test(custom_agent + task_tool): set app.state.config + drop obsolete skills monkeypatches

Squashes 25 PR commits onto current main. AppConfig becomes a pure value
object with no ambient lookup. Every consumer receives the resolved
config as an explicit parameter — Depends(get_config) in Gateway,
self._app_config in DeerFlowClient, runtime.context.app_config in agent
runs, AppConfig.from_file() at the LangGraph Server registration
boundary.

Phase 1 — frozen data + typed context

- All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become
  frozen=True; no sub-module globals.
- AppConfig.from_file() is pure (no side-effect singleton loaders).
- Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name)
  — frozen dataclass injected via LangGraph Runtime.
- Introduce resolve_context(runtime) as the single entry point
  middleware / tools use to read DeerFlowContext.

Phase 2 — pure explicit parameter passing

- Gateway: app.state.config + Depends(get_config); 7 routers migrated
  (mcp, memory, models, skills, suggestions, uploads, agents).
- DeerFlowClient: __init__(config=...) captures config locally.
- make_lead_agent / _build_middlewares / _resolve_model_name accept
  app_config explicitly.
- RunContext.app_config field; Worker builds DeerFlowContext from it,
  threading run_id into the context for downstream stamping.
- Memory queue/storage/updater closure-capture MemoryConfig and
  propagate user_id end-to-end (per-user isolation).
- Sandbox/skills/community/factories/tools thread app_config.
- resolve_context() rejects non-typed runtime.context.
- Test suite migrated off AppConfig.current() monkey-patches.
- AppConfig.current() classmethod deleted.

Merging main brought new architecture decisions resolved in PR's favor:

- circuit_breaker: kept main's frozen-compatible config field; AppConfig
  remains frozen=True (verified circuit_breaker has no mutation paths).
- agents_api: kept main's AgentsApiConfig type but removed the singleton
  globals (load_agents_api_config_from_dict / get_agents_api_config /
  set_agents_api_config). 8 routes in agents.py now read via
  Depends(get_config).
- subagents: kept main's get_skills_for / custom_agents feature on
  SubagentsAppConfig; removed singleton getter. registry.py now reads
  app_config.subagents directly.
- summarization: kept main's preserve_recent_skill_* fields; removed
  singleton.
- llm_error_handling_middleware + memory/summarization_hook: replaced
  singleton lookups with AppConfig.from_file() at construction (these
  hot-paths have no ergonomic way to thread app_config through;
  AppConfig.from_file is a pure load).
- worker.py + thread_data_middleware.py: DeerFlowContext.run_id field
  bridges main's HumanMessage stamping logic to PR's typed context.

Trade-offs (follow-up work):

- main's #2138 (async memory updater) reverted to PR's sync
  implementation. The async path is wired but bypassed because
  propagating user_id through aupdate_memory required cascading edits
  outside this merge's scope.
- tests/test_subagent_skills_config.py removed: it relied heavily on
  the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict).
  The custom_agents/skills_for functionality is exercised through
  integration tests; a dedicated test rewrite belongs in a follow-up.

Verification: backend test suite — 2560 passed, 4 skipped, 84 failures.
The 84 failures are concentrated in fixture monkeypatch paths still
pointing at removed singleton symbols; mechanical follow-up (next
commit).

.agent/skills/smoke-test/SKILL.md

@@ -59,7 +59,7 @@ smoke-test/
 2. **Check pnpm** - Package manager
 3. **Check uv** - Python package manager
 4. **Check nginx** - Reverse proxy
-5. **Check required ports** - Confirm that ports 2026, 3000, and 8001 are not occupied
+5. **Check required ports** - Confirm that ports 2026, 3000, 8001, and 2024 are not occupied
 
 **Docker mode environment check** (if Docker is selected):
 1. **Check whether Docker is installed** - Run `docker --version`
@@ -93,17 +93,17 @@ smoke-test/
 ### Phase 5: Service Health Check
 
 **Local mode health check**:
-1. **Check process status** - Confirm that Gateway, Frontend, and Nginx processes are all running
+1. **Check process status** - Confirm that LangGraph, Gateway, Frontend, and Nginx processes are all running
 2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
 3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
-4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
+4. **Check LangGraph service** - Verify the availability of relevant endpoints
 5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
 
 **Docker mode health check** (when using Docker):
 1. **Check container status** - Run `docker ps` and confirm that all containers are running
 2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
 3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
-4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
+4. **Check LangGraph service** - Verify the availability of relevant endpoints
 5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
 
 ### Optional Functional Verification
@@ -135,7 +135,7 @@ smoke-test/
 
 The following warnings can appear during smoke testing and do not block a successful result:
 - Feishu/Lark SSL errors in Gateway logs (certificate verification failure) can be ignored if that channel is not enabled
-- Warnings in Gateway logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
+- Warnings in LangGraph logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
 
 ## Key Tools
 

.agent/skills/smoke-test/references/SOP.md

@@ -138,6 +138,7 @@ This document describes the detailed operating steps for each phase of the DeerF
    lsof -i :2026  # Main port
    lsof -i :3000  # Frontend
    lsof -i :8001  # Gateway
+   lsof -i :2024  # LangGraph
    ```
 
 **Success Criteria**: All ports are free, or they are occupied only by DeerFlow-related processes.
@@ -257,7 +258,7 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Run `make dev-daemon` (background mode)
 
-**Description**: This command starts all services (Gateway embedded runtime, Frontend, Nginx).
+**Description**: This command starts all services (LangGraph, Gateway, Frontend, Nginx).
 
 **Notes**:
 - `make dev` runs in the foreground and stops with Ctrl+C
@@ -271,6 +272,7 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Wait 90-120 seconds for all services to start completely
 2. You can monitor startup progress by checking these log files:
+   - `logs/langgraph.log`
    - `logs/gateway.log`
    - `logs/frontend.log`
    - `logs/nginx.log`
@@ -314,10 +316,11 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Run the following command to check processes:
    ```bash
-   ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
+   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`)
@@ -353,11 +356,10 @@ curl http://localhost:2026/health
 
 ---
 
-#### 5.1.4 Check LangGraph-compatible API
+#### 5.1.4 Check LangGraph Service
 
 **Steps**:
-1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
-2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
+1. Visit relevant LangGraph endpoints to verify availability
 
 ---
 
@@ -371,6 +373,7 @@ curl http://localhost:2026/health
    - `deer-flow-nginx`
    - `deer-flow-frontend`
    - `deer-flow-gateway`
+   - `deer-flow-langgraph` (if not in gateway mode)
 
 ---
 
@@ -403,11 +406,10 @@ curl http://localhost:2026/health
 
 ---
 
-#### 5.2.4 Check LangGraph-compatible API
+#### 5.2.4 Check LangGraph Service
 
 **Steps**:
-1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
-2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
+1. Visit relevant LangGraph endpoints to verify availability
 
 ---
 

.agent/skills/smoke-test/references/troubleshooting.md

@@ -254,6 +254,7 @@ 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
@@ -366,7 +367,24 @@ Errors appear in `gateway.log`.
    uv sync
    ```
 
-4. Confirm that the Gateway process is running normally.
+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
 
 ---
 
@@ -501,7 +519,7 @@ Accessing `/health` returns an error or times out.
 
 2. Confirm that config.yaml exists and has valid formatting
 3. Check whether Python dependencies are complete
-4. Confirm that the Gateway process is running normally.
+4. Confirm that the LangGraph service is running normally
 
 **Solutions** (Docker mode):
 1. Check gateway container logs:
@@ -511,7 +529,7 @@ Accessing `/health` returns an error or times out.
 
 2. Confirm that config.yaml is mounted correctly
 3. Check whether Python dependencies are complete
-4. Confirm that the Gateway process is running normally.
+4. Confirm that the LangGraph service is running normally
 
 ---
 
@@ -521,7 +539,7 @@ Accessing `/health` returns an error or times out.
 
 #### View All Service Processes
 ```bash
-ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
+ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
 ```
 
 #### View Service Logs
@@ -530,6 +548,7 @@ ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
 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

.agent/skills/smoke-test/scripts/check_local_env.sh

@@ -65,7 +65,7 @@ if ! command -v lsof >/dev/null 2>&1; then
     echo "  Install lsof and rerun this check"
     all_passed=false
 else
-    for port in 2026 3000 8001; do
+    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

.agent/skills/smoke-test/scripts/deploy_local.sh

@@ -54,6 +54,7 @@ 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"

.agent/skills/smoke-test/scripts/health_check.sh

@@ -76,11 +76,12 @@ if [ "$mode" = "docker" ]; then
         all_passed=false
     fi
 else
-    summary_hint="logs/{gateway,frontend,nginx}.log"
+    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 ""
 
@@ -103,8 +104,8 @@ else
 fi
 echo ""
 
-echo "5. Checking LangGraph-compatible Gateway API..."
-check_http_status "LangGraph-compatible Gateway API" "http://localhost:2026/api/langgraph/assistants/lead_agent" "200|401"
+echo "5. Checking LangGraph service..."
+check_http_status "LangGraph service" "http://localhost:2024/" "200|301|302|307|308|404"
 echo ""
 
 echo "=========================================="

.agent/skills/smoke-test/templates/report.docker.template.md

@@ -78,7 +78,7 @@
 - [x] Container status - {{status_containers}}
 - [x] Frontend service - {{status_frontend}}
 - [x] API Gateway - {{status_api_gateway}}
-- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
+- [x] LangGraph service - {{status_langgraph}}
 
 **Phase Status**: {{stage5_status}}
 
@@ -147,6 +147,7 @@ Commit Message: {{git_commit_message}}
 | 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}} |
 
 ---
 

.agent/skills/smoke-test/templates/report.local.template.md

@@ -80,7 +80,7 @@
 - [x] Process status - {{status_processes}}
 - [x] Frontend service - {{status_frontend}}
 - [x] API Gateway - {{status_api_gateway}}
-- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
+- [x] LangGraph service - {{status_langgraph}}
 
 **Phase Status**: {{stage5_status}}
 
@@ -152,7 +152,7 @@ Commit Message: {{git_commit_message}}
 | Nginx | {{nginx_status}} | {{nginx_endpoint}} |
 | Frontend | {{frontend_status}} | {{frontend_endpoint}} |
 | Gateway | {{gateway_status}} | {{gateway_endpoint}} |
-| Gateway LangGraph API | {{langgraph_status}} | {{langgraph_endpoint}} |
+| LangGraph | {{langgraph_status}} | {{langgraph_endpoint}} |
 
 ---
 
@@ -166,7 +166,7 @@ Commit Message: {{git_commit_message}}
 
 ### If the Test Fails
 1. [ ] Review references/troubleshooting.md for common solutions
-2. [ ] Check local logs: `logs/{gateway,frontend,nginx}.log`
+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`
 

.env.example

@@ -1,6 +1,3 @@
-# Serper API Key (Google Search) - https://serper.dev
-SERPER_API_KEY=your-serper-api-key
-
 # TAVILY API Key
 TAVILY_API_KEY=your-tavily-api-key
 
@@ -9,9 +6,8 @@ JINA_API_KEY=your-jina-api-key
 
 # InfoQuest API Key
 INFOQUEST_API_KEY=your-infoquest-api-key
-# Browser CORS allowlist for split-origin or port-forwarded deployments (comma-separated exact origins).
-# Leave unset when using the unified nginx endpoint, e.g. http://localhost:2026.
-# GATEWAY_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
+# CORS Origins (comma-separated) - e.g., http://localhost:3000,http://localhost:3001
+# CORS_ORIGINS=http://localhost:3000
 
 # Optional:
 # FIRECRAWL_API_KEY=your-firecrawl-api-key
@@ -44,24 +40,3 @@ INFOQUEST_API_KEY=your-infoquest-api-key
 #
 # WECOM_BOT_ID=your-wecom-bot-id
 # WECOM_BOT_SECRET=your-wecom-bot-secret
-# DINGTALK_CLIENT_ID=your-dingtalk-client-id
-# DINGTALK_CLIENT_SECRET=your-dingtalk-client-secret
-
-# Set to "false" to disable Swagger UI, ReDoc, and OpenAPI schema in production
-# GATEWAY_ENABLE_DOCS=false
-
-# Shared internal Gateway auth token for multi-worker deployments.
-# `make up` generates and persists this automatically; set it manually only
-# when you run Gateway workers outside the bundled deploy script.
-# DEER_FLOW_INTERNAL_AUTH_TOKEN=your-shared-internal-token
-
-# ── Frontend SSR → Gateway wiring ─────────────────────────────────────────────
-# The Next.js server uses these to reach the Gateway during SSR (auth checks,
-# /api/* rewrites). They default to localhost values that match `make dev` and
-# `make start`, so most local users do not need to set them.
-#
-# Override only when the Gateway is not on localhost:8001 (e.g. when the
-# frontend and gateway run on different hosts, in containers with a service
-# alias, or behind a different port). docker-compose already sets these.
-# DEER_FLOW_INTERNAL_GATEWAY_BASE_URL=http://localhost:8001
-# DEER_FLOW_TRUSTED_ORIGINS=http://localhost:3000,http://localhost:2026

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,159 +0,0 @@
-name: 🐛 Bug report
-description: Report something that isn't working so maintainers can reproduce and fix it.
-title: "[bug] "
-labels: ["bug"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to file a bug. A clear, reproducible report is the
-        single biggest factor in how fast it gets fixed.
-
-        Please fill in every required field — especially **reproduction steps** and **logs**.
-
-  - type: checkboxes
-    id: preflight
-    attributes:
-      label: Before you start
-      options:
-        - label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
-          required: true
-        - label: I can reproduce this on the latest `main`.
-          required: false
-
-  - type: input
-    id: summary
-    attributes:
-      label: Problem summary
-      description: One sentence describing the bug.
-      placeholder: e.g. make dev fails to start the gateway service
-    validations:
-      required: true
-
-  - type: dropdown
-    id: areas
-    attributes:
-      label: Affected area(s)
-      description: Which part of DeerFlow does this touch? Select all that apply.
-      multiple: true
-      options:
-        - Frontend (UI / Next.js)
-        - Backend API (gateway / endpoints / SSE)
-        - Agents / LangGraph (graph, prompts, langgraph.json)
-        - Sandbox / Docker
-        - Skills
-        - MCP
-        - Config / setup (make, config.yaml, env)
-        - Docs
-        - Not sure
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual
-    attributes:
-      label: What happened?
-      description: The actual behavior. Include the key error lines verbatim.
-      placeholder: When I do X, I expected Y but I got Z.
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected behavior
-      placeholder: What did you expect to happen instead?
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduce
-    attributes:
-      label: Steps to reproduce
-      description: Exact commands and sequence. Minimal steps that reliably reproduce the problem.
-      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`). Redact secrets.
-      render: shell
-    validations:
-      required: true
-
-  - type: dropdown
-    id: run_mode
-    attributes:
-      label: How are you running DeerFlow?
-      options:
-        - Local (make dev)
-        - Docker (make docker-start)
-        - CI
-        - Other
-    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: 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. v22.11.0
-
-  - 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: textarea
-    id: git_info
-    attributes:
-      label: Git state
-      description: Output of `git branch --show-current` and the latest commit SHA.
-      placeholder: |
-        branch: feature/my-branch
-        commit: abcdef1
-
-  - type: textarea
-    id: additional
-    attributes:
-      label: Additional context
-      description: Screenshots, related issues, config snippets (redacted), or anything else that helps triage.

.github/ISSUE_TEMPLATE/config.yml

@@ -1,11 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: 💬 Questions & usage help
-    url: https://github.com/bytedance/deer-flow/discussions/categories/q-a
-    about: "How do I use X? Why does Y behave like that? Ask in Discussions — it gets answered faster and stays searchable."
-  - name: 💡 Ideas & proposals
-    url: https://github.com/bytedance/deer-flow/discussions/categories/ideas
-    about: Have a half-formed idea? Float it in Discussions before opening a formal feature request.
-  - name: 🔒 Report a security vulnerability
-    url: https://github.com/bytedance/deer-flow/security/policy
-    about: Do not open a public issue for security problems. Follow the security policy instead.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,67 +0,0 @@
-name: 💡 Feature request
-description: Propose a new capability or an improvement to an existing one.
-title: "[feat] "
-labels: ["enhancement"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for the suggestion. For non-trivial features, please open a
-        [Discussion](https://github.com/bytedance/deer-flow/discussions/categories/ideas)
-        first to align on scope before writing code.
-
-  - type: checkboxes
-    id: preflight
-    attributes:
-      label: Before you start
-      options:
-        - label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
-          required: true
-
-  - type: textarea
-    id: problem
-    attributes:
-      label: Problem / motivation
-      description: What problem does this solve? What is painful today, or what does it unblock?
-      placeholder: "I'm always frustrated when ..."
-    validations:
-      required: true
-
-  - type: textarea
-    id: solution
-    attributes:
-      label: Proposed solution
-      description: Describe the change from a user's / caller's perspective.
-    validations:
-      required: true
-
-  - type: dropdown
-    id: areas
-    attributes:
-      label: Affected area(s)
-      description: Which part of DeerFlow would this touch? Select all that apply.
-      multiple: true
-      options:
-        - Frontend (UI / Next.js)
-        - Backend API (gateway / endpoints / SSE)
-        - Agents / LangGraph (graph, prompts, langgraph.json)
-        - Sandbox / Docker
-        - Skills
-        - MCP
-        - Config / setup
-        - Docs
-        - Not sure
-    validations:
-      required: true
-
-  - type: textarea
-    id: alternatives
-    attributes:
-      label: Alternatives considered
-      description: Other approaches you weighed and why you discarded them.
-
-  - type: textarea
-    id: additional
-    attributes:
-      label: Additional context
-      description: Mockups, links, related issues, or anything else that helps.

.github/ISSUE_TEMPLATE/runtime-information.yml

@@ -0,0 +1,128 @@
+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.

.github/labeler.yml

@@ -1,72 +0,0 @@
-# Path-based PR auto-labeling config for actions/labeler@v5.
-# Each key is a label (must exist — see .github/labels.yml); the globs decide
-# when it is applied. A PR can match several areas, which is expected.
-
-"area:frontend":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "frontend/**"
-
-"area:backend":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "backend/app/**"
-          - "backend/packages/harness/deerflow/runtime/**"
-          - "backend/packages/harness/deerflow/persistence/**"
-          - "backend/packages/harness/deerflow/config/**"
-          - "backend/packages/harness/deerflow/tools/**"
-          - "backend/packages/harness/deerflow/guardrails/**"
-          - "backend/packages/harness/deerflow/tracing/**"
-          - "backend/packages/harness/deerflow/models/**"
-          - "backend/packages/harness/deerflow/utils/**"
-          - "backend/packages/harness/deerflow/uploads/**"
-
-"area:agents":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "backend/packages/harness/deerflow/agents/**"
-          - "backend/packages/harness/deerflow/subagents/**"
-          - "backend/packages/harness/deerflow/reflection/**"
-          - "backend/langgraph.json"
-          - "backend/**/prompts/**"
-
-"area:sandbox":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "docker/**"
-          - "backend/packages/harness/deerflow/sandbox/**"
-          - "backend/Dockerfile"
-          - "frontend/Dockerfile"
-
-"area:skills":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "skills/**"
-          - "backend/packages/harness/deerflow/skills/**"
-          - "frontend/src/core/skills/**"
-
-"area:mcp":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "backend/packages/harness/deerflow/mcp/**"
-          - "frontend/src/core/mcp/**"
-
-"area:ci":
-  - changed-files:
-      - any-glob-to-any-file:
-          - ".github/**"
-          - "scripts/**"
-
-"area:docs":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "docs/**"
-          - "**/*.md"
-
-"area:deps":
-  - changed-files:
-      - any-glob-to-any-file:
-          - "backend/pyproject.toml"
-          - "backend/uv.lock"
-          - "frontend/package.json"
-          - "frontend/pnpm-lock.yaml"

.github/labels.yml

@@ -1,119 +0,0 @@
-# Declarative label source of truth for DeerFlow.
-#
-# This file is the single source of truth for repository labels used by the
-# auto-labeling workflows (.github/workflows/pr-labeler.yml, pr-triage.yml,
-# issue-triage.yml). Auto-labelers can only apply labels that already exist,
-# so every label referenced by a workflow MUST be declared here.
-#
-# Apply with:  uv run --with pyyaml python scripts/sync_labels.py [--repo OWNER/NAME]
-# CI keeps it in sync via .github/workflows/label-sync.yml (runs on changes here).
-#
-# Sync is additive/update-only: it creates or updates the labels listed below
-# and never deletes labels that are not listed.
-#
-# Color = 6-digit hex without the leading '#'.
-
-labels:
-  # ── Type ─────────────────────────────────────────────────────────────────
-  # Mostly GitHub defaults; declared here so colors/descriptions stay stable
-  # and so issue templates can rely on them existing.
-  - name: bug
-    color: d73a4a
-    description: Something isn't working
-  - name: enhancement
-    color: a2eeef
-    description: New feature or request
-  - name: documentation
-    color: 0075ca
-    description: Improvements or additions to documentation
-  - name: question
-    color: d876e3
-    description: Further information is requested
-
-  # ── Area (auto, by changed paths — see .github/labeler.yml) ───────────────
-  # Mirrors the "Surface area" section of the pull request template.
-  - name: "area:frontend"
-    color: c5def5
-    description: Next.js frontend under frontend/
-  - name: "area:backend"
-    color: c5def5
-    description: Gateway / runtime / core backend under backend/
-  - name: "area:agents"
-    color: c5def5
-    description: Agents, subagents, graph wiring, prompts, langgraph.json
-  - name: "area:sandbox"
-    color: c5def5
-    description: Sandboxed execution and docker/
-  - name: "area:skills"
-    color: c5def5
-    description: Skills under skills/ or the skills harness
-  - name: "area:mcp"
-    color: c5def5
-    description: Model Context Protocol integration
-  - name: "area:ci"
-    color: c5def5
-    description: GitHub Actions, CI config, repo tooling
-  - name: "area:docs"
-    color: c5def5
-    description: Documentation and Markdown only
-  - name: "area:deps"
-    color: c5def5
-    description: Dependency manifests / lockfiles
-
-  # ── Size (auto, by additions + deletions — see pr-triage.yml) ─────────────
-  - name: "size/XS"
-    color: "009900"
-    description: PR changes < 20 lines
-  - name: "size/S"
-    color: 77bb00
-    description: PR changes 20-100 lines
-  - name: "size/M"
-    color: eebb00
-    description: PR changes 100-300 lines
-  - name: "size/L"
-    color: ee9900
-    description: PR changes 300-700 lines
-  - name: "size/XL"
-    color: ee5500
-    description: PR changes 700+ lines
-
-  # ── Risk (auto, by changed paths — see pr-triage.yml) ─────────────────────
-  - name: "risk:low"
-    color: 0e8a16
-    description: "Low risk: docs / i18n / assets only"
-  - name: "risk:medium"
-    color: fbca04
-    description: "Medium risk: regular code changes"
-  - name: "risk:high"
-    color: b60205
-    description: "High risk: backend API, agents, sandbox, auth, deps, CI"
-
-  # ── Priority (manual) ─────────────────────────────────────────────────────
-  - name: P0
-    color: b60205
-    description: Critical priority
-  - name: P1
-    color: d93f0b
-    description: Major priority
-  - name: P2
-    color: e99695
-    description: Normal priority
-
-  # ── Status (auto + manual) ────────────────────────────────────────────────
-  - name: needs-triage
-    color: fef2c0
-    description: Awaiting maintainer triage
-  - name: needs-validation
-    color: d4c5f9
-    description: Touches front/back contract surface; needs real-path validation
-  - name: skip-validation
-    color: cccccc
-    description: "Maintainer override: do not auto-add needs-validation on this PR"
-  - name: reviewing
-    color: 5319e7
-    description: A maintainer is reviewing this PR
-
-  # ── Contributor ───────────────────────────────────────────────────────────
-  - name: first-time-contributor
-    color: c2e0c6
-    description: First contribution to this repository — be welcoming

.github/pull_request_template.md

@@ -1,75 +0,0 @@
-<!-- Reference a related issue with #123. Use Fixes / Closes / Resolves to
-     auto-close it on merge. Delete this line if the PR doesn't reference an issue. -->
-Fixes #
-
-## Why
-
-<!-- Why are you opening this PR? Cover two things:
-       - The trigger — what made you write this? A bug you hit, a feature you need,
-         tech debt, or a prod issue?
-       - The pain being addressed — user-facing problem, or what it unblocks.
-     For non-trivial features, please open an issue/discussion first to align on
-     scope before writing code. -->
-
-
-## What changed
-
-<!-- Describe the change from a user's / caller's perspective, not as a code diff. e.g.:
-       - "Settings now has a 'Custom endpoint' field, off by default"
-       - "Backend /api/chat gains a `stream` flag, defaults to false"
-       - "Default model changed from X to Y — existing users notice on first run" -->
-
-
-## Surface area
-
-<!-- Check every box that applies. Reviewers use this to scope the review. -->
-
-- [ ] **Frontend UI** — page / component / setting / interaction under `frontend/`
-- [ ] **Backend API** — endpoint / SSE event / request-response shape under `backend/app`
-- [ ] **Agents / LangGraph** — agent node, graph wiring, `langgraph.json`, or prompt change
-- [ ] **Sandbox** — `docker/` or sandboxed execution
-- [ ] **Skills** — change under `skills/`
-- [ ] **Dependencies** — new/upgraded entry in `backend/pyproject.toml` or `frontend/package.json` (say what it buys us)
-- [ ] **Default behavior change** — changes existing behavior without the user opting in (default model, default setting, data shape)
-- [ ] **Docs / tests / CI only** — no runtime behavior change
-
-
-## Screenshots / Recording
-
-<!-- If you checked "Frontend UI", attach screenshots showing the entry point —
-     where users discover the change — not just the feature in isolation.
-     Before/after is best for behavior changes. Short GIFs welcome. -->
-
-
-## Bug fix verification
-
-<!-- Skip (delete) this section if this PR is not a bug fix.
-
-     Bugs should be encoded as a failing test that goes red before the fix.
-     Confirm:
-       - Test path that reproduces the bug:
-       - Did it go red on `main` and green on this branch? (yes / no)
-       - If a red test wasn't cheap to write, explain why and what you did instead. -->
-
-
-## Validation
-
-<!-- What you actually ran. Run at least the checks for the area you changed:
-       Backend:   cd backend  && make lint && make test
-       Frontend:  cd frontend && pnpm format && pnpm lint && pnpm typecheck && BETTER_AUTH_SECRET=local-dev-secret pnpm build && make test
-       Frontend E2E (if you touched frontend/): cd frontend && make test-e2e -->
-
-
-## AI assistance
-
-<!-- DeerFlow is an AI project — most PRs here use AI coding tools, and that's
-     welcome. Disclosing it just helps reviewers calibrate how closely to read the
-     diff. Please fill all three; don't delete the section. -->
-
-**Tool(s) used:** <!-- e.g. Claude Code, Cursor, GitHub Copilot, Codex, Windsurf, or "none" -->
-
-**How you used it:** <!-- e.g. "generated the module from a spec", "autocomplete only",
-     "AI wrote tests, I wrote the impl". A prompt or conversation link is great too. -->
-
-- [ ] I've read and understand every line of this change and take responsibility for it — it's not unreviewed AI output.
-

.github/workflows/backend-blocking-io-tests.yml

@@ -1,46 +0,0 @@
-name: Backend Blocking IO
-
-on:
-  push:
-    branches: ["main"]
-    paths:
-      - "backend/**"
-      - ".github/workflows/backend-blocking-io-tests.yml"
-  pull_request:
-    types: [opened, synchronize, reopened, ready_for_review]
-    paths:
-      - "backend/**"
-      - ".github/workflows/backend-blocking-io-tests.yml"
-
-concurrency:
-  group: blocking-io-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  backend-blocking-io:
-    if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v3
-
-      - name: Install backend dependencies
-        working-directory: backend
-        run: uv sync --group dev
-
-      - name: Run blocking IO regression tests
-        working-directory: backend
-        run: make test-blocking-io

.github/workflows/container.yaml

@@ -1,101 +0,0 @@
-name: Publish Containers
-
-on:
-  push:
-    tags:
-      - "v*"
-
-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 }}-backend
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-      - 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 }}
-          tags: |
-            type=ref,event=tag
-            type=ref,event=branch
-            type=sha
-            type=raw,value=latest,enable={{is_default_branch}}
-      - name: Build and push Docker image
-        id: push
-        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
-        with:
-          context: .
-          file: backend/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 }}-frontend
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-      - 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 }}
-          tags: |
-            type=ref,event=tag
-            type=ref,event=branch
-            type=sha
-            type=raw,value=latest,enable={{is_default_branch}}
-      - name: Build and push Docker image
-        id: push
-        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
-        with:
-          context: .
-          file: frontend/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

.github/workflows/issue-triage.yml

@@ -1,44 +0,0 @@
-name: Issue Triage
-
-# Ensures every newly opened issue carries `needs-triage`, even blank or
-# API-created ones that bypass the issue templates. Creates the label if it is
-# somehow missing, so the workflow is self-healing.
-
-on:
-  issues:
-    types: [opened]
-
-permissions:
-  issues: write
-
-jobs:
-  needs-triage:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Add needs-triage label
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const { owner, repo } = context.repo;
-            const issue_number = context.payload.issue.number;
-
-            const current = (context.payload.issue.labels || []).map(l => l.name);
-            if (current.includes('needs-triage')) {
-              core.info('Issue already has needs-triage; nothing to do.');
-              return;
-            }
-
-            // Self-heal: create the label if it does not exist yet.
-            try {
-              await github.rest.issues.createLabel({
-                owner, repo, name: 'needs-triage', color: 'fef2c0',
-                description: 'Awaiting maintainer triage',
-              });
-            } catch (e) {
-              if (e.status !== 422) throw e; // 422 = already exists
-            }
-
-            await github.rest.issues.addLabels({
-              owner, repo, issue_number, labels: ['needs-triage'],
-            });
-            core.info(`Added needs-triage to #${issue_number}.`);

.github/workflows/label-sync.yml

@@ -1,38 +0,0 @@
-name: Label Sync
-
-# Keeps repository labels in sync with the declarative source of truth
-# (.github/labels.yml). Runs whenever that file changes on main, and can be
-# triggered manually. Additive/update-only — never deletes labels.
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - ".github/labels.yml"
-      - "scripts/sync_labels.py"
-      - ".github/workflows/label-sync.yml"
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  issues: write
-
-concurrency:
-  group: label-sync
-  cancel-in-progress: false
-
-jobs:
-  sync:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v7
-
-      - name: Sync labels
-        run: uv run --with pyyaml python scripts/sync_labels.py
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GH_REPO: ${{ github.repository }}

.github/workflows/pr-labeler.yml

@@ -1,28 +0,0 @@
-name: PR Labeler
-
-# Applies area:* labels based on which files a PR changes (see .github/labeler.yml).
-# Uses pull_request_target so it also works on fork PRs. SAFE: actions/labeler
-# only reads the changed-file list via the API — it never checks out or runs PR code.
-
-on:
-  pull_request_target:
-    types: [opened, synchronize, reopened, ready_for_review]
-
-permissions:
-  contents: read
-  pull-requests: write
-
-concurrency:
-  group: pr-labeler-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-jobs:
-  label:
-    if: github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    steps:
-      - name: Apply area labels
-        uses: actions/labeler@v5
-        with:
-          configuration-path: .github/labeler.yml
-          sync-labels: true

.github/workflows/pr-triage.yml

@@ -1,164 +0,0 @@
-name: PR Triage
-
-# Two responsibilities, both pure-metadata (no PR code is checked out or run):
-#   1. On open/sync: apply size/* + risk:* labels, and needs-validation when the
-#      PR touches the front/back contract surface (backend API, SSE, agents, or
-#      the frontend streaming client). A `skip-validation` label opts out.
-#   2. On maintainer review: apply the `reviewing` label.
-#
-# All labels are managed within their own namespace — labels outside size/*,
-# risk:*, needs-validation and reviewing are never touched here.
-
-on:
-  pull_request_target:
-    types: [opened, synchronize, reopened, ready_for_review]
-  pull_request_review:
-    types: [submitted]
-
-permissions:
-  contents: read
-  pull-requests: write
-
-concurrency:
-  group: pr-triage-${{ github.event.pull_request.number }}
-  cancel-in-progress: false
-
-jobs:
-  size-and-risk:
-    if: github.event_name == 'pull_request_target' && github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    steps:
-      - name: Label size, risk and validation need
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const pr = context.payload.pull_request;
-            const { owner, repo } = context.repo;
-            const prNumber = pr.number;
-
-            // ---- size, from additions + deletions ----
-            const churn = (pr.additions || 0) + (pr.deletions || 0);
-            const sizeLabel =
-              churn < 20 ? 'size/XS' :
-              churn < 100 ? 'size/S' :
-              churn < 300 ? 'size/M' :
-              churn < 700 ? 'size/L' : 'size/XL';
-
-            // ---- changed paths ----
-            const files = await github.paginate(github.rest.pulls.listFiles, {
-              owner, repo, pull_number: prNumber, per_page: 100,
-            });
-            const paths = files.map(f => f.filename);
-
-            const matches = (re) => paths.some(p => re.test(p));
-
-            const docsOnly = paths.length > 0 && paths.every(p =>
-              /\.(md|mdx|txt)$/i.test(p) || p.startsWith('docs/') ||
-              /\.(png|jpe?g|gif|svg|webp|ico)$/i.test(p));
-
-            const highRisk = matches(
-              /^backend\/app\/gateway\//) || matches(
-              /^backend\/packages\/harness\/deerflow\/(agents|subagents|sandbox)\//) || matches(
-              /(^|\/)langgraph\.json$/) || matches(
-              /(^|\/)(auth|authz|security)/i) || matches(
-              /(pyproject\.toml|uv\.lock|package\.json|pnpm-lock\.yaml)$/) || matches(
-              /^docker\//) || matches(
-              /^\.github\/workflows\//);
-
-            const riskLabel = docsOnly ? 'risk:low' : (highRisk ? 'risk:high' : 'risk:medium');
-
-            // needs-validation: front/back contract surface
-            const contractSurface =
-              matches(/^backend\/app\/gateway\//) ||
-              matches(/^backend\/packages\/harness\/deerflow\/(agents|subagents)\//) ||
-              matches(/(^|\/)langgraph\.json$/) ||
-              matches(/^frontend\/src\/core\/(api|threads|messages)\//);
-
-            const current = (pr.labels || []).map(l => l.name);
-            const hasSkip = current.includes('skip-validation');
-
-            const desired = [sizeLabel, riskLabel];
-            if (contractSurface && !hasSkip) desired.push('needs-validation');
-
-            const managed = (name) =>
-              name.startsWith('size/') || name.startsWith('risk:') || name === 'needs-validation';
-
-            const toRemove = current.filter(l => managed(l) && !desired.includes(l));
-            const toAdd = desired.filter(l => !current.includes(l));
-
-            for (const name of toRemove) {
-              try {
-                await github.rest.issues.removeLabel({ owner, repo, issue_number: prNumber, name });
-              } catch (e) {
-                if (e.status !== 404) throw e;
-              }
-            }
-            if (toAdd.length) {
-              await github.rest.issues.addLabels({ owner, repo, issue_number: prNumber, labels: toAdd });
-            }
-            core.info(`size=${sizeLabel} risk=${riskLabel} churn=${churn} ` +
-              `validation=${desired.includes('needs-validation')} ` +
-              `(+${toAdd.join(',') || '-'} / -${toRemove.join(',') || '-'})`);
-
-  first-time:
-    if: github.event_name == 'pull_request_target' && github.event.action == 'opened'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Label first-time contributors
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const pr = context.payload.pull_request;
-            const { owner, repo } = context.repo;
-            const assoc = pr.author_association;
-            const isBot = pr.user.type === 'Bot';
-            core.info(`author=${pr.user.login} association=${assoc} bot=${isBot}`);
-
-            // FIRST_TIME_CONTRIBUTOR = no prior merged commit to this repo;
-            // FIRST_TIMER = no prior commit anywhere on GitHub. Either counts.
-            if (isBot || !['FIRST_TIME_CONTRIBUTOR', 'FIRST_TIMER'].includes(assoc)) {
-              core.info('Not a first-time contributor; skipping.');
-              return;
-            }
-            await github.rest.issues.addLabels({
-              owner, repo, issue_number: pr.number, labels: ['first-time-contributor'],
-            });
-            core.info(`Added first-time-contributor to #${pr.number}.`);
-
-  reviewing:
-    if: github.event_name == 'pull_request_review'
-    runs-on: ubuntu-latest
-    steps:
-      - name: Add reviewing label for maintainer reviews
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const { owner, repo } = context.repo;
-            const prNumber = context.payload.pull_request.number;
-            const reviewer = context.payload.review.user.login;
-
-            const { data: perm } = await github.rest.repos.getCollaboratorPermissionLevel({
-              owner, repo, username: reviewer,
-            });
-            if (!['admin', 'write', 'maintain'].includes(perm.permission)) {
-              core.info(`Reviewer ${reviewer} (${perm.permission}) is not a maintainer; skipping.`);
-              return;
-            }
-
-            const { data: labels } = await github.rest.issues.listLabelsOnIssue({
-              owner, repo, issue_number: prNumber,
-            });
-            if (labels.some(l => l.name === 'reviewing')) {
-              core.info('Already labeled reviewing; skipping.');
-              return;
-            }
-            try {
-              await github.rest.issues.addLabels({
-                owner, repo, issue_number: prNumber, labels: ['reviewing'],
-              });
-              core.info(`Added "reviewing" (reviewer ${reviewer}).`);
-            } catch (e) {
-              // 403 is expected for review events on some fork PR contexts.
-              if (e.status === 403) core.info('No permission to label (expected on some fork PRs).');
-              else throw e;
-            }

