Results added to messages
|
||||
|
||||
d. Stream response via SSE
|
||||
|
||||
4. Client receives streaming response
|
||||
```
|
||||
|
||||
## Data Flow
|
||||
|
||||
### File Upload Flow
|
||||
|
||||
```
|
||||
1. Client uploads file
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
2. Gateway receives file
|
||||
- Validates file
|
||||
- Stores in .deer-flow/threads/{thread_id}/user-data/uploads/
|
||||
- If document: converts to Markdown via markitdown
|
||||
|
||||
3. Returns response
|
||||
{
|
||||
"files": [{
|
||||
"filename": "doc.pdf",
|
||||
"path": ".deer-flow/.../uploads/doc.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/doc.pdf",
|
||||
"artifact_url": "/api/threads/.../artifacts/mnt/.../doc.pdf"
|
||||
}]
|
||||
}
|
||||
|
||||
4. Next agent run
|
||||
- UploadsMiddleware lists files
|
||||
- Injects file list into messages
|
||||
- Agent can access via virtual_path
|
||||
```
|
||||
|
||||
### Thread Cleanup Flow
|
||||
|
||||
```
|
||||
1. Client deletes conversation via LangGraph
|
||||
DELETE /api/langgraph/threads/{thread_id}
|
||||
|
||||
2. Web UI follows up with Gateway cleanup
|
||||
DELETE /api/threads/{thread_id}
|
||||
|
||||
3. Gateway removes local DeerFlow-managed files
|
||||
- Deletes .deer-flow/threads/{thread_id}/ recursively
|
||||
- Missing directories are treated as a no-op
|
||||
- Invalid thread IDs are rejected before filesystem access
|
||||
```
|
||||
|
||||
### Configuration Reload
|
||||
|
||||
```
|
||||
1. Client updates MCP config
|
||||
PUT /api/mcp/config
|
||||
|
||||
2. Gateway writes extensions_config.json
|
||||
- Updates mcpServers section
|
||||
- File mtime changes
|
||||
|
||||
3. MCP Manager detects change
|
||||
- get_cached_mcp_tools() checks mtime
|
||||
- If changed: reinitializes MCP client
|
||||
- Loads updated server configurations
|
||||
|
||||
4. Next agent run uses new tools
|
||||
```
|
||||
|
||||
## Security Considerations
|
||||
|
||||
### Sandbox Isolation
|
||||
|
||||
- Agent code executes within sandbox boundaries
|
||||
- Local sandbox: Direct execution (development only)
|
||||
- Docker sandbox: Container isolation (production recommended)
|
||||
- Path traversal prevention in file operations
|
||||
|
||||
### API Security
|
||||
|
||||
- Thread isolation: Each thread has separate data directories
|
||||
- File validation: Uploads checked for path safety
|
||||
- Environment variable resolution: Secrets not stored in config
|
||||
|
||||
### MCP Security
|
||||
|
||||
- Each MCP server runs in its own process
|
||||
- Environment variables resolved at runtime
|
||||
- Servers can be enabled/disabled independently
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Caching
|
||||
|
||||
- MCP tools cached with file mtime invalidation
|
||||
- Configuration loaded once, reloaded on file change
|
||||
- Skills parsed once at startup, cached in memory
|
||||
|
||||
### Streaming
|
||||
|
||||
- SSE used for real-time response streaming
|
||||
- Reduces time to first token
|
||||
- Enables progress visibility for long operations
|
||||
|
||||
### Context Management
|
||||
|
||||
- Summarization middleware reduces context when limits approached
|
||||
- Configurable triggers: tokens, messages, or fraction
|
||||
- Preserves recent messages while summarizing older ones
|
||||
@@ -1,258 +0,0 @@
|
||||
# 自动 Thread Title 生成功能
|
||||
|
||||
## 功能说明
|
||||
|
||||
自动为对话线程生成标题，在用户首次提问并收到回复后自动触发。
|
||||
|
||||
## 实现方式
|
||||
|
||||
使用 `TitleMiddleware` 在 `after_model` 钩子中：
|
||||
1. 检测是否是首次对话（1个用户消息 + 1个助手回复）
|
||||
2. 检查 state 是否已有 title
|
||||
3. 调用 LLM 生成简洁的标题（默认最多6个词）
|
||||
4. 将 title 存储到 `ThreadState` 中（会被 checkpointer 持久化）
|
||||
|
||||
TitleMiddleware 会先把 LangChain message content 里的结构化 block/list 内容归一化为纯文本，再拼到 title prompt 里，避免把 Python/JSON 的原始 repr 泄漏到标题生成模型。
|
||||
|
||||
## ⚠️ 重要：存储机制
|
||||
|
||||
### Title 存储位置
|
||||
|
||||
Title 存储在 **`ThreadState.title`** 中，而非 thread metadata：
|
||||
|
||||
```python
|
||||
class ThreadState(AgentState):
|
||||
sandbox: SandboxState | None = None
|
||||
title: str | None = None # ✅ Title stored here
|
||||
```
|
||||
|
||||
### 持久化说明
|
||||
|
||||
| 部署方式 | 持久化 | 说明 |
|
||||
|---------|--------|------|
|
||||
| **LangGraph Studio (本地)** | ❌ 否 | 仅内存存储，重启后丢失 |
|
||||
| **LangGraph Platform** | ✅ 是 | 自动持久化到数据库 |
|
||||
| **自定义 + Checkpointer** | ✅ 是 | 需配置 PostgreSQL/SQLite checkpointer |
|
||||
|
||||
### 如何启用持久化
|
||||
|
||||
如果需要在本地开发时也持久化 title，需要配置 checkpointer：
|
||||
|
||||
```python
|
||||
# 在 langgraph.json 同级目录创建 checkpointer.py
|
||||
from langgraph.checkpoint.postgres import PostgresSaver
|
||||
|
||||
checkpointer = PostgresSaver.from_conn_string(
|
||||
"postgresql://user:pass@localhost/dbname"
|
||||
)
|
||||
```
|
||||
|
||||
然后在 `langgraph.json` 中引用：
|
||||
|
||||
```json
|
||||
{
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:lead_agent"
|
||||
},
|
||||
"checkpointer": "checkpointer:checkpointer"
|
||||
}
|
||||
```
|
||||
|
||||
## 配置
|
||||
|
||||
在 `config.yaml` 中添加（可选）：
|
||||
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 6
|
||||
max_chars: 60
|
||||
model_name: null # 使用默认模型
|
||||
```
|
||||
|
||||
或在代码中配置：
|
||||
|
||||
```python
|
||||
from deerflow.config.title_config import TitleConfig, set_title_config
|
||||
|
||||
set_title_config(TitleConfig(
|
||||
enabled=True,
|
||||
max_words=8,
|
||||
max_chars=80,
|
||||
))
|
||||
```
|
||||
|
||||
## 客户端使用
|
||||
|
||||
### 获取 Thread Title
|
||||
|
||||
```typescript
|
||||
// 方式1: 从 thread state 获取
|
||||
const state = await client.threads.getState(threadId);
|
||||
const title = state.values.title || "New Conversation";
|
||||
|
||||
// 方式2: 监听 stream 事件
|
||||
for await (const chunk of client.runs.stream(threadId, assistantId, {
|
||||
input: { messages: [{ role: "user", content: "Hello" }] }
|
||||
})) {
|
||||
if (chunk.event === "values" && chunk.data.title) {
|
||||
console.log("Title:", chunk.data.title);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 显示 Title
|
||||
|
||||
```typescript
|
||||
// 在对话列表中显示
|
||||
function ConversationList() {
|
||||
const [threads, setThreads] = useState([]);
|
||||
|
||||
useEffect(() => {
|
||||
async function loadThreads() {
|
||||
const allThreads = await client.threads.list();
|
||||
|
||||
// 获取每个 thread 的 state 来读取 title
|
||||
const threadsWithTitles = await Promise.all(
|
||||
allThreads.map(async (t) => {
|
||||
const state = await client.threads.getState(t.thread_id);
|
||||
return {
|
||||
id: t.thread_id,
|
||||
title: state.values.title || "New Conversation",
|
||||
updatedAt: t.updated_at,
|
||||
};
|
||||
})
|
||||
);
|
||||
|
||||
setThreads(threadsWithTitles);
|
||||
}
|
||||
loadThreads();
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<ul>
|
||||
{threads.map(thread => (
|
||||
<li key={thread.id}>
|
||||
<a href={`/chat/${thread.id}`}>{thread.title}</a>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
## 工作流程
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant User
|
||||
participant Client
|
||||
participant LangGraph
|
||||
participant TitleMiddleware
|
||||
participant LLM
|
||||
participant Checkpointer
|
||||
|
||||
User->>Client: 发送首条消息
|
||||
Client->>LangGraph: POST /threads/{id}/runs
|
||||
LangGraph->>Agent: 处理消息
|
||||
Agent-->>LangGraph: 返回回复
|
||||
LangGraph->>TitleMiddleware: after_agent()
|
||||
TitleMiddleware->>TitleMiddleware: 检查是否需要生成 title
|
||||
TitleMiddleware->>LLM: 生成 title
|
||||
LLM-->>TitleMiddleware: 返回 title
|
||||
TitleMiddleware->>LangGraph: return {"title": "..."}
|
||||
LangGraph->>Checkpointer: 保存 state (含 title)
|
||||
LangGraph-->>Client: 返回响应
|
||||
Client->>Client: 从 state.values.title 读取
|
||||
```
|
||||
|
||||
## 优势
|
||||
|
||||
✅ **可靠持久化** - 使用 LangGraph 的 state 机制，自动持久化
|
||||
✅ **完全后端处理** - 客户端无需额外逻辑
|
||||
✅ **自动触发** - 首次对话后自动生成
|
||||
✅ **可配置** - 支持自定义长度、模型等
|
||||
✅ **容错性强** - 失败时使用 fallback 策略
|
||||
✅ **架构一致** - 与现有 SandboxMiddleware 保持一致
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **读取方式不同**：Title 在 `state.values.title` 而非 `thread.metadata.title`
|
||||
2. **性能考虑**：title 生成会增加约 0.5-1 秒延迟，可通过使用更快的模型优化
|
||||
3. **并发安全**：middleware 在 agent 执行后运行，不会阻塞主流程
|
||||
4. **Fallback 策略**：如果 LLM 调用失败，会使用用户消息的前几个词作为 title
|
||||
|
||||
## 测试
|
||||
|
||||
```python
|
||||
# 测试 title 生成
|
||||
import pytest
|
||||
from deerflow.agents.title_middleware import TitleMiddleware
|
||||
|
||||
def test_title_generation():
|
||||
# TODO: 添加单元测试
|
||||
pass
|
||||
```
|
||||
|
||||
## 故障排查
|
||||
|
||||
### Title 没有生成
|
||||
|
||||
1. 检查配置是否启用：`get_title_config().enabled == True`
|
||||
2. 检查日志：查找 "Generated thread title" 或错误信息
|
||||
3. 确认是首次对话：只有 1 个用户消息和 1 个助手回复时才会触发
|
||||
|
||||
### Title 生成但客户端看不到
|
||||
|
||||
1. 确认读取位置：应该从 `state.values.title` 读取，而非 `thread.metadata.title`
|
||||
2. 检查 API 响应：确认 state 中包含 title 字段
|
||||
3. 尝试重新获取 state：`client.threads.getState(threadId)`
|
||||
|
||||
### Title 重启后丢失
|
||||
|
||||
1. 检查是否配置了 checkpointer（本地开发需要）
|
||||
2. 确认部署方式：LangGraph Platform 会自动持久化
|
||||
3. 查看数据库：确认 checkpointer 正常工作
|
||||
|
||||
## 架构设计
|
||||
|
||||
### 为什么使用 State 而非 Metadata？
|
||||
|
||||
| 特性 | State | Metadata |
|
||||
|------|-------|----------|
|
||||
| **持久化** | ✅ 自动（通过 checkpointer） | ⚠️ 取决于实现 |
|
||||
| **版本控制** | ✅ 支持时间旅行 | ❌ 不支持 |
|
||||
| **类型安全** | ✅ TypedDict 定义 | ❌ 任意字典 |
|
||||
| **可追溯** | ✅ 每次更新都记录 | ⚠️ 只有最新值 |
|
||||
| **标准化** | ✅ LangGraph 核心机制 | ⚠️ 扩展功能 |
|
||||
|
||||
### 实现细节
|
||||
|
||||
```python
|
||||
# TitleMiddleware 核心逻辑
|
||||
@override
|
||||
def after_agent(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
"""Generate and set thread title after the first agent response."""