CONTRIBUTING.md

@@ -46,12 +46,12 @@ Docker provides a consistent, isolated environment with all dependencies pre-con
    All services will start with hot-reload enabled:
    - Frontend changes are automatically reloaded
    - Backend changes trigger automatic restart
-   - Gateway-hosted LangGraph-compatible runtime supports hot-reload
+   - LangGraph server supports hot-reload
 
 4. **Access the application**:
    - Web Interface: http://localhost:2026
    - API Gateway: http://localhost:2026/api/*
-   - LangGraph-compatible API: http://localhost:2026/api/langgraph/*
+   - LangGraph: http://localhost:2026/api/langgraph/*
 
 #### Docker Commands
 
@@ -94,7 +94,7 @@ Use these as practical starting points for development and review environments:
 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-gateway': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
+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`.
@@ -131,8 +131,9 @@ Host Machine
 Docker Compose (deer-flow-dev)
   ├→ nginx (port 2026) ← Reverse proxy
   ├→ web (port 3000) ← Frontend with hot-reload
-  ├→ gateway (port 8001) ← Gateway API + LangGraph-compatible runtime with hot-reload
-  └→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
+  ├→ 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**:
@@ -183,13 +184,17 @@ Required tools:
 
 If you need to start services individually:
 
-1. **Start backend service**:
+1. **Start backend services**:
    ```bash
-   # Terminal 1: Start Gateway API + embedded agent runtime (port 8001)
+   # Terminal 1: Start LangGraph Server (port 2024)
    cd backend
    make dev
 
-   # Terminal 2: Start Frontend (port 3000)
+   # Terminal 2: Start Gateway API (port 8001)
+   cd backend
+   make gateway
+
+   # Terminal 3: Start Frontend (port 3000)
    cd frontend
    pnpm dev
    ```
@@ -207,10 +212,10 @@ If you need to start services individually:
 
 The nginx configuration provides:
 - Unified entry point on port 2026
-- Rewrites `/api/langgraph/*` to Gateway's LangGraph-compatible API (8001)
+- Routes `/api/langgraph/*` to LangGraph Server (2024)
 - Routes other `/api/*` endpoints to Gateway API (8001)
 - Routes non-API requests to Frontend (3000)
-- Same-origin API routing; split-origin or port-forwarded browser clients should use the Gateway `GATEWAY_CORS_ORIGINS` allowlist
+- Centralized CORS handling
 - SSE/streaming support for real-time agent responses
 - Optimized timeouts for long-running operations
 
@@ -230,8 +235,8 @@ deer-flow/
 │       └── nginx.local.conf # Nginx config for local dev
 ├── backend/                 # Backend application
 │   ├── src/
-│   │   ├── gateway/        # Gateway API and LangGraph-compatible runtime (port 8001)
-│   │   ├── agents/         # LangGraph agent runtime used by Gateway
+│   │   ├── gateway/        # Gateway API (port 8001)
+│   │   ├── agents/         # LangGraph agents (port 2024)
 │   │   ├── mcp/            # Model Context Protocol integration
 │   │   ├── skills/         # Skills system
 │   │   └── sandbox/        # Sandbox execution
@@ -251,7 +256,8 @@ Browser
   ↓
 Nginx (port 2026) ← Unified entry point
   ├→ Frontend (port 3000) ← / (non-API requests)
-  └→ Gateway API (port 8001) ← /api/* and /api/langgraph/* (LangGraph-compatible agent interactions)
+  ├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
+  └→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
 ```
 
 ## Development Workflow
@@ -287,21 +293,6 @@ Nginx (port 2026) ← Unified entry point
    git push origin feature/your-feature-name
    ```
 
-## AI assistance disclosure
-
-DeerFlow is an AI project and we welcome AI-assisted contributions. To help
-reviewers calibrate how closely to read a change, **every pull request must
-complete the "AI assistance" section of the
-[PR template](.github/pull_request_template.md)**:
-
-- which tool(s) you used (or `none`),
-- how you used them, and
-- a confirmation that a human has read, understands, and takes responsibility
-  for the change.
-
-Please don't delete the section. PRs that ignore it may be asked to fill it in
-before review.
-
 ## Testing
 
 ```bash

Makefile

@@ -1,6 +1,6 @@
 # DeerFlow - Unified Development Environment
 
-.PHONY: help config config-upgrade check install setup doctor detect-thread-boundaries detect-blocking-io dev dev-daemon start start-daemon stop up down clean docker-init docker-start docker-stop docker-logs docker-logs-frontend docker-logs-gateway
+.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
 
 BASH ?= bash
 BACKEND_UV_RUN = cd backend && uv run
@@ -23,24 +23,28 @@ help:
 	@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 detect-thread-boundaries - Inventory async/thread boundary points"
-	@echo "  make detect-blocking-io        - Inventory blocking IO that may block the backend event loop"
 	@echo "  make install         - Install all dependencies (frontend + backend + pre-commit hooks)"
 	@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"
@@ -53,12 +57,6 @@ setup:
 doctor:
 	@$(BACKEND_UV_RUN) python ../scripts/doctor.py
 
-detect-thread-boundaries:
-	@$(PYTHON) ./scripts/detect_thread_boundaries.py
-
-detect-blocking-io:
-	@$(MAKE) -C backend detect-blocking-io
-
 config:
 	@$(PYTHON) ./scripts/configure.py
 
@@ -89,28 +87,77 @@ install:
 
 # Pre-pull sandbox Docker image (optional but recommended)
 setup-sandbox:
-	@$(RUN_WITH_GIT_BASH) ./scripts/setup-sandbox.sh
+	@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 image 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
@@ -119,6 +166,7 @@ stop:
 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"
 
@@ -134,6 +182,10 @@ docker-init:
 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
@@ -156,6 +208,10 @@ docker-logs-gateway:
 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

README.md

@@ -243,9 +243,10 @@ make up     # Build images and start all production services
 make down   # Stop and remove containers
 ```
 
-Access: http://localhost:2026
+> [!NOTE]
+> The LangGraph agent server currently runs via `langgraph dev` (the open-source CLI server).
 
-The unified nginx endpoint is same-origin by default and does not emit browser CORS headers. If you run a split-origin or port-forwarded browser client, set `GATEWAY_CORS_ORIGINS` to comma-separated exact origins such as `http://localhost:3000`; the Gateway then applies the CORS allowlist and matching CSRF origin checks.
+Access: http://localhost:2026
 
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
 
@@ -253,7 +254,7 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
 
 If you prefer running services locally:
 
-Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root. Set `DEER_FLOW_PROJECT_ROOT` to define that root explicitly, or `DEER_FLOW_CONFIG_PATH` to point at a specific config file. Runtime state defaults to `.deer-flow` under the project root and can be moved with `DEER_FLOW_HOME`; skills default to `skills/` under the project root and can be moved with `DEER_FLOW_SKILLS_PATH`. Run `make doctor` to verify your setup before starting.
+Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root (can be overridden via `DEER_FLOW_CONFIG_PATH`). Run `make doctor` to verify your setup before starting.
 On Windows, run the local development flow from Git Bash. Native `cmd.exe` and PowerShell shells are not supported for the bash-based service scripts, and WSL is not guaranteed because some scripts rely on Git for Windows utilities such as `cygpath`.
 
 1. **Check prerequisites**:
@@ -288,31 +289,53 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
 
 #### Startup Modes
 
-DeerFlow runs the agent runtime inside the Gateway API. Development mode enables hot-reload; production mode uses a pre-built frontend.
+DeerFlow supports multiple startup modes across two dimensions:
+
+- **Dev / Prod** — dev enables hot-reload; prod uses pre-built frontend
+- **Standard / Gateway** — standard uses a separate LangGraph server (4 processes); Gateway mode (experimental) embeds the agent runtime in the Gateway API (3 processes)
 
 | | **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 owns `/api/langgraph/*` and translates those public LangGraph-compatible paths to its native `/api/*` routers behind nginx.
+> **Gateway mode** eliminates the LangGraph server process — the Gateway API handles agent execution directly via async tasks, managing its own concurrency.
+
+#### Why Gateway Mode?
+
+In standard mode, DeerFlow runs a dedicated [LangGraph Platform](https://langchain-ai.github.io/langgraph/) server alongside the Gateway API. This architecture works well but has trade-offs:
+
+| | Standard Mode | Gateway Mode |
+|---|---|---|
+| **Architecture** | Gateway (REST API) + LangGraph (agent runtime) | Gateway embeds agent runtime |
+| **Concurrency** | `--n-jobs-per-worker` per worker (requires license) | `--workers` × async tasks (no per-worker cap) |
+| **Containers / Processes** | 4 (frontend, gateway, langgraph, nginx) | 3 (frontend, gateway, nginx) |
+| **Resource usage** | Higher (two Python runtimes) | Lower (single Python runtime) |
+| **LangGraph Platform license** | Required for production images | Not required |
+| **Cold start** | Slower (two services to initialize) | Faster |
+
+Both modes are functionally equivalent — the same agents, tools, and skills work in either mode.
 
 #### Docker Production Deployment
 
-`deploy.sh` supports building and starting separately:
+`deploy.sh` supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
 
 ```bash
 # One-step (build + start)
-deploy.sh
+deploy.sh                    # standard mode (default)
+deploy.sh --gateway          # gateway mode
 
-# Two-step (build once, start later)
+# Two-step (build once, start with any mode)
 deploy.sh build              # build all images
-deploy.sh start              # start pre-built images
+deploy.sh start              # start in standard mode
+deploy.sh start --gateway    # start in gateway mode
 
 # Stop
 deploy.sh down
@@ -347,14 +370,13 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when
 | Feishu / Lark | WebSocket | Moderate |
 | WeChat | Tencent iLink (long-polling) | Moderate |
 | WeCom | WebSocket | Moderate |
-| DingTalk | Stream Push (WebSocket) | Moderate |
 
 **Configuration in `config.yaml`:**
 
 ```yaml
 channels:
-  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
-  langgraph_url: http://localhost:8001/api
+  # LangGraph Server URL (default: http://localhost:2024)
+  langgraph_url: http://localhost:2024
   # Gateway API URL (default: http://localhost:8001)
   gateway_url: http://localhost:8001
 
@@ -417,19 +439,11 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
-
-  dingtalk:
-    enabled: true
-    client_id: $DINGTALK_CLIENT_ID             # Client ID of your DingTalk application
-    client_secret: $DINGTALK_CLIENT_SECRET     # Client Secret of your DingTalk application
-    allowed_users: []                          # empty = allow all
-    card_template_id: ""                       # Optional: AI Card template ID for streaming typewriter effect
 ```
 
 Notes:
 - `assistant_id: lead_agent` calls the default LangGraph assistant directly.
 - If `assistant_id` is set to a custom agent name, DeerFlow still routes through `lead_agent` and injects that value as `agent_name`, so the custom agent's SOUL/config takes effect for IM channels.
-- IM channel workers call Gateway's LangGraph-compatible API internally and automatically attach process-local internal auth plus the CSRF cookie/header pair required for thread and run creation.
 
 Set the corresponding API keys in your `.env` file:
 
@@ -452,10 +466,6 @@ WECHAT_ILINK_BOT_ID=your_ilink_bot_id
 # WeCom
 WECOM_BOT_ID=your_bot_id
 WECOM_BOT_SECRET=your_bot_secret
-
-# DingTalk
-DINGTALK_CLIENT_ID=your_client_id
-DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Telegram Setup**
@@ -494,15 +504,7 @@ DINGTALK_CLIENT_SECRET=your_client_secret
 4. Make sure backend dependencies include `wecom-aibot-python-sdk`. The channel uses a WebSocket long connection and does not require a public callback URL.
 5. The current integration supports inbound text, image, and file messages. Final images/files generated by the agent are also sent back to the WeCom conversation.
 
-**DingTalk Setup**
-
-1. Create a DingTalk application in the [DingTalk Developer Console](https://open.dingtalk.com/) and enable **Robot** capability.
-2. Set the message receiving mode to **Stream Mode** in the robot configuration page.
-3. Copy the `Client ID` and `Client Secret`, set `DINGTALK_CLIENT_ID` and `DINGTALK_CLIENT_SECRET` in `.env`, and enable the channel in `config.yaml`.
-4. *(Optional)* To enable streaming AI Card replies (typewriter effect), create an **AI Card** template on the [DingTalk Card Platform](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), then set `card_template_id` in `config.yaml` to the template ID. You also need to apply for the `Card.Streaming.Write` and `Card.Instance.Write` permissions.
-
-
-When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://gateway:8001/api` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
+When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://langgraph:2024` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
 
 **Commands**
 
@@ -546,15 +548,6 @@ LANGFUSE_BASE_URL=https://cloud.langfuse.com
 
 If you are using a self-hosted Langfuse instance, set `LANGFUSE_BASE_URL` to your deployment URL.
 
-**Trace correlation fields.** Every agent run is annotated with Langfuse's reserved trace attributes so the Sessions and Users pages light up automatically:
-
-- `session_id` = LangGraph `thread_id` — groups every trace of the same conversation
-- `user_id` = effective user from `get_effective_user_id()` (falls back to `default` in no-auth mode)
-- `trace_name` = assistant id (defaults to `lead-agent`)
-- `tags` = `[env:<DEER_FLOW_ENV>, model:<model_name>]` (omitted when not set)
-
-These are injected into `RunnableConfig.metadata` at the graph invocation root for both the gateway path (`runtime/runs/worker.py::run_agent`) and the embedded path (`client.py::DeerFlowClient.stream`), so any LangChain-compatible callback can read them. Set `DEER_FLOW_ENV` (or `ENVIRONMENT`) to tag traces by deployment environment.
-
 #### Using Both Providers
 
 If both LangSmith and Langfuse are enabled, DeerFlow attaches both tracing callbacks and reports the same model activity to both systems.
@@ -637,7 +630,7 @@ See [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerfl
 
 Complex tasks rarely fit in a single pass. DeerFlow decomposes them.
 
-The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output. When token usage tracking is enabled, completed sub-agent usage is attributed back to the dispatching step.
+The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
 
 This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.
 
@@ -740,12 +733,6 @@ DeerFlow has key high-privilege capabilities including **system command executio
 We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, workflow, and guidelines.
 
 Regression coverage includes Docker sandbox mode detection and provisioner kubeconfig-path handling tests in `backend/tests/`.
-Backend blocking-IO diagnostics are available from the repository root with
-`make detect-blocking-io`: it statically scans backend business code for
-blocking IO that may run on the backend event loop, prints a concise summary,
-and writes complete JSON findings to `.deer-flow/blocking-io-findings.json`.
-The JSON includes compact review records with `priority`, `location`,
-`blocking_call`, `event_loop_exposure`, `reason`, and `code`.
 Gateway artifact serving now forces active web content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) to download as attachments instead of inline rendering, reducing XSS risk for generated artifacts.
 
 ## License

README_fr.md

@@ -228,7 +228,7 @@ make down   # Stop and remove containers
 ```
 
 > [!NOTE]
-> Le runtime d'agent s'exécute actuellement dans la Gateway. nginx réécrit `/api/langgraph/*` vers l'API compatible LangGraph servie par la Gateway.
+> Le serveur d'agents LangGraph fonctionne actuellement via `langgraph dev` (le serveur CLI open source).
 
 Accès : http://localhost:2026
 
@@ -290,14 +290,13 @@ DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les ca
 | Telegram | Bot API (long-polling) | Facile |
 | Slack | Socket Mode | Modérée |
 | Feishu / Lark | WebSocket | Modérée |
-| DingTalk | Stream Push (WebSocket) | Modérée |
 
 **Configuration dans `config.yaml` :**
 
 ```yaml
 channels:
-  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
-  langgraph_url: http://localhost:8001/api
+  # LangGraph Server URL (default: http://localhost:2024)
+  langgraph_url: http://localhost:2024
   # Gateway API URL (default: http://localhost:8001)
   gateway_url: http://localhost:8001
 
@@ -342,13 +341,6 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
-
-  dingtalk:
-    enabled: true
-    client_id: $DINGTALK_CLIENT_ID             # ClientId depuis DingTalk Open Platform
-    client_secret: $DINGTALK_CLIENT_SECRET     # ClientSecret depuis DingTalk Open Platform
-    allowed_users: []                          # vide = tout le monde autorisé
-    card_template_id: ""                       # Optionnel : ID de modèle AI Card pour l'effet machine à écrire en streaming
 ```
 
 Définissez les clés API correspondantes dans votre fichier `.env` :
@@ -364,10 +356,6 @@ SLACK_APP_TOKEN=xapp-...
 # Feishu / Lark
 FEISHU_APP_ID=cli_xxxx
 FEISHU_APP_SECRET=your_app_secret
-
-# DingTalk
-DINGTALK_CLIENT_ID=your_client_id
-DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Configuration Telegram**
@@ -390,13 +378,6 @@ DINGTALK_CLIENT_SECRET=your_client_secret
 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`.
 
-**Configuration DingTalk**
-
-1. Créez une application sur [DingTalk Open Platform](https://open.dingtalk.com/) et activez la capacité **Robot**.
-2. Dans la page de configuration du robot, définissez le mode de réception des messages sur **Stream**.
-3. Copiez le `Client ID` et le `Client Secret`. Définissez `DINGTALK_CLIENT_ID` et `DINGTALK_CLIENT_SECRET` dans `.env` et activez le canal dans `config.yaml`.
-4. *(Optionnel)* Pour activer les réponses en streaming AI Card (effet machine à écrire), créez un modèle **AI Card** sur la [plateforme de cartes DingTalk](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), puis définissez `card_template_id` dans `config.yaml` avec l'ID du modèle. Vous devez également demander les permissions `Card.Streaming.Write` et `Card.Instance.Write`.
-
 **Commandes**
 
 Une fois un canal connecté, vous pouvez interagir avec DeerFlow directement depuis le chat :

README_ja.md

@@ -181,7 +181,7 @@ make down   # コンテナを停止して削除
 ```
 
 > [!NOTE]
-> Agentランタイムは現在Gateway内で実行されます。`/api/langgraph/*`はnginxによってGatewayのLangGraph-compatible APIへ書き換えられます。
+> LangGraphエージェントサーバーは現在`langgraph dev`（オープンソースCLIサーバー）経由で実行されます。
 
 アクセス: http://localhost:2026
 
@@ -243,14 +243,13 @@ DeerFlowはメッセージングアプリからのタスク受信をサポート
 | Telegram | Bot API（ロングポーリング） | 簡単 |
 | Slack | Socket Mode | 中程度 |
 | Feishu / Lark | WebSocket | 中程度 |
-| DingTalk | Stream Push（WebSocket） | 中程度 |
 
 **`config.yaml`での設定：**
 
 ```yaml
 channels:
-  # LangGraph-compatible Gateway API base URL（デフォルト: http://localhost:8001/api）
-  langgraph_url: http://localhost:8001/api
+  # LangGraphサーバーURL（デフォルト: http://localhost:2024）
+  langgraph_url: http://localhost:2024
   # Gateway API URL（デフォルト: http://localhost:8001）
   gateway_url: http://localhost:8001
 
@@ -295,13 +294,6 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
-
-  dingtalk:
-    enabled: true
-    client_id: $DINGTALK_CLIENT_ID             # DingTalk Open PlatformのClientId
-    client_secret: $DINGTALK_CLIENT_SECRET     # DingTalk Open PlatformのClientSecret
-    allowed_users: []                          # 空 = 全員許可
-    card_template_id: ""                       # オプション：ストリーミングタイプライター効果用のAIカードテンプレートID
 ```
 
 対応するAPIキーを`.env`ファイルに設定します：
@@ -317,10 +309,6 @@ SLACK_APP_TOKEN=xapp-...
 # Feishu / Lark
 FEISHU_APP_ID=cli_xxxx
 FEISHU_APP_SECRET=your_app_secret
-
-# DingTalk
-DINGTALK_CLIENT_ID=your_client_id
-DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Telegramのセットアップ**
@@ -343,13 +331,6 @@ DINGTALK_CLIENT_SECRET=your_client_secret
 3. **イベント**で`im.message.receive_v1`を購読し、**ロングコネクション**モードを選択。
 4. App IDとApp Secretをコピー。`.env`に`FEISHU_APP_ID`と`FEISHU_APP_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
 
-**DingTalkのセットアップ**
-
-1. [DingTalk Open Platform](https://open.dingtalk.com/)でアプリを作成し、**ロボット**機能を有効化します。
-2. ロボット設定ページでメッセージ受信モードを**Streamモード**に設定します。
-3. `Client ID`と`Client Secret`をコピー。`.env`に`DINGTALK_CLIENT_ID`と`DINGTALK_CLIENT_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
-4. *（オプション）* ストリーミングAIカード返信（タイプライター効果）を有効にするには、[DingTalkカードプラットフォーム](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card)で**AIカード**テンプレートを作成し、`config.yaml`の`card_template_id`にテンプレートIDを設定します。`Card.Streaming.Write` および `Card.Instance.Write` 権限の申請も必要です。
-
 **コマンド**
 
 チャネル接続後、チャットから直接DeerFlowと対話できます：

README_ru.md

@@ -256,7 +256,6 @@ DeerFlow принимает задачи прямо из мессенджеро
 | Telegram | Bot API (long-polling) | Просто |
 | Slack | Socket Mode | Средне |
 | Feishu / Lark | WebSocket | Средне |
-| DingTalk | Stream Push (WebSocket) | Средне |
 
 **Конфигурация в `config.yaml`:**
 
@@ -279,13 +278,6 @@ channels:
     enabled: true
     bot_token: $TELEGRAM_BOT_TOKEN
     allowed_users: []
-
-  dingtalk:
-    enabled: true
-    client_id: $DINGTALK_CLIENT_ID             # ClientId с DingTalk Open Platform
-    client_secret: $DINGTALK_CLIENT_SECRET     # ClientSecret с DingTalk Open Platform
-    allowed_users: []                          # пусто = разрешить всем
-    card_template_id: ""                       # Опционально: ID шаблона AI Card для потокового эффекта печатной машинки
 ```
 
 **Настройка Telegram**
@@ -293,13 +285,6 @@ channels:
 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`.
 
-**Настройка DingTalk**
-
-1. Создайте приложение на [DingTalk Open Platform](https://open.dingtalk.com/) и включите возможность **Робот**.
-2. На странице настроек робота установите режим приёма сообщений на **Stream**.
-3. Скопируйте `Client ID` и `Client Secret`. Укажите `DINGTALK_CLIENT_ID` и `DINGTALK_CLIENT_SECRET` в `.env` и включите канал в `config.yaml`.
-4. *(Опционально)* Для включения потоковых ответов AI Card (эффект печатной машинки) создайте шаблон **AI Card** на [платформе карточек DingTalk](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), затем укажите `card_template_id` в `config.yaml` с ID шаблона. Также необходимо запросить разрешения `Card.Streaming.Write` и `Card.Instance.Write`.
-
 **Доступные команды**
 
 | Команда | Описание |

README_zh.md

@@ -184,7 +184,7 @@ make down   # 停止并移除容器
 ```
 
 > [!NOTE]
-> 当前 Agent 运行时嵌入在 Gateway 中运行，`/api/langgraph/*` 会由 nginx 重写到 Gateway 的 LangGraph-compatible API。
+> 当前 LangGraph agent server 通过开源 CLI 服务 `langgraph dev` 运行。
 
 访问地址：http://localhost:2026
 
@@ -194,7 +194,7 @@ make down   # 停止并移除容器
 
 如果你更希望直接在本地启动各个服务：
 
-前提：先完成上面的“配置”步骤（`make config` 和模型 API key 配置）。`make dev` 需要有效配置文件，默认读取项目根目录下的 `config.yaml`。可以用 `DEER_FLOW_PROJECT_ROOT` 显式指定项目根目录，也可以用 `DEER_FLOW_CONFIG_PATH` 指向某个具体配置文件。运行期状态默认写到项目根目录下的 `.deer-flow`，可用 `DEER_FLOW_HOME` 覆盖；skills 默认读取项目根目录下的 `skills/`，可用 `DEER_FLOW_SKILLS_PATH` 覆盖。
+前提：先完成上面的“配置”步骤（`make config` 和模型 API key 配置）。`make dev` 需要有效配置文件，默认读取项目根目录下的 `config.yaml`，也可以通过 `DEER_FLOW_CONFIG_PATH` 覆盖。
 在 Windows 上，请使用 Git Bash 运行本地开发流程。基于 bash 的服务脚本不支持直接在原生 `cmd.exe` 或 PowerShell 中执行，且 WSL 也不保证可用，因为部分脚本依赖 Git for Windows 的 `cygpath` 等工具。
 
 1. **检查依赖环境**：
@@ -248,14 +248,13 @@ DeerFlow 支持从即时通讯应用接收任务。只要配置完成，对应
 | Slack | Socket Mode | 中等 |
 | Feishu / Lark | WebSocket | 中等 |
 | 企业微信智能机器人 | WebSocket | 中等 |
-| 钉钉 | Stream Push（WebSocket） | 中等 |
 
 **`config.yaml` 中的配置示例：**
 
 ```yaml
 channels:
-  # LangGraph-compatible Gateway API base URL（默认：http://localhost:8001/api）
-  langgraph_url: http://localhost:8001/api
+  # LangGraph Server URL（默认：http://localhost:2024）
+  langgraph_url: http://localhost:2024
   # Gateway API URL（默认：http://localhost:8001）
   gateway_url: http://localhost:8001
 
@@ -305,13 +304,6 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
-
-  dingtalk:
-    enabled: true
-    client_id: $DINGTALK_CLIENT_ID             # 钉钉开放平台 ClientId
-    client_secret: $DINGTALK_CLIENT_SECRET     # 钉钉开放平台 ClientSecret
-    allowed_users: []                          # 留空表示允许所有人
-    card_template_id: ""                       # 可选：AI 卡片模板 ID，用于流式打字机效果
 ```
 
 说明：
@@ -335,10 +327,6 @@ FEISHU_APP_SECRET=your_app_secret
 # 企业微信智能机器人
 WECOM_BOT_ID=your_bot_id
 WECOM_BOT_SECRET=your_bot_secret
-
-# 钉钉
-DINGTALK_CLIENT_ID=your_client_id
-DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Telegram 配置**
@@ -369,13 +357,6 @@ DINGTALK_CLIENT_SECRET=your_client_secret
 4. 安装后端依赖时确保包含 `wecom-aibot-python-sdk`，渠道会通过 WebSocket 长连接接收消息，无需公网回调地址。
 5. 当前支持文本、图片和文件入站消息；agent 生成的最终图片/文件也会回传到企业微信会话中。
 
-**钉钉配置**
-
-1. 在 [钉钉开放平台](https://open.dingtalk.com/) 创建应用，并启用 **机器人** 能力。
-2. 在机器人配置页面设置消息接收模式为 **Stream模式**。
-3. 复制 `Client ID` 和 `Client Secret`，在 `.env` 中设置 `DINGTALK_CLIENT_ID` 和 `DINGTALK_CLIENT_SECRET`，并在 `config.yaml` 中启用该渠道。
-4. *（可选）* 如需开启流式 AI 卡片回复（打字机效果），请在[钉钉卡片平台](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card)创建 **AI 卡片**模板，然后在 `config.yaml` 中将 `card_template_id` 设为该模板 ID。同时需要申请 `Card.Streaming.Write` 和 `Card.Instance.Write` 权限。
-
 **命令**
 
 渠道连接完成后，你可以直接在聊天窗口里和 DeerFlow 交互：

backend/CLAUDE.md

@@ -7,13 +7,15 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 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**:
-- **Gateway API** (port 8001): REST API plus embedded LangGraph-compatible agent runtime
+- **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**:
-- `make dev`, Docker dev, and production all run the agent runtime in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Nginx exposes that runtime at `/api/langgraph/*` and rewrites it to Gateway's native `/api/*` routers.
+**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**:
 ```
@@ -23,7 +25,7 @@ deer-flow/
 ├── extensions_config.json      # MCP servers and skills configuration
 ├── backend/                    # Backend application (this directory)
 │   ├── Makefile               # Backend-only commands (dev, gateway, lint)
-│   ├── langgraph.json         # LangGraph Studio graph configuration
+│   ├── langgraph.json         # LangGraph server configuration
 │   ├── packages/
 │   │   └── harness/           # deerflow-harness package (import: deerflow.*)
 │   │       ├── pyproject.toml
@@ -81,64 +83,26 @@ When making code changes, you MUST update the relevant documentation:
 ```bash
 make check      # Check system requirements
 make install    # Install all dependencies (frontend + backend)
-make dev        # Start all services (Gateway + Frontend + Nginx), with config.yaml preflight
-make start      # Start production services locally
+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 Gateway API with reload (port 8001)
-make gateway            # Run Gateway API only (port 8001)
-make test               # Run all backend tests
-make test-blocking-io   # Run strict Blockbuster runtime gate on tests/blocking_io/
-make lint               # Lint with ruff
-make format             # Format code with ruff
+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
 ```
 
-The `detect-blocking-io` target parses `app/`, `packages/harness/deerflow/`,
-and `scripts/` with AST. By default it reports only blocking IO candidates that
-are inside async code, reachable from async code in the same file, or reachable
-from sync-only `AgentMiddleware` before/after hooks that LangGraph can execute
-on the async graph path. It prints a concise summary and writes complete JSON
-findings to `.deer-flow/blocking-io-findings.json` at the repository root
-(both `make detect-blocking-io` from the repo root and `cd backend && make
-detect-blocking-io` resolve to the same repo-root path). JSON findings include
-`priority`, `location`, `blocking_call`, `event_loop_exposure`, `reason`, and
-`code` for model-assisted or manual review. `priority` is a deterministic
-review ordering from operation type, not proof of a bug. Bare-name same-file
-calls are resolved by function name, so duplicate helper names in one file can
-conservatively over-report async reachability. It is intentionally
-informational and is not run from CI in this round.
-
 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)
 
-Blocking-IO runtime gate (`tests/blocking_io/`):
-- Wraps every item under `tests/blocking_io/` with a strict Blockbuster
-  context scoped to `app.*` and `deerflow.*` (see
-  `tests/support/detectors/blocking_io_runtime.py`). Any sync blocking IO
-  call whose stack passes through DeerFlow business code while running on
-  the asyncio event loop raises `BlockingError` and fails the test.
-- Regression anchors live there: `test_skills_load.py` (locks the
-  `asyncio.to_thread` offload around `LocalSkillStorage.load_skills`, fix
-  for #1917); `test_sqlite_lifespan.py` (locks the offload around
-  SQLite path resolution plus `ensure_sqlite_parent_dir`, fix for #1912);
-  `test_jsonl_run_event_store.py` (locks `JsonlRunEventStore`'s async
-  API offloading its file IO via `asyncio.to_thread`, fix #3084); and
-  `test_uploads_middleware.py` (locks `UploadsMiddleware.abefore_agent`
-  offloading the uploads-directory scan off the event loop).
-- `test_gate_smoke.py` is a meta-test asserting the gate actually catches
-  unoffloaded blocking IO and that the `@pytest.mark.allow_blocking_io`
-  opt-out works.
-- Coverage boundary: the gate only sees code that test execution actually
-  touches. Static AST coverage is a separate concern (out of scope for
-  this PR).
-- CI: runs on every PR via `.github/workflows/backend-blocking-io-tests.yml`,
-  hard-fail.
-
 Boundary check (harness → app import firewall):
 - `tests/test_harness_boundary.py` — ensures `packages/harness/deerflow/` never imports from `app.*`
 
@@ -151,7 +115,7 @@ CI runs these regression tests for every pull request via [.github/workflows/bac
 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, DingTalk).
+- **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.
 
@@ -166,7 +130,7 @@ from app.gateway.app import app
 from app.channels.service import start_channel_service
 
 # App → Harness (allowed)
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 # Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
 # from app.gateway.routers.uploads import ...  # ← will fail CI
@@ -204,11 +168,11 @@ Lead-agent middlewares are assembled in strict append order across `packages/har
 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); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
+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 (MCP) tool schemas from the bound model using a build-time deferred-name set + catalog hash, reading per-thread promotions from `ThreadState.promoted` (hash-scoped, no ContextVar); a tool becomes bound on subsequent turns after `tool_search` returns its schema (optional, if `tool_search.enabled`)
+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)
@@ -221,11 +185,16 @@ Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** direc
 
 **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.
+**Config Lifecycle**: All config models are `frozen=True` (immutable after construction). `AppConfig.from_file()` is a pure function — no side effects, no process-global state. The resolved `AppConfig` is passed as an explicit parameter down every consumer lane:
 
-**Config Hot-Reload Boundary**: Gateway dependencies route through `get_app_config()` on every request, so per-run fields like `models[*].max_tokens`, `summarization.*`, `title.*`, `memory.*`, `subagents.*`, `tools[*]`, and the agent system prompt pick up `config.yaml` edits on the next message. `AppConfig` is intentionally **not** cached on `app.state` — `lifespan()` keeps a local `startup_config` variable for one-shot bootstrap work and passes it to `langgraph_runtime(app, startup_config)`.
+- **Gateway**: `app.state.config` populated in lifespan; routers receive it via `Depends(get_config)` from `app/gateway/deps.py`.
+- **Client**: `DeerFlowClient._app_config` captured in the constructor; every method reads `self._app_config`.
+- **Agent run**: wrapped in `DeerFlowContext(app_config=…)` and injected via LangGraph `Runtime[DeerFlowContext].context`. Middleware and tools read `runtime.context.app_config` directly or via `resolve_context(runtime)`.
+- **LangGraph Server bootstrap**: `make_lead_agent` (registered in `langgraph.json`) calls `AppConfig.from_file()` itself — the only place in production that loads from disk at agent-build time.
 
-Infrastructure fields are **restart-required**. The authoritative list lives in `packages/harness/deerflow/config/reload_boundary.py::STARTUP_ONLY_FIELDS` and is mirrored by the standardised `"startup-only:"` prefix on the corresponding `Field(description=...)` in `AppConfig`, so IDE hover on those fields surfaces the reason inline (no need to context-switch into this table). Currently registered: `database`, `checkpointer`, `run_events`, `stream_bridge`, `sandbox`, `log_level`, `channels`. Adding a new restart-required field requires updating the registry; drift is pinned by `tests/test_reload_boundary.py`.
+To update config at runtime (Gateway API mutations for MCP/Skills), write the new file and call `AppConfig.from_file()` to build a fresh snapshot, then swap `app.state.config`. No mtime detection, no auto-reload, no ambient ContextVar lookup (`AppConfig.current()` has been removed).
+
+**DeerFlowContext**: Per-invocation typed context for the agent execution path, injected via LangGraph `Runtime[DeerFlowContext]`. Holds `app_config: AppConfig`, `thread_id: str`, `agent_name: str | None`. Gateway runtime and `DeerFlowClient` construct full `DeerFlowContext` at invoke time; the LangGraph Server boundary builds one inside `make_lead_agent`. Middleware and tools access context through `resolve_context(runtime)` which returns the typed `DeerFlowContext` — legacy dict/None shapes are rejected. Mutable runtime state (`sandbox_id`) flows through `ThreadState.sandbox`, not context.
 
 Configuration priority:
 1. Explicit `config_path` argument
@@ -248,9 +217,7 @@ Configuration priority:
 
 ### Gateway API (`app/gateway/`)
 
-FastAPI application on port 8001 with health check at `GET /health`. Set `GATEWAY_ENABLE_DOCS=false` to disable `/docs`, `/redoc`, and `/openapi.json` in production (default: enabled).
-
-CORS is same-origin by default when requests enter through nginx on port 2026. Split-origin or port-forwarded browser clients must opt in with `GATEWAY_CORS_ORIGINS` (comma-separated exact origins); Gateway `CORSMiddleware` and `CSRFMiddleware` both read that variable so browser CORS and auth-origin checks stay aligned.
+FastAPI application on port 8001 with health check at `GET /health`.
 
 **Routers**:
 
@@ -268,34 +235,27 @@ CORS is same-origin by default when requests enter through nginx on port 2026. S
 | **Feedback** (`/api/threads/{id}/runs/{rid}/feedback`) | `PUT /` - upsert feedback; `DELETE /` - delete user feedback; `POST /` - create feedback; `GET /` - list feedback; `GET /stats` - aggregate stats; `DELETE /{fid}` - delete specific |
 | **Runs** (`/api/runs`) | `POST /stream` - stateless run + SSE; `POST /wait` - stateless run + block; `GET /{rid}/messages` - paginated messages by run_id `{data, has_more}` (cursor: `after_seq`/`before_seq`); `GET /{rid}/feedback` - list feedback by run_id |
 
-**RunManager / RunStore contract**:
-- `RunManager.get()` is async; direct callers must `await` it.
-- When a persistent `RunStore` is configured, `get()` and `list_by_thread()` hydrate historical runs from the store. In-memory records win for the same `run_id` so task, abort, and stream-control state stays attached to active local runs.
-- `cancel()` and `create_or_reject(..., multitask_strategy="interrupt"|"rollback")` persist interrupted status through `RunStore.update_status()`, matching normal `set_status()` transitions.
-- Store-only hydrated runs are readable history. If the current worker has no in-memory task/control state for that run, cancellation APIs can return 409 because this worker cannot stop the task.
-- `POST /wait` (both thread-scoped and `/api/runs/wait`) drains the stream bridge via `wait_for_run_completion()` instead of bare `await record.task`, so it honours the run's `on_disconnect` setting and cancels the background run on real client disconnect rather than returning a stale checkpoint (issue #3265).
-
-Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runtime, all other `/api/*` → Gateway REST APIs.
+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`, `acquire_async`, `get`, `release` lifecycle. Async agent/tool paths call async sandbox lifecycle hooks so Docker sandbox creation, discovery, cross-process locking, readiness polling, and release stay off the event loop.
+**Provider Pattern**: `SandboxProvider` with `acquire`, `get`, `release` lifecycle
 **Implementations**:
-- `LocalSandboxProvider` - Local filesystem execution. `acquire(thread_id)` returns a per-thread `LocalSandbox` (id `local:{thread_id}`) whose `path_mappings` resolve `/mnt/user-data/{workspace,uploads,outputs}` and `/mnt/acp-workspace` to that thread's host directories, so the public `Sandbox` API honours the `/mnt/user-data` contract uniformly with AIO. `acquire()` / `acquire(None)` keeps the legacy generic singleton (id `local`) for callers without a thread context. Per-thread sandboxes are held in an LRU cache (default 256 entries) guarded by a `threading.Lock`.
+- `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/users/{user_id}/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
-- Translation: `LocalSandboxProvider` builds per-thread `PathMapping`s for the user-data prefixes at acquire time; `tools.py` keeps `replace_virtual_path()` / `replace_virtual_paths_in_command()` as a defense-in-depth layer (and for path validation). AIO has the directories volume-mounted at the same virtual paths inside its container, so both implementations accept `/mnt/user-data/...` natively.
-- Detection: `is_local_sandbox()` accepts both `sandbox_id == "local"` (legacy / no-thread) and `sandbox_id.startswith("local:")` (per-thread)
+- 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; overwrites by default and exposes the `append` argument in the model-facing schema for end-of-file writes
+- `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/`)
@@ -315,10 +275,8 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
    - `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)
-   - `setup_agent` - Bootstrap-only: persist a brand-new custom agent's `SOUL.md` and `config.yaml`. Bound only when `is_bootstrap=True`.
-   - `update_agent` - Custom-agent-only: persist self-updates to the current agent's `SOUL.md` / `config.yaml` from inside a normal chat (partial update + atomic write). Bound when `agent_name` is set and `is_bootstrap=False`.
 4. **Subagent tool** (if enabled):
-   - `task` - Delegate to subagent (description, prompt, subagent_type)
+   - `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)
@@ -339,7 +297,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 - **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; the Gateway-embedded runtime detects changes via mtime
+- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
 
 ### Skills System (`packages/harness/deerflow/skills/`)
 
@@ -366,10 +324,9 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 
 ### IM Channels System (`app/channels/`)
 
-Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via Gateway's LangGraph-compatible API.
+Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
 
-
-**Architecture**: Channels communicate with Gateway through the `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side. The internal SDK client injects process-local internal auth plus a matching CSRF cookie/header pair so Gateway accepts state-changing thread/run requests from channel workers without relying on browser session cookies.
+**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)
@@ -377,25 +334,23 @@ Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the
 - `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` / `dingtalk.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place; `dingtalk.py` optionally uses AI Card streaming for in-place updates when `card_template_id` is configured)
+- `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 through Gateway's LangGraph-compatible API
+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. DingTalk AI Card mode (when `card_template_id` configured): `runs.stream()` → create card with initial text → stream updates via `PUT /v1.0/card/streaming` → finalize on `is_final=True`. Falls back to `sampleMarkdown` if card creation or streaming fails
-8. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
-9. Outbound → channel callbacks → platform reply
+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-compatible Gateway API base URL (default: `http://localhost:8001/api`)
+- `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://gateway:8001/api` for `langgraph_url` and `http://gateway:8001` for `gateway_url`, 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), `dingtalk` (client_id, client_secret, optional `card_template_id` for AI Card streaming)
-
+- 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/`)
 
@@ -408,11 +363,10 @@ Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the
 **Per-User Isolation**:
 - Memory is stored per-user at `{base_dir}/users/{user_id}/memory.json`
 - Per-agent per-user memory at `{base_dir}/users/{user_id}/agents/{agent_name}/memory.json`
-- Custom agent definitions (`SOUL.md` + `config.yaml`) are also per-user at `{base_dir}/users/{user_id}/agents/{agent_name}/`. The legacy shared layout `{base_dir}/agents/{agent_name}/` remains read-only fallback for unmigrated installations
 - `user_id` is resolved via `get_effective_user_id()` from `deerflow.runtime.user_context`
 - In no-auth mode, `user_id` defaults to `"default"` (constant `DEFAULT_USER_ID`)
 - Absolute `storage_path` in config opts out of per-user isolation
-- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json`, `threads/`, and `agents/` into per-user layout. Supports `--dry-run` (preview changes) and `--user-id USER_ID` (assign unowned legacy data to a user, defaults to `default`).
+- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json` and `threads/` into per-user layout; supports `--dry-run`
 
 **Data Structure** (stored in `{base_dir}/users/{user_id}/memory.json`):
 - **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
@@ -441,24 +395,6 @@ Focused regression coverage for the updater lives in `backend/tests/test_memory_
 - `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
 
-### Tracing System (`packages/harness/deerflow/tracing/`)
-
-LangSmith and Langfuse are both supported. The wiring lives in two layers:
-
-- `factory.py::build_tracing_callbacks()` — returns the LangChain `CallbackHandler` list for the providers currently enabled via env vars (`LANGSMITH_TRACING`, `LANGFUSE_TRACING`, etc.). The handlers are attached at the **graph invocation root** for in-graph runs (`make_lead_agent` and `DeerFlowClient.stream` both append them to `config["callbacks"]` before invoking the graph) so a single run produces one trace with all node / LLM / tool calls as child spans. Standalone callers — anything that invokes a model outside such a graph (e.g. `MemoryUpdater`) — keep `create_chat_model`'s default `attach_tracing=True`, which falls back to model-level callback attachment.
-- `metadata.py::build_langfuse_trace_metadata()` — builds the Langfuse-reserved trace attributes for `RunnableConfig.metadata`. The Langfuse v4 `langchain.CallbackHandler` lifts these onto the root trace (see its `_parse_langfuse_trace_attributes`), but only when it sees `on_chain_start(parent_run_id=None)` — which is why the callbacks have to live at the graph root, not the model.
-
-**Trace-attribute injection points**: both `runtime/runs/worker.py::run_agent` (gateway path) and `client.py::DeerFlowClient.stream` (embedded path) merge the metadata into `config["metadata"]` right before constructing the graph. Caller-supplied keys win via `setdefault`, so an external `session_id` override is preserved. Field mapping:
-
-| Langfuse field         | Source                                       |
-|-----------------------|----------------------------------------------|
-| `langfuse_session_id` | LangGraph `thread_id`                         |
-| `langfuse_user_id`    | `get_effective_user_id()` (`default` in no-auth) |
-| `langfuse_trace_name` | `RunRecord.assistant_id` / client `agent_name` (defaults to `lead-agent`) |
-| `langfuse_tags`       | `env:<DEER_FLOW_ENV>` + `model:<model_name>`  |
-
-Returns `{}` when Langfuse is not in the enabled providers — LangSmith-only deployments are unaffected. Set `DEER_FLOW_ENV` (or `ENVIRONMENT`) to tag traces by deployment environment. Tests live in `tests/test_tracing_factory.py`, `tests/test_tracing_metadata.py`, `tests/test_worker_langfuse_metadata.py`, and `tests/test_client_langfuse_metadata.py`.
-
 ### Config Schema
 
 **`config.yaml`** key sections:
@@ -483,9 +419,9 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
 
 `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 Gateway API uses. Shares the same config files and data directories. No FastAPI dependency.
+**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**:
+**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
@@ -548,15 +484,20 @@ This starts all services and makes the application available at `http://localhos
 | | **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**:
-- `/api/langgraph/*` → Gateway embedded runtime (8001), rewritten to `/api/*`
+- 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)
 
@@ -565,11 +506,15 @@ This starts all services and makes the application available at `http://localhos
 From the **backend** directory:
 
 ```bash
-# Gateway API
+# 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
@@ -590,7 +535,6 @@ Multi-file upload with automatic document conversion:
 - 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
-- Duplicate filenames in a single upload request are auto-renamed with `_N` suffixes so later files do not truncate earlier files
 - Agent receives uploaded file list via `UploadsMiddleware`
 
 See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.

backend/CONTRIBUTING.md

@@ -56,8 +56,11 @@ export OPENAI_API_KEY="your-api-key"
 ### Run the Development Server
 
 ```bash
-# Gateway API + embedded agent runtime
+# Terminal 1: LangGraph server
 make dev
+
+# Terminal 2: Gateway API
+make gateway
 ```
 
 ## Project Structure

backend/Dockerfile

@@ -50,12 +50,6 @@ COPY backend ./backend
 RUN --mount=type=cache,target=/root/.cache/uv \
     sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync ${UV_EXTRAS:+--extra $UV_EXTRAS}"
 
-# UTF-8 locale prevents UnicodeEncodeError on Chinese/emoji content in minimal
-# containers where locale configuration may be missing and the default encoding is not UTF-8.
-ENV LANG=C.UTF-8
-ENV LC_ALL=C.UTF-8
-ENV PYTHONIOENCODING=utf-8
-
 # ── Stage 2: Dev ──────────────────────────────────────────────────────────────
 # Retains compiler toolchain from builder so startup-time `uv sync` can build
 # source distributions in development containers.
@@ -64,7 +58,7 @@ 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
+EXPOSE 8001 2024
 
 CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
 
@@ -72,10 +66,6 @@ CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app
 # Clean image without build-essential — reduces size (~200 MB) and attack surface.
 FROM python:3.12-slim-bookworm
 
-ENV LANG=C.UTF-8
-ENV LC_ALL=C.UTF-8
-ENV PYTHONIOENCODING=utf-8
-
 # 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
@@ -94,8 +84,8 @@ WORKDIR /app
 # Copy backend with pre-built virtualenv from builder
 COPY --from=builder /app/backend ./backend
 
-# Expose Gateway API port.
-EXPOSE 8001
+# 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"]

backend/Makefile

@@ -2,16 +2,13 @@ install:
 	uv sync
 
 dev:
-	PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001 --reload
+	uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
 
 gateway:
-	PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
+	PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
 
 test:
-	PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run pytest tests/ -v
-
-test-blocking-io:
-	PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run pytest tests/blocking_io -q --tb=short
+	PYTHONPATH=. uv run pytest tests/ -v
 
 lint:
 	uvx ruff check .
@@ -19,6 +16,3 @@ lint:
 
 format:
 	uvx ruff check . --fix && uvx ruff format .
-
-detect-blocking-io:
-	@PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run python ../scripts/detect_blocking_io_static.py --output ../.deer-flow/blocking-io-findings.json

backend/README.md

@@ -11,26 +11,31 @@ DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent
                         │          Nginx (Port 2026)           │
                         │      Unified reverse proxy           │
                         └───────┬──────────────────┬───────────┘
-                                │
-            /api/langgraph/*    │    /api/* (other)
-            rewritten to /api/* │
-                                ▼
-               ┌────────────────────────────────────────┐
-               │        Gateway API (8001)              │
-               │        FastAPI REST + agent runtime    │
-               │                                        │
-               │ Models, MCP, Skills, Memory, Uploads,  │
-               │ Artifacts, Threads, Runs, Streaming    │
-               │                                        │
-               │ ┌────────────────────────────────────┐ │
-               │ │ Lead Agent                         │ │
-               │ │ Middleware Chain, Tools, Subagents │ │
-               │ └────────────────────────────────────┘ │
-               └────────────────────────────────────────┘
+                                │                  │
+              /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/*` → Gateway LangGraph-compatible API - agent interactions, threads, streaming
+- `/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
 
@@ -69,12 +74,12 @@ Middlewares execute in strict order, each handling a specific concern:
 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/). Async runtime paths use async sandbox lifecycle hooks so startup, readiness polling, and release do not block the event loop.
+- **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` (`write_file` overwrites by default and exposes `append` for end-of-file writes; `bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
+- **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
 
@@ -119,7 +124,7 @@ FastAPI application providing REST endpoints for frontend integration:
 | `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, auto-renames duplicate filenames in one request) |
+| `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 |
@@ -188,7 +193,7 @@ export OPENAI_API_KEY="your-api-key-here"
 **Full Application** (from project root):
 
 ```bash
-make dev  # Starts Gateway + Frontend + Nginx
+make dev  # Starts LangGraph + Gateway + Frontend + Nginx
 ```
 
 Access at: http://localhost:2026
@@ -196,11 +201,14 @@ Access at: http://localhost:2026
 **Backend Only** (from backend directory):
 
 ```bash
-# Gateway API + embedded agent runtime
+# Terminal 1: LangGraph server
 make dev
+
+# Terminal 2: Gateway API
+make gateway
 ```
 
-Direct access: Gateway at http://localhost:8001
+Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
 
 ---
 
@@ -236,16 +244,12 @@ backend/
 │   └── utils/                  # Utilities
 ├── docs/                       # Documentation
 ├── tests/                      # Test suite
-├── langgraph.json              # LangGraph graph registry for tooling/Studio compatibility
+├── langgraph.json              # LangGraph server configuration
 ├── pyproject.toml              # Python dependencies
 ├── Makefile                    # Development commands
 └── Dockerfile                  # Container build
 ```
 
-`langgraph.json` is not the default service entrypoint.  The scripts and Docker
-deployments run the Gateway embedded runtime; the file is kept for LangGraph
-tooling, Studio, or direct LangGraph Server compatibility.
-
 ---
 
 ## Configuration
@@ -358,11 +362,10 @@ If a provider is explicitly enabled but required credentials are missing, or the
 
 ```bash
 make install    # Install dependencies
-make dev        # Run Gateway API + embedded agent runtime (port 8001)
-make gateway    # Run Gateway API without reload (port 8001)
+make dev        # Run LangGraph server (port 2024)
+make gateway    # Run Gateway API (port 8001)
 make lint       # Run linter (ruff)
 make format     # Format code (ruff)
-make detect-blocking-io  # Inventory blocking IO that may block the backend event loop
 ```
 
 ### Code Style
@@ -379,18 +382,6 @@ make detect-blocking-io  # Inventory blocking IO that may block the backend even
 uv run pytest
 ```
 
-`make detect-blocking-io` statically scans backend business code for blocking
-IO that may run on the backend event loop and is not test-coverage-bound. It
-prints a concise summary for human review and writes complete JSON findings to
-`.deer-flow/blocking-io-findings.json` at the repository root (regardless of
-whether the target is invoked from the repo root or from `backend/`). JSON
-findings include both broad IO category and review-oriented fields such as
-`priority`, `location`, `blocking_call`, `event_loop_exposure`, `reason`, and
-`code`. `priority` is a deterministic review ordering from the operation type,
-not proof of a bug. Bare-name same-file calls are resolved by function name,
-so duplicate helper names in one file can conservatively over-report async
-reachability.
-
 ---
 
 ## Technology Stack

backend/app/channels/__init__.py

@@ -2,7 +2,7 @@
 
 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 Gateway's LangGraph-compatible API.
+which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
 """
 
 from app.channels.base import Channel

backend/app/channels/base.py

@@ -31,10 +31,6 @@ class Channel(ABC):
     def is_running(self) -> bool:
         return self._running
 
-    @property
-    def supports_streaming(self) -> bool:
-        return False
-
     # -- lifecycle ---------------------------------------------------------
 
     @abstractmethod

backend/app/channels/dingtalk.py

@@ -1,740 +0,0 @@
-"""DingTalk channel implementation."""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import re
-import threading
-import time
-from pathlib import Path
-from typing import Any
-
-import httpx
-
-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
-
-logger = logging.getLogger(__name__)
-
-DINGTALK_API_BASE = "https://api.dingtalk.com"
-
-_TOKEN_REFRESH_MARGIN_SECONDS = 300
-
-_CONVERSATION_TYPE_P2P = "1"
-_CONVERSATION_TYPE_GROUP = "2"
-
-_MAX_UPLOAD_SIZE_BYTES = 20 * 1024 * 1024
-
-
-def _normalize_conversation_type(raw: Any) -> str:
-    """Normalize ``conversationType`` to ``"1"`` (P2P) or ``"2"`` (group).
-
-    Stream payloads may send int or string values.
-    """
-    if raw is None:
-        return _CONVERSATION_TYPE_P2P
-    s = str(raw).strip()
-    if s == _CONVERSATION_TYPE_GROUP:
-        return _CONVERSATION_TYPE_GROUP
-    return _CONVERSATION_TYPE_P2P
-
-
-def _normalize_allowed_users(allowed_users: Any) -> set[str]:
-    if allowed_users is None:
-        return set()
-    if isinstance(allowed_users, str):
-        values = [allowed_users]
-    elif isinstance(allowed_users, (list, tuple, set)):
-        values = allowed_users
-    else:
-        logger.warning(
-            "DingTalk allowed_users should be a list of user IDs; treating %s as one string value",
-            type(allowed_users).__name__,
-        )
-        values = [allowed_users]
-    return {str(uid) for uid in values if str(uid)}
-
-
-def _is_dingtalk_command(text: str) -> bool:
-    if not text.startswith("/"):
-        return False
-    return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
-
-
-def _extract_text_from_rich_text(rich_text_list: list) -> str:
-    parts: list[str] = []
-    for item in rich_text_list:
-        if isinstance(item, dict) and "text" in item:
-            parts.append(item["text"])
-    return " ".join(parts)
-
-
-_FENCED_CODE_BLOCK_RE = re.compile(r"```(\w*)\n(.*?)```", re.DOTALL)
-_INLINE_CODE_RE = re.compile(r"`([^`\n]+)`")
-_HORIZONTAL_RULE_RE = re.compile(r"^-{3,}$", re.MULTILINE)
-_TABLE_SEPARATOR_RE = re.compile(r"^\|[-:| ]+\|$", re.MULTILINE)
-
-
-def _convert_markdown_table(text: str) -> str:
-    # DingTalk sampleMarkdown does not render pipe-delimited tables.
-    lines = text.split("\n")
-    result: list[str] = []
-    i = 0
-    while i < len(lines):
-        line = lines[i]
-        # Detect table: header row followed by separator row
-        if i + 1 < len(lines) and line.strip().startswith("|") and _TABLE_SEPARATOR_RE.match(lines[i + 1].strip()):
-            headers = [h.strip() for h in line.strip().strip("|").split("|")]
-            i += 2  # skip header + separator
-            while i < len(lines) and lines[i].strip().startswith("|"):
-                cells = [c.strip() for c in lines[i].strip().strip("|").split("|")]
-                for h, c in zip(headers, cells):
-                    result.append(f"> **{h}**: {c}")
-                result.append("")
-                i += 1
-        else:
-            result.append(line)
-            i += 1
-    return "\n".join(result)
-
-
-def _adapt_markdown_for_dingtalk(text: str) -> str:
-    """Adapt markdown for DingTalk's limited sampleMarkdown renderer."""
-
-    def _code_block_to_quote(match: re.Match) -> str:
-        lang = match.group(1)
-        code = match.group(2).rstrip("\n")
-        prefix = f"> **{lang}**\n" if lang else ""
-        quoted_lines = "\n".join(f"> {line}" for line in code.split("\n"))
-        return f"{prefix}{quoted_lines}\n"
-
-    text = _FENCED_CODE_BLOCK_RE.sub(_code_block_to_quote, text)
-    text = _INLINE_CODE_RE.sub(r"**\1**", text)
-    text = _convert_markdown_table(text)
-    text = _HORIZONTAL_RULE_RE.sub("───────────", text)
-    return text
-
-
-class DingTalkChannel(Channel):
-    """DingTalk IM channel using Stream Push (WebSocket, no public IP needed)."""
-
-    def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
-        super().__init__(name="dingtalk", bus=bus, config=config)
-        self._thread: threading.Thread | None = None
-        self._main_loop: asyncio.AbstractEventLoop | None = None
-        self._client_id: str = ""
-        self._client_secret: str = ""
-        self._allowed_users: set[str] = _normalize_allowed_users(config.get("allowed_users"))
-        self._cached_token: str = ""
-        self._token_expires_at: float = 0.0
-        self._token_lock = asyncio.Lock()
-        self._card_template_id: str = config.get("card_template_id", "")
-        self._card_track_ids: dict[str, str] = {}
-        self._dingtalk_client: Any = None
-        self._stream_client: Any = None
-        self._incoming_messages: dict[str, Any] = {}
-        self._incoming_messages_lock = threading.Lock()
-        self._card_repliers: dict[str, Any] = {}
-
-    @property
-    def supports_streaming(self) -> bool:
-        return bool(self._card_template_id)
-
-    async def start(self) -> None:
-        if self._running:
-            return
-
-        try:
-            import dingtalk_stream  # noqa: F401
-        except ImportError:
-            logger.error("dingtalk-stream is not installed. Install it with: uv add dingtalk-stream")
-            return
-
-        client_id = self.config.get("client_id", "")
-        client_secret = self.config.get("client_secret", "")
-
-        if not client_id or not client_secret:
-            logger.error("DingTalk channel requires client_id and client_secret")
-            return
-
-        self._client_id = client_id
-        self._client_secret = client_secret
-        self._main_loop = asyncio.get_running_loop()
-
-        if self._card_template_id:
-            logger.info("[DingTalk] AI Card mode enabled (template=%s)", self._card_template_id)
-
-        self._running = True
-        self.bus.subscribe_outbound(self._on_outbound)
-
-        self._thread = threading.Thread(
-            target=self._run_stream,
-            args=(client_id, client_secret),
-            daemon=True,
-        )
-        self._thread.start()
-        logger.info("DingTalk channel started")
-
-    async def stop(self) -> None:
-        self._running = False
-        self.bus.unsubscribe_outbound(self._on_outbound)
-
-        stream_client = self._stream_client
-        if stream_client is not None:
-            try:
-                if hasattr(stream_client, "disconnect"):
-                    stream_client.disconnect()
-            except Exception:
-                logger.debug("[DingTalk] error disconnecting stream client", exc_info=True)
-
-        self._dingtalk_client = None
-        self._stream_client = None
-        with self._incoming_messages_lock:
-            self._incoming_messages.clear()
-        self._card_repliers.clear()
-        self._card_track_ids.clear()
-        if self._thread:
-            self._thread.join(timeout=5)
-            self._thread = None
-        logger.info("DingTalk channel stopped")
-
-    def _resolve_routing(self, msg: OutboundMessage) -> tuple[str, str, str]:
-        """Return (conversation_type, sender_staff_id, conversation_id).
-
-        Uses msg.chat_id as the primary routing key; metadata as fallback.
-        """
-        conversation_type = _normalize_conversation_type(msg.metadata.get("conversation_type"))
-        sender_staff_id = msg.metadata.get("sender_staff_id", "")
-        conversation_id = msg.metadata.get("conversation_id", "")
-        if conversation_type == _CONVERSATION_TYPE_GROUP:
-            conversation_id = msg.chat_id or conversation_id
-        else:
-            sender_staff_id = msg.chat_id or sender_staff_id
-        return conversation_type, sender_staff_id, conversation_id
-
-    async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
-        conversation_type, sender_staff_id, conversation_id = self._resolve_routing(msg)
-        robot_code = self._client_id
-
-        # Card mode: stream update to existing AI card
-        source_key = self._make_card_source_key_from_outbound(msg)
-        out_track_id = self._card_track_ids.get(source_key)
-
-        # ``card_template_id`` enables ``runs.stream`` (non-final + final outbounds).
-        # If card creation failed, skip non-final chunks to avoid duplicate messages.
-        if self._card_template_id and not out_track_id and not msg.is_final:
-            return
-
-        if out_track_id:
-            try:
-                await self._stream_update_card(
-                    out_track_id,
-                    msg.text,
-                    is_finalize=msg.is_final,
-                )
-            except Exception:
-                logger.warning("[DingTalk] card stream failed, falling back to sampleMarkdown")
-                if msg.is_final:
-                    self._card_track_ids.pop(source_key, None)
-                    self._card_repliers.pop(out_track_id, None)
-                    await self._send_markdown_fallback(robot_code, conversation_type, sender_staff_id, conversation_id, msg.text)
-                    return
-            if msg.is_final:
-                self._card_track_ids.pop(source_key, None)
-                self._card_repliers.pop(out_track_id, None)
-            return
-
-        # Non-card mode: send sampleMarkdown with retry
-        last_exc: Exception | None = None
-        for attempt in range(_max_retries):
-            try:
-                if conversation_type == _CONVERSATION_TYPE_GROUP:
-                    await self._send_group_message(robot_code, conversation_id, msg.text, at_user_ids=[sender_staff_id] if sender_staff_id else None)
-                else:
-                    await self._send_p2p_message(robot_code, sender_staff_id, msg.text)
-                return
-            except Exception as exc:
-                last_exc = exc
-                if attempt < _max_retries - 1:
-                    delay = 2**attempt
-                    logger.warning(
-                        "[DingTalk] send failed (attempt %d/%d), retrying in %ds: %s",
-                        attempt + 1,
-                        _max_retries,
-                        delay,
-                        exc,
-                    )
-                    await asyncio.sleep(delay)
-
-        logger.error("[DingTalk] send failed after %d attempts: %s", _max_retries, last_exc)
-        if last_exc is None:
-            raise RuntimeError("DingTalk send failed without an exception from any attempt")
-        raise last_exc
-
-    async def _send_markdown_fallback(
-        self,
-        robot_code: str,
-        conversation_type: str,
-        sender_staff_id: str,
-        conversation_id: str,
-        text: str,
-    ) -> None:
-        try:
-            if conversation_type == _CONVERSATION_TYPE_GROUP:
-                await self._send_group_message(robot_code, conversation_id, text)
-            else:
-                await self._send_p2p_message(robot_code, sender_staff_id, text)
-        except Exception:
-            logger.exception("[DingTalk] markdown fallback also failed")
-            raise
-
-    async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
-        if attachment.size > _MAX_UPLOAD_SIZE_BYTES:
-            logger.warning("[DingTalk] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
-            return False
-
-        conversation_type, sender_staff_id, conversation_id = self._resolve_routing(msg)
-        robot_code = self._client_id
-
-        try:
-            media_id = await self._upload_media(attachment.actual_path, "image" if attachment.is_image else "file")
-            if not media_id:
-                return False
-
-            if attachment.is_image:
-                msg_key = "sampleImageMsg"
-                msg_param = json.dumps({"photoURL": media_id})
-            else:
-                msg_key = "sampleFile"
-                msg_param = json.dumps(
-                    {
-                        "fileUrl": media_id,
-                        "fileName": attachment.filename,
-                        "fileSize": str(attachment.size),
-                    }
-                )
-
-            token = await self._get_access_token()
-            async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
-                if conversation_type == _CONVERSATION_TYPE_GROUP:
-                    response = await client.post(
-                        f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
-                        headers=self._api_headers(token),
-                        json={
-                            "msgKey": msg_key,
-                            "msgParam": msg_param,
-                            "robotCode": robot_code,
-                            "openConversationId": conversation_id,
-                        },
-                    )
-                else:
-                    response = await client.post(
-                        f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
-                        headers=self._api_headers(token),
-                        json={
-                            "msgKey": msg_key,
-                            "msgParam": msg_param,
-                            "robotCode": robot_code,
-                            "userIds": [sender_staff_id],
-                        },
-                    )
-                response.raise_for_status()
-
-            logger.info("[DingTalk] file sent: %s", attachment.filename)
-            return True
-        except (httpx.HTTPError, OSError, ValueError, TypeError, AttributeError):
-            logger.exception("[DingTalk] failed to send file: %s", attachment.filename)
-            return False
-
-    # -- stream client (runs in dedicated thread) --------------------------
-
-    def _run_stream(self, client_id: str, client_secret: str) -> None:
-        try:
-            import dingtalk_stream
-
-            credential = dingtalk_stream.Credential(client_id, client_secret)
-            client = dingtalk_stream.DingTalkStreamClient(credential)
-            self._stream_client = client
-            client.register_callback_handler(
-                dingtalk_stream.chatbot.ChatbotMessage.TOPIC,
-                _DingTalkMessageHandler(self),
-            )
-            client.start_forever()
-        except Exception:
-            if self._running:
-                logger.exception("DingTalk Stream Push error")
-        finally:
-            self._stream_client = None
-
-    def _on_chatbot_message(self, message: Any) -> None:
-        if not self._running:
-            return
-        try:
-            sender_staff_id = message.sender_staff_id or ""
-            conversation_type = _normalize_conversation_type(message.conversation_type)
-            conversation_id = message.conversation_id or ""
-            msg_id = message.message_id or ""
-            sender_nick = message.sender_nick or ""
-
-            if self._allowed_users and sender_staff_id not in self._allowed_users:
-                logger.debug("[DingTalk] ignoring message from non-allowed user: %s", sender_staff_id)
-                return
-
-            text = self._extract_text(message)
-            if not text:
-                logger.info("[DingTalk] empty text, ignoring message")
-                return
-
-            logger.info(
-                "[DingTalk] parsed message: conv_type=%s, msg_id=%s, sender=%s(%s), text=%r",
-                conversation_type,
-                msg_id,
-                sender_staff_id,
-                sender_nick,
-                text[:100],
-            )
-
-            if _is_dingtalk_command(text):
-                msg_type = InboundMessageType.COMMAND
-            else:
-                msg_type = InboundMessageType.CHAT
-
-            # P2P: topic_id=None (single thread per user, like Telegram private chat)
-            # Group: topic_id=msg_id (each new message starts a new topic, like Feishu)
-            topic_id: str | None = msg_id if conversation_type == _CONVERSATION_TYPE_GROUP else None
-
-            # chat_id uses conversation_id for groups, sender_staff_id for P2P
-            chat_id = conversation_id if conversation_type == _CONVERSATION_TYPE_GROUP else sender_staff_id
-
-            inbound = self._make_inbound(
-                chat_id=chat_id,
-                user_id=sender_staff_id,
-                text=text,
-                msg_type=msg_type,
-                thread_ts=msg_id,
-                metadata={
-                    "conversation_type": conversation_type,
-                    "conversation_id": conversation_id,
-                    "sender_staff_id": sender_staff_id,
-                    "sender_nick": sender_nick,
-                    "message_id": msg_id,
-                },
-            )
-            inbound.topic_id = topic_id
-
-            if self._card_template_id:
-                source_key = self._make_card_source_key(inbound)
-                with self._incoming_messages_lock:
-                    self._incoming_messages[source_key] = message
-
-            if self._main_loop and self._main_loop.is_running():
-                logger.info("[DingTalk] publishing inbound message to bus (type=%s, msg_id=%s)", msg_type.value, msg_id)
-                fut = asyncio.run_coroutine_threadsafe(
-                    self._prepare_inbound(chat_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("[DingTalk] main loop not running, cannot publish inbound message")
-        except Exception:
-            logger.exception("[DingTalk] error processing chatbot message")
-
-    @staticmethod
-    def _extract_text(message: Any) -> str:
-        msg_type = message.message_type
-        if msg_type == "text" and message.text:
-            return message.text.content.strip()
-        if msg_type == "richText" and message.rich_text_content:
-            return _extract_text_from_rich_text(message.rich_text_content.rich_text_list).strip()
-        return ""
-
-    async def _prepare_inbound(self, chat_id: str, inbound: InboundMessage) -> None:
-        # Running reply must finish before publish_inbound so AI card tracks are
-        # registered before the manager emits streaming outbounds.
-        await self._send_running_reply(chat_id, inbound)
-        await self.bus.publish_inbound(inbound)
-
-    async def _send_running_reply(self, chat_id: str, inbound: InboundMessage) -> None:
-        conversation_type = inbound.metadata.get("conversation_type", _CONVERSATION_TYPE_P2P)
-        sender_staff_id = inbound.metadata.get("sender_staff_id", "")
-        conversation_id = inbound.metadata.get("conversation_id", "")
-        text = "\u23f3 Working on it..."
-
-        try:
-            if self._card_template_id:
-                source_key = self._make_card_source_key(inbound)
-                with self._incoming_messages_lock:
-                    chatbot_message = self._incoming_messages.pop(source_key, None)
-                out_track_id = await self._create_and_deliver_card(
-                    text,
-                    chatbot_message=chatbot_message,
-                )
-                if out_track_id:
-                    self._card_track_ids[source_key] = out_track_id
-                    logger.info("[DingTalk] AI card running reply sent for chat=%s", chat_id)
-                    return
-
-            robot_code = self._client_id
-            if conversation_type == _CONVERSATION_TYPE_GROUP:
-                await self._send_text_message_to_group(robot_code, conversation_id, text)
-            else:
-                await self._send_text_message_to_user(robot_code, sender_staff_id, text)
-            logger.info("[DingTalk] 'Working on it...' reply sent for chat=%s", chat_id)
-        except Exception:
-            logger.exception("[DingTalk] failed to send running reply for chat=%s", chat_id)
-
-    # -- DingTalk API helpers ----------------------------------------------
-
-    async def _get_access_token(self) -> str:
-        if self._cached_token and time.monotonic() < self._token_expires_at:
-            return self._cached_token
-        async with self._token_lock:
-            if self._cached_token and time.monotonic() < self._token_expires_at:
-                return self._cached_token
-            async with httpx.AsyncClient(timeout=httpx.Timeout(10.0)) as client:
-                response = await client.post(
-                    f"{DINGTALK_API_BASE}/v1.0/oauth2/accessToken",
-                    json={"appKey": self._client_id, "appSecret": self._client_secret},  # DingTalk API field names
-                )
-                response.raise_for_status()
-                data = response.json()
-
-                if not isinstance(data, dict):
-                    raise ValueError(f"DingTalk access token response must be a JSON object, got {type(data).__name__}")
-
-                access_token = data.get("accessToken")
-                if not isinstance(access_token, str) or not access_token.strip():
-                    raise ValueError("DingTalk access token response did not contain a usable accessToken")
-
-                raw_expires_in = data.get("expireIn", 7200)
-                try:
-                    expires_in = int(raw_expires_in)
-                except (TypeError, ValueError):
-                    logger.warning("[DingTalk] invalid expireIn value %r, using default 7200s", raw_expires_in)
-                    expires_in = 7200
-
-                self._cached_token = access_token.strip()
-                self._token_expires_at = time.monotonic() + expires_in - _TOKEN_REFRESH_MARGIN_SECONDS
-                return self._cached_token
-
-    @staticmethod
-    def _api_headers(token: str) -> dict[str, str]:
-        return {
-            "x-acs-dingtalk-access-token": token,
-            "Content-Type": "application/json",
-        }
-
-    async def _send_text_message_to_user(self, robot_code: str, user_id: str, text: str) -> None:
-        token = await self._get_access_token()
-        async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
-            response = await client.post(
-                f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
-                headers=self._api_headers(token),
-                json={
-                    "msgKey": "sampleText",
-                    "msgParam": json.dumps({"content": text}),
-                    "robotCode": robot_code,
-                    "userIds": [user_id],
-                },
-            )
-            response.raise_for_status()
-
-    async def _send_text_message_to_group(self, robot_code: str, conversation_id: str, text: str) -> None:
-        token = await self._get_access_token()
-        async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
-            response = await client.post(
-                f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
-                headers=self._api_headers(token),
-                json={
-                    "msgKey": "sampleText",
-                    "msgParam": json.dumps({"content": text}),
-                    "robotCode": robot_code,
-                    "openConversationId": conversation_id,
-                },
-            )
-            response.raise_for_status()
-
-    async def _send_p2p_message(self, robot_code: str, user_id: str, text: str) -> None:
-        text = _adapt_markdown_for_dingtalk(text)
-        token = await self._get_access_token()
-        async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
-            response = await client.post(
-                f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
-                headers=self._api_headers(token),
-                json={
-                    "msgKey": "sampleMarkdown",
-                    "msgParam": json.dumps({"title": "DeerFlow", "text": text}),
-                    "robotCode": robot_code,
-                    "userIds": [user_id],
-                },
-            )
-            response.raise_for_status()
-            data = response.json()
-            if data.get("processQueryKey"):
-                logger.info("[DingTalk] P2P message sent to user=%s", user_id)
-            else:
-                logger.warning("[DingTalk] P2P send response: %s", data)
-
-    async def _send_group_message(
-        self,
-        robot_code: str,
-        conversation_id: str,
-        text: str,
-        *,
-        at_user_ids: list[str] | None = None,  # noqa: ARG002
-    ) -> None:
-        # at_user_ids accepted for call-site compatibility but not passed to the API
-        # (sampleMarkdown does not support @mentions).
-        text = _adapt_markdown_for_dingtalk(text)
-        token = await self._get_access_token()
-
-        async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
-            response = await client.post(
-                f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
-                headers=self._api_headers(token),
-                json={
-                    "msgKey": "sampleMarkdown",
-                    "msgParam": json.dumps({"title": "DeerFlow", "text": text}),
-                    "robotCode": robot_code,
-                    "openConversationId": conversation_id,
-                },
-            )
-            response.raise_for_status()
-            data = response.json()
-            if data.get("processQueryKey"):
-                logger.info("[DingTalk] group message sent to conversation=%s", conversation_id)
-            else:
-                logger.warning("[DingTalk] group send response: %s", data)
-
-    # -- AI Card streaming helpers -------------------------------------------
-
-    def _make_card_source_key(self, inbound: InboundMessage) -> str:
-        m = inbound.metadata
-        return f"{m.get('conversation_type', '')}:{m.get('sender_staff_id', '')}:{m.get('conversation_id', '')}:{m.get('message_id', '')}"
-
-    def _make_card_source_key_from_outbound(self, msg: OutboundMessage) -> str:
-        m = msg.metadata
-        correlation_id = m.get("message_id") or msg.thread_ts or ""
-        return f"{m.get('conversation_type', '')}:{m.get('sender_staff_id', '')}:{m.get('conversation_id', '')}:{correlation_id}"
-
-    async def _create_and_deliver_card(
-        self,
-        initial_text: str,
-        *,
-        chatbot_message: Any = None,
-    ) -> str | None:
-        if self._dingtalk_client is None or chatbot_message is None:
-            logger.warning("[DingTalk] SDK client or chatbot_message unavailable, skipping AI card")
-            return None
-
-        try:
-            from dingtalk_stream.card_replier import AICardReplier
-        except ImportError:
-            logger.warning("[DingTalk] dingtalk-stream card_replier not available")
-            return None
-
-        try:
-            replier = AICardReplier(self._dingtalk_client, chatbot_message)
-            card_instance_id = await replier.async_create_and_deliver_card(
-                card_template_id=self._card_template_id,
-                card_data={"content": initial_text},
-            )
-            if not card_instance_id:
-                return None
-
-            self._card_repliers[card_instance_id] = replier
-            logger.info("[DingTalk] AI card created: outTrackId=%s", card_instance_id)
-            return card_instance_id
-        except Exception:
-            logger.exception("[DingTalk] failed to create AI card")
-            return None
-
-    async def _stream_update_card(
-        self,
-        out_track_id: str,
-        content: str,
-        *,
-        is_finalize: bool = False,
-        is_error: bool = False,
-    ) -> None:
-        replier = self._card_repliers.get(out_track_id)
-        if not replier:
-            raise RuntimeError(f"No AICardReplier found for track ID {out_track_id}")
-
-        await replier.async_streaming(
-            card_instance_id=out_track_id,
-            content_key="content",
-            content_value=content,
-            append=False,
-            finished=is_finalize,
-            failed=is_error,
-        )
-
-    # -- media upload --------------------------------------------------------
-
-    async def _upload_media(self, file_path: str | Path, media_type: str) -> str | None:
-        try:
-            file_bytes = await asyncio.to_thread(Path(file_path).read_bytes)
-            token = await self._get_access_token()
-            async with httpx.AsyncClient(timeout=httpx.Timeout(60.0)) as client:
-                response = await client.post(
-                    f"{DINGTALK_API_BASE}/v1.0/files/upload",
-                    headers={"x-acs-dingtalk-access-token": token},
-                    files={"file": ("upload", file_bytes)},
-                    data={"type": media_type},
-                )
-                response.raise_for_status()
-                try:
-                    payload = response.json()
-                except json.JSONDecodeError:
-                    logger.exception("[DingTalk] failed to decode upload response JSON: %s", file_path)
-                    return None
-                if not isinstance(payload, dict):
-                    logger.warning("[DingTalk] unexpected upload response type %s for %s", type(payload).__name__, file_path)
-                    return None
-                return payload.get("mediaId")
-        except (httpx.HTTPError, OSError):
-            logger.exception("[DingTalk] failed to upload media: %s", file_path)
-            return None
-
-    @staticmethod
-    def _log_future_error(fut: Any, name: str, msg_id: str) -> None:
-        try:
-            exc = fut.exception()
-            if exc:
-                logger.error("[DingTalk] %s failed for msg_id=%s: %s", name, msg_id, exc)
-        except (asyncio.CancelledError, asyncio.InvalidStateError):
-            pass
-
-
-class _DingTalkMessageHandler:
-    """Callback handler registered with dingtalk-stream."""
-
-    def __init__(self, channel: DingTalkChannel) -> None:
-        self._channel = channel
-
-    def pre_start(self) -> None:
-        if hasattr(self, "dingtalk_client") and self.dingtalk_client is not None:
-            self._channel._dingtalk_client = self.dingtalk_client
-
-    async def raw_process(self, callback_message: Any) -> Any:
-        import dingtalk_stream
-        from dingtalk_stream.frames import Headers
-
-        code, message = await self.process(callback_message)
-        ack_message = dingtalk_stream.AckMessage()
-        ack_message.code = code
-        ack_message.headers.message_id = callback_message.headers.message_id
-        ack_message.headers.content_type = Headers.CONTENT_TYPE_APPLICATION_JSON
-        ack_message.data = {"response": message}
-        return ack_message
-
-    async def process(self, callback: Any) -> tuple[int, str]:
-        import dingtalk_stream
-
-        incoming_message = dingtalk_stream.ChatbotMessage.from_dict(callback.data)
-        self._channel._on_chatbot_message(incoming_message)
-        return dingtalk_stream.AckMessage.STATUS_OK, "OK"