|
||||
if self._should_generate_title(state, runtime):
|
||||
title = self._generate_title(runtime)
|
||||
print(f"Generated thread title: {title}")
|
||||
|
||||
# ✅ 返回 state 更新，会被 checkpointer 自动持久化
|
||||
return {"title": title}
|
||||
|
||||
return None
|
||||
```
|
||||
|
||||
## 相关文件
|
||||
|
||||
- [`packages/harness/deerflow/agents/thread_state.py`](../packages/harness/deerflow/agents/thread_state.py) - ThreadState 定义
|
||||
- [`packages/harness/deerflow/agents/middlewares/title_middleware.py`](../packages/harness/deerflow/agents/middlewares/title_middleware.py) - TitleMiddleware 实现
|
||||
- [`packages/harness/deerflow/config/title_config.py`](../packages/harness/deerflow/config/title_config.py) - 配置管理
|
||||
- [`config.yaml`](../../config.example.yaml) - 配置文件
|
||||
- [`packages/harness/deerflow/agents/lead_agent/agent.py`](../packages/harness/deerflow/agents/lead_agent/agent.py) - Middleware 注册
|
||||
|
||||
## 参考资料
|
||||
|
||||
- [LangGraph Checkpointer 文档](https://langchain-ai.github.io/langgraph/concepts/persistence/)
|
||||
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
|
||||
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)
|
||||
@@ -1,369 +0,0 @@
|
||||
# Configuration Guide
|
||||
|
||||
This guide explains how to configure DeerFlow for your environment.
|
||||
|
||||
## Config Versioning
|
||||
|
||||
`config.example.yaml` contains a `config_version` field that tracks schema changes. When the example version is higher than your local `config.yaml`, the application emits a startup warning:
|
||||
|
||||
```
|
||||
WARNING - Your config.yaml (version 0) is outdated — the latest version is 1.
|
||||
Run `make config-upgrade` to merge new fields into your config.
|
||||
```
|
||||
|
||||
- **Missing `config_version`** in your config is treated as version 0.
|
||||
- Run `make config-upgrade` to auto-merge missing fields (your existing values are preserved, a `.bak` backup is created).
|
||||
- When changing the config schema, bump `config_version` in `config.example.yaml`.
|
||||
|
||||
## Configuration Sections
|
||||
|
||||
### Models
|
||||
|
||||
Configure the LLM models available to the agent:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4 # Internal identifier
|
||||
display_name: GPT-4 # Human-readable name
|
||||
use: langchain_openai:ChatOpenAI # LangChain class path
|
||||
model: gpt-4 # Model identifier for API
|
||||
api_key: $OPENAI_API_KEY # API key (use env var)
|
||||
max_tokens: 4096 # Max tokens per request
|
||||
temperature: 0.7 # Sampling temperature
|
||||
```
|
||||
|
||||
**Supported Providers**:
|
||||
- OpenAI (`langchain_openai:ChatOpenAI`)
|
||||
- Anthropic (`langchain_anthropic:ChatAnthropic`)
|
||||
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
|
||||
- Claude Code OAuth (`deerflow.models.claude_provider:ClaudeChatModel`)
|
||||
- Codex CLI (`deerflow.models.openai_codex_provider:CodexChatModel`)
|
||||
- Any LangChain-compatible provider
|
||||
|
||||
CLI-backed provider examples:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5.4
|
||||
display_name: GPT-5.4 (Codex CLI)
|
||||
use: deerflow.models.openai_codex_provider:CodexChatModel
|
||||
model: gpt-5.4
|
||||
supports_thinking: true
|
||||
supports_reasoning_effort: true
|
||||
|
||||
- name: claude-sonnet-4.6
|
||||
display_name: Claude Sonnet 4.6 (Claude Code OAuth)
|
||||
use: deerflow.models.claude_provider:ClaudeChatModel
|
||||
model: claude-sonnet-4-6
|
||||
max_tokens: 4096
|
||||
supports_thinking: true
|
||||
```
|
||||
|
||||
**Auth behavior for CLI-backed providers**:
|
||||
- `CodexChatModel` loads Codex CLI auth from `~/.codex/auth.json`
|
||||
- The Codex Responses endpoint currently rejects `max_tokens` and `max_output_tokens`, so `CodexChatModel` does not expose a request-level token cap
|
||||
- `ClaudeChatModel` accepts `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR`, `CLAUDE_CODE_CREDENTIALS_PATH`, or plaintext `~/.claude/.credentials.json`
|
||||
- On macOS, DeerFlow does not probe Keychain automatically. Use `scripts/export_claude_code_oauth.py` to export Claude Code auth explicitly when needed
|
||||
|
||||
To use OpenAI's `/v1/responses` endpoint with LangChain, keep using `langchain_openai:ChatOpenAI` and set:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
```
|
||||
|
||||
For OpenAI-compatible gateways (for example Novita or OpenRouter), keep using `langchain_openai:ChatOpenAI` and set `base_url`:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: novita-deepseek-v3.2
|
||||
display_name: Novita DeepSeek V3.2
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: deepseek/deepseek-v3.2
|
||||
api_key: $NOVITA_API_KEY
|
||||
base_url: https://api.novita.ai/openai
|
||||
supports_thinking: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
|
||||
- name: minimax-m2.5
|
||||
display_name: MiniMax M2.5
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: MiniMax-M2.5
|
||||
api_key: $MINIMAX_API_KEY
|
||||
base_url: https://api.minimax.io/v1
|
||||
max_tokens: 4096
|
||||
temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
|
||||
supports_vision: true
|
||||
|
||||
- name: minimax-m2.5-highspeed
|
||||
display_name: MiniMax M2.5 Highspeed
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: MiniMax-M2.5-highspeed
|
||||
api_key: $MINIMAX_API_KEY
|
||||
base_url: https://api.minimax.io/v1
|
||||
max_tokens: 4096
|
||||
temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
|
||||
supports_vision: true
|
||||
- name: openrouter-gemini-2.5-flash
|
||||
display_name: Gemini 2.5 Flash (OpenRouter)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: google/gemini-2.5-flash-preview
|
||||
api_key: $OPENAI_API_KEY
|
||||
base_url: https://openrouter.ai/api/v1
|
||||
```
|
||||
|
||||
If your OpenRouter key lives in a different environment variable name, point `api_key` at that variable explicitly (for example `api_key: $OPENROUTER_API_KEY`).
|
||||
|
||||
**Thinking Models**:
|
||||
Some models support "thinking" mode for complex reasoning:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: deepseek-v3
|
||||
supports_thinking: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
```
|
||||
|
||||
**Gemini with thinking via OpenAI-compatible gateway**:
|
||||
|
||||
When routing Gemini through an OpenAI-compatible proxy (Vertex AI OpenAI compat endpoint, AI Studio, or third-party gateways) with thinking enabled, the API attaches a `thought_signature` to each tool-call object returned in the response. Every subsequent request that replays those assistant messages **must** echo those signatures back on the tool-call entries or the API returns:
|
||||
|
||||
```
|
||||
HTTP 400 INVALID_ARGUMENT: function call `<tool>` in the N. content block is
|
||||
missing a `thought_signature`.
|
||||
```
|
||||
|
||||
Standard `langchain_openai:ChatOpenAI` silently drops `thought_signature` when serialising messages. Use `deerflow.models.patched_openai:PatchedChatOpenAI` instead — it re-injects the tool-call signatures (sourced from `AIMessage.additional_kwargs["tool_calls"]`) into every outgoing payload:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gemini-2.5-pro-thinking
|
||||
display_name: Gemini 2.5 Pro (Thinking)
|
||||
use: deerflow.models.patched_openai:PatchedChatOpenAI
|
||||
model: google/gemini-2.5-pro-preview # model name as expected by your gateway
|
||||
api_key: $GEMINI_API_KEY
|
||||
base_url: https://<your-openai-compat-gateway>/v1
|
||||
max_tokens: 16384
|
||||
supports_thinking: true
|
||||
supports_vision: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
```
|
||||
|
||||
For Gemini accessed **without** thinking (e.g. via OpenRouter where thinking is not activated), the plain `langchain_openai:ChatOpenAI` with `supports_thinking: false` is sufficient and no patch is needed.
|
||||
|
||||
### Tool Groups
|
||||
|
||||
Organize tools into logical groups:
|
||||
|
||||
```yaml
|
||||
tool_groups:
|
||||
- name: web # Web browsing and search
|
||||
- name: file:read # Read-only file operations
|
||||
- name: file:write # Write file operations
|
||||
- name: bash # Shell command execution
|
||||
```
|
||||
|
||||
### Tools
|
||||
|
||||
Configure specific tools available to the agent:
|
||||
|
||||
```yaml
|
||||
tools:
|
||||
- name: web_search
|
||||
group: web
|
||||
use: deerflow.community.tavily.tools:web_search_tool
|
||||
max_results: 5
|
||||
# api_key: $TAVILY_API_KEY # Optional
|
||||
```
|
||||
|
||||
**Built-in Tools**:
|
||||
- `web_search` - Search the web (DuckDuckGo, Tavily, Exa, InfoQuest, Firecrawl)
|
||||
- `web_fetch` - Fetch web pages (Jina AI, Exa, InfoQuest, Firecrawl)
|
||||
- `ls` - List directory contents
|
||||
- `read_file` - Read file contents
|
||||
- `write_file` - Write file contents
|
||||
- `str_replace` - String replacement in files
|
||||
- `bash` - Execute bash commands
|
||||
|
||||
### Sandbox
|
||||
|
||||
DeerFlow supports multiple sandbox execution modes. Configure your preferred mode in `config.yaml`:
|
||||
|
||||
**Local Execution** (runs sandbox code directly on the host machine):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.sandbox.local:LocalSandboxProvider # Local execution
|
||||
allow_host_bash: false # default; host bash is disabled unless explicitly re-enabled
|
||||
```
|
||||
|
||||
**Docker Execution** (runs sandbox code in isolated Docker containers):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider # Docker-based sandbox
|
||||
```
|
||||
|
||||
**Docker Execution with Kubernetes** (runs sandbox code in Kubernetes pods via provisioner service):
|
||||
|
||||
This mode runs each sandbox in an isolated Kubernetes Pod on your **host machine's cluster**. Requires Docker Desktop K8s, OrbStack, or similar local K8s setup.
|
||||
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
provisioner_url: http://provisioner:8002
|
||||
```
|
||||
|
||||
When using Docker development (`make docker-start`), DeerFlow starts the `provisioner` service only if this provisioner mode is configured. In local or plain Docker sandbox modes, `provisioner` is skipped.
|
||||
|
||||
See [Provisioner Setup Guide](../../docker/provisioner/README.md) for detailed configuration, prerequisites, and troubleshooting.
|
||||
|
||||
Choose between local execution or Docker-based isolation:
|
||||
|
||||
**Option 1: Local Sandbox** (default, simpler setup):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.sandbox.local:LocalSandboxProvider
|
||||
allow_host_bash: false
|
||||
```
|
||||
|
||||
`allow_host_bash` is intentionally `false` by default. DeerFlow's local sandbox is a host-side convenience mode, not a secure shell isolation boundary. If you need `bash`, prefer `AioSandboxProvider`. Only set `allow_host_bash: true` for fully trusted single-user local workflows.
|
||||
|
||||
**Option 2: Docker Sandbox** (isolated, more secure):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
port: 8080
|
||||
auto_start: true
|
||||
container_prefix: deer-flow-sandbox
|
||||
|
||||
# Optional: Additional mounts
|
||||
mounts:
|
||||
- host_path: /path/on/host
|
||||
container_path: /path/in/container
|
||||
read_only: false
|
||||
```
|
||||
|
||||
When you configure `sandbox.mounts`, DeerFlow exposes those `container_path` values in the agent prompt so the agent can discover and operate on mounted directories directly instead of assuming everything must live under `/mnt/user-data`.
|
||||
|
||||
### Skills
|
||||
|
||||
Configure the skills directory for specialized workflows:
|
||||
|
||||
```yaml
|
||||
skills:
|
||||
# Host path (optional, default: ../skills)
|
||||
path: /custom/path/to/skills
|
||||
|
||||
# Container mount path (default: /mnt/skills)
|
||||
container_path: /mnt/skills
|
||||
```
|
||||
|
||||
**How Skills Work**:
|
||||
- Skills are stored in `deer-flow/skills/{public,custom}/`
|
||||
- Each skill has a `SKILL.md` file with metadata
|
||||
- Skills are automatically discovered and loaded
|
||||
- Available in both local and Docker sandbox via path mapping
|
||||
|
||||
**Per-Agent Skill Filtering**:
|
||||
Custom agents can restrict which skills they load by defining a `skills` field in their `config.yaml` (located at `workspace/agents/<agent_name>/config.yaml`):
|
||||
- **Omitted or `null`**: Loads all globally enabled skills (default fallback).
|
||||
- **`[]` (empty list)**: Disables all skills for this specific agent.
|
||||
- **`["skill-name"]`**: Loads only the explicitly specified skills.
|
||||
|
||||
### Title Generation
|
||||
|
||||
Automatic conversation title generation:
|
||||
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 6
|
||||
max_chars: 60
|
||||
model_name: null # Use first model in list
|
||||
```
|
||||
|
||||
### GitHub API Token (Optional for GitHub Deep Research Skill)
|
||||
|
||||
The default GitHub API rate limits are quite restrictive. For frequent project research, we recommend configuring a personal access token (PAT) with read-only permissions.
|
||||
|
||||
**Configuration Steps**:
|
||||
1. Uncomment the `GITHUB_TOKEN` line in the `.env` file and add your personal access token
|
||||
2. Restart the DeerFlow service to apply changes
|
||||
|
||||
## Environment Variables
|
||||
|
||||
DeerFlow supports environment variable substitution using the `$` prefix:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- api_key: $OPENAI_API_KEY # Reads from environment
|
||||
```
|
||||
|
||||
**Common Environment Variables**:
|
||||
- `OPENAI_API_KEY` - OpenAI API key
|
||||
- `ANTHROPIC_API_KEY` - Anthropic API key
|
||||
- `DEEPSEEK_API_KEY` - DeepSeek API key
|
||||
- `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
|
||||
- `TAVILY_API_KEY` - Tavily search API key
|
||||
- `DEER_FLOW_CONFIG_PATH` - Custom config file path
|
||||
|
||||
## Configuration Location
|
||||
|
||||
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
|
||||
|
||||
## Configuration Priority
|
||||
|
||||
DeerFlow searches for configuration in this order:
|
||||
|
||||
1. Path specified in code via `config_path` argument
|
||||
2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
|
||||
3. `config.yaml` in current working directory (typically `backend/` when running)
|
||||
4. `config.yaml` in parent directory (project root: `deer-flow/`)
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Place `config.yaml` in project root** - Not in `backend/` directory
|
||||
2. **Never commit `config.yaml`** - It's already in `.gitignore`
|
||||
3. **Use environment variables for secrets** - Don't hardcode API keys
|
||||
4. **Keep `config.example.yaml` updated** - Document all new options
|
||||
5. **Test configuration changes locally** - Before deploying
|
||||
6. **Use Docker sandbox for production** - Better isolation and security
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "Config file not found"
|
||||
- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
|
||||
- The backend searches parent directory by default, so root location is preferred
|
||||
- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
|
||||
|
||||
### "Invalid API key"
|
||||
- Verify environment variables are set correctly
|
||||
- Check that `$` prefix is used for env var references
|
||||
|
||||
### "Skills not loading"
|
||||
- Check that `deer-flow/skills/` directory exists
|
||||
- Verify skills have valid `SKILL.md` files
|
||||
- Check `skills.path` configuration if using custom path
|
||||
|
||||
### "Docker sandbox fails to start"
|
||||
- Ensure Docker is running
|
||||
- Check port 8080 (or configured port) is available
|
||||
- Verify Docker image is accessible
|
||||
|
||||
## Examples
|
||||
|
||||
See `config.example.yaml` for complete examples of all configuration options.