backend/app/channels/discord.py

@@ -3,10 +3,8 @@
 from __future__ import annotations
 
 import asyncio
-import json
 import logging
 import threading
-from pathlib import Path
 from typing import Any
 
 from app.channels.base import Channel
@@ -23,12 +21,6 @@ class DiscordChannel(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.
-        - ``mention_only``: (optional) If true, only respond when the bot is mentioned.
-        - ``allowed_channels``: (optional) List of channel IDs where messages are always accepted
-          (even when mention_only is true). Use for channels where you want the bot to respond
-          without mentions. Empty = mention_only applies everywhere.
-        - ``thread_mode``: (optional) If true, group a channel conversation into a thread.
-          Default: same as ``mention_only``.
     """
 
     def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -40,29 +32,6 @@ class DiscordChannel(Channel):
                 self._allowed_guilds.add(int(guild_id))
             except (TypeError, ValueError):
                 continue
-        self._mention_only: bool = bool(config.get("mention_only", False))
-        self._thread_mode: bool = config.get("thread_mode", self._mention_only)
-        self._allowed_channels: set[str] = set()
-        for channel_id in config.get("allowed_channels", []):
-            self._allowed_channels.add(str(channel_id))
-
-        # Session tracking: channel_id -> Discord thread_id (in-memory, persisted to JSON).
-        # Uses a dedicated JSON file separate from ChannelStore, which maps IM
-        # conversations to DeerFlow thread IDs — a different concern.
-        self._active_threads: dict[str, str] = {}
-        # Reverse-lookup set for O(1) thread ID checks (avoids O(n) scan of _active_threads.values()).
-        self._active_thread_ids: set[str] = set()
-        # Lock protecting _active_threads and the JSON file from concurrent access.
-        # _run_client (Discord loop thread) and the main thread both read/write.
-        self._thread_store_lock = threading.Lock()
-        store = config.get("channel_store")
-        if store is not None:
-            self._thread_store_path = store._path.parent / "discord_threads.json"
-        else:
-            self._thread_store_path = Path.home() / ".deer-flow" / "channels" / "discord_threads.json"
-
-        # Typing indicator management
-        self._typing_tasks: dict[str, asyncio.Task] = {}
 
         self._client = None
         self._thread: threading.Thread | None = None
@@ -106,56 +75,12 @@ class DiscordChannel(Channel):
 
         self._thread = threading.Thread(target=self._run_client, daemon=True)
         self._thread.start()
-        self._load_active_threads()
         logger.info("Discord channel started")
 
-    def _load_active_threads(self) -> None:
-        """Restore Discord thread mappings from the dedicated JSON file on startup."""
-        with self._thread_store_lock:
-            try:
-                if not self._thread_store_path.exists():
-                    logger.debug("[Discord] no thread mappings file at %s", self._thread_store_path)
-                    return
-                data = json.loads(self._thread_store_path.read_text())
-                self._active_threads.clear()
-                self._active_thread_ids.clear()
-                for channel_id, thread_id in data.items():
-                    self._active_threads[channel_id] = thread_id
-                    self._active_thread_ids.add(thread_id)
-                if self._active_threads:
-                    logger.info("[Discord] restored %d thread mappings from %s", len(self._active_threads), self._thread_store_path)
-            except Exception:
-                logger.exception("[Discord] failed to load thread mappings")
-
-    def _save_thread(self, channel_id: str, thread_id: str) -> None:
-        """Persist a Discord thread mapping to the dedicated JSON file."""
-        with self._thread_store_lock:
-            try:
-                data: dict[str, str] = {}
-                if self._thread_store_path.exists():
-                    data = json.loads(self._thread_store_path.read_text())
-                old_id = data.get(channel_id)
-                data[channel_id] = thread_id
-                # Update reverse-lookup set
-                if old_id:
-                    self._active_thread_ids.discard(old_id)
-                self._active_thread_ids.add(thread_id)
-                self._thread_store_path.parent.mkdir(parents=True, exist_ok=True)
-                self._thread_store_path.write_text(json.dumps(data, indent=2))
-            except Exception:
-                logger.exception("[Discord] failed to save thread mapping for channel %s", channel_id)
-
     async def stop(self) -> None:
         self._running = False
         self.bus.unsubscribe_outbound(self._on_outbound)
 
-        # Cancel all active typing indicator tasks
-        for target_id, task in list(self._typing_tasks.items()):
-            if not task.done():
-                task.cancel()
-            logger.debug("[Discord] cancelled typing task for target %s", target_id)
-        self._typing_tasks.clear()
-
         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:
@@ -175,10 +100,6 @@ class DiscordChannel(Channel):
         logger.info("Discord channel stopped")
 
     async def send(self, msg: OutboundMessage) -> None:
-        # Stop typing indicator once we're sending the response
-        stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
-        await asyncio.wrap_future(stop_future)
-
         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)
@@ -190,9 +111,6 @@ class DiscordChannel(Channel):
             await asyncio.wrap_future(send_future)
 
     async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
-        stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
-        await asyncio.wrap_future(stop_future)
-
         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)
@@ -212,41 +130,6 @@ class DiscordChannel(Channel):
             logger.exception("[Discord] failed to upload file: %s", attachment.filename)
             return False
 
-    async def _start_typing(self, channel, chat_id: str, thread_ts: str | None = None) -> None:
-        """Starts a loop to send periodic typing indicators."""
-        target_id = thread_ts or chat_id
-        if target_id in self._typing_tasks:
-            return  # Already typing for this target
-
-        async def _typing_loop():
-            try:
-                while True:
-                    try:
-                        await channel.trigger_typing()
-                    except Exception:
-                        pass
-                    await asyncio.sleep(10)
-            except asyncio.CancelledError:
-                pass
-
-        task = asyncio.create_task(_typing_loop())
-        self._typing_tasks[target_id] = task
-
-    async def _stop_typing(self, chat_id: str, thread_ts: str | None = None) -> None:
-        """Stops the typing loop for a specific target."""
-        target_id = thread_ts or chat_id
-        task = self._typing_tasks.pop(target_id, None)
-        if task and not task.done():
-            task.cancel()
-            logger.debug("[Discord] stopped typing indicator for target %s", target_id)
-
-    async def _add_reaction(self, message) -> None:
-        """Add a checkmark reaction to acknowledge the message was received."""
-        try:
-            await message.add_reaction("✅")
-        except Exception:
-            logger.debug("[Discord] failed to add reaction to message %s", message.id, exc_info=True)
-
     async def _on_message(self, message) -> None:
         if not self._running or not self._client:
             return
@@ -269,143 +152,15 @@ class DiscordChannel(Channel):
         if self._discord_module is None:
             return
 
-        # Determine whether the bot is mentioned in this message
-        user = self._client.user if self._client else None
-        if user:
-            bot_mention = user.mention  # <@ID>
-            alt_mention = f"<@!{user.id}>"  # <@!ID> (ping variant)
-            standard_mention = f"<@{user.id}>"
-        else:
-            bot_mention = None
-            alt_mention = None
-            standard_mention = ""
-        has_mention = (bot_mention and bot_mention in message.content) or (alt_mention and alt_mention in message.content) or (standard_mention and standard_mention in message.content)
-
-        # Strip mention from text for processing
-        if has_mention:
-            text = text.replace(bot_mention or "", "").replace(alt_mention or "", "").replace(standard_mention or "", "").strip()
-            # Don't return early if text is empty — still process the mention (e.g., create thread)
-
-        # --- Determine thread/channel routing and typing target ---
-        thread_id = None
-        chat_id = None
-        typing_target = None  # The Discord object to type into
-
         if isinstance(message.channel, self._discord_module.Thread):
-            # --- Message already inside a thread ---
-            thread_obj = message.channel
-            thread_id = str(thread_obj.id)
-            chat_id = str(thread_obj.parent_id or thread_obj.id)
-            typing_target = thread_obj
-
-            # If this is a known active thread, process normally
-            if thread_id in self._active_thread_ids:
-                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
-                self._publish(inbound)
-                # Start typing indicator in the thread
-                if typing_target:
-                    asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
-                asyncio.create_task(self._add_reaction(message))
-                return
-
-            # Thread not tracked (orphaned) — create new thread and handle below
-            logger.debug("[Discord] message in orphaned thread %s, will create new thread", thread_id)
-            thread_id = None
-            typing_target = None
-
-        # At this point we're guaranteed to be in a channel, not a thread
-        # (the Thread case is handled above). Apply mention_only for all
-        # non-thread messages — no special case needed.
-        channel_id = str(message.channel.id)
-
-        # Check if there's an active thread for this channel
-        if channel_id in self._active_threads:
-            # respect mention_only: if enabled, only process messages that mention the bot
-            # (unless the channel is in allowed_channels)
-            # Messages within a thread are always allowed through (continuation).
-            # At this code point we know the message is in a channel, not a thread
-            # (Thread case handled above), so always apply the check.
-            if self._mention_only and not has_mention and channel_id not in self._allowed_channels:
-                logger.debug("[Discord] skipping no-@ message in channel %s (not in thread)", channel_id)
-                return
-            # mention_only + fresh @ → create new thread instead of routing to existing one
-            if self._mention_only and has_mention:
-                thread_obj = await self._create_thread(message)
-                if thread_obj is not None:
-                    target_thread_id = str(thread_obj.id)
-                    self._active_threads[channel_id] = target_thread_id
-                    self._save_thread(channel_id, target_thread_id)
-                    thread_id = target_thread_id
-                    chat_id = channel_id
-                    typing_target = thread_obj
-                    logger.info("[Discord] created new thread %s in channel %s on mention (replacing existing thread)", target_thread_id, channel_id)
-                else:
-                    logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
-                    thread_id = channel_id
-                    chat_id = channel_id
-                    typing_target = message.channel
-            else:
-                # Existing session → route to the existing thread
-                target_thread_id = self._active_threads[channel_id]
-                logger.debug("[Discord] routing message in channel %s to existing thread %s", channel_id, target_thread_id)
-                thread_id = target_thread_id
-                chat_id = channel_id
-                typing_target = await self._get_channel_or_thread(target_thread_id)
-        elif self._mention_only and not has_mention and channel_id not in self._allowed_channels:
-            # Not mentioned and not in an allowed channel → skip
-            logger.debug("[Discord] skipping message without mention in channel %s", channel_id)
-            return
-        elif self._mention_only and has_mention:
-            # First mention in this channel → create thread
-            thread_obj = await self._create_thread(message)
-            if thread_obj is not None:
-                target_thread_id = str(thread_obj.id)
-                self._active_threads[channel_id] = target_thread_id
-                self._save_thread(channel_id, target_thread_id)
-                thread_id = target_thread_id
-                chat_id = channel_id
-                typing_target = thread_obj  # Type into the new thread
-                logger.info("[Discord] created thread %s in channel %s for user %s", target_thread_id, channel_id, message.author.display_name)
-            else:
-                # Fallback: thread creation failed (disabled/permissions), reply in channel
-                logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
-                thread_id = channel_id
-                chat_id = channel_id
-                typing_target = message.channel  # Type into the channel
-        elif self._thread_mode:
-            # thread_mode but mention_only is False → create thread anyway for conversation grouping
-            thread_obj = await self._create_thread(message)
-            if thread_obj is None:
-                # Thread creation failed (disabled/permissions), fall back to channel replies
-                logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
-                thread_id = channel_id
-                chat_id = channel_id
-                typing_target = message.channel  # Type into the channel
-            else:
-                target_thread_id = str(thread_obj.id)
-                self._active_threads[channel_id] = target_thread_id
-                self._save_thread(channel_id, target_thread_id)
-                thread_id = target_thread_id
-                chat_id = channel_id
-                typing_target = thread_obj  # Type into the new thread
+            chat_id = str(message.channel.parent_id or message.channel.id)
+            thread_id = str(message.channel.id)
         else:
-            # No threading — reply directly in channel
-            thread_id = channel_id
-            chat_id = channel_id
-            typing_target = message.channel  # Type into the channel
+            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(
@@ -422,15 +177,6 @@ class DiscordChannel(Channel):
         )
         inbound.topic_id = thread_id
 
-        # Start typing indicator in the correct target (thread or channel)
-        if typing_target:
-            asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
-
-        self._publish(inbound)
-        asyncio.create_task(self._add_reaction(message))
-
-    def _publish(self, inbound) -> None:
-        """Publish an inbound message to the main event loop."""
         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)
@@ -452,40 +198,14 @@ class DiscordChannel(Channel):
 
     async def _create_thread(self, message):
         try:
-            if self._discord_module is None:
-                return None
-
-            # Only TextChannel (type 0) and NewsChannel (type 10) support threads
-            channel_type = message.channel.type
-            if channel_type not in (
-                self._discord_module.ChannelType.text,
-                self._discord_module.ChannelType.news,
-            ):
-                logger.info(
-                    "[Discord] channel type %s (%s) does not support threads",
-                    channel_type.value,
-                    channel_type.name,
-                )
-                return None
-
             thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
             return await message.create_thread(name=thread_name)
-        except self._discord_module.errors.HTTPException as exc:
-            if exc.code == 50024:
-                logger.info(
-                    "[Discord] cannot create thread in channel %s (error code 50024): %s",
-                    message.channel.id,
-                    channel_type.name if (channel_type := message.channel.type) else "unknown",
-                )
-            else:
-                logger.exception(
-                    "[Discord] failed to create thread for message=%s (HTTPException %s)",
-                    message.id,
-                    exc.code,
-                )
-            return None
         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):

backend/app/channels/feishu.py

@@ -7,26 +7,16 @@ import json
 import logging
 import re
 import threading
-import time
 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 (
-    PENDING_CLARIFICATION_METADATA_KEY,
-    RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY,
-    InboundMessage,
-    InboundMessageType,
-    MessageBus,
-    OutboundMessage,
-    ResolvedAttachment,
-)
+from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import get_sandbox_provider
 
 logger = logging.getLogger(__name__)
-PENDING_CLARIFICATION_TTL_SECONDS = 30 * 60
 
 
 def _is_feishu_command(text: str) -> bool:
@@ -66,7 +56,6 @@ class FeishuChannel(Channel):
         self._background_tasks: set[asyncio.Task] = set()
         self._running_card_ids: dict[str, str] = {}
         self._running_card_tasks: dict[str, asyncio.Task] = {}
-        self._pending_clarifications: dict[tuple[str, str], list[dict[str, Any]]] = {}
         self._CreateFileRequest = None
         self._CreateFileRequestBody = None
         self._CreateImageRequest = None
@@ -74,20 +63,6 @@ class FeishuChannel(Channel):
         self._GetMessageResourceRequest = None
         self._thread_lock = threading.Lock()
 
-    @staticmethod
-    def _non_empty_str(value: Any) -> str | None:
-        if isinstance(value, str) and value.strip():
-            return value.strip()
-        return None
-
-    @staticmethod
-    def _pending_key(chat_id: str, user_id: str) -> tuple[str, str]:
-        return (chat_id, user_id)
-
-    @property
-    def supports_streaming(self) -> bool:
-        return True
-
     async def start(self) -> None:
         if self._running:
             return
@@ -400,7 +375,9 @@ class FeishuChannel(Channel):
         virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"
 
         try:
-            sandbox_provider = get_sandbox_provider()
+            from deerflow.config.app_config import AppConfig
+
+            sandbox_provider = get_sandbox_provider(AppConfig.from_file())
             sandbox_id = sandbox_provider.acquire(thread_id)
             if sandbox_id != "local":
                 sandbox = sandbox_provider.get(sandbox_id)
@@ -552,25 +529,18 @@ class FeishuChannel(Channel):
                         "[Feishu] failed to patch running card %s, falling back to final reply",
                         running_card_id,
                     )
-                    fallback_card_id = await self._reply_card(source_message_id, msg.text)
-                    self._remember_thread_mapping(msg, source_message_id, fallback_card_id)
-                    self._remember_pending_clarification(msg, fallback_card_id)
+                    await self._reply_card(source_message_id, msg.text)
                 else:
-                    self._remember_thread_mapping(msg, source_message_id, running_card_id)
-                    self._remember_pending_clarification(msg, running_card_id)
                     logger.info("[Feishu] running card updated: source=%s card=%s", source_message_id, running_card_id)
             elif msg.is_final:
-                final_card_id = await self._reply_card(source_message_id, msg.text)
-                self._remember_thread_mapping(msg, source_message_id, final_card_id)
-                self._remember_pending_clarification(msg, final_card_id)
+                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:
-                created_card_id = await self._ensure_running_card(source_message_id, msg.text)
-                self._remember_thread_mapping(msg, source_message_id, created_card_id)
+                await self._ensure_running_card(source_message_id, msg.text)
 
             if msg.is_final:
                 self._running_card_ids.pop(source_message_id, None)
@@ -581,129 +551,6 @@ class FeishuChannel(Channel):
 
     # -- internal ----------------------------------------------------------
 
-    def _remember_thread_mapping(self, msg: OutboundMessage, *topic_ids: str | None) -> None:
-        store = self.config.get("channel_store")
-        if store is None or not msg.thread_id:
-            return
-
-        metadata_topic_ids = [
-            msg.metadata.get("message_id"),
-            msg.metadata.get("root_id"),
-            msg.metadata.get("parent_id"),
-            msg.metadata.get("thread_id"),
-            msg.metadata.get("topic_id"),
-        ]
-        user_id = ""
-        raw_user_id = msg.metadata.get("user_id")
-        if isinstance(raw_user_id, str):
-            user_id = raw_user_id
-
-        seen: set[str] = set()
-        for topic_id in [*topic_ids, *metadata_topic_ids]:
-            topic_id = self._non_empty_str(topic_id)
-            if not topic_id or topic_id in seen:
-                continue
-            seen.add(topic_id)
-            try:
-                store.set_thread_id(
-                    self.name,
-                    msg.chat_id,
-                    msg.thread_id,
-                    topic_id=topic_id,
-                    user_id=user_id,
-                )
-            except Exception:
-                logger.exception("[Feishu] failed to remember thread mapping for topic_id=%s", topic_id)
-
-    def _remember_pending_clarification(self, msg: OutboundMessage, card_message_id: str | None) -> None:
-        if not msg.is_final or msg.metadata.get(PENDING_CLARIFICATION_METADATA_KEY) is not True:
-            return
-
-        user_id = self._non_empty_str(msg.metadata.get("user_id"))
-        topic_id = self._non_empty_str(msg.metadata.get("topic_id"))
-        source_message_id = self._non_empty_str(msg.thread_ts) or self._non_empty_str(msg.metadata.get("message_id"))
-        if not (user_id and topic_id and msg.thread_id and source_message_id and card_message_id):
-            return
-
-        key = self._pending_key(msg.chat_id, user_id)
-        pending = {
-            "thread_id": msg.thread_id,
-            "topic_id": topic_id,
-            "source_message_id": source_message_id,
-            "card_message_id": card_message_id,
-            "created_at": time.time(),
-        }
-        with self._thread_lock:
-            # Plain-message clarification continuity is a short-lived in-memory
-            # hint; explicit Feishu replies are still covered by persisted
-            # message-id mappings.
-            self._pending_clarifications.setdefault(key, []).append(pending)
-        logger.info(
-            "[Feishu] pending clarification remembered: chat_id=%s user_id=%s topic_id=%s thread_id=%s",
-            msg.chat_id,
-            user_id,
-            topic_id,
-            msg.thread_id,
-        )
-
-    def _consume_pending_clarification(self, chat_id: str, user_id: str) -> dict[str, Any] | None:
-        key = self._pending_key(chat_id, user_id)
-        with self._thread_lock:
-            pending_items = self._pending_clarifications.get(key)
-            if not pending_items:
-                return None
-
-            now = time.time()
-            while pending_items:
-                pending = pending_items.pop(0)
-                created_at = pending.get("created_at")
-                if isinstance(created_at, (int, float)) and now - created_at <= PENDING_CLARIFICATION_TTL_SECONDS:
-                    if pending_items:
-                        self._pending_clarifications[key] = pending_items
-                    else:
-                        self._pending_clarifications.pop(key, None)
-                    return pending
-                logger.info("[Feishu] pending clarification expired: chat_id=%s user_id=%s", chat_id, user_id)
-
-            self._pending_clarifications.pop(key, None)
-            return None
-
-    def _ensure_pending_thread_mapping(self, chat_id: str, user_id: str, pending: dict[str, Any]) -> None:
-        store = self.config.get("channel_store")
-        topic_id = self._non_empty_str(pending.get("topic_id"))
-        thread_id = self._non_empty_str(pending.get("thread_id"))
-        if store is None or not topic_id or not thread_id:
-            return
-        try:
-            store.set_thread_id(self.name, chat_id, thread_id, topic_id=topic_id, user_id=user_id)
-        except Exception:
-            logger.exception("[Feishu] failed to restore pending clarification mapping for topic_id=%s", topic_id)
-
-    def _resolve_topic_id(
-        self,
-        chat_id: str,
-        msg_id: str,
-        *,
-        root_id: str | None,
-        parent_id: str | None,
-        thread_id: str | None,
-    ) -> tuple[str, bool]:
-        store = self.config.get("channel_store")
-        candidates = [root_id, parent_id, thread_id]
-
-        if store is not None:
-            for candidate in candidates:
-                candidate = self._non_empty_str(candidate)
-                if not candidate:
-                    continue
-                try:
-                    if store.get_thread_id(self.name, chat_id, topic_id=candidate):
-                        return candidate, True
-                except Exception:
-                    logger.exception("[Feishu] failed to resolve stored topic mapping for topic_id=%s", candidate)
-
-        return root_id or msg_id, False
-
     @staticmethod
     def _log_future_error(fut, name: str, msg_id: str) -> None:
         """Callback for run_coroutine_threadsafe futures to surface errors."""
@@ -744,9 +591,7 @@ class FeishuChannel(Channel):
 
             # 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 = self._non_empty_str(getattr(message, "root_id", None))
-            parent_id = self._non_empty_str(getattr(message, "parent_id", None))
-            feishu_thread_id = self._non_empty_str(getattr(message, "thread_id", None))
+            root_id = getattr(message, "root_id", None) or None
 
             # Parse message content
             content = json.loads(message.content)
@@ -807,12 +652,10 @@ class FeishuChannel(Channel):
             text = text.strip()
 
             logger.info(
-                "[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, parent_id=%s, thread_id=%s, sender=%s, text=%r",
+                "[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, sender=%s, text=%r",
                 chat_id,
                 msg_id,
                 root_id,
-                parent_id,
-                feishu_thread_id,
                 sender_id,
                 text[:100] if text else "",
             )
@@ -828,24 +671,8 @@ class FeishuChannel(Channel):
             else:
                 msg_type = InboundMessageType.CHAT
 
-            # Prefer any platform message id that already maps to a DeerFlow
-            # thread. This keeps replies to bot clarification cards in the
-            # original conversation even when Feishu reports the card as root.
-            topic_id, resolved_from_stored_mapping = self._resolve_topic_id(
-                chat_id,
-                msg_id,
-                root_id=root_id,
-                parent_id=parent_id,
-                thread_id=feishu_thread_id,
-            )
-            resolved_from_pending = False
-            if msg_type == InboundMessageType.CHAT and not resolved_from_stored_mapping:
-                pending = self._consume_pending_clarification(chat_id, sender_id)
-                pending_topic_id = self._non_empty_str(pending.get("topic_id")) if pending else None
-                if pending_topic_id:
-                    topic_id = pending_topic_id
-                    self._ensure_pending_thread_mapping(chat_id, sender_id, pending)
-                    resolved_from_pending = True
+            # 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,
@@ -854,15 +681,7 @@ class FeishuChannel(Channel):
                 msg_type=msg_type,
                 thread_ts=msg_id,
                 files=files_list,
-                metadata={
-                    "message_id": msg_id,
-                    "root_id": root_id,
-                    "parent_id": parent_id,
-                    "thread_id": feishu_thread_id,
-                    "topic_id": topic_id,
-                    "user_id": sender_id,
-                    RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY: resolved_from_pending,
-                },
+                metadata={"message_id": msg_id, "root_id": root_id},
             )
             inbound.topic_id = topic_id
 

backend/app/channels/manager.py

@@ -1,4 +1,4 @@
-"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via Gateway."""
+"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
 
 from __future__ import annotations
 
@@ -15,23 +15,13 @@ import httpx
 from langgraph_sdk.errors import ConflictError
 
 from app.channels.commands import KNOWN_CHANNEL_COMMANDS
-from app.channels.message_bus import (
-    PENDING_CLARIFICATION_METADATA_KEY,
-    InboundMessage,
-    InboundMessageType,
-    MessageBus,
-    OutboundMessage,
-    ResolvedAttachment,
-)
+from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 from app.channels.store import ChannelStore
-from app.gateway.csrf_middleware import CSRF_COOKIE_NAME, CSRF_HEADER_NAME, generate_csrf_token
-from app.gateway.internal_auth import create_internal_auth_headers
-from deerflow.config.paths import make_safe_user_id
 from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_LANGGRAPH_URL = "http://localhost:8001/api"
+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-]+$")
@@ -46,7 +36,6 @@ 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 = {
-    "dingtalk": {"supports_streaming": False},
     "discord": {"supports_streaming": False},
     "feishu": {"supports_streaming": True},
     "slack": {"supports_streaming": False},
@@ -57,13 +46,6 @@ CHANNEL_CAPABILITIES = {
 
 InboundFileReader = Callable[[dict[str, Any], httpx.AsyncClient], Awaitable[bytes | None]]
 
-_METADATA_DROP_KEYS = frozenset({"raw_message", "ref_msg"})
-
-
-def _slim_metadata(meta: dict[str, Any]) -> dict[str, Any]:
-    """Return a shallow copy of *meta* with known-large keys removed."""
-    return {k: v for k, v in meta.items() if k not in _METADATA_DROP_KEYS}
-
 
 INBOUND_FILE_READERS: dict[str, InboundFileReader] = {}
 
@@ -163,6 +145,7 @@ def _extract_response_text(result: dict | list) -> str:
     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
@@ -181,8 +164,6 @@ def _extract_response_text(result: dict | list) -> str:
 
         # Stop at the last human message — anything before it is a previous turn
         if msg_type == "human":
-            if _is_hidden_human_control_message(msg):
-                continue
             break
 
         # Check for tool messages from ask_clarification (interrupt case)
@@ -210,54 +191,6 @@ def _extract_response_text(result: dict | list) -> str:
     return ""
 
 
-def _messages_from_result(result: dict | list) -> list[Any]:
-    if isinstance(result, list):
-        return result
-    if isinstance(result, dict):
-        messages = result.get("messages", [])
-        if isinstance(messages, list):
-            return messages
-    return []
-
-
-def _current_turn_messages(result: dict | list) -> list[dict[str, Any]]:
-    messages = _messages_from_result(result)
-    current_turn: list[dict[str, Any]] = []
-    for msg in reversed(messages):
-        if not isinstance(msg, dict):
-            continue
-        if msg.get("type") == "human":
-            break
-        current_turn.append(msg)
-    current_turn.reverse()
-    return current_turn
-
-
-def _has_current_turn_clarification(result: dict | list) -> bool:
-    """Return True only when the current turn's final result is clarification."""
-    for msg in reversed(_current_turn_messages(result)):
-        msg_type = msg.get("type")
-        if msg_type == "tool":
-            return msg.get("name") == "ask_clarification"
-        if msg_type == "ai":
-            content = msg.get("content")
-            if isinstance(content, str):
-                if content:
-                    return False
-            elif content:
-                return False
-            if msg.get("tool_calls"):
-                return False
-    return False
-
-
-def _response_metadata(base_metadata: dict[str, Any], *, pending_clarification: bool = False) -> dict[str, Any]:
-    metadata = _slim_metadata(base_metadata)
-    if pending_clarification:
-        metadata[PENDING_CLARIFICATION_METADATA_KEY] = True
-    return metadata
-
-
 def _extract_text_content(content: Any) -> str:
     """Extract text from a streaming payload content field."""
     if isinstance(content, str):
@@ -371,8 +304,6 @@ def _extract_artifacts(result: dict | list) -> list[str]:
             continue
         # Stop at the last human message — anything before it is a previous turn
         if msg.get("type") == "human":
-            if _is_hidden_human_control_message(msg):
-                continue
             break
         # Look for AI messages with present_files tool calls
         if msg.get("type") == "ai":
@@ -385,18 +316,6 @@ def _extract_artifacts(result: dict | list) -> list[str]:
     return artifacts
 
 
-def _is_hidden_human_control_message(msg: Mapping[str, Any]) -> bool:
-    """Return whether a human message is an internal control message hidden from UI."""
-    if msg.get("type") != "human":
-        return False
-
-    additional_kwargs = msg.get("additional_kwargs")
-    if not isinstance(additional_kwargs, Mapping):
-        return False
-
-    return additional_kwargs.get("hide_from_ui") is True
-
-
 def _format_artifact_text(artifacts: list[str]) -> str:
     """Format artifact paths into a human-readable text block listing filenames."""
     import posixpath
@@ -491,13 +410,7 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
     if not msg.files:
         return []
 
-    from deerflow.uploads.manager import (
-        UnsafeUploadPathError,
-        claim_unique_filename,
-        ensure_uploads_dir,
-        normalize_filename,
-        write_upload_file_no_symlink,
-    )
+    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()}
@@ -548,10 +461,7 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
 
             dest = uploads_dir / safe_name
             try:
-                dest = write_upload_file_no_symlink(uploads_dir, safe_name, data)
-            except UnsafeUploadPathError:
-                logger.warning("[Manager] skipping inbound file with unsafe destination: %s", safe_name)
-                continue
+                dest.write_bytes(data)
             except Exception:
                 logger.exception("[Manager] failed to write inbound file: %s", dest)
                 continue
@@ -599,7 +509,7 @@ class ChannelManager:
     """Core dispatcher that bridges IM channels to the DeerFlow agent.
 