|
||||
@@ -1,296 +0,0 @@
|
||||
# 文件上传功能
|
||||
|
||||
## 概述
|
||||
|
||||
DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并可选地将 Office 文档和 PDF 转换为 Markdown 格式。
|
||||
|
||||
## 功能特性
|
||||
|
||||
- ✅ 支持多文件同时上传
|
||||
- ✅ 可选地转换文档为 Markdown（PDF、PPT、Excel、Word）
|
||||
- ✅ 文件存储在线程隔离的目录中
|
||||
- ✅ Agent 自动感知已上传的文件
|
||||
- ✅ 支持文件列表查询和删除
|
||||
|
||||
## API 端点
|
||||
|
||||
### 1. 上传文件
|
||||
```
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
```
|
||||
|
||||
**请求体：** `multipart/form-data`
|
||||
- `files`: 一个或多个文件
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"markdown_file": "document.md",
|
||||
"markdown_path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.md",
|
||||
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
|
||||
"markdown_artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.md"
|
||||
}
|
||||
],
|
||||
"message": "Successfully uploaded 1 file(s)"
|
||||
}
|
||||
```
|
||||
|
||||
**路径说明：**
|
||||
- `path`: 实际文件系统路径（相对于 `backend/` 目录）
|
||||
- `virtual_path`: Agent 在沙箱中使用的虚拟路径
|
||||
- `artifact_url`: 前端通过 HTTP 访问文件的 URL
|
||||
|
||||
### 2. 列出已上传文件
|
||||
```
|
||||
GET /api/threads/{thread_id}/uploads/list
|
||||
```
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"extension": ".pdf",
|
||||
"modified": 1705997600.0
|
||||
}
|
||||
],
|
||||
"count": 1
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 删除文件
|
||||
```
|
||||
DELETE /api/threads/{thread_id}/uploads/{filename}
|
||||
```
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted document.pdf"
|
||||
}
|
||||
```
|
||||
|
||||
## 支持的文档格式
|
||||
|
||||
以下格式在显式启用 `uploads.auto_convert_documents: true` 时会自动转换为 Markdown：
|
||||
- PDF (`.pdf`)
|
||||
- PowerPoint (`.ppt`, `.pptx`)
|
||||
- Excel (`.xls`, `.xlsx`)
|
||||
- Word (`.doc`, `.docx`)
|
||||
|
||||
转换后的 Markdown 文件会保存在同一目录下，文件名为原文件名 + `.md` 扩展名。
|
||||
|
||||
默认情况下，自动转换是关闭的，以避免在网关主机上对不受信任的 Office/PDF 上传执行解析。只有在受信任部署中明确接受此风险时，才应将 `uploads.auto_convert_documents` 设置为 `true`。
|
||||
|
||||
## Agent 集成
|
||||
|
||||
### 自动文件列举
|
||||
|
||||
Agent 在每次请求时会自动收到已上传文件的列表，格式如下：
|
||||
|
||||
```xml
|
||||
<uploaded_files>
|
||||
The following files have been uploaded and are available for use:
|
||||
|
||||
- document.pdf (1.2 MB)
|
||||
Path: /mnt/user-data/uploads/document.pdf
|
||||
|
||||
- document.md (45.3 KB)
|
||||
Path: /mnt/user-data/uploads/document.md
|
||||
|
||||
You can read these files using the `read_file` tool with the paths shown above.
|
||||
</uploaded_files>
|
||||
```
|
||||
|
||||
### 使用上传的文件
|
||||
|
||||
Agent 在沙箱中运行，使用虚拟路径访问文件。Agent 可以直接使用 `read_file` 工具读取上传的文件：
|
||||
|
||||
```python
|
||||
# 读取原始 PDF（如果支持）
|
||||
read_file(path="/mnt/user-data/uploads/document.pdf")
|
||||
|
||||
# 读取转换后的 Markdown（推荐）
|
||||
read_file(path="/mnt/user-data/uploads/document.md")
|
||||
```
|
||||
|
||||
**路径映射关系：**
|
||||
- Agent 使用：`/mnt/user-data/uploads/document.pdf`（虚拟路径）
|
||||
- 实际存储：`backend/.deer-flow/threads/{thread_id}/user-data/uploads/document.pdf`
|
||||
- 前端访问：`/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf`（HTTP URL）
|
||||
|
||||
上传流程采用“线程目录优先”策略：
|
||||
- 先写入 `backend/.deer-flow/threads/{thread_id}/user-data/uploads/` 作为权威存储
|
||||
- 本地沙箱（`sandbox_id=local`）直接使用线程目录内容
|
||||
- 非本地沙箱会额外同步到 `/mnt/user-data/uploads/*`，确保运行时可见
|
||||
|
||||
## 测试示例
|
||||
|
||||
### 使用 curl 测试
|
||||
|
||||
```bash
|
||||
# 1. 上传单个文件
|
||||
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
|
||||
-F "files=@/path/to/document.pdf"
|
||||
|
||||
# 2. 上传多个文件
|
||||
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
|
||||
-F "files=@/path/to/document.pdf" \
|
||||
-F "files=@/path/to/presentation.pptx" \
|
||||
-F "files=@/path/to/spreadsheet.xlsx"
|
||||
|
||||
# 3. 列出已上传文件
|
||||
curl http://localhost:2026/api/threads/test-thread/uploads/list
|
||||
|
||||
# 4. 删除文件
|
||||
curl -X DELETE http://localhost:2026/api/threads/test-thread/uploads/document.pdf
|
||||
```
|
||||
|
||||
### 使用 Python 测试
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
thread_id = "test-thread"
|
||||
base_url = "http://localhost:2026"
|
||||
|
||||
# 上传文件
|
||||
files = [
|
||||
("files", open("document.pdf", "rb")),
|
||||
("files", open("presentation.pptx", "rb")),
|
||||
]
|
||||
response = requests.post(
|
||||
f"{base_url}/api/threads/{thread_id}/uploads",
|
||||
files=files
|
||||
)
|
||||
print(response.json())
|
||||
|
||||
# 列出文件
|
||||
response = requests.get(f"{base_url}/api/threads/{thread_id}/uploads/list")
|
||||
print(response.json())
|
||||
|
||||
# 删除文件
|
||||
response = requests.delete(
|
||||
f"{base_url}/api/threads/{thread_id}/uploads/document.pdf"
|
||||
)
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
## 文件存储结构
|
||||
|
||||
```
|
||||
backend/.deer-flow/threads/
|
||||
└── {thread_id}/
|
||||
└── user-data/
|
||||
└── uploads/
|
||||
├── document.pdf # 原始文件
|
||||
├── document.md # 转换后的 Markdown
|
||||
├── presentation.pptx
|
||||
├── presentation.md
|
||||
└── ...
|
||||
```
|
||||
|
||||
## 限制
|
||||
|
||||
- 最大文件大小：100MB（可在 nginx.conf 中配置 `client_max_body_size`）
|
||||
- 文件名安全性：系统会自动验证文件路径，防止目录遍历攻击
|
||||
- 线程隔离：每个线程的上传文件相互隔离，无法跨线程访问
|
||||
- 自动文档转换默认关闭；如需启用，需在 `config.yaml` 中显式设置 `uploads.auto_convert_documents: true`
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 组件
|
||||
|
||||
1. **Upload Router** (`app/gateway/routers/uploads.py`)
|
||||
- 处理文件上传、列表、删除请求
|
||||
- 使用 markitdown 转换文档
|
||||
|
||||
2. **Uploads Middleware** (`packages/harness/deerflow/agents/middlewares/uploads_middleware.py`)
|
||||
- 在每次 Agent 请求前注入文件列表
|
||||
- 自动生成格式化的文件列表消息
|
||||
|
||||
3. **Nginx 配置** (`nginx.conf`)
|
||||
- 路由上传请求到 Gateway API
|
||||
- 配置大文件上传支持
|
||||
|
||||
### 依赖
|
||||
|
||||
- `markitdown>=0.0.1a2` - 文档转换
|
||||
- `python-multipart>=0.0.20` - 文件上传处理
|
||||
|
||||
## 故障排查
|
||||
|
||||
### 文件上传失败
|
||||
|
||||
1. 检查文件大小是否超过限制
|
||||
2. 检查 Gateway API 是否正常运行
|
||||
3. 检查磁盘空间是否充足
|
||||
4. 查看 Gateway 日志：`make gateway`
|
||||
|
||||
### 文档转换失败
|
||||
|
||||
1. 检查 markitdown 是否正确安装：`uv run python -c "import markitdown"`
|
||||
2. 查看日志中的具体错误信息
|
||||
3. 某些损坏或加密的文档可能无法转换，但原文件仍会保存
|
||||
|
||||
### Agent 看不到上传的文件
|
||||
|
||||
1. 确认 UploadsMiddleware 已在 agent.py 中注册
|
||||
2. 检查 thread_id 是否正确
|
||||
3. 确认文件确实已上传到 `backend/.deer-flow/threads/{thread_id}/user-data/uploads/`
|
||||
4. 非本地沙箱场景下，确认上传接口没有报错（需要成功完成 sandbox 同步）
|
||||
|
||||
## 开发建议
|
||||
|
||||
### 前端集成
|
||||
|
||||
```typescript
|
||||
// 上传文件示例
|
||||
async function uploadFiles(threadId: string, files: File[]) {
|
||||
const formData = new FormData();
|
||||
files.forEach(file => {
|
||||
formData.append('files', file);
|
||||
});
|
||||
|
||||
const response = await fetch(
|
||||
`/api/threads/${threadId}/uploads`,
|
||||
{
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
}
|
||||
);
|
||||
|
||||
return response.json();
|
||||
}
|
||||
|
||||
// 列出文件
|
||||
async function listFiles(threadId: string) {
|
||||
const response = await fetch(
|
||||
`/api/threads/${threadId}/uploads/list`
|
||||
);
|
||||
return response.json();
|
||||
}
|
||||
```
|
||||
|
||||
### 扩展功能建议
|
||||
|
||||
1. **文件预览**：添加预览端点，支持在浏览器中直接查看文件
|
||||
2. **批量删除**：支持一次删除多个文件
|
||||
3. **文件搜索**：支持按文件名或类型搜索
|
||||
4. **版本控制**：保留文件的多个版本
|
||||
5. **压缩包支持**：自动解压 zip 文件
|
||||
6. **图片 OCR**：对上传的图片进行 OCR 识别
|
||||
@@ -1,385 +0,0 @@
|
||||
# Guardrails: Pre-Tool-Call Authorization
|
||||
|
||||
> **Context:** [Issue #1213](https://github.com/bytedance/deer-flow/issues/1213) — DeerFlow has Docker sandboxing and human approval via `ask_clarification`, but no deterministic, policy-driven authorization layer for tool calls. An agent running autonomous multi-step tasks can execute any loaded tool with any arguments. Guardrails add a middleware that evaluates every tool call against a policy **before** execution.