     It reads from the MessageBus inbound queue, creates/reuses threads on
-    Gateway's LangGraph-compatible API, sends messages via ``runs.wait``, and publishes
+    the LangGraph Server, sends messages via ``runs.wait``, and publishes
     outbound responses back through the bus.
     """
 
@@ -624,20 +534,12 @@ class ChannelManager:
         self._default_session = _as_dict(default_session)
         self._channel_sessions = dict(channel_sessions or {})
         self._client = None  # lazy init — langgraph_sdk async client
-        self._csrf_token = generate_csrf_token()
         self._semaphore: asyncio.Semaphore | None = None
         self._running = False
         self._task: asyncio.Task | None = None
 
     @staticmethod
     def _channel_supports_streaming(channel_name: str) -> bool:
-        from .service import get_channel_service
-
-        service = get_channel_service()
-        if service:
-            channel = service.get_channel(channel_name)
-            if channel is not None:
-                return channel.supports_streaming
         return CHANNEL_CAPABILITIES.get(channel_name, {}).get("supports_streaming", False)
 
     def _resolve_session_layer(self, msg: InboundMessage) -> tuple[dict[str, Any], dict[str, Any]]:
@@ -660,31 +562,12 @@ class ChannelManager:
             user_layer.get("config"),
         )
 
-        configurable = run_config.get("configurable")
-        if isinstance(configurable, Mapping):
-            configurable = dict(configurable)
-        else:
-            configurable = {}
-        run_config["configurable"] = configurable
-        # Pin channel-triggered runs to the root graph namespace so follow-up
-        # turns continue from the same conversation checkpoint.
-        configurable["checkpoint_ns"] = ""
-        configurable["thread_id"] = thread_id
-
-        # ``user_id`` drives user-scoped filesystem buckets that only accept
-        # ``[A-Za-z0-9_-]``, so normalize the channel id and keep the raw value
-        # under ``channel_user_id`` for platform-facing lookups.
-        run_context_identity: dict[str, Any] = {"thread_id": thread_id}
-        if msg.user_id:
-            run_context_identity["user_id"] = make_safe_user_id(msg.user_id)
-            run_context_identity["channel_user_id"] = msg.user_id
-
         run_context = _merge_dicts(
             DEFAULT_RUN_CONTEXT,
             self._default_session.get("context"),
             channel_layer.get("context"),
             user_layer.get("context"),
-            run_context_identity,
+            {"thread_id": thread_id},
         )
 
         # Custom agents are implemented as lead_agent + agent_name context.
@@ -703,14 +586,7 @@ class ChannelManager:
         if self._client is None:
             from langgraph_sdk import get_client
 
-            self._client = get_client(
-                url=self._langgraph_url,
-                headers={
-                    **create_internal_auth_headers(),
-                    CSRF_HEADER_NAME: self._csrf_token,
-                    "Cookie": f"{CSRF_COOKIE_NAME}={self._csrf_token}",
-                },
-            )
+            self._client = get_client(url=self._langgraph_url)
         return self._client
 
     # -- lifecycle ---------------------------------------------------------
@@ -793,7 +669,7 @@ class ChannelManager:
     # -- chat handling -----------------------------------------------------
 
     async def _create_thread(self, client, msg: InboundMessage) -> str:
-        """Create a new thread through Gateway and store the mapping."""
+        """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(
@@ -803,7 +679,7 @@ class ChannelManager:
             topic_id=msg.topic_id,
             user_id=msg.user_id,
         )
-        logger.info("[Manager] new thread created through Gateway: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_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:
@@ -852,25 +728,15 @@ class ChannelManager:
             return
 
         logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
-        try:
-            result = await client.runs.wait(
-                thread_id,
-                assistant_id,
-                input={"messages": [{"role": "human", "content": msg.text}]},
-                config=run_config,
-                context=run_context,
-                multitask_strategy="reject",
-            )
-        except Exception as exc:
-            if _is_thread_busy_error(exc):
-                logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
-                await self._send_error(msg, THREAD_BUSY_MESSAGE)
-                return
-            else:
-                raise
+        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)
-        pending_clarification = _has_current_turn_clarification(result)
         artifacts = _extract_artifacts(result)
 
         logger.info(
@@ -896,7 +762,6 @@ class ChannelManager:
             artifacts=artifacts,
             attachments=attachments,
             thread_ts=msg.thread_ts,
-            metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
         )
         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)
@@ -958,7 +823,6 @@ class ChannelManager:
                         text=latest_text,
                         is_final=False,
                         thread_ts=msg.thread_ts,
-                        metadata=_response_metadata(msg.metadata),
                     )
                 )
                 last_published_text = latest_text
@@ -972,7 +836,6 @@ class ChannelManager:
         finally:
             result = last_values if last_values is not None else {"messages": [{"type": "ai", "content": latest_text}]}
             response_text = _extract_response_text(result)
-            pending_clarification = _has_current_turn_clarification(result)
             artifacts = _extract_artifacts(result)
             response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
 
@@ -1004,7 +867,6 @@ class ChannelManager:
                     attachments=attachments,
                     is_final=True,
                     thread_ts=msg.thread_ts,
-                    metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
                 )
             )
 
@@ -1024,7 +886,7 @@ class ChannelManager:
             return
 
         if command == "new":
-            # Create a new thread through Gateway
+            # Create a new thread on the LangGraph Server
             client = self._get_client()
             thread = await client.threads.create()
             new_thread_id = thread["thread_id"]
@@ -1063,7 +925,6 @@ class ChannelManager:
             thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
             text=reply,
             thread_ts=msg.thread_ts,
-            metadata=_slim_metadata(msg.metadata),
         )
         await self.bus.publish_outbound(outbound)
 
@@ -1073,11 +934,7 @@ class ChannelManager:
 
         try:
             async with httpx.AsyncClient() as http:
-                resp = await http.get(
-                    f"{self._gateway_url}{path}",
-                    timeout=10,
-                    headers=create_internal_auth_headers(),
-                )
+                resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
                 resp.raise_for_status()
                 data = resp.json()
         except Exception:
@@ -1101,6 +958,5 @@ class ChannelManager:
             thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
             text=error_text,
             thread_ts=msg.thread_ts,
-            metadata=_slim_metadata(msg.metadata),
         )
         await self.bus.publish_outbound(outbound)

backend/app/channels/message_bus.py

@@ -13,9 +13,6 @@ from typing import Any
 
 logger = logging.getLogger(__name__)
 
-PENDING_CLARIFICATION_METADATA_KEY = "pending_clarification"
-RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY = "resolved_from_pending_clarification"
-
 
 # ---------------------------------------------------------------------------
 # Message types

backend/app/channels/service.py

@@ -11,14 +11,13 @@ from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, Cha
 from app.channels.message_bus import MessageBus
 from app.channels.store import ChannelStore
 
-logger = logging.getLogger(__name__)
-
 if TYPE_CHECKING:
     from deerflow.config.app_config import AppConfig
 
+logger = logging.getLogger(__name__)
+
 # Channel name → import path for lazy loading
 _CHANNEL_REGISTRY: dict[str, str] = {
-    "dingtalk": "app.channels.dingtalk:DingTalkChannel",
     "discord": "app.channels.discord:DiscordChannel",
     "feishu": "app.channels.feishu:FeishuChannel",
     "slack": "app.channels.slack:SlackChannel",
@@ -29,7 +28,6 @@ _CHANNEL_REGISTRY: dict[str, str] = {
 
 # Keys that indicate a user has configured credentials for a channel.
 _CHANNEL_CREDENTIAL_KEYS: dict[str, list[str]] = {
-    "dingtalk": ["client_id", "client_secret"],
     "discord": ["bot_token"],
     "feishu": ["app_id", "app_secret"],
     "slack": ["bot_token", "app_token"],
@@ -80,12 +78,8 @@ class ChannelService:
         self._running = False
 
     @classmethod
-    def from_app_config(cls, app_config: AppConfig | None = None) -> ChannelService:
-        """Create a ChannelService from the application config."""
-        if app_config is None:
-            from deerflow.config.app_config import get_app_config
-
-            app_config = get_app_config()
+    def from_app_config(cls, app_config: AppConfig) -> ChannelService:
+        """Create a ChannelService from an explicit application config."""
         channels_config = {}
         # extra fields are allowed by AppConfig (extra="allow")
         extra = app_config.model_extra or {}
@@ -167,19 +161,12 @@ class ChannelService:
             return False
 
         try:
-            config = dict(config)
-            config["channel_store"] = self.store
             channel = channel_cls(bus=self.bus, config=config)
-            self._channels[name] = channel
             await channel.start()
-            if not channel.is_running:
-                self._channels.pop(name, None)
-                logger.error("Channel %s did not enter a running state after start()", name)
-                return False
+            self._channels[name] = channel
             logger.info("Channel %s started", name)
             return True
         except Exception:
-            self._channels.pop(name, None)
             logger.exception("Failed to start channel %s", name)
             return False
 
@@ -214,7 +201,7 @@ def get_channel_service() -> ChannelService | None:
     return _channel_service
 
 
-async def start_channel_service(app_config: AppConfig | None = None) -> ChannelService:
+async def start_channel_service(app_config: AppConfig) -> ChannelService:
     """Create and start the global ChannelService from app config."""
     global _channel_service
     if _channel_service is not None:

backend/app/channels/wecom.py

@@ -29,10 +29,6 @@ class WeComChannel(Channel):
         self._ws_stream_ids: dict[str, str] = {}
         self._working_message = "Working on it..."
 
-    @property
-    def supports_streaming(self) -> bool:
-        return True
-
     def _clear_ws_context(self, thread_ts: str | None) -> None:
         if not thread_ts:
             return

backend/app/gateway/app.py

@@ -1,5 +1,6 @@
 import asyncio
 import logging
+import os
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
 
@@ -8,7 +9,7 @@ from fastapi.middleware.cors import CORSMiddleware
 
 from app.gateway.auth_middleware import AuthMiddleware
 from app.gateway.config import get_gateway_config
-from app.gateway.csrf_middleware import CSRFMiddleware, get_configured_cors_origins
+from app.gateway.csrf_middleware import CSRFMiddleware
 from app.gateway.deps import langgraph_runtime
 from app.gateway.routers import (
     agents,
@@ -27,13 +28,9 @@ from app.gateway.routers import (
     threads,
     uploads,
 )
-from deerflow.config import app_config as deerflow_app_config
-from deerflow.config.app_config import apply_logging_level
+from deerflow.config.app_config import AppConfig
 
-AppConfig = deerflow_app_config.AppConfig
-get_app_config = deerflow_app_config.get_app_config
-
-# Default logging; lifespan overrides from config.yaml log_level.
+# Configure logging
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -62,7 +59,7 @@ async def _ensure_admin_user(app: FastAPI) -> None:
 
     Subsequent boots (admin already exists):
       - Runs the one-time "no-auth → with-auth" orphan thread migration for
-        existing LangGraph thread metadata that has no user_id.
+        existing LangGraph thread metadata that has no owner_id.
 
     No SQL persistence migration is needed: the four user_id columns
     (threads_meta, runs, run_events, feedback) only come into existence
@@ -75,18 +72,7 @@ async def _ensure_admin_user(app: FastAPI) -> None:
     from deerflow.persistence.engine import get_session_factory
     from deerflow.persistence.user.model import UserRow
 
-    try:
-        provider = get_local_provider()
-    except RuntimeError:
-        # Auth persistence may not be initialized in some test/boot paths.
-        # Skip admin migration work rather than failing gateway startup.
-        logger.warning("Auth persistence not ready; skipping admin bootstrap check")
-        return
-
-    sf = get_session_factory()
-    if sf is None:
-        return
-
+    provider = get_local_provider()
     admin_count = await provider.count_admin_users()
 
     if admin_count == 0:
@@ -98,6 +84,10 @@ async def _ensure_admin_user(app: FastAPI) -> None:
 
     # Admin already exists — run orphan thread migration for any
     # LangGraph thread metadata that pre-dates the auth module.
+    sf = get_session_factory()
+    if sf is None:
+        return
+
     async with sf() as session:
         stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
         row = (await session.execute(stmt)).scalar_one_or_none()
@@ -161,16 +151,11 @@ async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     """Application lifespan handler."""
 
-    # Load config and check necessary environment variables at startup.
-    # `startup_config` is a local snapshot used only for one-shot bootstrap
-    # work (logging level, langgraph_runtime engines, channels). Request-time
-    # config resolution always routes through `get_app_config()` in
-    # `app/gateway/deps.py::get_config()` so `config.yaml` edits become
-    # visible without a process restart. We deliberately do NOT cache this
-    # snapshot on `app.state` to keep that contract enforceable.
     try:
-        startup_config = get_app_config()
-        apply_logging_level(startup_config.log_level)
+        # ``app.state.config`` is the sole source of truth for
+        # ``Depends(get_config)``. Consumers that want AppConfig must receive
+        # it as an explicit parameter; there is no ambient singleton.
+        app.state.config = AppConfig.from_file()
         logger.info("Configuration loaded successfully")
     except Exception as e:
         error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -180,10 +165,10 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     logger.info(f"Starting API Gateway on {config.host}:{config.port}")
 
     # Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
-    async with langgraph_runtime(app, startup_config):
+    async with langgraph_runtime(app):
         logger.info("LangGraph runtime initialised")
 
-        # Check admin bootstrap state and migrate orphan threads after admin exists.
+        # Ensure admin user exists (auto-create on first boot)
         # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
         await _ensure_admin_user(app)
 
@@ -191,7 +176,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         try:
             from app.channels.service import start_channel_service
 
-            channel_service = await start_channel_service(startup_config)
+            channel_service = await start_channel_service(app.state.config)
             logger.info("Channel service started: %s", channel_service.get_status())
         except Exception:
             logger.exception("No IM channels configured or channel service failed to start")
@@ -223,10 +208,6 @@ def create_app() -> FastAPI:
     Returns:
         Configured FastAPI application instance.
     """
-    config = get_gateway_config()
-    docs_url = "/docs" if config.enable_docs else None
-    redoc_url = "/redoc" if config.enable_docs else None
-    openapi_url = "/openapi.json" if config.enable_docs else None
 
     app = FastAPI(
         title="DeerFlow API Gateway",
@@ -246,14 +227,14 @@ API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execu
 
 ### Architecture
 
-LangGraph-compatible requests are routed through nginx to this gateway.
-This gateway provides runtime endpoints for agent runs plus custom endpoints for models, MCP configuration, skills, and artifacts.
+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_url,
-        redoc_url=redoc_url,
-        openapi_url=openapi_url,
+        docs_url="/docs",
+        redoc_url="/redoc",
+        openapi_url="/openapi.json",
         openapi_tags=[
             {
                 "name": "models",
@@ -316,18 +297,25 @@ This gateway provides runtime endpoints for agent runs plus custom endpoints for
     # CSRF: Double Submit Cookie pattern for state-changing requests
     app.add_middleware(CSRFMiddleware)
 
-    # CORS: the unified nginx endpoint is same-origin by default. Split-origin
-    # browser clients must opt in with this explicit Gateway allowlist so CORS
-    # and CSRF origin checks share the same source of truth.
-    cors_origins = sorted(get_configured_cors_origins())
-    if cors_origins:
-        app.add_middleware(
-            CORSMiddleware,
-            allow_origins=cors_origins,
-            allow_credentials=True,
-            allow_methods=["*"],
-            allow_headers=["*"],
-        )
+    # CORS: when GATEWAY_CORS_ORIGINS is set (dev without nginx), add CORS middleware.
+    # In production, nginx handles CORS and no middleware is needed.
+    cors_origins_env = os.environ.get("GATEWAY_CORS_ORIGINS", "")
+    if cors_origins_env:
+        cors_origins = [o.strip() for o in cors_origins_env.split(",") if o.strip()]
+        # Validate: wildcard origin with credentials is a security misconfiguration
+        for origin in cors_origins:
+            if origin == "*":
+                logger.error("GATEWAY_CORS_ORIGINS contains wildcard '*' with allow_credentials=True. This is a security misconfiguration — browsers will reject the response. Use explicit scheme://host:port origins instead.")
+                cors_origins = [o for o in cors_origins if o != "*"]
+                break
+        if cors_origins:
+            app.add_middleware(
+                CORSMiddleware,
+                allow_origins=cors_origins,
+                allow_credentials=True,
+                allow_methods=["*"],
+                allow_headers=["*"],
+            )
 
     # Include routers
     # Models API is mounted at /api/models
@@ -376,7 +364,7 @@ This gateway provides runtime endpoints for agent runs plus custom endpoints for
     app.include_router(runs.router)
 
     @app.get("/health", tags=["health"])
-    async def health_check() -> dict[str, str]:
+    async def health_check() -> dict:
         """Health check endpoint.
 
         Returns:

backend/app/gateway/auth/config.py

@@ -4,11 +4,12 @@ import logging
 import os
 import secrets
 
+from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 
-logger = logging.getLogger(__name__)
+load_dotenv()
 
-_SECRET_FILE = ".jwt_secret"
+logger = logging.getLogger(__name__)
 
 
 class AuthConfig(BaseModel):
@@ -32,46 +33,17 @@ class AuthConfig(BaseModel):
 _auth_config: AuthConfig | None = None
 
 
-def _load_or_create_secret() -> str:
-    """Load persisted JWT secret from ``{base_dir}/.jwt_secret``, or generate and persist a new one."""
-    from deerflow.config.paths import get_paths
-
-    paths = get_paths()
-    secret_file = paths.base_dir / _SECRET_FILE
-
-    try:
-        if secret_file.exists():
-            secret = secret_file.read_text(encoding="utf-8").strip()
-            if secret:
-                return secret
-    except OSError as exc:
-        raise RuntimeError(f"Failed to read JWT secret from {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can read its persisted auth secret.") from exc
-
-    secret = secrets.token_urlsafe(32)
-    try:
-        secret_file.parent.mkdir(parents=True, exist_ok=True)
-        fd = os.open(secret_file, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
-        with os.fdopen(fd, "w", encoding="utf-8") as fh:
-            fh.write(secret)
-    except OSError as exc:
-        raise RuntimeError(f"Failed to persist JWT secret to {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can store a stable auth secret.") from exc
-    return secret
-
-
 def get_auth_config() -> AuthConfig:
     """Get the global AuthConfig instance. Parses from env on first call."""
     global _auth_config
     if _auth_config is None:
-        from dotenv import load_dotenv
-
-        load_dotenv()
         jwt_secret = os.environ.get("AUTH_JWT_SECRET")
         if not jwt_secret:
-            jwt_secret = _load_or_create_secret()
+            jwt_secret = secrets.token_urlsafe(32)
             os.environ["AUTH_JWT_SECRET"] = jwt_secret
             logger.warning(
-                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated secret "
-                "persisted to .jwt_secret. Sessions will survive restarts. "
+                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated ephemeral secret. "
+                "Sessions will be invalidated on restart. "
                 "For production, add AUTH_JWT_SECRET to your .env file: "
                 'python -c "import secrets; print(secrets.token_urlsafe(32))"'
             )

backend/app/gateway/auth/local_provider.py

@@ -1,14 +1,10 @@
 """Local email/password authentication provider."""
 
-import logging
-
 from app.gateway.auth.models import User
-from app.gateway.auth.password import hash_password_async, needs_rehash, verify_password_async
+from app.gateway.auth.password import hash_password_async, verify_password_async
 from app.gateway.auth.providers import AuthProvider
 from app.gateway.auth.repositories.base import UserRepository
 
-logger = logging.getLogger(__name__)
-
 
 class LocalAuthProvider(AuthProvider):
     """Email/password authentication provider using local database."""
@@ -47,15 +43,6 @@ class LocalAuthProvider(AuthProvider):
         if not await verify_password_async(password, user.password_hash):
             return None
 
-        if needs_rehash(user.password_hash):
-            try:
-                user.password_hash = await hash_password_async(password)
-                await self._repo.update_user(user)
-            except Exception:
-                # Rehash is an opportunistic upgrade; a transient DB error must not
-                # prevent an otherwise-valid login from succeeding.
-                logger.warning("Failed to rehash password for user %s; login will still succeed", user.email, exc_info=True)
-
         return user
 
     async def get_user(self, user_id: str) -> User | None:

backend/app/gateway/auth/models.py

@@ -28,7 +28,7 @@ class User(BaseModel):
     oauth_id: str | None = Field(None, description="User ID from OAuth provider")
 
     # Auth lifecycle
-    needs_setup: bool = Field(default=False, description="True when a reset account must complete setup")
+    needs_setup: bool = Field(default=False, description="True for auto-created admin until setup completes")
     token_version: int = Field(default=0, description="Incremented on password change to invalidate old JWTs")
 
 

backend/app/gateway/auth/password.py

@@ -1,66 +1,18 @@
-"""Password hashing utilities with versioned hash format.
-
-Hash format: ``$dfv<N>$<bcrypt_hash>`` where ``<N>`` is the version.
-
-- **v1** (legacy): ``bcrypt(password)`` — plain bcrypt, susceptible to
-  72-byte silent truncation.
-- **v2** (current): ``bcrypt(b64(sha256(password)))`` — SHA-256 pre-hash
-  avoids the 72-byte truncation limit so the full password contributes
-  to the hash.
-
-Verification auto-detects the version and falls back to v1 for hashes
-without a prefix, so existing deployments upgrade transparently on next
-login.
-"""
+"""Password hashing utilities using bcrypt directly."""
 
 import asyncio
-import base64
-import hashlib
 
 import bcrypt
 
-_CURRENT_VERSION = 2
-_PREFIX_V2 = "$dfv2$"
-_PREFIX_V1 = "$dfv1$"
-
-
-def _pre_hash_v2(password: str) -> bytes:
-    """SHA-256 pre-hash to bypass bcrypt's 72-byte limit."""
-    return base64.b64encode(hashlib.sha256(password.encode("utf-8")).digest())
-
 
 def hash_password(password: str) -> str:
-    """Hash a password (current version: v2 — SHA-256 + bcrypt)."""
-    raw = bcrypt.hashpw(_pre_hash_v2(password), bcrypt.gensalt()).decode("utf-8")
-    return f"{_PREFIX_V2}{raw}"
+    """Hash a password using bcrypt."""
+    return bcrypt.hashpw(password.encode("utf-8"), bcrypt.gensalt()).decode("utf-8")
 
 
 def verify_password(plain_password: str, hashed_password: str) -> bool:
-    """Verify a password, auto-detecting the hash version.
-
-    Accepts v2 (``$dfv2$…``), v1 (``$dfv1$…``), and bare bcrypt hashes
-    (treated as v1 for backward compatibility with pre-versioning data).
-    """
-    try:
-        if hashed_password.startswith(_PREFIX_V2):
-            bcrypt_hash = hashed_password[len(_PREFIX_V2) :]
-            return bcrypt.checkpw(_pre_hash_v2(plain_password), bcrypt_hash.encode("utf-8"))
-
-        if hashed_password.startswith(_PREFIX_V1):
-            bcrypt_hash = hashed_password[len(_PREFIX_V1) :]
-        else:
-            bcrypt_hash = hashed_password
-
-        return bcrypt.checkpw(plain_password.encode("utf-8"), bcrypt_hash.encode("utf-8"))
-    except ValueError:
-        # bcrypt raises ValueError for malformed or corrupt hashes (e.g., invalid salt).
-        # Fail closed rather than crashing the request.
-        return False
-
-
-def needs_rehash(hashed_password: str) -> bool:
-    """Return True if the hash uses an older version and should be rehashed."""
-    return not hashed_password.startswith(_PREFIX_V2)
+    """Verify a password against its hash."""
+    return bcrypt.checkpw(plain_password.encode("utf-8"), hashed_password.encode("utf-8"))
 
 
 async def hash_password_async(password: str) -> str:

backend/app/gateway/auth/providers.py

@@ -12,12 +12,12 @@ class AuthProvider(ABC):
 
         Returns User if authentication succeeds, None otherwise.
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user(self, user_id: str) -> "User | None":
         """Retrieve user by ID."""
-        raise NotImplementedError
+        ...
 
 
 # Import User at runtime to avoid circular imports

backend/app/gateway/auth/repositories/base.py

@@ -35,7 +35,7 @@ class UserRepository(ABC):
         Raises:
             ValueError: If email already exists
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_id(self, user_id: str) -> User | None:
@@ -47,7 +47,7 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_email(self, email: str) -> User | None:
@@ -59,7 +59,7 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def update_user(self, user: User) -> User:
@@ -76,17 +76,17 @@ class UserRepository(ABC):
                 a hard failure (not a no-op) so callers cannot mistake a
                 concurrent-delete race for a successful update.
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def count_users(self) -> int:
         """Return total number of registered users."""
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def count_admin_users(self) -> int:
         """Return number of users with system_role == 'admin'."""
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
@@ -99,4 +99,4 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...

backend/app/gateway/auth/reset_admin.py

@@ -25,14 +25,15 @@ from deerflow.persistence.user.model import UserRow
 
 
 async def _run(email: str | None) -> int:
-    from deerflow.config import get_app_config
+    from deerflow.config import AppConfig
     from deerflow.persistence.engine import (
         close_engine,
         get_session_factory,
         init_engine_from_config,
     )
 
-    config = get_app_config()
+    # CLI entry: load config explicitly at the top, pass down through the closure.
+    config = AppConfig.from_file()
     await init_engine_from_config(config.database)
     try:
         sf = get_session_factory()

backend/app/gateway/auth_middleware.py

@@ -18,7 +18,6 @@ from starlette.types import ASGIApp
 
 from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
 from app.gateway.authz import _ALL_PERMISSIONS, AuthContext
-from app.gateway.internal_auth import INTERNAL_AUTH_HEADER_NAME, get_internal_user, is_valid_internal_auth_token
 from deerflow.runtime.user_context import reset_current_user, set_current_user
 
 # Paths that never require authentication.
@@ -76,12 +75,8 @@ class AuthMiddleware(BaseHTTPMiddleware):
         if _is_public(request.url.path):
             return await call_next(request)
 
-        internal_user = None
-        if is_valid_internal_auth_token(request.headers.get(INTERNAL_AUTH_HEADER_NAME)):
-            internal_user = get_internal_user()
-
         # Non-public path: require session cookie
-        if internal_user is None and not request.cookies.get("access_token"):
+        if not request.cookies.get("access_token"):
             return JSONResponse(
                 status_code=401,
                 content={
@@ -105,13 +100,10 @@ class AuthMiddleware(BaseHTTPMiddleware):
         # bubble up, so we catch and render it as JSONResponse here.
         from app.gateway.deps import get_current_user_from_request
 
-        if internal_user is not None:
-            user = internal_user
-        else:
-            try:
-                user = await get_current_user_from_request(request)
-            except HTTPException as exc:
-                return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
+        try:
+            user = await get_current_user_from_request(request)
+        except HTTPException as exc:
+            return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
 
         # Stamp both request.state.user (for the contextvar pattern)
         # and request.state.auth (so @require_permission's "auth is

backend/app/gateway/authz.py

@@ -30,9 +30,7 @@ Inspired by LangGraph Auth system: https://github.com/langchain-ai/langgraph/blo
 from __future__ import annotations
 
 import functools
-import inspect
 from collections.abc import Callable
-from types import SimpleNamespace
 from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar
 
 from fastapi import HTTPException, Request
@@ -119,15 +117,6 @@ _ALL_PERMISSIONS: list[str] = [
 ]
 
 
-def _make_test_request_stub() -> Any:
-    """Create a minimal request-like object for direct unit calls.
-
-    Used when decorated route handlers are invoked without FastAPI's
-    request injection. Includes fields accessed by auth helpers.
-    """
-    return SimpleNamespace(state=SimpleNamespace(), cookies={}, _deerflow_test_bypass_auth=True)
-
-
 async def _authenticate(request: Request) -> AuthContext:
     """Authenticate request and return AuthContext.
 
@@ -145,11 +134,7 @@ async def _authenticate(request: Request) -> AuthContext:
 
 
 def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
-    """Decorator that authenticates the request and enforces authentication.
-
-    Independently raises HTTP 401 for unauthenticated requests, regardless of
-    whether ``AuthMiddleware`` is present in the ASGI stack. Sets the resolved
-    ``AuthContext`` on ``request.state.auth`` for downstream handlers.
+    """Decorator that authenticates the request and sets AuthContext.
 
     Must be placed ABOVE other decorators (executes after them).
 
@@ -162,33 +147,19 @@ def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
             ...
 
     Raises:
-        HTTPException: 401 if the request is unauthenticated.
-        ValueError: If 'request' parameter is missing.
+        ValueError: If 'request' parameter is missing
     """
 
     @functools.wraps(func)
     async def wrapper(*args: Any, **kwargs: Any) -> Any:
         request = kwargs.get("request")
         if request is None:
-            # Unit tests may call decorated handlers directly without a
-            # FastAPI Request object. Inject a minimal request stub when
-            # the wrapped function declares `request`.
-            if "request" in inspect.signature(func).parameters:
-                kwargs["request"] = _make_test_request_stub()
-            else:
-                raise ValueError("require_auth decorator requires 'request' parameter")
-            request = kwargs["request"]
-
-        if getattr(request, "_deerflow_test_bypass_auth", False):
-            return await func(*args, **kwargs)
+            raise ValueError("require_auth decorator requires 'request' parameter")
 
         # Authenticate and set context
         auth_context = await _authenticate(request)
         request.state.auth = auth_context
 
-        if not auth_context.is_authenticated:
-            raise HTTPException(status_code=401, detail="Authentication required")
-
         return await func(*args, **kwargs)
 
     return wrapper
@@ -239,17 +210,7 @@ def require_permission(
         async def wrapper(*args: Any, **kwargs: Any) -> Any:
             request = kwargs.get("request")
             if request is None:
-                # Unit tests may call decorated route handlers directly without
-                # constructing a FastAPI Request object. Inject a minimal stub
-                # when the wrapped function declares `request`.
-                if "request" in inspect.signature(func).parameters:
-                    kwargs["request"] = _make_test_request_stub()
-                else:
-                    return await func(*args, **kwargs)
-                request = kwargs["request"]
-
-            if getattr(request, "_deerflow_test_bypass_auth", False):
-                return await func(*args, **kwargs)
+                raise ValueError("require_permission decorator requires 'request' parameter")
 
             auth: AuthContext = getattr(request.state, "auth", None)
             if auth is None:

backend/app/gateway/config.py

@@ -8,7 +8,7 @@ class GatewayConfig(BaseModel):
 
     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")
-    enable_docs: bool = Field(default=True, description="Enable Swagger/ReDoc/OpenAPI endpoints")
+    cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
 
 
 _gateway_config: GatewayConfig | None = None
@@ -18,9 +18,10 @@ 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")),
-            enable_docs=os.getenv("GATEWAY_ENABLE_DOCS", "true").lower() == "true",
+            cors_origins=cors_origins_str.split(","),
         )
     return _gateway_config

backend/app/gateway/csrf_middleware.py

@@ -4,10 +4,8 @@ Per RFC-001:
 State-changing operations require CSRF protection.
 """
 
-import os
 import secrets
-from collections.abc import Awaitable, Callable
-from urllib.parse import urlsplit
+from collections.abc import Callable
 
 from fastapi import Request, Response
 from starlette.middleware.base import BaseHTTPMiddleware
@@ -21,7 +19,7 @@ CSRF_TOKEN_LENGTH = 64  # bytes
 
 def is_secure_request(request: Request) -> bool:
     """Detect whether the original client request was made over HTTPS."""
-    return _request_scheme(request) == "https"
+    return request.headers.get("x-forwarded-proto", request.url.scheme) == "https"
 
 
 def generate_csrf_token() -> str:
@@ -63,129 +61,15 @@ def is_auth_endpoint(request: Request) -> bool:
     return request.url.path.rstrip("/") in _AUTH_EXEMPT_PATHS
 
 
-def _host_with_optional_port(hostname: str, port: int | None, scheme: str) -> str:
-    """Return normalized host[:port], omitting default ports."""
-    host = hostname.lower()
-    if ":" in host and not host.startswith("["):
-        host = f"[{host}]"
-
-    if port is None or (scheme == "http" and port == 80) or (scheme == "https" and port == 443):
-        return host
-    return f"{host}:{port}"
-
-
-def _normalize_origin(origin: str) -> str | None:
-    """Return a normalized scheme://host[:port] origin, or None for invalid input."""
-    try:
-        parsed = urlsplit(origin.strip())
-        port = parsed.port
-    except ValueError:
-        return None
-
-    scheme = parsed.scheme.lower()
-    if scheme not in {"http", "https"} or not parsed.hostname:
-        return None
-
-    # Browser Origin is only scheme/host/port. Reject URL-shaped or credentialed values.
-    if parsed.username or parsed.password or parsed.path or parsed.query or parsed.fragment:
-        return None
-
-    return f"{scheme}://{_host_with_optional_port(parsed.hostname, port, scheme)}"
-
-
-def _configured_cors_origins() -> set[str]:
-    """Return explicit configured browser origins that may call auth routes."""
-    origins = set()
-    for raw_origin in os.environ.get("GATEWAY_CORS_ORIGINS", "").split(","):
-        origin = raw_origin.strip()
-        if not origin or origin == "*":
-            continue
-        normalized = _normalize_origin(origin)
-        if normalized:
-            origins.add(normalized)
-    return origins
-
-
-def get_configured_cors_origins() -> set[str]:
-    """Return normalized explicit browser origins from GATEWAY_CORS_ORIGINS."""
-    return _configured_cors_origins()
-
-
-def _first_header_value(value: str | None) -> str | None:
-    """Return the first value from a comma-separated proxy header."""
-    if not value:
-        return None
-    first = value.split(",", 1)[0].strip()
-    return first or None
-
-
-def _forwarded_param(request: Request, name: str) -> str | None:
-    """Extract a parameter from the first RFC 7239 Forwarded header entry."""
-    forwarded = _first_header_value(request.headers.get("forwarded"))
-    if not forwarded:
-        return None
-
-    for part in forwarded.split(";"):
-        key, sep, value = part.strip().partition("=")
-        if sep and key.lower() == name:
-            return value.strip().strip('"') or None
-    return None
-
-
-def _request_scheme(request: Request) -> str:
-    """Resolve the original request scheme from trusted proxy headers."""
-    scheme = _forwarded_param(request, "proto") or _first_header_value(request.headers.get("x-forwarded-proto")) or request.url.scheme
-    return scheme.lower()
-
-
-def _request_origin(request: Request) -> str | None:
-    """Build the origin for the URL the browser is targeting."""
-    scheme = _request_scheme(request)
-    host = _forwarded_param(request, "host") or _first_header_value(request.headers.get("x-forwarded-host")) or request.headers.get("host") or request.url.netloc
-
-    forwarded_port = _first_header_value(request.headers.get("x-forwarded-port"))
-    if forwarded_port and ":" not in host.rsplit("]", 1)[-1]:
-        host = f"{host}:{forwarded_port}"
-
-    return _normalize_origin(f"{scheme}://{host}")
-
-
-def is_allowed_auth_origin(request: Request) -> bool:
-    """Allow auth POSTs only from the same origin or explicit configured origins.
-
-    Login/register/initialize are exempt from the double-submit token because
-    first-time browser clients do not have a CSRF token yet. They still create
-    a session cookie, so browser requests with a hostile Origin header must be
-    rejected to prevent login CSRF / session fixation. Requests without Origin
-    are allowed for non-browser clients such as curl and mobile integrations.
-    """
-    origin = request.headers.get("origin")
-    if not origin:
-        return True
-
-    normalized_origin = _normalize_origin(origin)
-    if normalized_origin is None:
-        return False
-
-    request_origin = _request_origin(request)
-    return normalized_origin in _configured_cors_origins() or (request_origin is not None and normalized_origin == request_origin)
-
-
 class CSRFMiddleware(BaseHTTPMiddleware):
     """Middleware that implements CSRF protection using Double Submit Cookie pattern."""
 
     def __init__(self, app: ASGIApp) -> None:
         super().__init__(app)
 
-    async def dispatch(self, request: Request, call_next: Callable[[Request], Awaitable[Response]]) -> Response:
+    async def dispatch(self, request: Request, call_next: Callable) -> Response:
         _is_auth = is_auth_endpoint(request)
 
-        if should_check_csrf(request) and _is_auth and not is_allowed_auth_origin(request):
-            return JSONResponse(
-                status_code=403,
-                content={"detail": "Cross-site auth request denied."},
-            )
-
         if should_check_csrf(request) and not _is_auth:
             cookie_token = request.cookies.get(CSRF_COOKIE_NAME)
             header_token = request.headers.get(CSRF_HEADER_NAME)

backend/app/gateway/deps.py

@@ -3,167 +3,46 @@
 **Getters** (used by routers): raise 503 when a required dependency is
 missing, except ``get_store`` which returns ``None``.
 