|
||||
|
||||
## Why Guardrails
|
||||
|
||||
```
|
||||
Without guardrails: With guardrails:
|
||||
|
||||
Agent Agent
|
||||
│ │
|
||||
▼ ▼
|
||||
┌──────────┐ ┌──────────┐
|
||||
│ bash │──▶ executes immediately │ bash │──▶ GuardrailMiddleware
|
||||
│ rm -rf / │ │ rm -rf / │ │
|
||||
└──────────┘ └──────────┘ ▼
|
||||
┌──────────────┐
|
||||
│ Provider │
|
||||
│ evaluates │
|
||||
│ against │
|
||||
│ policy │
|
||||
└──────┬───────┘
|
||||
│
|
||||
┌─────┴─────┐
|
||||
│ │
|
||||
ALLOW DENY
|
||||
│ │
|
||||
▼ ▼
|
||||
Tool runs Agent sees:
|
||||
normally "Guardrail denied:
|
||||
rm -rf blocked"
|
||||
```
|
||||
|
||||
- **Sandboxing** provides process isolation but not semantic authorization. A sandboxed `bash` can still `curl` data out.
|
||||
- **Human approval** (`ask_clarification`) requires a human in the loop for every action. Not viable for autonomous workflows.
|
||||
- **Guardrails** provide deterministic, policy-driven authorization that works without human intervention.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ Middleware Chain │
|
||||
│ │
|
||||
│ 1. ThreadDataMiddleware ─── per-thread dirs │
|
||||
│ 2. UploadsMiddleware ─── file upload tracking │
|
||||
│ 3. SandboxMiddleware ─── sandbox acquisition │
|
||||
│ 4. DanglingToolCallMiddleware ── fix incomplete tool calls │
|
||||
│ 5. GuardrailMiddleware ◄──── EVALUATES EVERY TOOL CALL │
|
||||
│ 6. ToolErrorHandlingMiddleware ── convert exceptions to messages │
|
||||
│ 7-12. (Summarization, Title, Memory, Vision, Subagent, Clarify) │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ GuardrailProvider │ ◄── pluggable: any class
|
||||
│ (configured in YAML) │ with evaluate/aevaluate
|
||||
└────────────┬─────────────┘
|
||||
│
|
||||
┌─────────┼──────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
Built-in OAP Passport Custom
|
||||
Allowlist Provider Provider
|
||||
(zero dep) (open standard) (your code)
|
||||
│
|
||||
Any implementation
|
||||
(e.g. APort, or
|
||||
your own evaluator)
|
||||
```
|
||||
|
||||
The `GuardrailMiddleware` implements `wrap_tool_call` / `awrap_tool_call` (the same `AgentMiddleware` pattern used by `ToolErrorHandlingMiddleware`). It:
|
||||
|
||||
1. Builds a `GuardrailRequest` with tool name, arguments, and passport reference
|
||||
2. Calls `provider.evaluate(request)` on whatever provider is configured
|
||||
3. If **deny**: returns `ToolMessage(status="error")` with the reason -- agent sees the denial and adapts
|
||||
4. If **allow**: passes through to the actual tool handler
|
||||
5. If **provider error** and `fail_closed=true` (default): blocks the call
|
||||
6. `GraphBubbleUp` exceptions (LangGraph control signals) are always propagated, never caught
|
||||
|
||||
## Three Provider Options
|
||||
|
||||
### Option 1: Built-in AllowlistProvider (Zero Dependencies)
|
||||
|
||||
The simplest option. Ships with DeerFlow. Block or allow tools by name. No external packages, no passport, no network.
|
||||
|
||||
**config.yaml:**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config:
|
||||
denied_tools: ["bash", "write_file"]
|
||||
```
|
||||
|
||||
This blocks `bash` and `write_file` for all requests. All other tools pass through.
|
||||
|
||||
You can also use an allowlist (only these tools are permitted):
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config:
|
||||
allowed_tools: ["web_search", "read_file", "ls"]
|
||||
```
|
||||
|
||||
**Try it:**
|
||||
1. Add the config above to your `config.yaml`
|
||||
2. Start DeerFlow: `make dev`
|
||||
3. Ask the agent: "Use bash to run echo hello"
|
||||
4. The agent sees: `Guardrail denied: tool 'bash' was blocked (oap.tool_not_allowed)`
|
||||
|
||||
### Option 2: OAP Passport Provider (Policy-Based)
|
||||
|
||||
For policy enforcement based on the [Open Agent Passport (OAP)](https://github.com/aporthq/aport-spec) open standard. An OAP passport is a JSON document that declares an agent's identity, capabilities, and operational limits. Any provider that reads an OAP passport and returns OAP-compliant decisions works with DeerFlow.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ OAP Passport (JSON) │
|
||||
│ (open standard, any provider) │
|
||||
│ { │
|
||||
│ "spec_version": "oap/1.0", │
|
||||
│ "status": "active", │
|
||||
│ "capabilities": [ │
|
||||
│ {"id": "system.command.execute"}, │
|
||||
│ {"id": "data.file.read"}, │
|
||||
│ {"id": "data.file.write"}, │
|
||||
│ {"id": "web.fetch"}, │
|
||||
│ {"id": "mcp.tool.execute"} │
|
||||
│ ], │
|
||||
│ "limits": { │
|
||||
│ "system.command.execute": { │
|
||||
│ "allowed_commands": ["git", "npm", "node", "ls"], │
|
||||
│ "blocked_patterns": ["rm -rf", "sudo", "chmod 777"] │
|
||||
│ } │
|
||||
│ } │
|
||||
│ } │
|
||||
└──────────────────────────┬──────────────────────────────────┘
|
||||
│
|
||||
Any OAP-compliant provider
|
||||
┌────────────────┼────────────────┐
|
||||
│ │ │
|
||||
Your own APort (ref. Other future
|
||||
evaluator implementation) implementations
|
||||
```
|
||||
|
||||
**Creating a passport manually:**
|
||||
|
||||
An OAP passport is just a JSON file. You can create one by hand following the [OAP specification](https://github.com/aporthq/aport-spec/blob/main/oap/oap-spec.md) and validate it against the [JSON schema](https://github.com/aporthq/aport-spec/blob/main/oap/passport-schema.json). See the [examples](https://github.com/aporthq/aport-spec/tree/main/oap/examples) directory for templates.
|
||||
|
||||
**Using APort as a reference implementation:**
|
||||
|
||||
[APort Agent Guardrails](https://github.com/aporthq/aport-agent-guardrails) is one open-source (Apache 2.0) implementation of an OAP provider. It handles passport creation, local evaluation, and optional hosted API evaluation.
|
||||
|
||||
```bash
|
||||
pip install aport-agent-guardrails
|
||||
aport setup --framework deerflow
|
||||
```
|
||||
|
||||
This creates:
|
||||
- `~/.aport/deerflow/config.yaml` -- evaluator config (local or API mode)
|
||||
- `~/.aport/deerflow/aport/passport.json` -- OAP passport with capabilities and limits
|
||||
|
||||
**config.yaml (using APort as the provider):**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: aport_guardrails.providers.generic:OAPGuardrailProvider
|
||||
```
|
||||
|
||||
**config.yaml (using your own OAP provider):**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: my_oap_provider:MyOAPProvider
|
||||
config:
|
||||
passport_path: ./my-passport.json
|
||||
```
|
||||
|
||||
Any provider that accepts `framework` as a kwarg and implements `evaluate`/`aevaluate` works. The OAP standard defines the passport format and decision codes; DeerFlow doesn't care which provider reads them.
|
||||
|
||||
**What the passport controls:**
|
||||
|
||||
| Passport field | What it does | Example |
|
||||
|---|---|---|
|
||||
| `capabilities[].id` | Which tool categories the agent can use | `system.command.execute`, `data.file.write` |
|
||||
| `limits.*.allowed_commands` | Which commands are allowed | `["git", "npm", "node"]` or `["*"]` for all |
|
||||
| `limits.*.blocked_patterns` | Patterns always denied | `["rm -rf", "sudo", "chmod 777"]` |
|
||||
| `status` | Kill switch | `active`, `suspended`, `revoked` |
|
||||
|
||||
**Evaluation modes (provider-dependent):**
|
||||
|
||||
OAP providers may support different evaluation modes. For example, the APort reference implementation supports:
|
||||
|
||||
| Mode | How it works | Network | Latency |
|
||||
|---|---|---|---|
|
||||
| **Local** | Evaluates passport locally (bash script). | None | ~300ms |
|
||||
| **API** | Sends passport + context to a hosted evaluator. Signed decisions. | Yes | ~65ms |
|
||||
|
||||
A custom OAP provider can implement any evaluation strategy -- the DeerFlow middleware doesn't care how the provider reaches its decision.
|
||||
|
||||
**Try it:**
|
||||
1. Install and set up as above
|
||||
2. Start DeerFlow and ask: "Create a file called test.txt with content hello"
|
||||
3. Then ask: "Now delete it using bash rm -rf"
|
||||
4. Guardrail blocks it: `oap.blocked_pattern: Command contains blocked pattern: rm -rf`
|
||||
|
||||
### Option 3: Custom Provider (Bring Your Own)
|
||||
|
||||
Any Python class with `evaluate(request)` and `aevaluate(request)` methods works. No base class or inheritance needed -- it's a structural protocol.
|
||||
|
||||
```python
|
||||
# my_guardrail.py
|
||||
|
||||
class MyGuardrailProvider:
|
||||
name = "my-company"
|
||||
|
||||
def evaluate(self, request):
|
||||
from deerflow.guardrails.provider import GuardrailDecision, GuardrailReason
|
||||
|
||||
# Example: block any bash command containing "delete"
|
||||
if request.tool_name == "bash" and "delete" in str(request.tool_input):
|
||||
return GuardrailDecision(
|
||||
allow=False,
|
||||
reasons=[GuardrailReason(code="custom.blocked", message="delete not allowed")],
|
||||
policy_id="custom.v1",
|
||||
)
|
||||
return GuardrailDecision(allow=True, reasons=[GuardrailReason(code="oap.allowed")])
|
||||
|
||||
async def aevaluate(self, request):
|
||||
return self.evaluate(request)
|
||||
```
|
||||
|
||||
**config.yaml:**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: my_guardrail:MyGuardrailProvider
|
||||
```
|
||||
|
||||
Make sure `my_guardrail.py` is on the Python path (e.g. in the backend directory or installed as a package).
|
||||
|
||||
**Try it:**
|
||||
1. Create `my_guardrail.py` in the backend directory
|
||||
2. Add the config
|
||||
3. Start DeerFlow and ask: "Use bash to delete test.txt"
|
||||
4. Your provider blocks it
|
||||
|
||||
## Implementing a Provider
|
||||
|
||||
### Required Interface
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────┐
|
||||
│ GuardrailProvider Protocol │
|
||||
│ │
|
||||
│ name: str │
|
||||
│ │
|
||||
│ evaluate(request: GuardrailRequest) │
|
||||
│ -> GuardrailDecision │
|
||||
│ │
|
||||
│ aevaluate(request: GuardrailRequest) (async) │
|
||||
│ -> GuardrailDecision │
|
||||
└──────────────────────────────────────────────────┘
|
||||
|
||||
┌──────────────────────────┐ ┌──────────────────────────┐
|
||||
│ GuardrailRequest │ │ GuardrailDecision │
|
||||
│ │ │ │
|
||||
│ tool_name: str │ │ allow: bool │
|
||||
│ tool_input: dict │ │ reasons: [GuardrailReason]│
|
||||
│ agent_id: str | None │ │ policy_id: str | None │
|
||||
│ thread_id: str | None │ │ metadata: dict │
|
||||
│ is_subagent: bool │ │ │
|
||||
│ timestamp: str │ │ GuardrailReason: │
|
||||
│ │ │ code: str │
|
||||
└──────────────────────────┘ │ message: str │
|
||||
└──────────────────────────┘
|
||||
```
|
||||
|
||||
### DeerFlow Tool Names
|
||||
|
||||
These are the tool names your provider will see in `request.tool_name`:
|
||||
|
||||
| Tool | What it does |
|
||||
|---|---|
|
||||
| `bash` | Shell command execution |
|
||||
| `write_file` | Create/overwrite a file |
|
||||
| `str_replace` | Edit a file (find and replace) |
|
||||
| `read_file` | Read file content |
|
||||
| `ls` | List directory |
|
||||
| `web_search` | Web search query |
|
||||
| `web_fetch` | Fetch URL content |
|
||||
| `image_search` | Image search |
|
||||
| `present_file` | Present file to user |
|
||||
| `view_image` | Display image |
|
||||
| `ask_clarification` | Ask user a question |
|
||||
| `task` | Delegate to subagent |
|
||||
| `mcp__*` | MCP tools (dynamic) |
|
||||
|
||||
### OAP Reason Codes
|
||||
|
||||
Standard codes used by the [OAP specification](https://github.com/aporthq/aport-spec):
|
||||
|
||||
| Code | Meaning |
|
||||
|---|---|
|
||||
| `oap.allowed` | Tool call authorized |
|
||||
| `oap.tool_not_allowed` | Tool not in allowlist |
|
||||
| `oap.command_not_allowed` | Command not in allowed_commands |
|
||||
| `oap.blocked_pattern` | Command matches a blocked pattern |
|
||||
| `oap.limit_exceeded` | Operation exceeds a limit |
|
||||
| `oap.passport_suspended` | Passport status is suspended/revoked |
|
||||
| `oap.evaluator_error` | Provider crashed (fail-closed) |
|
||||
|
||||
### Provider Loading
|
||||
|
||||
DeerFlow loads providers via `resolve_variable()` -- the same mechanism used for models, tools, and sandbox providers. The `use:` field is a Python class path: `package.module:ClassName`.
|
||||
|
||||
The provider is instantiated with `**config` kwargs if `config:` is set, plus `framework="deerflow"` is always injected. Accept `**kwargs` to stay forward-compatible:
|
||||
|
||||
```python
|
||||
class YourProvider:
|
||||
def __init__(self, framework: str = "generic", **kwargs):
|
||||
# framework="deerflow" tells you which config dir to use
|
||||
...
|
||||
```
|
||||
|
||||
## Configuration Reference
|
||||
|
||||
```yaml
|
||||
guardrails:
|
||||
# Enable/disable guardrail middleware (default: false)
|
||||
enabled: true
|
||||
|
||||
# Block tool calls if provider raises an exception (default: true)
|
||||
fail_closed: true
|
||||
|
||||
# Passport reference -- passed as request.agent_id to the provider.
|
||||
# File path, hosted agent ID, or null (provider resolves from its config).
|
||||
passport: null
|
||||
|
||||
# Provider: loaded by class path via resolve_variable
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config: # optional kwargs passed to provider.__init__
|
||||
denied_tools: ["bash"]
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
uv run python -m pytest tests/test_guardrail_middleware.py -v
|
||||
```
|
||||
|
||||
25 tests covering:
|
||||
- AllowlistProvider: allow, deny, both allowlist+denylist, async
|
||||
- GuardrailMiddleware: allow passthrough, deny with OAP codes, fail-closed, fail-open, passport forwarding, empty reasons fallback, empty tool name, protocol isinstance check
|
||||
- Async paths: awrap_tool_call for allow, deny, fail-closed, fail-open
|
||||
- GraphBubbleUp: LangGraph control signals propagate through (not caught)
|
||||
- Config: defaults, from_dict, singleton load/reset
|
||||
|
||||
## Files
|
||||
|
||||
```
|
||||
packages/harness/deerflow/guardrails/
|
||||
__init__.py # Public exports
|
||||
provider.py # GuardrailProvider protocol, GuardrailRequest, GuardrailDecision
|
||||
middleware.py # GuardrailMiddleware (AgentMiddleware subclass)
|
||||
builtin.py # AllowlistProvider (zero deps)
|
||||
|
||||
packages/harness/deerflow/config/
|
||||
guardrails_config.py # GuardrailsConfig Pydantic model + singleton
|
||||
|
||||
packages/harness/deerflow/agents/middlewares/
|
||||
tool_error_handling_middleware.py # Registers GuardrailMiddleware in chain
|
||||
|
||||
config.example.yaml # Three provider options documented
|
||||
tests/test_guardrail_middleware.py # 25 tests
|
||||
docs/GUARDRAILS.md # This file
|
||||
```
|
||||
@@ -1,343 +0,0 @@
|
||||
# DeerFlow 后端拆分设计文档：Harness + App
|
||||
|
||||
> 状态：Draft
|
||||
> 作者：DeerFlow Team
|
||||
> 日期：2026-03-13
|
||||
|
||||
## 1. 背景与动机
|
||||
|
||||
DeerFlow 后端当前是一个单一 Python 包（`src.*`），包含了从底层 agent 编排到上层用户产品的所有代码。随着项目发展，这种结构带来了几个问题：
|
||||
|
||||
- **复用困难**：其他产品（CLI 工具、Slack bot、第三方集成）想用 agent 能力，必须依赖整个后端，包括 FastAPI、IM SDK 等不需要的依赖
|
||||
- **职责模糊**：agent 编排逻辑和用户产品逻辑混在同一个 `src/` 下，边界不清晰
|
||||
- **依赖膨胀**：LangGraph Server 运行时不需要 FastAPI/uvicorn/Slack SDK，但当前必须安装全部依赖
|
||||
|
||||
本文档提出将后端拆分为两部分：**deerflow-harness**（可发布的 agent 框架包）和 **app**（不打包的用户产品代码）。
|
||||
|
||||
## 2. 核心概念
|
||||
|
||||
### 2.1 Harness（线束/框架层）
|
||||
|
||||
Harness 是 agent 的构建与编排框架，回答 **"如何构建和运行 agent"** 的问题：
|
||||
|
||||
- Agent 工厂与生命周期管理
|
||||
- Middleware pipeline
|
||||
- 工具系统（内置工具 + MCP + 社区工具）
|
||||
- 沙箱执行环境
|
||||
- 子 agent 委派
|
||||
- 记忆系统
|
||||
- 技能加载与注入
|
||||
- 模型工厂
|
||||
- 配置系统
|
||||
|
||||
**Harness 是一个可发布的 Python 包**（`deerflow-harness`），可以独立安装和使用。
|
||||
|
||||
**Harness 的设计原则**：对上层应用完全无感知。它不知道也不关心谁在调用它——可以是 Web App、CLI、Slack Bot、或者一个单元测试。
|
||||
|
||||
### 2.2 App（应用层）
|
||||
|
||||
App 是面向用户的产品代码，回答 **"如何将 agent 呈现给用户"** 的问题：
|
||||
|
||||
- Gateway API（FastAPI REST 接口）
|
||||
- IM Channels（飞书、Slack、Telegram 集成）
|
||||
- Custom Agent 的 CRUD 管理
|
||||
- 文件上传/下载的 HTTP 接口
|
||||
|
||||
**App 不打包、不发布**，它是 DeerFlow 项目内部的应用代码，直接运行。
|
||||
|
||||
**App 依赖 Harness，但 Harness 不依赖 App。**
|
||||
|
||||
### 2.3 边界划分
|
||||
|
||||
| 模块 | 归属 | 说明 |
|
||||
|------|------|------|
|
||||
| `config/` | Harness | 配置系统是基础设施 |
|
||||
| `reflection/` | Harness | 动态模块加载工具 |
|
||||
| `utils/` | Harness | 通用工具函数 |
|
||||
| `agents/` | Harness | Agent 工厂、middleware、state、memory |
|
||||
| `subagents/` | Harness | 子 agent 委派系统 |
|
||||
| `sandbox/` | Harness | 沙箱执行环境 |
|
||||
| `tools/` | Harness | 工具注册与发现 |
|
||||
| `mcp/` | Harness | MCP 协议集成 |
|
||||
| `skills/` | Harness | 技能加载、解析、定义 schema |
|
||||
| `models/` | Harness | LLM 模型工厂 |
|
||||
| `community/` | Harness | 社区工具（tavily、jina 等） |
|
||||
| `client.py` | Harness | 嵌入式 Python 客户端 |
|
||||
| `gateway/` | App | FastAPI REST API |
|
||||
| `channels/` | App | IM 平台集成 |
|
||||
|
||||
**关于 Custom Agents**：agent 定义格式（`config.yaml` + `SOUL.md` schema）由 Harness 层的 `config/agents_config.py` 定义，但文件的存储、CRUD、发现机制由 App 层的 `gateway/routers/agents.py` 负责。
|
||||
|
||||