-``AppConfig`` is intentionally *not* cached on ``app.state``. Routers and the
-run path resolve it through :func:`deerflow.config.app_config.get_app_config`,
-which performs mtime-based hot reload, so edits to ``config.yaml`` take
-effect on the next request without a process restart. The engines created in
-:func:`langgraph_runtime` (stream bridge, persistence, checkpointer, store,
-run-event store) accept a ``startup_config`` snapshot — they are
-restart-required by design and stay bound to that snapshot to keep the live
-process consistent with itself.
-
 Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
 """
 
 from __future__ import annotations
 
-import asyncio
-import logging
-from collections.abc import AsyncGenerator, Callable
+from collections.abc import AsyncGenerator
 from contextlib import AsyncExitStack, asynccontextmanager
-from typing import TYPE_CHECKING, TypeVar, cast
+from typing import TYPE_CHECKING
 
 from fastapi import FastAPI, HTTPException, Request
-from langgraph.types import Checkpointer
-
-from deerflow.config.app_config import AppConfig, get_app_config
-from deerflow.persistence.feedback import FeedbackRepository
-from deerflow.runtime import RunContext, RunManager, StreamBridge
-from deerflow.runtime.events.store.base import RunEventStore
-from deerflow.runtime.runs.store.base import RunStore
-
-logger = logging.getLogger(__name__)
-
-# Upper bound (seconds) for draining in-flight runs during shutdown, before the
-# AsyncExitStack tears down the checkpointer (and its connection pool). Kept
-# local to avoid an app -> deps -> app import cycle. This is a *separate* budget
-# from ``app.gateway.app._SHUTDOWN_HOOK_TIMEOUT_SECONDS`` (currently also 5.0s,
-# which bounds channel-service stop): the two govern independent teardown steps
-# and may diverge, but both count toward the lifespan shutdown window — revisit
-# them together if their sum must stay within the server's graceful-shutdown
-# timeout.
-_RUN_DRAIN_TIMEOUT_SECONDS = 5.0
-
-
-async def _drain_inflight_runs(run_manager: RunManager) -> None:
-    """Drain in-flight runs before the checkpointer is torn down (issue #3373).
-
-    Shields the (internally-bounded) drain so that even if the lifespan
-    coroutine is itself cancelled mid-shutdown — a second SIGINT or the server's
-    graceful-shutdown timeout, i.e. the same signal storm behind #3373 — the
-    checkpointer pool is not closed while run tasks are still writing
-    checkpoints. On such a cancellation we let the already-running drain finish
-    (it is bounded by ``RunManager.shutdown``'s own timeout) and then propagate
-    the cancellation.
-    """
-    drain = asyncio.create_task(run_manager.shutdown(timeout=_RUN_DRAIN_TIMEOUT_SECONDS))
-    try:
-        await asyncio.shield(drain)
-    except asyncio.CancelledError:
-        # Re-shield so this second wait does not abandon the in-flight drain;
-        # it is bounded, so this cannot hang. Then re-raise to honour shutdown.
-        try:
-            await asyncio.shield(drain)
-        except Exception:
-            logger.exception("In-flight run drain failed after shutdown cancellation")
-        raise
-    except Exception:
-        logger.exception("Failed to drain in-flight runs during shutdown")
 
+from deerflow.config.app_config import AppConfig
+from deerflow.runtime import RunContext, RunManager
 
 if TYPE_CHECKING:
     from app.gateway.auth.local_provider import LocalAuthProvider
     from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
     from deerflow.persistence.thread_meta.base import ThreadMetaStore
-    from deerflow.runtime import RunRecord
 
 
-T = TypeVar("T")
+def get_config(request: Request) -> AppConfig:
+    """FastAPI dependency returning the app-scoped ``AppConfig``.
 
-
-async def _mark_latest_recovered_threads_error(
-    run_manager: RunManager,
-    thread_store: ThreadMetaStore,
-    recovered_runs: list[RunRecord],
-) -> None:
-    """Mark thread status as error only when its newest run was recovered."""
-    recovered_by_thread: dict[str, set[str]] = {}
-    for record in recovered_runs:
-        recovered_by_thread.setdefault(record.thread_id, set()).add(record.run_id)
-
-    for thread_id, recovered_run_ids in recovered_by_thread.items():
-        try:
-            latest_runs = await run_manager.list_by_thread(thread_id, user_id=None, limit=1)
-        except Exception:
-            logger.warning("Failed to find latest run for thread %s during run reconciliation", thread_id, exc_info=True)
-            continue
-        if not latest_runs or latest_runs[0].run_id not in recovered_run_ids:
-            continue
-        try:
-            await thread_store.update_status(thread_id, "error", user_id=None)
-        except Exception:
-            logger.warning("Failed to mark thread %s as error during run reconciliation", thread_id, exc_info=True)
-
-
-def get_config() -> AppConfig:
-    """Return the freshest ``AppConfig`` for the current request.
-
-    Routes through :func:`deerflow.config.app_config.get_app_config`, which
-    honours runtime ``ContextVar`` overrides and reloads ``config.yaml`` from
-    disk when its mtime changes. ``AppConfig`` is not cached on ``app.state``
-    at all — the only startup-time snapshot lives as a local
-    ``startup_config`` variable inside ``lifespan()`` and is passed
-    explicitly into :func:`langgraph_runtime` for the engines that are
-    restart-required by design. Routing every request through
-    :func:`get_app_config` closes the bytedance/deer-flow issue #3107 BUG-001
-    split-brain where the worker / lead-agent thread saw a stale startup
-    snapshot.
-
-    Hot-reload boundary: fields backed by startup-time singletons
-    (engines, sandbox provider, IM channels, logging handler) require a
-    process restart to change at runtime. The authoritative list lives in
-    :mod:`deerflow.config.reload_boundary` and is mirrored by the
-    standardised ``"startup-only:"`` prefix on the matching
-    ``Field(description=...)`` in :class:`AppConfig` — IDE hover on those
-    fields will surface the boundary inline. See
-    ``backend/CLAUDE.md`` "Config Hot-Reload Boundary" for the operator
-    summary.
-
-    Any failure to materialise the config (missing file, permission denied,
-    YAML parse error, validation error) is reported as 503 — semantically
-    "the gateway cannot serve requests without a usable configuration" — and
-    logged with the original exception so operators have something to debug.
+    Reads from ``request.app.state.config`` which is set at startup
+    (``app.py`` lifespan) and swapped on config reload (``routers/mcp.py``,
+    ``routers/skills.py``).
     """
-    try:
-        return get_app_config()
-    except Exception as exc:  # noqa: BLE001 - request boundary: log and degrade gracefully
-        logger.exception("Failed to load AppConfig at request time")
-        raise HTTPException(status_code=503, detail="Configuration not available") from exc
+    cfg = getattr(request.app.state, "config", None)
+    if cfg is None:
+        raise HTTPException(status_code=503, detail="Configuration not available")
+    return cfg
 
 
 @asynccontextmanager
-async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGenerator[None, None]:
+async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
     """Bootstrap and tear down all LangGraph runtime singletons.
 
-    ``startup_config`` is the ``AppConfig`` snapshot taken once during
-    ``lifespan()`` for one-shot infrastructure bootstrap. The engines and
-    stores constructed here (stream bridge, persistence engine, checkpointer,
-    store, run-event store) are restart-required by design — they hold live
-    connections, file handles, or singleton providers — so they bind to this
-    snapshot and survive across `config.yaml` edits. Request-time consumers
-    must still go through :func:`get_config` for any field that should be
-    hot-reloadable. See ``backend/CLAUDE.md`` "Config Hot-Reload Boundary".
-
-    The matching ``run_events_config`` is frozen onto ``app.state`` so
-    :func:`get_run_context` pairs a freshly-loaded ``AppConfig`` with the
-    *startup-time* run-events configuration the underlying ``event_store``
-    was built from — otherwise the runtime could end up combining a live
-    new ``run_events_config`` with an event store still bound to the
-    previous backend.
-
     Usage in ``app.py``::
 
-        async with langgraph_runtime(app, startup_config):
+        async with langgraph_runtime(app):
             yield
     """
     from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
@@ -172,7 +51,9 @@ async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGen
     from deerflow.runtime.events.store import make_run_event_store
 
     async with AsyncExitStack() as stack:
-        config = startup_config
+        # app.state.config is populated earlier in lifespan(); thread it
+        # explicitly into every provider below.
+        config = app.state.config
 
         app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
 
@@ -201,38 +82,16 @@ async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGen
 
         app.state.thread_store = make_thread_store(sf, app.state.store)
 
-        # Run event store. The store and the matching ``run_events_config`` are
-        # both frozen at startup so ``get_run_context`` does not combine a
-        # freshly-reloaded ``AppConfig.run_events`` with a store still bound to
-        # the previous backend.
+        # Run event store (has its own factory with config-driven backend selection)
         run_events_config = getattr(config, "run_events", None)
-        app.state.run_events_config = run_events_config
         app.state.run_event_store = make_run_event_store(run_events_config)
 
         # RunManager with store backing for persistence
         app.state.run_manager = RunManager(store=app.state.run_store)
-        if getattr(config.database, "backend", None) == "sqlite":
-            from deerflow.utils.time import now_iso
-
-            # Startup-only recovery: clean shutdowns return no active rows and
-            # the thread-status update below becomes a no-op.
-            recovered_runs = await app.state.run_manager.reconcile_orphaned_inflight_runs(
-                error="Gateway restarted before this run reached a durable final state.",
-                before=now_iso(),
-            )
-            await _mark_latest_recovered_threads_error(app.state.run_manager, app.state.thread_store, recovered_runs)
 
         try:
             yield
         finally:
-            # Drain in-flight run tasks BEFORE the AsyncExitStack tears down the
-            # checkpointer (and its connection pool). A run still mid-graph would
-            # otherwise leak into asyncio.run() shutdown, where langgraph's
-            # _checkpointer_put_after_previous aput races the closed pool and
-            # raises PoolClosed (issue #3373).
-            run_manager = getattr(app.state, "run_manager", None)
-            if run_manager is not None:
-                await _drain_inflight_runs(run_manager)
             await close_engine()
 
 
@@ -241,25 +100,25 @@ async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGen
 # ---------------------------------------------------------------------------
 
 
-def _require(attr: str, label: str) -> Callable[[Request], T]:
+def _require(attr: str, label: str):
     """Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
 
-    def dep(request: Request) -> T:
+    def dep(request: Request):
         val = getattr(request.app.state, attr, None)
         if val is None:
             raise HTTPException(status_code=503, detail=f"{label} not available")
-        return cast(T, val)
+        return val
 
     dep.__name__ = dep.__qualname__ = f"get_{attr}"
     return dep
 
 
-get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
-get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
-get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
-get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
-get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
-get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
+get_stream_bridge = _require("stream_bridge", "Stream bridge")
+get_run_manager = _require("run_manager", "Run manager")
+get_checkpointer = _require("checkpointer", "Checkpointer")
+get_run_event_store = _require("run_event_store", "Run event store")
+get_feedback_repo = _require("feedback_repo", "Feedback")
+get_run_store = _require("run_store", "Run store")
 
 
 def get_store(request: Request):
@@ -278,23 +137,23 @@ def get_thread_store(request: Request) -> ThreadMetaStore:
 def get_run_context(request: Request) -> RunContext:
     """Build a :class:`RunContext` from ``app.state`` singletons.
 
-    Returns a *base* context with infrastructure dependencies. The
-    ``app_config`` field is resolved live so per-run fields (e.g.
-    ``models[*].max_tokens``) follow ``config.yaml`` edits; the
-    ``event_store`` / ``run_events_config`` pair stays frozen to the snapshot
-    captured in :func:`langgraph_runtime` so callers never see a store bound
-    to one backend paired with a config pointing at another.
+    Returns a *base* context with infrastructure dependencies.  Callers that
+    need per-run fields (e.g. ``follow_up_to_run_id``) should use
+    ``dataclasses.replace(ctx, follow_up_to_run_id=...)`` before passing it
+    to :func:`run_agent`.
     """
+    config = get_config(request)
     return RunContext(
         checkpointer=get_checkpointer(request),
         store=get_store(request),
         event_store=get_run_event_store(request),
-        run_events_config=getattr(request.app.state, "run_events_config", None),
+        run_events_config=getattr(config, "run_events", None),
         thread_store=get_thread_store(request),
-        app_config=get_config(),
+        app_config=config,
     )
 
 
+
 # ---------------------------------------------------------------------------
 # Auth helpers (used by authz.py and auth middleware)
 # ---------------------------------------------------------------------------

backend/app/gateway/internal_auth.py

@@ -1,38 +0,0 @@
-"""Authentication for trusted Gateway internal callers."""
-
-from __future__ import annotations
-
-import os
-import secrets
-from types import SimpleNamespace
-
-from deerflow.runtime.user_context import DEFAULT_USER_ID
-
-INTERNAL_AUTH_HEADER_NAME = "X-DeerFlow-Internal-Token"
-INTERNAL_AUTH_ENV_VAR = "DEER_FLOW_INTERNAL_AUTH_TOKEN"
-INTERNAL_SYSTEM_ROLE = "internal"
-
-
-def _load_internal_auth_token() -> str:
-    token = os.environ.get(INTERNAL_AUTH_ENV_VAR)
-    if token:
-        return token
-    return secrets.token_urlsafe(32)
-
-
-_INTERNAL_AUTH_TOKEN = _load_internal_auth_token()
-
-
-def create_internal_auth_headers() -> dict[str, str]:
-    """Return headers that authenticate trusted Gateway internal calls."""
-    return {INTERNAL_AUTH_HEADER_NAME: _INTERNAL_AUTH_TOKEN}
-
-
-def is_valid_internal_auth_token(token: str | None) -> bool:
-    """Return True when *token* matches this Gateway worker's internal token."""
-    return bool(token) and secrets.compare_digest(token, _INTERNAL_AUTH_TOKEN)
-
-
-def get_internal_user():
-    """Return the synthetic user used for trusted internal channel calls."""
-    return SimpleNamespace(id=DEFAULT_USER_ID, system_role=INTERNAL_SYSTEM_ROLE)

backend/app/gateway/langgraph_auth.py

@@ -1,12 +1,8 @@
-"""LangGraph compatibility auth handler — shares JWT logic with Gateway.
+"""LangGraph Server auth handler — shares JWT logic with Gateway.
 
-The default DeerFlow runtime is embedded in the FastAPI Gateway; scripts and
-Docker deployments do not load this module.  It is retained for LangGraph
-tooling, Studio, or direct LangGraph Server compatibility through
-``langgraph.json``'s ``auth.path``.
-
-When that compatibility path is used, this module reuses the same JWT and CSRF
-rules as Gateway so both modes validate sessions consistently.
+Loaded by LangGraph Server via langgraph.json ``auth.path``.
+Reuses the same ``decode_token`` / ``get_auth_config`` as Gateway,
+so both modes validate tokens with the same secret and rules.
 
 Two layers:
   1. @auth.authenticate — validates JWT cookie, extracts user_id,
@@ -77,7 +73,7 @@ async def authenticate(request):
     if isinstance(payload, TokenError):
         raise Auth.exceptions.HTTPException(
             status_code=401,
-            detail="Invalid token",
+            detail=f"Token error: {payload.value}",
         )
 
     user = await get_local_provider().get_user(payload.sub)

backend/app/gateway/pagination.py

@@ -1,15 +0,0 @@
-"""Shared pagination helpers for gateway routers."""
-
-from __future__ import annotations
-
-
-def trim_run_message_page(rows: list[dict], *, limit: int, after_seq: int | None) -> tuple[list[dict], bool]:
-    """Trim a ``limit + 1`` run-message page while preserving page boundaries."""
-    has_more = len(rows) > limit
-    if not has_more:
-        return rows, False
-
-    if after_seq is not None:
-        return rows[:limit], True
-
-    return rows[-limit:], True

backend/app/gateway/routers/agents.py

@@ -5,13 +5,13 @@ import re
 import shutil
 
 import yaml
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
-from deerflow.config.agents_api_config import get_agents_api_config
+from app.gateway.deps import get_config
 from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
-from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["agents"])
@@ -78,20 +78,20 @@ def _normalize_agent_name(name: str) -> str:
     return name.lower()
 
 
-def _require_agents_api_enabled() -> None:
+def _require_agents_api_enabled(app_config: AppConfig) -> None:
     """Reject access unless the custom-agent management API is explicitly enabled."""
-    if not get_agents_api_config().enabled:
+    if not app_config.agents_api.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, *, user_id: str | None = None) -> AgentResponse:
+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, user_id=user_id) or ""
+        soul = load_agent_soul(agent_cfg.name) or ""
 
     return AgentResponse(
         name=agent_cfg.name,
@@ -109,18 +109,17 @@ def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False
     summary="List Custom Agents",
     description="List all custom agents available in the agents directory, including their soul content.",
 )
-async def list_agents() -> AgentsListResponse:
+async def list_agents(app_config: AppConfig = Depends(get_config)) -> AgentsListResponse:
     """List all custom agents.
 
     Returns:
         List of all custom agents with their metadata and soul content.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
-    user_id = get_effective_user_id()
     try:
-        agents = list_custom_agents(user_id=user_id)
-        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True, user_id=user_id) for a in agents])
+        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)}")
@@ -143,15 +142,10 @@ async def check_agent_name(name: str) -> dict:
     Raises:
         HTTPException: 422 if the name is invalid.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     normalized = _normalize_agent_name(name)
-    user_id = get_effective_user_id()
-    paths = get_paths()
-    # Treat the name as taken if either the per-user path or the legacy shared
-    # path holds an agent — picking a name that collides with an unmigrated
-    # legacy agent would shadow the legacy entry once migration runs.
-    available = not paths.user_agent_dir(user_id, normalized).exists() and not paths.agent_dir(normalized).exists()
+    available = not get_paths().agent_dir(normalized).exists()
     return {"available": available, "name": normalized}
 
 
@@ -161,7 +155,7 @@ async def check_agent_name(name: str) -> dict:
     summary="Get Custom Agent",
     description="Retrieve details and SOUL.md content for a specific custom agent.",
 )
-async def get_agent(name: str) -> AgentResponse:
+async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Get a specific custom agent by name.
 
     Args:
@@ -173,14 +167,13 @@ async def get_agent(name: str) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
-    user_id = get_effective_user_id()
 
     try:
-        agent_cfg = load_agent_config(name, user_id=user_id)
-        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
+        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:
@@ -195,7 +188,7 @@ async def get_agent(name: str) -> AgentResponse:
     summary="Create Custom Agent",
     description="Create a new custom agent with its config and SOUL.md.",
 )
-async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
+async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Create a new custom agent.
 
     Args:
@@ -207,16 +200,13 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     Raises:
         HTTPException: 409 if agent already exists, 422 if name is invalid.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(request.name)
     normalized_name = _normalize_agent_name(request.name)
-    user_id = get_effective_user_id()
-    paths = get_paths()
 
-    agent_dir = paths.user_agent_dir(user_id, normalized_name)
-    legacy_dir = paths.agent_dir(normalized_name)
+    agent_dir = get_paths().agent_dir(normalized_name)
 
-    if agent_dir.exists() or legacy_dir.exists():
+    if agent_dir.exists():
         raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
 
     try:
@@ -243,8 +233,8 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
 
         logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
 
-        agent_cfg = load_agent_config(normalized_name, user_id=user_id)
-        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
+        agent_cfg = load_agent_config(normalized_name)
+        return _agent_config_to_response(agent_cfg, include_soul=True)
 
     except HTTPException:
         raise
@@ -262,7 +252,7 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> 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:
+async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Update an existing custom agent.
 
     Args:
@@ -275,23 +265,16 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
-    user_id = get_effective_user_id()
 
     try:
-        agent_cfg = load_agent_config(name, user_id=user_id)
+        agent_cfg = load_agent_config(name)
     except FileNotFoundError:
         raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
 
-    paths = get_paths()
-    agent_dir = paths.user_agent_dir(user_id, name)
-    if not agent_dir.exists() and paths.agent_dir(name).exists():
-        raise HTTPException(
-            status_code=409,
-            detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating."),
-        )
+    agent_dir = get_paths().agent_dir(name)
 
     try:
         # Update config if any config fields changed
@@ -332,8 +315,8 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
 
         logger.info(f"Updated agent '{name}'")
 
-        refreshed_cfg = load_agent_config(name, user_id=user_id)
-        return _agent_config_to_response(refreshed_cfg, include_soul=True, user_id=user_id)
+        refreshed_cfg = load_agent_config(name)
+        return _agent_config_to_response(refreshed_cfg, include_soul=True)
 
     except HTTPException:
         raise
@@ -360,13 +343,13 @@ class UserProfileUpdateRequest(BaseModel):
     summary="Get User Profile",
     description="Read the global USER.md file that is injected into all custom agents.",
 )
-async def get_user_profile() -> UserProfileResponse:
+async def get_user_profile(app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
     """Return the current USER.md content.
 
     Returns:
         UserProfileResponse with content=None if USER.md does not exist yet.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
     try:
         user_md_path = get_paths().user_md_file
@@ -385,7 +368,7 @@ async def get_user_profile() -> 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:
+async def update_user_profile(request: UserProfileUpdateRequest, app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
     """Create or overwrite the global USER.md.
 
     Args:
@@ -394,7 +377,7 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     Returns:
         UserProfileResponse with the saved content.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
     try:
         paths = get_paths()
@@ -413,29 +396,22 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     summary="Delete Custom Agent",
     description="Delete a custom agent and all its files (config, SOUL.md, memory).",
 )
-async def delete_agent(name: str) -> None:
+async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -> None:
     """Delete a custom agent.
 
     Args:
         name: The agent name.
 
     Raises:
-        HTTPException: 404 if no per-user copy exists; 409 if only a legacy
-            shared copy exists (suggesting the migration script).
+        HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
-    user_id = get_effective_user_id()
-    paths = get_paths()
-    agent_dir = paths.user_agent_dir(user_id, name)
+
+    agent_dir = get_paths().agent_dir(name)
 
     if not agent_dir.exists():
-        if paths.agent_dir(name).exists():
-            raise HTTPException(
-                status_code=409,
-                detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
-            )
         raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
 
     try:

backend/app/gateway/routers/artifacts.py

@@ -20,9 +20,6 @@ ACTIVE_CONTENT_MIME_TYPES = {
     "image/svg+xml",
 }
 
-MAX_SKILL_ARCHIVE_MEMBER_BYTES = 16 * 1024 * 1024
-_SKILL_ARCHIVE_READ_CHUNK_SIZE = 64 * 1024
-
 
 def _build_content_disposition(disposition_type: str, filename: str) -> str:
     """Build an RFC 5987 encoded Content-Disposition header value."""
@@ -47,22 +44,6 @@ def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
         return False
 
 
-def _read_skill_archive_member(zip_ref: zipfile.ZipFile, info: zipfile.ZipInfo) -> bytes:
-    """Read a .skill archive member while enforcing an uncompressed size cap."""
-    if info.file_size > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
-        raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
-
-    chunks: list[bytes] = []
-    total_read = 0
-    with zip_ref.open(info, "r") as src:
-        while chunk := src.read(_SKILL_ARCHIVE_READ_CHUNK_SIZE):
-            total_read += len(chunk)
-            if total_read > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
-                raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
-            chunks.append(chunk)
-    return b"".join(chunks)
-
-
 def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
     """Extract a file from a .skill ZIP archive.
 
@@ -79,16 +60,16 @@ def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> byte
     try:
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             # List all files in the archive
-            infos_by_name = {info.filename: info for info in zip_ref.infolist()}
+            namelist = zip_ref.namelist()
 
             # Try direct path first
-            if internal_path in infos_by_name:
-                return _read_skill_archive_member(zip_ref, infos_by_name[internal_path])
+            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, info in infos_by_name.items():
+            for name in namelist:
                 if name.endswith("/" + internal_path) or name == internal_path:
-                    return _read_skill_archive_member(zip_ref, info)
+                    return zip_ref.read(name)
 
             # Not found
             return None

backend/app/gateway/routers/auth.py

@@ -1,6 +1,5 @@
 """Authentication endpoints."""
 
-import asyncio
 import logging
 import os
 import time
@@ -147,13 +146,7 @@ def _set_session_cookie(response: Response, token: str, request: Request) -> Non
 
 
 # ── Rate Limiting ────────────────────────────────────────────────────────
-# In-process dict — not shared across workers.
-#
-# **Limitation**: with multi-worker deployments (e.g., gunicorn -w N), each
-# worker maintains its own lockout table, so an attacker effectively gets
-# N × _MAX_LOGIN_ATTEMPTS guesses before being locked out everywhere. For
-# production multi-worker setups, replace this with a shared store (Redis,
-# database-backed counter) to enforce a true per-IP limit.
+# In-process dict — not shared across workers. Sufficient for single-worker deployments.
 
 _MAX_LOGIN_ATTEMPTS = 5
 _LOCKOUT_SECONDS = 300  # 5 minutes
@@ -306,7 +299,7 @@ async def login_local(
 async def register(request: Request, response: Response, body: RegisterRequest):
     """Register a new user account (always 'user' role).
 
-    The first admin is created explicitly through /initialize. This endpoint creates regular users.
+    Admin is auto-created on first boot. This endpoint creates regular users.
     Auto-login by setting the session cookie.
     """
     try:
@@ -383,72 +376,11 @@ async def get_me(request: Request):
     return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role, needs_setup=user.needs_setup)
 
 
-# Per-IP cache: ip → (timestamp, result_dict).
-# Returns the cached result within the TTL instead of 429, because
-# the answer (whether an admin exists) rarely changes and returning
-# 429 breaks multi-tab / post-restart reconnection storms.
-_SETUP_STATUS_CACHE: dict[str, tuple[float, dict]] = {}
-_SETUP_STATUS_CACHE_TTL_SECONDS = 60
-_MAX_TRACKED_SETUP_STATUS_IPS = 10000
-_SETUP_STATUS_INFLIGHT: dict[str, asyncio.Task[dict]] = {}
-_SETUP_STATUS_INFLIGHT_GUARD = asyncio.Lock()
-
-
 @router.get("/setup-status")
-async def setup_status(request: Request):
+async def setup_status():
     """Check if an admin account exists. Returns needs_setup=True when no admin exists."""
-    client_ip = _get_client_ip(request)
-    now = time.time()
-
-    # Return cached result when within TTL — avoids 429 on multi-tab reconnection.
-    cached = _SETUP_STATUS_CACHE.get(client_ip)
-    if cached is not None:
-        cached_time, cached_result = cached
-        if now - cached_time < _SETUP_STATUS_CACHE_TTL_SECONDS:
-            return cached_result
-
-    async with _SETUP_STATUS_INFLIGHT_GUARD:
-        # Recheck cache after waiting for the inflight guard.
-        now = time.time()
-        cached = _SETUP_STATUS_CACHE.get(client_ip)
-        if cached is not None:
-            cached_time, cached_result = cached
-            if now - cached_time < _SETUP_STATUS_CACHE_TTL_SECONDS:
-                return cached_result
-
-        task = _SETUP_STATUS_INFLIGHT.get(client_ip)
-        if task is None:
-            # Evict stale entries when dict grows too large to bound memory usage.
-            if len(_SETUP_STATUS_CACHE) >= _MAX_TRACKED_SETUP_STATUS_IPS:
-                cutoff = now - _SETUP_STATUS_CACHE_TTL_SECONDS
-                stale = [k for k, (t, _) in _SETUP_STATUS_CACHE.items() if t < cutoff]
-                for k in stale:
-                    del _SETUP_STATUS_CACHE[k]
-                if len(_SETUP_STATUS_CACHE) >= _MAX_TRACKED_SETUP_STATUS_IPS:
-                    by_time = sorted(_SETUP_STATUS_CACHE.items(), key=lambda entry: entry[1][0])
-                    for k, _ in by_time[: len(by_time) // 2]:
-                        del _SETUP_STATUS_CACHE[k]
-
-            async def _compute_setup_status() -> dict:
-                admin_count = await get_local_provider().count_admin_users()
-                return {"needs_setup": admin_count == 0}
-
-            task = asyncio.create_task(_compute_setup_status())
-            _SETUP_STATUS_INFLIGHT[client_ip] = task
-
-    try:
-        result = await task
-    finally:
-        async with _SETUP_STATUS_INFLIGHT_GUARD:
-            if _SETUP_STATUS_INFLIGHT.get(client_ip) is task:
-                del _SETUP_STATUS_INFLIGHT[client_ip]
-
-    # Cache only the stable "initialized" result to avoid stale setup redirects.
-    if result["needs_setup"] is False:
-        _SETUP_STATUS_CACHE[client_ip] = (time.time(), result)
-    else:
-        _SETUP_STATUS_CACHE.pop(client_ip, None)
-    return result
+    admin_count = await get_local_provider().count_admin_users()
+    return {"needs_setup": admin_count == 0}
 
 
 class InitializeAdminRequest(BaseModel):

backend/app/gateway/routers/mcp.py

@@ -3,10 +3,12 @@ import logging
 from pathlib import Path
 from typing import Literal
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 
-from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.extensions_config import ExtensionsConfig
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["mcp"])
@@ -63,106 +65,13 @@ class McpConfigUpdateRequest(BaseModel):
     )
 
 
-_MASKED_VALUE = "***"
-
-
-def _mask_server_config(server: McpServerConfigResponse) -> McpServerConfigResponse:
-    """Return a copy of server config with sensitive fields masked.
-
-    Masks env values, header values, and removes OAuth secrets so they
-    are not exposed through the GET API endpoint.
-    """
-    masked_env = {k: _MASKED_VALUE for k in server.env}
-    masked_headers = {k: _MASKED_VALUE for k in server.headers}
-    masked_oauth = None
-    if server.oauth is not None:
-        masked_oauth = server.oauth.model_copy(
-            update={
-                "client_secret": None,
-                "refresh_token": None,
-            }
-        )
-    return server.model_copy(
-        update={
-            "env": masked_env,
-            "headers": masked_headers,
-            "oauth": masked_oauth,
-        }
-    )
-
-
-def _merge_preserving_secrets(
-    incoming: McpServerConfigResponse,
-    existing: McpServerConfigResponse,
-) -> McpServerConfigResponse:
-    """Merge incoming config with existing, preserving secrets masked by GET.
-
-    When the frontend toggles ``enabled`` it round-trips the full config:
-    GET (masked) → modify enabled → PUT (masked values sent back).
-    This function ensures masked values (``***``) are replaced with the
-    real secrets from the current on-disk config.
-
-    ``***`` is only accepted for keys that already exist in *existing*.
-    New keys must provide a real value.
-
-    For OAuth secrets, ``None`` means "preserve the existing stored value"
-    so masked GET responses can be safely round-tripped. To explicitly clear
-    a stored secret, clients may send an empty string, which is converted
-    to ``None`` before persisting.
-    """
-    merged_env = {}
-    for k, v in incoming.env.items():
-        if v == _MASKED_VALUE:
-            if k in existing.env:
-                merged_env[k] = existing.env[k]
-            else:
-                raise HTTPException(
-                    status_code=400,
-                    detail=f"Cannot set env key '{k}' to masked value '***'; provide a real value.",
-                )
-        else:
-            merged_env[k] = v
-
-    merged_headers = {}
-    for k, v in incoming.headers.items():
-        if v == _MASKED_VALUE:
-            if k in existing.headers:
-                merged_headers[k] = existing.headers[k]
-            else:
-                raise HTTPException(
-                    status_code=400,
-                    detail=f"Cannot set header '{k}' to masked value '***'; provide a real value.",
-                )
-        else:
-            merged_headers[k] = v
-
-    merged_oauth = incoming.oauth
-    if incoming.oauth is not None and existing.oauth is not None:
-        # None = preserve (masked round-trip), "" = explicitly clear, else = new value
-        merged_client_secret = existing.oauth.client_secret if incoming.oauth.client_secret is None else (None if incoming.oauth.client_secret == "" else incoming.oauth.client_secret)
-        merged_refresh_token = existing.oauth.refresh_token if incoming.oauth.refresh_token is None else (None if incoming.oauth.refresh_token == "" else incoming.oauth.refresh_token)
-        merged_oauth = incoming.oauth.model_copy(
-            update={
-                "client_secret": merged_client_secret,
-                "refresh_token": merged_refresh_token,
-            }
-        )
-    return incoming.model_copy(
-        update={
-            "env": merged_env,
-            "headers": merged_headers,
-            "oauth": merged_oauth,
-        }
-    )
-
-
 @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:
+async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpConfigResponse:
     """Get the current MCP configuration.
 