## 3. 目标架构
|
||||
|
||||
### 3.1 目录结构
|
||||
|
||||
```
|
||||
backend/
|
||||
├── packages/
|
||||
│ └── harness/
|
||||
│ ├── pyproject.toml # deerflow-harness 包定义
|
||||
│ └── deerflow/ # Python 包根（import 前缀: deerflow.*）
|
||||
│ ├── __init__.py
|
||||
│ ├── config/
|
||||
│ ├── reflection/
|
||||
│ ├── utils/
|
||||
│ ├── agents/
|
||||
│ │ ├── lead_agent/
|
||||
│ │ ├── middlewares/
|
||||
│ │ ├── memory/
|
||||
│ │ ├── checkpointer/
|
||||
│ │ └── thread_state.py
|
||||
│ ├── subagents/
|
||||
│ ├── sandbox/
|
||||
│ ├── tools/
|
||||
│ ├── mcp/
|
||||
│ ├── skills/
|
||||
│ ├── models/
|
||||
│ ├── community/
|
||||
│ └── client.py
|
||||
├── app/ # 不打包（import 前缀: app.*）
|
||||
│ ├── __init__.py
|
||||
│ ├── gateway/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── app.py
|
||||
│ │ ├── config.py
|
||||
│ │ ├── path_utils.py
|
||||
│ │ └── routers/
|
||||
│ └── channels/
|
||||
│ ├── __init__.py
|
||||
│ ├── base.py
|
||||
│ ├── manager.py
|
||||
│ ├── service.py
|
||||
│ ├── store.py
|
||||
│ ├── message_bus.py
|
||||
│ ├── feishu.py
|
||||
│ ├── slack.py
|
||||
│ └── telegram.py
|
||||
├── pyproject.toml # uv workspace root
|
||||
├── langgraph.json
|
||||
├── tests/
|
||||
├── docs/
|
||||
└── Makefile
|
||||
```
|
||||
|
||||
### 3.2 Import 规则
|
||||
|
||||
两个层使用不同的 import 前缀，职责边界一目了然：
|
||||
|
||||
```python
|
||||
# ---------------------------------------------------------------
|
||||
# Harness 内部互相引用（deerflow.* 前缀）
|
||||
# ---------------------------------------------------------------
|
||||
from deerflow.agents import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.config import get_app_config
|
||||
from deerflow.tools import get_available_tools
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# App 内部互相引用（app.* 前缀）
|
||||
# ---------------------------------------------------------------
|
||||
from app.gateway.app import app
|
||||
from app.gateway.routers.uploads import upload_files
|
||||
from app.channels.service import start_channel_service
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# App 调用 Harness（单向依赖，Harness 永远不 import app）
|
||||
# ---------------------------------------------------------------
|
||||
from deerflow.agents import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.skills import load_skills
|
||||
from deerflow.config.extensions_config import get_extensions_config
|
||||
```
|
||||
|
||||
**App 调用 Harness 示例 — Gateway 中启动 agent**：
|
||||
|
||||
```python
|
||||
# app/gateway/routers/chat.py
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
async def create_chat_session(thread_id: str, model_name: str):
|
||||
config = get_app_config()
|
||||
model = create_chat_model(name=model_name)
|
||||
agent = make_lead_agent(config=...)
|
||||
# ... 使用 agent 处理用户消息
|
||||
```
|
||||
|
||||
**App 调用 Harness 示例 — Channel 中查询 skills**：
|
||||
|
||||
```python
|
||||
# app/channels/manager.py
|
||||
from deerflow.skills import load_skills
|
||||
from deerflow.agents.memory.updater import get_memory_data
|
||||
|
||||
def handle_status_command():
|
||||
skills = load_skills(enabled_only=True)
|
||||
memory = get_memory_data()
|
||||
return f"Skills: {len(skills)}, Memory facts: {len(memory.get('facts', []))}"
|
||||
```
|
||||
|
||||
**禁止方向**：Harness 代码中绝不能出现 `from app.` 或 `import app.`。
|
||||
|
||||
### 3.3 为什么 App 不打包
|
||||
|
||||
| 方面 | 打包（放 packages/ 下） | 不打包（放 backend/app/） |
|
||||
|------|------------------------|--------------------------|
|
||||
| 命名空间 | 需要 pkgutil `extend_path` 合并，或独立前缀 | 天然独立，`app.*` vs `deerflow.*` |
|
||||
| 发布需求 | 没有——App 是项目内部代码 | 不需要 pyproject.toml |
|
||||
| 复杂度 | 需要管理两个包的构建、版本、依赖声明 | 直接运行，零额外配置 |
|
||||
| 运行方式 | `pip install deerflow-app` | `PYTHONPATH=. uvicorn app.gateway.app:app` |
|
||||
|
||||
App 的唯一消费者是 DeerFlow 项目自身，没有独立发布的需求。放在 `backend/app/` 下作为普通 Python 包，通过 `PYTHONPATH` 或 editable install 让 Python 找到即可。
|
||||
|
||||
### 3.4 依赖关系
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ app/ (不打包，直接运行) │
|
||||
│ ├── fastapi, uvicorn │
|
||||
│ ├── slack-sdk, lark-oapi, ... │
|
||||
│ └── import deerflow.* │
|
||||
└──────────────┬──────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────┐
|
||||
│ deerflow-harness (可发布的包) │
|
||||
│ ├── langgraph, langchain │
|
||||
│ ├── markitdown, pydantic, ... │
|
||||
│ └── 零 app 依赖 │
|
||||
└─────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**依赖分类**：
|
||||
|
||||
| 分类 | 依赖包 |
|
||||
|------|--------|
|
||||
| Harness only | agent-sandbox, langchain*, langgraph*, markdownify, markitdown, pydantic, pyyaml, readabilipy, tavily-python, firecrawl-py, tiktoken, ddgs, duckdb, httpx, kubernetes, dotenv |
|
||||
| App only | fastapi, uvicorn, sse-starlette, python-multipart, lark-oapi, slack-sdk, python-telegram-bot, markdown-to-mrkdwn |
|
||||
| Shared | langgraph-sdk（channels 用 HTTP client）, pydantic, httpx |
|
||||
|
||||
### 3.5 Workspace 配置
|
||||
|
||||
`backend/pyproject.toml`（workspace root）：
|
||||
|
||||
```toml
|
||||
[project]
|
||||
name = "deer-flow"
|
||||
version = "0.1.0"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = ["deerflow-harness"]
|
||||
|
||||
[dependency-groups]
|
||||
dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
|
||||
# App 的额外依赖（fastapi 等）也声明在 workspace root，因为 app 不打包
|
||||
app = ["fastapi", "uvicorn", "sse-starlette", "python-multipart"]
|
||||
channels = ["lark-oapi", "slack-sdk", "python-telegram-bot"]
|
||||
|
||||
[tool.uv.workspace]
|
||||
members = ["packages/harness"]
|
||||
|
||||
[tool.uv.sources]
|
||||
deerflow-harness = { workspace = true }
|
||||
```
|
||||
|
||||
## 4. 当前的跨层依赖问题
|
||||
|
||||
在拆分之前，需要先解决 `client.py` 中两处从 harness 到 app 的反向依赖：
|
||||
|
||||
### 4.1 `_validate_skill_frontmatter`
|
||||
|
||||
```python
|
||||
# client.py — harness 导入了 app 层代码
|
||||
from src.gateway.routers.skills import _validate_skill_frontmatter
|
||||
```
|
||||
|
||||
**解决方案**：将该函数提取到 `deerflow/skills/validation.py`。这是一个纯逻辑函数（解析 YAML frontmatter、校验字段），与 FastAPI 无关。
|
||||
|
||||
### 4.2 `CONVERTIBLE_EXTENSIONS` + `convert_file_to_markdown`
|
||||
|
||||
```python
|
||||
# client.py — harness 导入了 app 层代码
|
||||
from src.gateway.routers.uploads import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
|
||||
```
|
||||
|
||||
**解决方案**：将它们提取到 `deerflow/utils/file_conversion.py`。仅依赖 `markitdown` + `pathlib`，是通用工具函数。
|
||||
|
||||
## 5. 基础设施变更
|
||||
|
||||
### 5.1 LangGraph Server
|
||||
|
||||
LangGraph Server 只需要 harness 包。`langgraph.json` 更新：
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["./packages/harness"],
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:make_lead_agent"
|
||||
},
|
||||
"checkpointer": {
|
||||
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5.2 Gateway API
|
||||
|
||||
```bash
|
||||
# serve.sh / Makefile
|
||||
# PYTHONPATH 包含 backend/ 根目录，使 app.* 和 deerflow.* 都能被找到
|
||||
PYTHONPATH=. uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
|
||||
```
|
||||
|
||||
### 5.3 Nginx
|
||||
|
||||
无需变更（只做 URL 路由，不涉及 Python 模块路径）。
|
||||
|
||||
### 5.4 Docker
|
||||
|
||||
Dockerfile 中的 module 引用从 `src.` 改为 `deerflow.` / `app.`，`COPY` 命令需覆盖 `packages/` 和 `app/` 目录。
|
||||
|
||||
## 6. 实施计划
|
||||
|
||||
分 3 个 PR 递进执行：
|
||||
|
||||
### PR 1：提取共享工具函数（Low Risk）
|
||||
|
||||
1. 创建 `src/skills/validation.py`，从 `gateway/routers/skills.py` 提取 `_validate_skill_frontmatter`
|
||||
2. 创建 `src/utils/file_conversion.py`，从 `gateway/routers/uploads.py` 提取文件转换逻辑
|
||||
3. 更新 `client.py`、`gateway/routers/skills.py`、`gateway/routers/uploads.py` 的 import
|
||||
4. 运行全部测试确认无回归
|
||||
|
||||
### PR 2：Rename + 物理拆分（High Risk，原子操作）
|
||||
|
||||
1. 创建 `packages/harness/` 目录，创建 `pyproject.toml`