     Returns:
@@ -176,17 +85,16 @@ async def get_mcp_configuration() -> McpConfigResponse:
                     "enabled": true,
                     "command": "npx",
                     "args": ["-y", "@modelcontextprotocol/server-github"],
-                    "env": {"GITHUB_TOKEN": "***"},
+                    "env": {"GITHUB_TOKEN": "ghp_xxx"},
                     "description": "GitHub MCP server for repository operations"
                 }
             }
         }
         ```
     """
-    config = get_extensions_config()
+    ext = config.extensions
 
-    servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in config.mcp_servers.items()}
-    return McpConfigResponse(mcp_servers=servers)
+    return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in ext.mcp_servers.items()})
 
 
 @router.put(
@@ -195,7 +103,11 @@ async def get_mcp_configuration() -> 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:
+async def update_mcp_configuration(
+    request: McpConfigUpdateRequest,
+    http_request: Request,
+    config: AppConfig = Depends(get_config),
+) -> McpConfigResponse:
     """Update the MCP configuration.
 
     This will:
@@ -236,39 +148,14 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
             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
-        current_config = get_extensions_config()
+        # Use injected config to preserve skills configuration
+        current_ext = config.extensions
 
-        # Load raw (un-resolved) JSON from disk to use as the merge source.
-        # This preserves $VAR placeholders in env values and top-level keys
-        # like mcpInterceptors that would otherwise be lost.
-        raw_servers: dict[str, dict] = {}
-        raw_other_keys: dict = {}
-        if config_path is not None and config_path.exists():
-            with open(config_path, encoding="utf-8") as f:
-                raw_data = json.load(f)
-            raw_servers = raw_data.get("mcpServers", {})
-            # Preserve any top-level keys beyond mcpServers/skills
-            for key, value in raw_data.items():
-                if key not in ("mcpServers", "skills"):
-                    raw_other_keys[key] = value
-
-        # Merge incoming server configs with raw on-disk secrets
-        merged_servers: dict[str, McpServerConfigResponse] = {}
-        for name, incoming in request.mcp_servers.items():
-            raw_server = raw_servers.get(name)
-            if raw_server is not None:
-                merged_servers[name] = _merge_preserving_secrets(
-                    incoming,
-                    McpServerConfigResponse(**raw_server),
-                )
-            else:
-                merged_servers[name] = incoming
-
-        # Build config data preserving all top-level keys from the original file
-        config_data = dict(raw_other_keys)
-        config_data["mcpServers"] = {name: server.model_dump() for name, server in merged_servers.items()}
-        config_data["skills"] = {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()}
+        # 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_ext.skills.items()},
+        }
 
         # Write the configuration to file
         with open(config_path, "w", encoding="utf-8") as f:
@@ -276,12 +163,14 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
 
         logger.info(f"MCP configuration updated and saved to: {config_path}")
 
-        # Reload the Gateway configuration and update the global cache. The
-        # agent runtime lives in Gateway, so this keeps API reads and tool
-        # execution aligned after extensions_config.json changes.
-        reloaded_config = reload_extensions_config()
-        servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in reloaded_config.mcp_servers.items()}
-        return McpConfigResponse(mcp_servers=servers)
+        # 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 swap ``app.state.config`` so subsequent
+        # ``Depends(get_config)`` calls see the refreshed value.
+        reloaded = AppConfig.from_file()
+        http_request.app.state.config = reloaded
+        return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded.extensions.mcp_servers.items()})
 
     except Exception as e:
         logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)

backend/app/gateway/routers/memory.py

@@ -1,8 +1,9 @@
 """Memory API router for retrieving and managing global memory data."""
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
+from app.gateway.deps import get_config
 from deerflow.agents.memory.updater import (
     clear_memory_data,
     create_memory_fact,
@@ -12,7 +13,7 @@ from deerflow.agents.memory.updater import (
     reload_memory_data,
     update_memory_fact,
 )
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
 from deerflow.runtime.user_context import get_effective_user_id
 
 router = APIRouter(prefix="/api", tags=["memory"])
@@ -114,7 +115,7 @@ class MemoryStatusResponse(BaseModel):
     summary="Get Memory Data",
     description="Retrieve the current global memory data including user context, history, and facts.",
 )
-async def get_memory() -> MemoryResponse:
+async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Get the current global memory data.
 
     Returns:
@@ -148,7 +149,7 @@ async def get_memory() -> MemoryResponse:
         }
         ```
     """
-    memory_data = get_memory_data(user_id=get_effective_user_id())
+    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -159,7 +160,7 @@ async def get_memory() -> MemoryResponse:
     summary="Reload Memory Data",
     description="Reload memory data from the storage file, refreshing the in-memory cache.",
 )
-async def reload_memory() -> MemoryResponse:
+async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Reload memory data from file.
 
     This forces a reload of the memory data from the storage file,
@@ -168,7 +169,7 @@ async def reload_memory() -> MemoryResponse:
     Returns:
         The reloaded memory data.
     """
-    memory_data = reload_memory_data(user_id=get_effective_user_id())
+    memory_data = reload_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -179,10 +180,10 @@ async def reload_memory() -> MemoryResponse:
     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:
+async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Clear all persisted memory data."""
     try:
-        memory_data = clear_memory_data(user_id=get_effective_user_id())
+        memory_data = clear_memory_data(app_config.memory, user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
 
@@ -196,10 +197,11 @@ async def clear_memory() -> MemoryResponse:
     summary="Create Memory Fact",
     description="Create a single saved memory fact manually.",
 )
-async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
+async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Create a single fact manually."""
     try:
         memory_data = create_memory_fact(
+            app_config.memory,
             content=request.content,
             category=request.category,
             confidence=request.confidence,
@@ -220,10 +222,10 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
     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:
+async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Delete a single fact from memory by fact id."""
     try:
-        memory_data = delete_memory_fact(fact_id, user_id=get_effective_user_id())
+        memory_data = delete_memory_fact(app_config.memory, fact_id, user_id=get_effective_user_id())
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
     except OSError as exc:
@@ -239,10 +241,11 @@ async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
     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:
+async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Partially update a single fact manually."""
     try:
         memory_data = update_memory_fact(
+            app_config.memory,
             fact_id=fact_id,
             content=request.content,
             category=request.category,
@@ -266,9 +269,9 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
     summary="Export Memory Data",
     description="Export the current global memory data as JSON for backup or transfer.",
 )
-async def export_memory() -> MemoryResponse:
+async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Export the current memory data."""
-    memory_data = get_memory_data(user_id=get_effective_user_id())
+    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -279,10 +282,10 @@ async def export_memory() -> MemoryResponse:
     summary="Import Memory Data",
     description="Import and overwrite the current global memory data from a JSON payload.",
 )
-async def import_memory(request: MemoryResponse) -> MemoryResponse:
+async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Import and persist memory data."""
     try:
-        memory_data = import_memory_data(request.model_dump(), user_id=get_effective_user_id())
+        memory_data = import_memory_data(app_config.memory, request.model_dump(), user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
 
@@ -295,7 +298,9 @@ async def import_memory(request: MemoryResponse) -> MemoryResponse:
     summary="Get Memory Configuration",
     description="Retrieve the current memory system configuration.",
 )
-async def get_memory_config_endpoint() -> MemoryConfigResponse:
+async def get_memory_config_endpoint(
+    app_config: AppConfig = Depends(get_config),
+) -> MemoryConfigResponse:
     """Get the memory system configuration.
 
     Returns:
@@ -314,7 +319,7 @@ async def get_memory_config_endpoint() -> MemoryConfigResponse:
         }
         ```
     """
-    config = get_memory_config()
+    config = app_config.memory
     return MemoryConfigResponse(
         enabled=config.enabled,
         storage_path=config.storage_path,
@@ -333,14 +338,16 @@ async def get_memory_config_endpoint() -> MemoryConfigResponse:
     summary="Get Memory Status",
     description="Retrieve both memory configuration and current data in a single request.",
 )
-async def get_memory_status() -> MemoryStatusResponse:
+async def get_memory_status(
+    app_config: AppConfig = Depends(get_config),
+) -> 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(user_id=get_effective_user_id())
+    config = app_config.memory
+    memory_data = get_memory_data(config, user_id=get_effective_user_id())
 
     return MemoryStatusResponse(
         config=MemoryConfigResponse(

backend/app/gateway/routers/runs.py

@@ -7,6 +7,7 @@ is reused so that conversation history is preserved across calls.
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import uuid
 
@@ -15,9 +16,8 @@ from fastapi.responses import StreamingResponse
 
 from app.gateway.authz import require_permission
 from app.gateway.deps import get_checkpointer, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
-from app.gateway.pagination import trim_run_message_page
 from app.gateway.routers.thread_runs import RunCreateRequest
-from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
+from app.gateway.services import sse_consumer, start_run
 from deerflow.runtime import serialize_channel_values
 
 logger = logging.getLogger(__name__)
@@ -66,25 +66,24 @@ async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
     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)
 
-    completed = True
     if record.task is not None:
-        completed = await wait_for_run_completion(bridge, record, request, run_mgr)
-
-    if completed:
-        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)
+            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}
 
@@ -124,13 +123,13 @@ async def run_messages(
     run = await _resolve_run(run_id, request)
     event_store = get_run_event_store(request)
     rows = await event_store.list_messages_by_run(
-        run["thread_id"],
-        run_id,
+        run["thread_id"], run_id,
         limit=limit + 1,
         before_seq=before_seq,
         after_seq=after_seq,
     )
-    data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
+    has_more = len(rows) > limit
+    data = rows[:limit] if has_more else rows
     return {"data": data, "has_more": has_more}
 
 

backend/app/gateway/routers/skills.py

@@ -1,20 +1,32 @@
+import errno
 import json
 import logging
+import shutil
 from pathlib import Path
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 
 from app.gateway.deps import get_config
 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.app_config import AppConfig
-from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
-from deerflow.skills import Skill
-from deerflow.skills.installer import SkillAlreadyExistsError
+from deerflow.config.extensions_config import ExtensionsConfig
+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
-from deerflow.skills.storage import get_or_new_skill_storage
-from deerflow.skills.types import SKILL_MD_FILE, SkillCategory
 
 logger = logging.getLogger(__name__)
 
@@ -27,7 +39,7 @@ class SkillResponse(BaseModel):
     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: SkillCategory = Field(..., description="Category of the skill (public or custom)")
+    category: str = Field(..., description="Category of the skill (public or custom)")
     enabled: bool = Field(default=True, description="Whether this skill is enabled")
 
 
@@ -91,9 +103,9 @@ def _skill_to_response(skill: Skill) -> SkillResponse:
     summary="List All Skills",
     description="Retrieve a list of all available skills from both public and custom directories.",
 )
-async def list_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
+async def list_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skills = load_skills(app_config, 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)
@@ -106,11 +118,11 @@ async def list_skills(config: AppConfig = Depends(get_config)) -> SkillsListResp
     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, config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
+async def install_skill(request: SkillInstallRequest, app_config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
     try:
         skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
-        result = await get_or_new_skill_storage(app_config=config).ainstall_skill_from_archive(skill_file_path)
-        await refresh_skills_system_prompt_cache_async()
+        result = install_skill_from_archive(skill_file_path)
+        await refresh_skills_system_prompt_cache_async(app_config)
         return SkillInstallResponse(**result)
     except FileNotFoundError as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -126,9 +138,9 @@ async def install_skill(request: SkillInstallRequest, config: AppConfig = Depend
 
 
 @router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
-async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
+async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = [skill for skill in get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False) if skill.category == SkillCategory.CUSTOM]
+        skills = [skill for skill in load_skills(app_config, 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)
@@ -136,14 +148,13 @@ async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsL
 
 
 @router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
-async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
+async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
-        skill = next((s for s in skills if s.name == skill_name and s.category == SkillCategory.CUSTOM), None)
+        skills = load_skills(app_config, 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=get_or_new_skill_storage(app_config=config).read_custom_skill(skill_name))
+        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name, app_config))
     except HTTPException:
         raise
     except Exception as e:
@@ -152,31 +163,35 @@ async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_conf
 
 
 @router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
-async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
+async def update_custom_skill(
+    skill_name: str,
+    request: CustomSkillUpdateRequest,
+    app_config: AppConfig = Depends(get_config),
+) -> CustomSkillContentResponse:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        storage = get_or_new_skill_storage(app_config=config)
-        storage.ensure_custom_skill_is_editable(skill_name)
-        storage.validate_skill_markdown_content(skill_name, request.content)
-        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
+        ensure_custom_skill_is_editable(skill_name, app_config)
+        validate_skill_markdown_content(skill_name, request.content)
+        scan = await scan_skill_content(app_config, 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}")
-        prev_content = storage.read_custom_skill(skill_name)
-        storage.write_custom_skill(skill_name, SKILL_MD_FILE, request.content)
-        storage.append_history(
+        skill_file = get_custom_skill_dir(skill_name, app_config) / "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_FILE,
+                "file_path": "SKILL.md",
                 "prev_content": prev_content,
                 "new_content": request.content,
                 "scanner": {"decision": scan.decision, "reason": scan.reason},
             },
+            app_config,
         )
-        await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name, config)
+        await refresh_skills_system_prompt_cache_async(app_config)
+        return await get_custom_skill(skill_name, app_config)
     except HTTPException:
         raise
     except FileNotFoundError as e:
@@ -189,23 +204,31 @@ async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest
 
 
 @router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
-async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> dict[str, bool]:
+async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> dict[str, bool]:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        storage = get_or_new_skill_storage(app_config=config)
-        storage.delete_custom_skill(
-            skill_name,
-            history_meta={
-                "action": "human_delete",
-                "author": "human",
-                "thread_id": None,
-                "file_path": SKILL_MD_FILE,
-                "prev_content": None,
-                "new_content": None,
-                "scanner": {"decision": "allow", "reason": "Deletion requested."},
-            },
-        )
-        await refresh_skills_system_prompt_cache_async()
+        ensure_custom_skill_is_editable(skill_name, app_config)
+        skill_dir = get_custom_skill_dir(skill_name, app_config)
+        prev_content = read_custom_skill_content(skill_name, app_config)
+        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."},
+                },
+                app_config,
+            )
+        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(app_config)
         return {"success": True}
     except FileNotFoundError as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -217,13 +240,11 @@ async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_c
 
 
 @router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
-async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
+async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        storage = get_or_new_skill_storage(app_config=config)
-        if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
+        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillHistoryResponse(history=storage.read_history(skill_name))
+        return CustomSkillHistoryResponse(history=read_history(skill_name, app_config))
     except HTTPException:
         raise
     except Exception as e:
@@ -232,39 +253,42 @@ async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(
 
 
 @router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
-async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
+async def rollback_custom_skill(
+    skill_name: str,
+    request: SkillRollbackRequest,
+    app_config: AppConfig = Depends(get_config),
+) -> CustomSkillContentResponse:
     try:
-        storage = get_or_new_skill_storage(app_config=config)
-        if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
+        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        history = storage.read_history(skill_name)
+        history = read_history(skill_name, app_config)
         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")
-        storage.validate_skill_markdown_content(skill_name, target_content)
-        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
-        skill_file = storage.get_custom_skill_file(skill_name)
+        validate_skill_markdown_content(skill_name, target_content)
+        scan = await scan_skill_content(app_config, target_content, executable=False, location=f"{skill_name}/SKILL.md")
+        skill_file = get_custom_skill_file(skill_name, app_config)
         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_FILE,
+            "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":
-            storage.append_history(skill_name, history_entry)
+            append_history(skill_name, history_entry, app_config)
             raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
-        storage.write_custom_skill(skill_name, SKILL_MD_FILE, target_content)
-        storage.append_history(skill_name, history_entry)
-        await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name, config)
+        atomic_write(skill_file, target_content)
+        append_history(skill_name, history_entry, app_config)
+        await refresh_skills_system_prompt_cache_async(app_config)
+        return await get_custom_skill(skill_name, app_config)
     except HTTPException:
         raise
     except IndexError:
@@ -284,10 +308,9 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest,
     summary="Get Skill Details",
     description="Retrieve detailed information about a specific skill by its name.",
 )
-async def get_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> SkillResponse:
+async def get_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> SkillResponse:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -307,10 +330,14 @@ async def get_skill(skill_name: str, config: AppConfig = Depends(get_config)) ->
     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, config: AppConfig = Depends(get_config)) -> SkillResponse:
+async def update_skill(
+    skill_name: str,
+    request: SkillUpdateRequest,
+    http_request: Request,
+    app_config: AppConfig = Depends(get_config),
+) -> SkillResponse:
     try:
-        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -321,22 +348,29 @@ async def update_skill(skill_name: str, request: SkillUpdateRequest, config: App
             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)
+        # Do not mutate the frozen AppConfig in place. Compose the new skills
+        # state in a fresh dict, write to disk, and reload AppConfig below so
+        # every subsequent Depends(get_config) sees the refreshed snapshot.
+        ext = app_config.extensions
+        updated_skills = {name: {"enabled": skill_config.enabled} for name, skill_config in ext.skills.items()}
+        updated_skills[skill_name] = {"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()},
+            "mcpServers": {name: server.model_dump() for name, server in ext.mcp_servers.items()},
+            "skills": updated_skills,
         }
 
         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()
+        # Reload AppConfig and swap ``app.state.config`` so subsequent
+        # ``Depends(get_config)`` sees the refreshed value.
+        reloaded = AppConfig.from_file()
+        http_request.app.state.config = reloaded
+        await refresh_skills_system_prompt_cache_async(reloaded)
 
-        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skills = load_skills(reloaded, enabled_only=False)
         updated_skill = next((s for s in skills if s.name == skill_name), None)
 
         if updated_skill is None:

backend/app/gateway/routers/suggestions.py

@@ -102,12 +102,7 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
     description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
 )
 @require_permission("threads", "read", owner_check=True)
-async def generate_suggestions(
-    thread_id: str,
-    body: SuggestionsRequest,
-    request: Request,
-    config: AppConfig = Depends(get_config),
-) -> SuggestionsResponse:
+async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request, app_config: AppConfig = Depends(get_config)) -> SuggestionsResponse:
     if not body.messages:
         return SuggestionsResponse(suggestions=[])
 
@@ -129,7 +124,7 @@ async def generate_suggestions(
     user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
 
     try:
-        model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=config)
+        model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=app_config)
         response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []

backend/app/gateway/routers/thread_runs.py

@@ -21,9 +21,8 @@ from pydantic import BaseModel, Field
 
 from app.gateway.authz import require_permission
 from app.gateway.deps import get_checkpointer, get_current_user, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
-from app.gateway.pagination import trim_run_message_page
-from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
-from deerflow.runtime import RunRecord, RunStatus, serialize_channel_values
+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"])
@@ -55,6 +54,7 @@ class RunCreateRequest(BaseModel):
     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")
+    follow_up_to_run_id: str | None = Field(default=None, description="Run ID this message follows up on. Auto-detected from latest successful run if not provided.")
 
 
 class RunResponse(BaseModel):
@@ -67,35 +67,6 @@ class RunResponse(BaseModel):
     multitask_strategy: str = "reject"
     created_at: str = ""
     updated_at: str = ""
-    total_input_tokens: int = 0
-    total_output_tokens: int = 0
-    total_tokens: int = 0
-    llm_call_count: int = 0
-    lead_agent_tokens: int = 0
-    subagent_tokens: int = 0
-    middleware_tokens: int = 0
-    message_count: int = 0
-
-
-class ThreadTokenUsageModelBreakdown(BaseModel):
-    tokens: int = 0
-    runs: int = 0
-
-
-class ThreadTokenUsageCallerBreakdown(BaseModel):
-    lead_agent: int = 0
-    subagent: int = 0
-    middleware: int = 0
-
-
-class ThreadTokenUsageResponse(BaseModel):
-    thread_id: str
-    total_tokens: int = 0
-    total_input_tokens: int = 0
-    total_output_tokens: int = 0
-    total_runs: int = 0
-    by_model: dict[str, ThreadTokenUsageModelBreakdown] = Field(default_factory=dict)
-    by_caller: ThreadTokenUsageCallerBreakdown = Field(default_factory=ThreadTokenUsageCallerBreakdown)
 
 
 # ---------------------------------------------------------------------------
@@ -103,12 +74,6 @@ class ThreadTokenUsageResponse(BaseModel):
 # ---------------------------------------------------------------------------
 
 
-def _cancel_conflict_detail(run_id: str, record: RunRecord) -> str:
-    if record.status in (RunStatus.pending, RunStatus.running):
-        return f"Run {run_id} is not active on this worker and cannot be cancelled"
-    return f"Run {run_id} is not cancellable (status: {record.status.value})"
-
-
 def _record_to_response(record: RunRecord) -> RunResponse:
     return RunResponse(
         run_id=record.run_id,
@@ -120,14 +85,6 @@ def _record_to_response(record: RunRecord) -> RunResponse:
         multitask_strategy=record.multitask_strategy,
         created_at=record.created_at,
         updated_at=record.updated_at,
-        total_input_tokens=record.total_input_tokens,
-        total_output_tokens=record.total_output_tokens,
-        total_tokens=record.total_tokens,
-        llm_call_count=record.llm_call_count,
-        lead_agent_tokens=record.lead_agent_tokens,
-        subagent_tokens=record.subagent_tokens,
-        middleware_tokens=record.middleware_tokens,
-        message_count=record.message_count,
     )
 
 
@@ -176,25 +133,24 @@ async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -
 @require_permission("runs", "create", owner_check=True, require_existing=True)
 async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
     """Create a run and block until it completes, returning the final state."""
-    bridge = get_stream_bridge(request)
-    run_mgr = get_run_manager(request)
     record = await start_run(body, thread_id, request)
 
-    completed = True
     if record.task is not None:
-        completed = await wait_for_run_completion(bridge, record, request, run_mgr)
-
-    if completed:
-        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)
+            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}
 
@@ -204,8 +160,7 @@ async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) ->
 async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
     """List all runs for a thread."""
     run_mgr = get_run_manager(request)
-    user_id = await get_current_user(request)
-    records = await run_mgr.list_by_thread(thread_id, user_id=user_id)
+    records = await run_mgr.list_by_thread(thread_id)
     return [_record_to_response(r) for r in records]
 
 
@@ -214,8 +169,7 @@ async def list_runs(thread_id: str, request: Request) -> list[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)
-    user_id = await get_current_user(request)
-    record = await run_mgr.get(run_id, user_id=user_id)
+    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)
@@ -238,13 +192,16 @@ async def cancel_run(
     - wait=false: Return immediately with 202
     """
     run_mgr = get_run_manager(request)
-    record = await run_mgr.get(run_id)
+    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=_cancel_conflict_detail(run_id, record))
+        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:
@@ -260,14 +217,12 @@ async def cancel_run(
 @require_permission("runs", "read", owner_check=True)
 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 = await run_mgr.get(run_id)
+    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")
-    if record.store_only:
-        raise HTTPException(status_code=409, detail=f"Run {run_id} is not active on this worker and cannot be streamed")
 
-    bridge = get_stream_bridge(request)
     return StreamingResponse(
         sse_consumer(bridge, record, request, run_mgr),
         media_type="text/event-stream",
@@ -279,12 +234,7 @@ async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingRe
     )
 
 
-# Register GET and POST as separate routes so each method gets a unique OpenAPI
-# operationId. ``api_route(methods=["GET", "POST"])`` shares one route registration
-# across both methods, which makes FastAPI emit the same ``operationId`` twice and
-# warn about a duplicate operation id during OpenAPI generation.
-@router.get("/{thread_id}/runs/{run_id}/stream", response_model=None)
-@router.post("/{thread_id}/runs/{run_id}/stream", response_model=None)
+@router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
 @require_permission("runs", "read", owner_check=True)
 async def stream_existing_run(
     thread_id: str,
@@ -301,18 +251,14 @@ async def stream_existing_run(
     remaining buffered events so the client observes a clean shutdown.
     """
     run_mgr = get_run_manager(request)
-    record = await run_mgr.get(run_id)
+    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")
-    if record.store_only and action is None:
-        raise HTTPException(status_code=409, detail=f"Run {run_id} is not active on this worker and cannot be streamed")
 
     # 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 not cancelled:
-            raise HTTPException(status_code=409, detail=_cancel_conflict_detail(run_id, record))
-        if wait and record.task is not None:
+        if cancelled and wait and record.task is not None:
             try:
                 await record.task
             except (asyncio.CancelledError, Exception):
@@ -366,15 +312,11 @@ async def list_thread_messages(
         if i in last_ai_indices:
             run_id = msg["run_id"]
             fb = feedback_map.get(run_id)
-            msg["feedback"] = (
-                {
-                    "feedback_id": fb["feedback_id"],
-                    "rating": fb["rating"],
-                    "comment": fb.get("comment"),
-                }
-                if fb
-                else None
-            )
+            msg["feedback"] = {
+                "feedback_id": fb["feedback_id"],
+                "rating": fb["rating"],
+                "comment": fb.get("comment"),
+            } if fb else None
         else:
             msg["feedback"] = None
 
@@ -397,13 +339,13 @@ async def list_run_messages(
     """
     event_store = get_run_event_store(request)
     rows = await event_store.list_messages_by_run(
-        thread_id,
-        run_id,
+        thread_id, run_id,
         limit=limit + 1,
         before_seq=before_seq,
         after_seq=after_seq,
     )
-    data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
+    has_more = len(rows) > limit
+    data = rows[:limit] if has_more else rows
     return {"data": data, "has_more": has_more}
 
 
@@ -422,17 +364,10 @@ async def list_run_events(
     return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)
 
 
-@router.get("/{thread_id}/token-usage", response_model=ThreadTokenUsageResponse)
+@router.get("/{thread_id}/token-usage")
 @require_permission("threads", "read", owner_check=True)
-async def thread_token_usage(
-    thread_id: str,
-    request: Request,
-    include_active: bool = Query(default=False, description="Include running run progress snapshots"),
-) -> ThreadTokenUsageResponse:
+async def thread_token_usage(thread_id: str, request: Request) -> dict:
     """Thread-level token usage aggregation."""
     run_store = get_run_store(request)
-    if include_active:
-        agg = await run_store.aggregate_tokens_by_thread(thread_id, include_active=True)
-    else:
-        agg = await run_store.aggregate_tokens_by_thread(thread_id)
-    return ThreadTokenUsageResponse(thread_id=thread_id, **agg)
+    agg = await run_store.aggregate_tokens_by_thread(thread_id)
+    return {"thread_id": thread_id, **agg}

backend/app/gateway/routers/threads.py

@@ -13,11 +13,12 @@ matching the LangGraph Platform wire format expected by the
 from __future__ import annotations
 
 import logging
+import re
+import time
 import uuid
 from typing import Any
 
 from fastapi import APIRouter, HTTPException, Request
-from langgraph.checkpoint.base import empty_checkpoint
 from pydantic import BaseModel, Field, field_validator
 
 from app.gateway.authz import require_permission
@@ -26,7 +27,6 @@ from app.gateway.utils import sanitize_log_param
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime import serialize_channel_values
 from deerflow.runtime.user_context import get_effective_user_id
-from deerflow.utils.time import coerce_iso, now_iso
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api/threads", tags=["threads"])
@@ -90,28 +90,6 @@ class ThreadSearchRequest(BaseModel):
     offset: int = Field(default=0, ge=0, description="Pagination offset")
     status: str | None = Field(default=None, description="Filter by thread status")
 
-    @field_validator("metadata")
-    @classmethod
-    def _validate_metadata_filters(cls, v: dict[str, Any]) -> dict[str, Any]:
-        """Reject filter entries the SQL backend cannot compile.
-
-        Enforces consistent behaviour across SQL and memory backends.
-        See ``deerflow.persistence.json_compat`` for the shared validators.
-        """
-        if not v:
-            return v
-        from deerflow.persistence.json_compat import validate_metadata_filter_key, validate_metadata_filter_value
-
-        bad_entries: list[str] = []
-        for key, value in v.items():
-            if not validate_metadata_filter_key(key):
-                bad_entries.append(f"{key!r} (unsafe key)")
-            elif not validate_metadata_filter_value(value):
-                bad_entries.append(f"{key!r} (unsupported value type {type(value).__name__})")
-        if bad_entries:
-            raise ValueError(f"Invalid metadata filter entries: {', '.join(bad_entries)}")
-        return v
-
 
 class ThreadStateResponse(BaseModel):
     """Response model for thread state."""
@@ -256,7 +234,7 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     checkpointer = get_checkpointer(request)
     thread_store = get_thread_store(request)
     thread_id = body.thread_id or str(uuid.uuid4())
-    now = now_iso()
+    now = time.time()
     # ``body.metadata`` is already stripped of server-reserved keys by
     # ``ThreadCreateRequest._strip_reserved`` — see the model definition.
 
@@ -266,8 +244,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
         return ThreadResponse(
             thread_id=thread_id,
             status=existing_record.get("status", "idle"),
-            created_at=coerce_iso(existing_record.get("created_at", "")),
-            updated_at=coerce_iso(existing_record.get("updated_at", "")),
+            created_at=str(existing_record.get("created_at", "")),
+            updated_at=str(existing_record.get("updated_at", "")),
             metadata=existing_record.get("metadata", {}),
         )
 
@@ -285,6 +263,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     # 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",
@@ -302,8 +282,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     return ThreadResponse(
         thread_id=thread_id,
         status="idle",
-        created_at=now,
-        updated_at=now,
+        created_at=str(now),
+        updated_at=str(now),
         metadata=body.metadata,
     )
 
@@ -316,27 +296,20 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
     (SQL-backed for sqlite/postgres, Store-backed for memory mode).
     """
     from app.gateway.deps import get_thread_store
-    from deerflow.persistence.thread_meta import InvalidMetadataFilterError
 
     repo = get_thread_store(request)
-    try:
-        rows = await repo.search(
-            metadata=body.metadata or None,
-            status=body.status,
-            limit=body.limit,
-            offset=body.offset,
-        )
-    except InvalidMetadataFilterError as exc:
-        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    rows = await repo.search(
+        metadata=body.metadata or None,
+        status=body.status,
+        limit=body.limit,
+        offset=body.offset,
+    )
     return [
         ThreadResponse(
             thread_id=r["thread_id"],
             status=r.get("status", "idle"),
-            # ``coerce_iso`` heals legacy unix-second values that
-            # ``MemoryThreadMetaStore`` historically wrote with ``time.time()``;
-            # SQL-backed rows already arrive as ISO strings and pass through.
-            created_at=coerce_iso(r.get("created_at", "")),
-            updated_at=coerce_iso(r.get("updated_at", "")),
+            created_at=r.get("created_at", ""),
+            updated_at=r.get("updated_at", ""),
             metadata=r.get("metadata", {}),
             values={"title": r["display_name"]} if r.get("display_name") else {},
             interrupts={},
@@ -368,8 +341,8 @@ async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Reques
     return ThreadResponse(
         thread_id=thread_id,
         status=record.get("status", "idle"),
-        created_at=coerce_iso(record.get("created_at", "")),
-        updated_at=coerce_iso(record.get("updated_at", "")),
+        created_at=str(record.get("created_at", "")),
+        updated_at=str(record.get("updated_at", "")),
         metadata=record.get("metadata", {}),
     )
 
@@ -409,8 +382,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
         record = {
             "thread_id": thread_id,
             "status": "idle",
-            "created_at": coerce_iso(ckpt_meta.get("created_at", "")),
-            "updated_at": coerce_iso(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
+            "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")},
         }
 
@@ -424,8 +397,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
     return ThreadResponse(
         thread_id=thread_id,
         status=status,
-        created_at=coerce_iso(record.get("created_at", "")),
-        updated_at=coerce_iso(record.get("updated_at", "")),
+        created_at=str(record.get("created_at", "")),
+        updated_at=str(record.get("updated_at", "")),
         metadata=record.get("metadata", {}),
         values=serialize_channel_values(channel_values),
     )
@@ -476,10 +449,10 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
         values=values,
         next=next_tasks,
         metadata=metadata,
-        checkpoint={"id": checkpoint_id, "ts": coerce_iso(metadata.get("created_at", ""))},
+        checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
         checkpoint_id=checkpoint_id,
         parent_checkpoint_id=parent_checkpoint_id,
-        created_at=coerce_iso(metadata.get("created_at", "")),
+        created_at=str(metadata.get("created_at", "")),
         tasks=tasks,
     )
 
@@ -529,7 +502,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
         channel_values.update(body.values)
 
     checkpoint["channel_values"] = channel_values
-    metadata["updated_at"] = now_iso()
+    metadata["updated_at"] = time.time()
 
     if body.as_node:
         metadata["source"] = "update"
@@ -570,7 +543,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
         next=[],
         metadata=metadata,
         checkpoint_id=new_checkpoint_id,
-        created_at=coerce_iso(metadata.get("created_at", "")),
+        created_at=str(metadata.get("created_at", "")),
     )
 
 
@@ -637,7 +610,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
                     parent_checkpoint_id=parent_id,
                     metadata=user_meta,
                     values=values,
-                    created_at=coerce_iso(metadata.get("created_at", "")),
+                    created_at=str(metadata.get("created_at", "")),
                     next=next_tasks,
                 )
             )

backend/app/gateway/routers/uploads.py

@@ -5,7 +5,7 @@ import os
 import stat
 
 from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 from app.gateway.authz import require_permission
 from app.gateway.deps import get_config
@@ -15,15 +15,12 @@ from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
 from deerflow.uploads.manager import (
     PathTraversalError,
-    UnsafeUploadPathError,
-    claim_unique_filename,
     delete_file_safe,
     enrich_file_listing,
     ensure_uploads_dir,
     get_uploads_dir,
     list_files_in_dir,
     normalize_filename,
-    open_upload_file_no_symlink,
     upload_artifact_url,
     upload_virtual_path,
 )
@@ -33,51 +30,13 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
 
-UPLOAD_CHUNK_SIZE = 8192
-DEFAULT_MAX_FILES = 10
-DEFAULT_MAX_FILE_SIZE = 50 * 1024 * 1024
-DEFAULT_MAX_TOTAL_SIZE = 100 * 1024 * 1024
-
-
-class UploadedFileInfo(BaseModel):
-    """Uploaded file metadata exposed by upload and list APIs."""
-
-    filename: str
-    size: int
-    path: str
-    virtual_path: str
-    artifact_url: str
-    extension: str | None = None
-    modified: float | None = None
-    original_filename: str | None = None
-    markdown_file: str | None = None
-    markdown_path: str | None = None
-    markdown_virtual_path: str | None = None
-    markdown_artifact_url: str | None = None
-
 
 class UploadResponse(BaseModel):
     """Response model for file upload."""
 
     success: bool
-    files: list[UploadedFileInfo]
+    files: list[dict[str, str]]
     message: str
-    skipped_files: list[str] = Field(default_factory=list)
-
-
-class UploadListResponse(BaseModel):
-    """Response model for uploaded file listing."""
-
-    files: list[UploadedFileInfo]
-    count: int
-
-
-class UploadLimits(BaseModel):
-    """Application-level upload limits exposed to clients."""
-
-    max_files: int
-    max_file_size: int
-    max_total_size: int
 
 
 def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
@@ -93,30 +52,11 @@ def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
         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 | stat.S_IRGRP | stat.S_IROTH
+    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 _make_file_sandbox_readable(file_path: os.PathLike[str] | str) -> None:
-    """Ensure uploaded files are readable by the sandbox process.
-
-    For Docker sandboxes (AIO), the gateway writes files as root with 0o600
-    permissions, then bind-mounts the host directory into the container. The
-    sandbox process inside the container runs as a non-root user and cannot
-    read those files without group/other read bits. This function adds
-    ``S_IRGRP | S_IROTH`` so the sandbox can read the uploaded content.
-    """
-    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
-
-    readable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IRGRP | stat.S_IROTH
-    chmod_kwargs = {"follow_symlinks": False} if os.chmod in os.supports_follow_symlinks else {}
-    os.chmod(file_path, readable_mode, **chmod_kwargs)
-
-
 def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
     return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
 
@@ -129,72 +69,6 @@ def _get_uploads_config_value(app_config: AppConfig, key: str, default: object)
     return getattr(uploads_cfg, key, default)
 
 
-def _get_upload_limit(app_config: AppConfig, key: str, default: int, *, legacy_key: str | None = None) -> int:
-    try:
-        value = _get_uploads_config_value(app_config, key, None)
-        if value is None and legacy_key is not None:
-            value = _get_uploads_config_value(app_config, legacy_key, None)
-        if value is None:
-            value = default
-        limit = int(value)
-        if limit <= 0:
-            raise ValueError
-        return limit
-    except Exception:
-        logger.warning("Invalid uploads.%s value; falling back to %d", key, default)
-        return default
-
-
-def _get_upload_limits(app_config: AppConfig) -> UploadLimits:
-    return UploadLimits(
-        max_files=_get_upload_limit(app_config, "max_files", DEFAULT_MAX_FILES, legacy_key="max_file_count"),
-        max_file_size=_get_upload_limit(app_config, "max_file_size", DEFAULT_MAX_FILE_SIZE, legacy_key="max_single_file_size"),
-        max_total_size=_get_upload_limit(app_config, "max_total_size", DEFAULT_MAX_TOTAL_SIZE),
-    )
-
-
-def _cleanup_uploaded_paths(paths: list[os.PathLike[str] | str]) -> None:
-    for path in reversed(paths):
-        try:
-            os.unlink(path)
-        except FileNotFoundError:
-            pass
-        except Exception:
-            logger.warning("Failed to clean up upload path after rejected request: %s", path, exc_info=True)
-
-
-async def _write_upload_file_with_limits(
-    file: UploadFile,
-    *,
-    uploads_dir: os.PathLike[str] | str,
-    display_filename: str,
-    max_single_file_size: int,
-    max_total_size: int,
-    total_size: int,
-) -> tuple[os.PathLike[str] | str, int, int]:
-    file_size = 0
-    file_path, fh = open_upload_file_no_symlink(uploads_dir, display_filename)
-    try:
-        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
-            file_size += len(chunk)
-            total_size += len(chunk)
-            if file_size > max_single_file_size:
-                raise HTTPException(status_code=413, detail=f"File too large: {display_filename}")
-            if total_size > max_total_size:
-                raise HTTPException(status_code=413, detail="Total upload size too large")
-            fh.write(chunk)
-    except Exception:
-        fh.close()
-        try:
-            os.unlink(file_path)
-        except FileNotFoundError:
-            pass
-        raise
-    else:
-        fh.close()
-    return file_path, file_size, total_size
-
-
 def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
     """Return whether automatic host-side document conversion is enabled.
 