|
||||
2. `git mv` 将 harness 相关模块从 `src/` 移入 `packages/harness/deerflow/`
|
||||
3. `git mv` 将 app 相关模块从 `src/` 移入 `app/`
|
||||
4. 全局替换 import：
|
||||
- harness 模块：`src.*` → `deerflow.*`（所有 `.py` 文件、`langgraph.json`、测试、文档）
|
||||
- app 模块：`src.gateway.*` → `app.gateway.*`、`src.channels.*` → `app.channels.*`
|
||||
5. 更新 workspace root `pyproject.toml`
|
||||
6. 更新 `langgraph.json`、`Makefile`、`Dockerfile`
|
||||
7. `uv sync` + 全部测试 + 手动验证服务启动
|
||||
|
||||
### PR 3：边界检查 + 文档（Low Risk）
|
||||
|
||||
1. 添加 lint 规则：检查 harness 不 import app 模块
|
||||
2. 更新 `CLAUDE.md`、`README.md`
|
||||
|
||||
## 7. 风险与缓解
|
||||
|
||||
| 风险 | 影响 | 缓解措施 |
|
||||
|------|------|----------|
|
||||
| 全局 rename 误伤 | 字符串中的 `src` 被错误替换 | 正则精确匹配 `\bsrc\.`，review diff |
|
||||
| LangGraph Server 找不到模块 | 服务启动失败 | `langgraph.json` 的 `dependencies` 指向正确的 harness 包路径 |
|
||||
| App 的 `PYTHONPATH` 缺失 | Gateway/Channel 启动 import 报错 | Makefile/Docker 统一设置 `PYTHONPATH=.` |
|
||||
| `config.yaml` 中的 `use` 字段引用旧路径 | 运行时模块解析失败 | `config.yaml` 中的 `use` 字段同步更新为 `deerflow.*` |
|
||||
| 测试中 `sys.path` 混乱 | 测试失败 | 用 editable install（`uv sync`）确保 deerflow 可导入，`conftest.py` 中添加 `app/` 到 `sys.path` |
|
||||
|
||||
## 8. 未来演进
|
||||
|
||||
- **独立发布**：harness 可以发布到内部 PyPI，让其他项目直接 `pip install deerflow-harness`
|
||||
- **插件化 App**：不同的 app（web、CLI、bot）可以各自独立，都依赖同一个 harness
|
||||
- **更细粒度拆分**：如果 harness 内部模块继续增长，可以进一步拆分（如 `deerflow-sandbox`、`deerflow-mcp`）
|
||||
@@ -1,65 +0,0 @@
|
||||
# MCP (Model Context Protocol) Configuration
|
||||
|
||||
DeerFlow supports configurable MCP servers and skills to extend its capabilities, which are loaded from a dedicated `extensions_config.json` file in the project root directory.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Copy `extensions_config.example.json` to `extensions_config.json` in the project root directory.
|
||||
```bash
|
||||
# Copy example configuration
|
||||
cp extensions_config.example.json extensions_config.json
|
||||
```
|
||||
|
||||
2. Enable the desired MCP servers or skills by setting `"enabled": true`.
|
||||
3. Configure each server’s command, arguments, and environment variables as needed.
|
||||
4. Restart the application to load and register MCP tools.
|
||||
|
||||
## OAuth Support (HTTP/SSE MCP Servers)
|
||||
|
||||
For `http` and `sse` MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.
|
||||
|
||||
- Supported grants: `client_credentials`, `refresh_token`
|
||||
- Configure per-server `oauth` block in `extensions_config.json`
|
||||
- Secrets should be provided via environment variables (for example: `$MCP_OAUTH_CLIENT_SECRET`)
|
||||
|
||||
Example:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"secure-http-server": {
|
||||
"enabled": true,
|
||||
"type": "http",
|
||||
"url": "https://api.example.com/mcp",
|
||||
"oauth": {
|
||||
"enabled": true,
|
||||
"token_url": "https://auth.example.com/oauth/token",
|
||||
"grant_type": "client_credentials",
|
||||
"client_id": "$MCP_OAUTH_CLIENT_ID",
|
||||
"client_secret": "$MCP_OAUTH_CLIENT_SECRET",
|
||||
"scope": "mcp.read",
|
||||
"refresh_skew_seconds": 60
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.
|
||||
|
||||
## Example Capabilities
|
||||
|
||||
MCP servers can provide access to:
|
||||
|
||||
- **File systems**
|
||||
- **Databases** (e.g., PostgreSQL)
|
||||
- **External APIs** (e.g., GitHub, Brave Search)
|
||||
- **Browser automation** (e.g., Puppeteer)
|
||||
- **Custom MCP server implementations**
|
||||
|
||||
## Learn More
|
||||
|
||||
For detailed documentation about the Model Context Protocol, visit:
|
||||
https://modelcontextprotocol.io
|
||||
@@ -1,65 +0,0 @@
|
||||
# Memory System Improvements
|
||||
|
||||
This document tracks memory injection behavior and roadmap status.
|
||||
|
||||
## Status (As Of 2026-03-10)
|
||||
|
||||
Implemented in `main`:
|
||||
- Accurate token counting via `tiktoken` in `format_memory_for_injection`.
|
||||
- Facts are injected into prompt memory context.
|
||||
- Facts are ranked by confidence (descending).
|
||||
- Injection respects `max_injection_tokens` budget.
|
||||
|
||||
Planned / not yet merged:
|
||||
- TF-IDF similarity-based fact retrieval.
|
||||
- `current_context` input for context-aware scoring.
|
||||
- Configurable similarity/confidence weights (`similarity_weight`, `confidence_weight`).
|
||||
- Middleware/runtime wiring for context-aware retrieval before each model call.
|
||||
|
||||
## Current Behavior
|
||||
|
||||
Function today:
|
||||
|
||||
```python
|
||||
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
|
||||
```
|
||||
|
||||
Current injection format:
|
||||
- `User Context` section from `user.*.summary`
|
||||
- `History` section from `history.*.summary`
|
||||
- `Facts` section from `facts[]`, sorted by confidence, appended until token budget is reached
|
||||
|
||||
Token counting:
|
||||
- Uses `tiktoken` (`cl100k_base`) when available
|
||||
- Falls back to `len(text) // 4` if tokenizer import fails
|
||||
|
||||
## Known Gap
|
||||
|
||||
Previous versions of this document described TF-IDF/context-aware retrieval as if it were already shipped.
|
||||
That was not accurate for `main` and caused confusion.
|
||||
|
||||
Issue reference: `#1059`
|
||||
|
||||
## Roadmap (Planned)
|
||||
|
||||
Planned scoring strategy:
|
||||
|
||||
```text
|
||||
final_score = (similarity * 0.6) + (confidence * 0.4)
|
||||
```
|
||||
|
||||
Planned integration shape:
|
||||
1. Extract recent conversational context from filtered user/final-assistant turns.
|
||||
2. Compute TF-IDF cosine similarity between each fact and current context.
|
||||
3. Rank by weighted score and inject under token budget.
|
||||
4. Fall back to confidence-only ranking if context is unavailable.
|
||||
|
||||
## Validation
|
||||
|
||||
Current regression coverage includes:
|
||||
- facts inclusion in memory injection output
|
||||
- confidence ordering
|
||||
- token-budget-limited fact inclusion
|
||||
|
||||
Tests:
|
||||
- `backend/tests/test_memory_prompt_injection.py`
|
||||
@@ -1,38 +0,0 @@
|
||||
# Memory System Improvements - Summary
|
||||
|
||||
## Sync Note (2026-03-10)
|
||||
|
||||
This summary is synchronized with the `main` branch implementation.
|
||||
TF-IDF/context-aware retrieval is **planned**, not merged yet.
|
||||
|
||||
## Implemented
|
||||
|
||||
- Accurate token counting with `tiktoken` in memory injection.
|
||||
- Facts are injected into `<memory>` prompt content.
|
||||
- Facts are ordered by confidence and bounded by `max_injection_tokens`.
|
||||
|
||||
## Planned (Not Yet Merged)
|
||||
|
||||
- TF-IDF cosine similarity recall based on recent conversation context.
|
||||
- `current_context` parameter for `format_memory_for_injection`.
|
||||
- Weighted ranking (`similarity` + `confidence`).
|
||||
- Runtime extraction/injection flow for context-aware fact selection.
|
||||
|
||||
## Why This Sync Was Needed
|
||||
|
||||
Earlier docs described TF-IDF behavior as already implemented, which did not match code in `main`.
|
||||
This mismatch is tracked in issue `#1059`.
|
||||
|
||||
## Current API Shape
|
||||
|
||||
```python
|
||||
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
|
||||
```
|
||||
|
||||
No `current_context` argument is currently available in `main`.
|
||||
|
||||
## Verification Pointers
|
||||
|
||||
- Implementation: `packages/harness/deerflow/agents/memory/prompt.py`
|
||||
- Prompt assembly: `packages/harness/deerflow/agents/lead_agent/prompt.py`
|
||||
- Regression tests: `backend/tests/test_memory_prompt_injection.py`
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user