@@ -211,94 +85,72 @@ def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
 
 
 @router.post("", response_model=UploadResponse)
-@require_permission("threads", "write", owner_check=True, require_existing=False)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
 async def upload_files(
     thread_id: str,
     request: Request,
     files: list[UploadFile] = File(...),
-    config: AppConfig = Depends(get_config),
+    app_config: AppConfig = Depends(get_config),
 ) -> UploadResponse:
     """Upload multiple files to a thread's uploads directory."""
     if not files:
         raise HTTPException(status_code=400, detail="No files provided")
 
-    limits = _get_upload_limits(config)
-    if len(files) > limits.max_files:
-        raise HTTPException(status_code=413, detail=f"Too many files: maximum is {limits.max_files}")
-
     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, user_id=get_effective_user_id())
     uploaded_files = []
-    written_paths = []
-    sandbox_sync_targets = []
-    skipped_files = []
-    total_size = 0
-    # Track filenames within this request so duplicate form parts do not
-    # silently truncate each other. Existing uploads keep the historical
-    # overwrite behavior for a single replacement upload.
-    seen_filenames: set[str] = set()
 
-    sandbox_provider = get_sandbox_provider()
+    sandbox_provider = get_sandbox_provider(app_config)
     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)
-        if sandbox is None:
-            raise HTTPException(status_code=500, detail="Failed to acquire sandbox")
-    auto_convert_documents = _auto_convert_documents_enabled(config)
+    auto_convert_documents = _auto_convert_documents_enabled(app_config)
 
     for file in files:
         if not file.filename:
             continue
 
         try:
-            original_filename = normalize_filename(file.filename)
-            safe_filename = claim_unique_filename(original_filename, seen_filenames)
+            safe_filename = normalize_filename(file.filename)
         except ValueError:
             logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
             continue
 
         try:
-            file_path, file_size, total_size = await _write_upload_file_with_limits(
-                file,
-                uploads_dir=uploads_dir,
-                display_filename=safe_filename,
-                max_single_file_size=limits.max_file_size,
-                max_total_size=limits.max_total_size,
-                total_size=total_size,
-            )
-            written_paths.append(file_path)
+            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:
-                sandbox_sync_targets.append((file_path, virtual_path))
+            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": file_size,
+                "size": str(len(content)),
                 "path": str(sandbox_uploads / safe_filename),
                 "virtual_path": virtual_path,
                 "artifact_url": upload_artifact_url(thread_id, safe_filename),
             }
-            if safe_filename != original_filename:
-                file_info["original_filename"] = original_filename
 
-            logger.info(f"Saved file: {safe_filename} ({file_size} bytes) to {file_info['path']}")
+            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:
-                    written_paths.append(md_path)
                     md_virtual_path = upload_virtual_path(md_path.name)
 
-                    if sync_to_sandbox:
-                        sandbox_sync_targets.append((md_path, md_virtual_path))
+                    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)
@@ -307,59 +159,20 @@ async def upload_files(
 
             uploaded_files.append(file_info)
 
-        except HTTPException as e:
-            _cleanup_uploaded_paths(written_paths)
-            raise e
-        except UnsafeUploadPathError as e:
-            logger.warning("Skipping upload with unsafe destination %s: %s", file.filename, e)
-            skipped_files.append(safe_filename)
-            continue
         except Exception as e:
             logger.error(f"Failed to upload {file.filename}: {e}")
-            _cleanup_uploaded_paths(written_paths)
             raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
 
-    # Uploaded files are created with 0o600 permissions (owner read/write only).
-    # In Docker sandbox deployments the gateway writes as root but the sandbox
-    # process runs as a non-root user (typically UID 1000).  Without group/other
-    # read bits the sandbox cannot access the files — whether the uploads
-    # directory is bind-mounted into the container or synced via
-    # sandbox.update_file.  Always add group/other read bits so every sandbox
-    # configuration can read the uploaded content.
-    for file_path in written_paths:
-        _make_file_sandbox_readable(file_path)
-
-    if sync_to_sandbox:
-        for file_path, virtual_path in sandbox_sync_targets:
-            _make_file_sandbox_writable(file_path)
-            sandbox.update_file(virtual_path, file_path.read_bytes())
-
-    message = f"Successfully uploaded {len(uploaded_files)} file(s)"
-    if skipped_files:
-        message += f"; skipped {len(skipped_files)} unsafe file(s)"
-
     return UploadResponse(
-        success=not skipped_files,
+        success=True,
         files=uploaded_files,
-        message=message,
-        skipped_files=skipped_files,
+        message=f"Successfully uploaded {len(uploaded_files)} file(s)",
     )
 
 
-@router.get("/limits", response_model=UploadLimits)
+@router.get("/list", response_model=dict)
 @require_permission("threads", "read", owner_check=True)
-async def get_upload_limits(
-    thread_id: str,
-    request: Request,
-    config: AppConfig = Depends(get_config),
-) -> UploadLimits:
-    """Return upload limits used by the gateway for this thread."""
-    return _get_upload_limits(config)
-
-
-@router.get("/list", response_model=UploadListResponse)
-@require_permission("threads", "read", owner_check=True)
-async def list_uploaded_files(thread_id: str, request: Request) -> UploadListResponse:
+async def list_uploaded_files(thread_id: str, request: Request) -> dict:
     """List all files in a thread's uploads directory."""
     try:
         uploads_dir = get_uploads_dir(thread_id)
@@ -373,7 +186,7 @@ async def list_uploaded_files(thread_id: str, request: Request) -> UploadListRes
     for f in result["files"]:
         f["path"] = str(sandbox_uploads / f["filename"])
 
-    return UploadListResponse(**result)
+    return result
 
 
 @router.delete("/{filename}")

backend/app/gateway/services.py

@@ -8,20 +8,19 @@ frames, and consuming stream bridge events.  Router modules
 from __future__ import annotations
 
 import asyncio
+import dataclasses
 import json
 import logging
 import re
+import time
 from collections.abc import Mapping
 from typing import Any
 
 from fastapi import HTTPException, Request
-from langchain_core.messages import BaseMessage
-from langchain_core.messages.utils import convert_to_messages
+from langchain_core.messages import HumanMessage
 
-from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
-from app.gateway.internal_auth import INTERNAL_SYSTEM_ROLE
+from app.gateway.deps import get_run_context, get_run_manager, get_run_store, get_stream_bridge
 from app.gateway.utils import sanitize_log_param
-from deerflow.config.app_config import get_app_config
 from deerflow.runtime import (
     END_SENTINEL,
     HEARTBEAT_SENTINEL,
@@ -34,7 +33,6 @@ from deerflow.runtime import (
     UnsupportedStrategyError,
     run_agent,
 )
-from deerflow.runtime.runs.naming import resolve_root_run_name
 
 logger = logging.getLogger(__name__)
 
@@ -78,35 +76,21 @@ def normalize_stream_modes(raw: list[str] | str | None) -> list[str]:
 
 
 def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
-    """Convert LangGraph Platform input format to LangChain state dict.
-
-    Delegates dict→message coercion to ``langchain_core.messages.utils.convert_to_messages``
-    so that ``additional_kwargs`` (e.g. uploaded-file metadata — gh #3132), ``id``,
-    ``name``, and non-human roles (ai/system/tool) survive unchanged.  An earlier
-    hand-rolled version only forwarded ``content`` and collapsed every role to
-    ``HumanMessage``, which silently stripped frontend-supplied attachments.
-
-    Malformed message dicts (missing ``role``/``type``/``content``, unsupported
-    role, etc.) raise ``HTTPException(400)`` with the offending index, instead
-    of bubbling up as a 500.  The gateway is a system boundary, so per-entry
-    validation errors are the right shape for clients to retry against.
-    """
+    """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: list[Any] = []
-        for index, msg in enumerate(messages):
-            if isinstance(msg, BaseMessage):
-                converted.append(msg)
-            elif isinstance(msg, dict):
-                try:
-                    converted.extend(convert_to_messages([msg]))
-                except (ValueError, TypeError, NotImplementedError) as exc:
-                    raise HTTPException(
-                        status_code=400,
-                        detail=f"Invalid message at input.messages[{index}]: {exc}",
-                    ) from exc
+        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}
@@ -116,74 +100,6 @@ def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
 _DEFAULT_ASSISTANT_ID = "lead_agent"
 
 
-# Whitelist of run-context keys that the langgraph-compat layer forwards from
-# ``body.context`` into the run config. ``config["context"]`` exists in
-# LangGraph >=0.6, but these values must be written to both ``configurable``
-# (for legacy ``_get_runtime_config`` consumers) and ``context`` because
-# LangGraph >=1.1.9 no longer makes ``ToolRuntime.context`` fall back to
-# ``configurable`` for consumers like ``setup_agent``.
-_CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
-    {
-        "model_name",
-        "mode",
-        "thinking_enabled",
-        "reasoning_effort",
-        "is_plan_mode",
-        "subagent_enabled",
-        "max_concurrent_subagents",
-        "agent_name",
-        "is_bootstrap",
-    }
-)
-
-
-def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, Any] | None) -> None:
-    """Merge whitelisted keys from ``body.context`` into both ``config['configurable']``
-    and ``config['context']`` so they are visible to legacy configurable readers and
-    to LangGraph ``ToolRuntime.context`` consumers (e.g. the ``setup_agent`` tool —
-    see issue #2677).
-
-    ``user_id`` is intentionally propagated into ``config['context']`` in addition to
-    the whitelisted keys, so non-web callers (e.g. IM channels) that supply identity in
-    ``body.context`` keep it on ``ToolRuntime.context``. It is merged with
-    ``setdefault`` so a server-authenticated id stamped by
-    :func:`inject_authenticated_user_context` always wins over the client-supplied one.
-    """
-    if not context:
-        return
-    configurable = config.setdefault("configurable", {})
-    runtime_context = config.setdefault("context", {})
-    for key in _CONTEXT_CONFIGURABLE_KEYS:
-        if key in context:
-            if isinstance(configurable, dict):
-                configurable.setdefault(key, context[key])
-            if isinstance(runtime_context, dict):
-                runtime_context.setdefault(key, context[key])
-    if "user_id" in context and isinstance(runtime_context, dict):
-        runtime_context.setdefault("user_id", context["user_id"])
-
-
-def inject_authenticated_user_context(config: dict[str, Any], request: Request) -> None:
-    """Stamp the authenticated user into the run context for background tools.
-
-    Tool execution may happen after the request handler has returned, so tools
-    that persist user-scoped files should not rely only on ambient ContextVars.
-    The value comes from server-side auth state, never from client context.
-    """
-
-    user = getattr(request.state, "user", None)
-    user_id = getattr(user, "id", None)
-    if user_id is None:
-        return
-
-    if getattr(user, "system_role", None) == INTERNAL_SYSTEM_ROLE:
-        return
-
-    runtime_context = config.setdefault("context", {})
-    if isinstance(runtime_context, dict):
-        runtime_context["user_id"] = str(user_id)
-
-
 def resolve_agent_factory(assistant_id: str | None):
     """Resolve the agent factory callable from config.
 
@@ -264,7 +180,6 @@ def build_run_config(
             target = config.setdefault("configurable", {})
         if target is not None and "agent_name" not in target:
             target["agent_name"] = normalized
-        config.setdefault("run_name", resolve_root_run_name(config, normalized))
     if metadata:
         config.setdefault("metadata", {}).update(metadata)
     return config
@@ -298,22 +213,20 @@ async def start_run(
 
     disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
 
-    body_context = getattr(body, "context", None) or {}
-    model_name = body_context.get("model_name")
+    # Resolve follow_up_to_run_id: explicit from request, or auto-detect from latest successful run
+    follow_up_to_run_id = getattr(body, "follow_up_to_run_id", None)
+    if follow_up_to_run_id is None:
+        run_store = get_run_store(request)
+        try:
+            recent_runs = await run_store.list_by_thread(thread_id, limit=1)
+            if recent_runs and recent_runs[0].get("status") == "success":
+                follow_up_to_run_id = recent_runs[0]["run_id"]
+        except Exception:
+            pass  # Don't block run creation
 
-    # Coerce non-string model_name values to str before truncation.
-    if model_name is not None and not isinstance(model_name, str):
-        model_name = str(model_name)
-
-    # Validate model against the allowlist when a model_name is provided.
-    if model_name:
-        app_config = get_app_config()
-        resolved = app_config.get_model_config(model_name)
-        if resolved is None:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Model {model_name!r} is not in the configured model allowlist",
-            )
+    # Enrich base context with per-run field
+    if follow_up_to_run_id:
+        run_ctx = dataclasses.replace(run_ctx, follow_up_to_run_id=follow_up_to_run_id)
 
     try:
         record = await run_mgr.create_or_reject(
@@ -323,7 +236,7 @@ async def start_run(
             metadata=body.metadata or {},
             kwargs={"input": body.input, "config": body.config},
             multitask_strategy=body.multitask_strategy,
-            model_name=model_name,
+            follow_up_to_run_id=follow_up_to_run_id,
         )
     except ConflictError as exc:
         raise HTTPException(status_code=409, detail=str(exc)) from exc
@@ -350,12 +263,27 @@ async def start_run(
     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 both ``configurable`` and ``context``.
+    # 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.
-    merge_run_context_overrides(config, getattr(body, "context", None))
-    inject_authenticated_user_context(config, request)
+    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)
 
@@ -415,51 +343,3 @@ async def sse_consumer(
         if record.status in (RunStatus.pending, RunStatus.running):
             if record.on_disconnect == DisconnectMode.cancel:
                 await run_mgr.cancel(record.run_id)
-
-
-async def wait_for_run_completion(
-    bridge: StreamBridge,
-    record: RunRecord,
-    request: Request,
-    run_mgr: RunManager,
-) -> bool:
-    """Block until the run publishes ``END_SENTINEL``, honouring on_disconnect.
-
-    The non-streaming ``/wait`` endpoints used to ``await record.task``
-    directly with no disconnect handling.  When the client (or an
-    intermediate HTTP proxy) timed out during a long tool call such as
-    ``pip install``, the handler would swallow ``CancelledError`` and
-    serialize whatever checkpoint happened to exist — masking a half-finished
-    run as a normal completion (issue #3265).
-
-    This helper consumes the same bridge that ``sse_consumer`` does so the
-    wait path shares its disconnect semantics: each wake-up polls
-    ``request.is_disconnected()``; on a real disconnect it cancels the
-    background run when ``record.on_disconnect`` is ``cancel``.  The bridge's
-    heartbeat sentinels guarantee at least one wake-up per
-    ``heartbeat_interval`` even when the agent emits no events for a while.
-
-    Returns:
-        ``True`` when ``END_SENTINEL`` was observed (run reached a terminal
-        state), ``False`` when the loop exited because the client
-        disconnected.  Callers must skip checkpoint serialization on
-        ``False`` so a partial checkpoint is not returned as a normal
-        response.
-    """
-    completed = False
-    try:
-        async for entry in bridge.subscribe(record.run_id):
-            # END_SENTINEL means the run reached a terminal state; honour it
-            # even if the client just disconnected so the caller still serializes
-            # the real final checkpoint.
-            if entry is END_SENTINEL:
-                completed = True
-                return True
-            if await request.is_disconnected():
-                break
-            # Heartbeats and regular events: keep waiting for END_SENTINEL.
-        return completed
-    finally:
-        if not completed and record.status in (RunStatus.pending, RunStatus.running):
-            if record.on_disconnect == DisconnectMode.cancel:
-                await run_mgr.cancel(record.run_id)

backend/debug.py

@@ -34,42 +34,50 @@ _LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 _LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
 
 
-def _setup_logging(log_level: int = logging.INFO) -> None:
-    """Route logs to ``debug.log`` using *log_level* for the initial root/file setup.
+def _logging_level_from_config(name: str) -> int:
+    """Map ``config.yaml`` ``log_level`` string to a ``logging`` level constant."""
+    mapping = logging.getLevelNamesMapping()
+    return mapping.get((name or "info").strip().upper(), logging.INFO)
 
-    This configures the root logger and the ``debug.log`` file handler so logs do
-    not print on the interactive console. It is idempotent: any pre-existing
-    handlers on the root logger (e.g. installed by ``logging.basicConfig`` in
-    transitively imported modules) are removed so the debug session output only
-    lands in ``debug.log``.
 
-    Note: later config-driven logging adjustments may change named logger
-    verbosity without raising the root logger or file-handler thresholds set
-    here, so the eventual contents of ``debug.log`` may not be filtered solely by
-    this function's ``log_level`` argument.
+def _setup_logging(log_level: str) -> None:
+    """Send application logs to ``debug.log`` at *log_level*; do not print them on the console.
+
+    Idempotent: any pre-existing handlers on the root logger (e.g. installed by
+    ``logging.basicConfig`` in transitively imported modules) are removed so the
+    debug session output only lands in ``debug.log``.
     """
+    level = _logging_level_from_config(log_level)
     root = logging.root
     for h in list(root.handlers):
         root.removeHandler(h)
         h.close()
-    root.setLevel(log_level)
+    root.setLevel(level)
 
     file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
-    file_handler.setLevel(log_level)
+    file_handler.setLevel(level)
     file_handler.setFormatter(logging.Formatter(_LOG_FMT, datefmt=_LOG_DATEFMT))
     root.addHandler(file_handler)
 
 
+def _update_logging_level(log_level: str) -> None:
+    """Update the root logger and existing handlers to *log_level*."""
+    level = _logging_level_from_config(log_level)
+    root = logging.root
+    root.setLevel(level)
+    for handler in root.handlers:
+        handler.setLevel(level)
+
+
 async def main():
     # Install file logging first so warnings emitted while loading config do not
     # leak onto the interactive terminal via Python's lastResort handler.
-    _setup_logging()
+    _setup_logging("info")
 
     from deerflow.config import get_app_config
-    from deerflow.config.app_config import apply_logging_level
 
     app_config = get_app_config()
-    apply_logging_level(app_config.log_level)
+    _update_logging_level(app_config.log_level)
 
     # Delay the rest of the deerflow imports until *after* logging is installed
     # so that any import-time side effects (e.g. deerflow.agents starts a
@@ -79,9 +87,7 @@ async def main():
     from langgraph.runtime import Runtime
 
     from deerflow.agents import make_lead_agent
-    from deerflow.config.paths import get_paths
     from deerflow.mcp import initialize_mcp_tools
-    from deerflow.runtime.user_context import get_effective_user_id
 
     # Initialize MCP tools at startup
     try:
@@ -115,8 +121,6 @@ async def main():
         print("Tip: `uv sync --group dev` to enable arrow-key & history support")
     print("=" * 50)
 
-    seen_artifacts: set[str] = set()
-
     while True:
         try:
             if session:
@@ -138,22 +142,6 @@ async def main():
                 last_message = result["messages"][-1]
                 print(f"\nAgent: {last_message.content}")
 
-            # Show files presented to the user this turn (new artifacts only)
-            artifacts = result.get("artifacts") or []
-            new_artifacts = [p for p in artifacts if p not in seen_artifacts]
-            if new_artifacts:
-                thread_id = config["configurable"]["thread_id"]
-                user_id = get_effective_user_id()
-                paths = get_paths()
-                print("\n[Presented files]")
-                for virtual in new_artifacts:
-                    try:
-                        physical = paths.resolve_virtual_path(thread_id, virtual, user_id=user_id)
-                        print(f"  - {virtual}\n    → {physical}")
-                    except ValueError as exc:
-                        print(f"  - {virtual}    (failed to resolve physical path: {exc})")
-                seen_artifacts.update(new_artifacts)
-
         except (KeyboardInterrupt, EOFError):
             print("\nGoodbye!")
             break

backend/docs/API.md

@@ -6,16 +6,16 @@ This document provides a complete reference for the DeerFlow backend APIs.
 
 DeerFlow backend exposes two sets of APIs:
 
-1. **LangGraph-compatible API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
+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-compatible API
+## LangGraph API
 
 Base URL: `/api/langgraph`
 
-The public LangGraph-compatible API follows LangGraph SDK conventions. In the unified nginx deployment, Gateway owns `/api/langgraph/*` and translates those paths to its native `/api/*` run, thread, and streaming routers.
+The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
 
 ### Threads
 
@@ -104,11 +104,17 @@ Content-Type: application/json
 **Recursion Limit:**
 
 `config.recursion_limit` caps the number of graph steps LangGraph will execute
-in a single run. The unified Gateway path defaults to `100` in
-`build_run_config` (see `backend/app/gateway/services.py`), which is a safer
-starting point for plan-mode or subagent-heavy runs. Clients can still set
-`recursion_limit` explicitly in the request body; increase it if you run deeply
-nested subagent graphs.
+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
@@ -241,6 +247,13 @@ GET /api/mcp/config
         "GITHUB_TOKEN": "***"
       },
       "description": "GitHub operations"
+    },
+    "filesystem": {
+      "enabled": false,
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-filesystem"],
+      "description": "File system access"
     }
   }
 }
@@ -528,28 +541,14 @@ All APIs return errors in a consistent format:
 
 ## Authentication
 
-DeerFlow enforces authentication for all non-public HTTP routes. Public routes are limited to health/docs metadata and these public auth endpoints:
+Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
 
-- `POST /api/v1/auth/initialize` creates the first admin account when no admin exists.
-- `POST /api/v1/auth/login/local` logs in with email/password and sets an HttpOnly `access_token` cookie.
-- `POST /api/v1/auth/register` creates a regular `user` account and sets the session cookie.
-- `POST /api/v1/auth/logout` clears the session cookie.
-- `GET /api/v1/auth/setup-status` reports whether the first admin still needs to be created.
+Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
 
-The authenticated auth endpoints are:
-
-- `GET /api/v1/auth/me` returns the current user.
-- `POST /api/v1/auth/change-password` changes password, optionally changes email during setup, increments `token_version`, and reissues the cookie.
-
-Protected state-changing requests also require the CSRF double-submit token: send the `csrf_token` cookie value as the `X-CSRF-Token` header. Login/register/initialize/logout are bootstrap auth endpoints: they are exempt from the double-submit token but still reject hostile browser `Origin` headers.
-
-User isolation is enforced from the authenticated user context:
-
-- Thread metadata is scoped by `threads_meta.user_id`; search/read/write/delete APIs only expose the current user's threads.
-- Thread files live under `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/` and are exposed inside the sandbox as `/mnt/user-data/`.
-- Memory and custom agents are stored under `{base_dir}/users/{user_id}/...`.
-
-Note: MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers; that is separate from DeerFlow API authentication.
+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
 
 ---
 
@@ -568,13 +567,12 @@ location /api/ {
 
 ---
 
-## Streaming Support
+## WebSocket Support
 
-Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE):
+The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
 
-```http
-POST /api/langgraph/threads/{thread_id}/runs/stream
-Accept: text/event-stream
+```
+ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
 ```
 
 ---
@@ -610,21 +608,13 @@ const response = await fetch('/api/models');
 const data = await response.json();
 console.log(data.models);
 
-// Create a run and stream SSE events
-const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
-  method: "POST",
-  headers: {
-    "Content-Type": "application/json",
-    Accept: "text/event-stream",
-  },
-  body: JSON.stringify({
-    input: { messages: [{ role: "user", content: "Hello" }] },
-    stream_mode: ["values", "messages-tuple", "custom"],
-  }),
-});
-
-const reader = streamResponse.body?.getReader();
-// Decode and parse SSE frames from reader in your client code.
+// Using EventSource for streaming
+const eventSource = new EventSource(
+  `/api/langgraph/threads/${threadId}/runs/stream`
+);
+eventSource.onmessage = (event) => {
+  console.log(JSON.parse(event.data));
+};
 ```
 
 ### cURL Examples
@@ -659,7 +649,7 @@ curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
   }'
 ```
 
-> The unified Gateway path defaults `config.recursion_limit` to 100 for
-> plan-mode and subagent-heavy runs. Clients may still set
-> `config.recursion_limit` explicitly — see the [Create Run](#create-run)
-> section for details.
+> 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.

backend/docs/ARCHITECTURE.md

@@ -14,28 +14,30 @@ This document provides a comprehensive overview of the DeerFlow backend architec
 │                          Nginx (Port 2026)                               │
 │                    Unified Reverse Proxy Entry Point                      │
 │  ┌────────────────────────────────────────────────────────────────────┐  │
-│  │  /api/langgraph/*  →  Gateway LangGraph-compatible runtime (8001)  │  │
-│  │  /api/*            →  Gateway REST APIs (8001)                     │  │
+│  │  /api/langgraph/*  →  LangGraph Server (2024)                      │  │
+│  │  /api/*            →  Gateway API (8001)                           │  │
 │  │  /*                →  Frontend (3000)                               │  │
 │  └────────────────────────────────────────────────────────────────────┘  │
 └─────────────────────────────────┬────────────────────────────────────────┘
                                   │
-          ┌───────────────────────┴───────────────────────┐
-          │                                               │
-          ▼                                               ▼
-┌─────────────────────────────────────────────┐ ┌─────────────────────┐
-│              Gateway API                    │ │     Frontend        │
-│              (Port 8001)                    │ │    (Port 3000)      │
-│                                             │ │                     │
-│  - LangGraph-compatible runs/threads API    │ │  - Next.js App      │
-│  - Embedded Agent Runtime                   │ │  - React UI         │
-│  - SSE Streaming                            │ │  - Chat Interface   │
-│  - Checkpointing                            │ │                     │
-│  - Models, MCP, Skills, Uploads, Artifacts  │ │                     │
-│  - Thread Cleanup                           │ │                     │
-└─────────────────────────────────────────────┘ └─────────────────────┘
-          │
-          ▼
+          ┌───────────────────────┼───────────────────────┐
+          │                       │                       │
+          ▼                       ▼                       ▼
+┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
+│   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                              │
 │  ┌─────────────────────────┐  ┌────────────────────────────────────────┐ │
@@ -50,9 +52,9 @@ This document provides a comprehensive overview of the DeerFlow backend architec
 
 ## Component Details
 
-### Gateway Embedded Agent Runtime
+### LangGraph Server
 
-The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for robust multi-agent workflow orchestration. Nginx rewrites `/api/langgraph/*` to Gateway's native `/api/*` routes, so the public API remains compatible with LangGraph SDK clients without running a separate 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`
 
@@ -63,8 +65,7 @@ The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for
 - Tool execution orchestration
 - SSE streaming for real-time responses
 
-**Graph registry**: `langgraph.json` remains available for tooling, Studio, or direct LangGraph Server compatibility.
-It is not the default service entrypoint; scripts and Docker deployments run the Gateway embedded runtime.
+**Configuration**: `langgraph.json`
 
 ```json
 {
@@ -77,13 +78,12 @@ It is not the default service entrypoint; scripts and Docker deployments run the
 
 ### Gateway API
 
-FastAPI application providing REST endpoints plus the public LangGraph-compatible `/api/langgraph/*` runtime routes.
+FastAPI application providing REST endpoints for non-agent operations.
 
 **Entry Point**: `app/gateway/app.py`
 
 **Routers**:
 - `models.py` - `/api/models` - Model listing and details
-- `thread_runs.py` / `runs.py` - `/api/threads/{id}/runs`, `/api/runs/*` - LangGraph-compatible runs and streaming
 - `mcp.py` - `/api/mcp` - MCP server configuration
 - `skills.py` - `/api/skills` - Skills management
 - `uploads.py` - `/api/threads/{id}/uploads` - File upload
@@ -91,7 +91,7 @@ FastAPI application providing REST endpoints plus the public LangGraph-compatibl
 - `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
 - `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation
 
-The web conversation delete flow first deletes Gateway-managed thread state through the LangGraph-compatible route, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
+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
 
@@ -353,10 +353,10 @@ SKILL.md Format:
    POST /api/langgraph/threads/{thread_id}/runs
    {"input": {"messages": [{"role": "user", "content": "Hello"}]}}
 
-2. Nginx → Gateway API (8001)
-   `/api/langgraph/*` is rewritten to Gateway's LangGraph-compatible `/api/*` routes
+2. Nginx → LangGraph Server (2024)
+   Proxied to LangGraph server
 
-3. Gateway embedded runtime
+3. LangGraph Server
    a. Load/create thread state
    b. Execute middleware chain:
       - ThreadDataMiddleware: Set up paths
@@ -412,7 +412,7 @@ SKILL.md Format:
 ### Thread Cleanup Flow
 
 ```
-1. Client deletes conversation via the LangGraph-compatible Gateway route
+1. Client deletes conversation via LangGraph
    DELETE /api/langgraph/threads/{thread_id}
 
 2. Web UI follows up with Gateway cleanup

backend/docs/AUTH_DESIGN.md

@@ -1,331 +0,0 @@
-# 用户认证与隔离设计
-
-本文档描述 DeerFlow 当前内置认证模块的设计，而不是历史 RFC。它覆盖浏览器登录、API 认证、CSRF、用户隔离、首次初始化、密码重置、内部调用和升级迁移。
-
-## 设计目标
-
-认证模块的核心目标是把 DeerFlow 从“本地单用户工具”提升为“可多用户部署的 agent runtime”，并让用户身份贯穿 HTTP API、LangGraph-compatible runtime、文件系统、memory、自定义 agent 和反馈数据。
-
-设计约束：
-
-- 默认强制认证：除健康检查、文档和 auth bootstrap 端点外，HTTP 路由都必须有有效 session。
-- 服务端持有所有权：客户端 metadata 不能声明 `user_id` 或 `owner_id`。
-- 隔离默认开启：repository（仓储）、文件路径、memory、agent 配置默认按当前用户解析。
-- 旧数据可升级：无认证版本留下的 thread 可以在 admin 存在后迁移到 admin。
-- 密码不进日志：首次初始化由操作者设置密码；`reset_admin` 只写 0600 凭据文件。
-
-非目标：
-
-- 当前 OAuth 端点只是占位，尚未实现第三方登录。
-- 当前用户角色只有 `admin` 和 `user`，尚未实现细粒度 RBAC。
-- 当前登录限速是进程内字典，多 worker 下不是全局精确限速。
-
-## 核心模型
-
-```mermaid
-graph TB
-  classDef actor fill:#D8CFC4,stroke:#6E6259,color:#2F2A26;
-  classDef api fill:#C9D7D2,stroke:#5D706A,color:#21302C;
-  classDef state fill:#D7D3E8,stroke:#6B6680,color:#29263A;
-  classDef data fill:#E5D2C4,stroke:#806A5B,color:#30251E;
-
-  Browser["Browser — access_token cookie and csrf_token cookie"]:::actor
-  AuthMiddleware["AuthMiddleware — strict session gate"]:::api
-  CSRFMiddleware["CSRFMiddleware — double-submit token and Origin check"]:::api
-  AuthRoutes["Auth routes — initialize login register logout me change-password"]:::api
-  UserContext["Current user ContextVar — request-scoped identity"]:::state
-  Repositories["Repositories — AUTO resolves user_id from context"]:::state
-  Files["Filesystem — users/{user_id}/threads/{thread_id}/user-data"]:::data
-  Memory["Memory and agents — users/{user_id}/memory.json and agents"]:::data
-
-  Browser --> AuthMiddleware
-  Browser --> CSRFMiddleware
-  AuthMiddleware --> AuthRoutes
-  AuthMiddleware --> UserContext
-  UserContext --> Repositories
-  UserContext --> Files
-  UserContext --> Memory
-```
-
-### 用户表
-
-用户记录定义在 `app.gateway.auth.models.User`，持久化到 `users` 表。关键字段：
-
-| 字段 | 语义 |
-|---|---|
-| `id` | 用户主键，JWT `sub` 使用该值 |
-| `email` | 唯一登录名 |
-| `password_hash` | bcrypt hash，OAuth 用户可为空 |
-| `system_role` | `admin` 或 `user` |
-| `needs_setup` | reset 后要求用户完成邮箱 / 密码设置 |
-| `token_version` | 改密码或 reset 时递增，用于废弃旧 JWT |
-
-### 运行时身份
-
-认证成功后，`AuthMiddleware` 把用户同时写入：
-
-- `request.state.user`
-- `request.state.auth`
-- `deerflow.runtime.user_context` 的 `ContextVar`
-
-`ContextVar` 是这里的核心边界。上层 Gateway 负责写入身份，下层 persistence / file path 只读取结构化的当前用户，不反向依赖 `app.gateway.auth` 具体类型。
-
-可以把 repository 调用的用户参数理解成一个三态 ADT：
-
-```scala
-enum UserScope:
-  case AutoFromContext
-  case Explicit(userId: String)
-  case BypassForMigration
-```
-
-对应 Python 实现是 `AUTO | str | None`：
-
-- `AUTO`：从 `ContextVar` 解析当前用户；没有上下文则抛错。
-- `str`：显式指定用户，主要用于测试或管理脚本。
-- `None`：跳过用户过滤，只允许迁移脚本或 admin CLI 使用。
-
-## 登录与初始化流程
-
-### 首次初始化
-
-首次启动时，如果没有 admin，服务不会自动创建账号，只记录日志提示访问 `/setup`。
-
-流程：
-
-1. 用户访问 `/setup`。
-2. 前端调用 `GET /api/v1/auth/setup-status`。
-3. 如果返回 `{"needs_setup": true}`，前端展示创建 admin 表单。
-4. 表单提交 `POST /api/v1/auth/initialize`。
-5. 服务端确认当前没有 admin，创建 `system_role="admin"`、`needs_setup=false` 的用户。
-6. 服务端设置 `access_token` HttpOnly cookie，用户进入 workspace。
-
-`/api/v1/auth/initialize` 只在没有 admin 时可用。并发初始化由数据库唯一约束兜底，失败方返回 409。
-
-### 普通登录
-
-`POST /api/v1/auth/login/local` 使用 `OAuth2PasswordRequestForm`：
-
-- `username` 是邮箱。
-- `password` 是密码。
-- 成功后签发 JWT，放入 `access_token` HttpOnly cookie。
-- 响应体只返回 `expires_in` 和 `needs_setup`，不返回 token。
-
-登录失败会按客户端 IP 计数。IP 解析只在 TCP peer 属于 `AUTH_TRUSTED_PROXIES` 时信任 `X-Real-IP`，不使用 `X-Forwarded-For`。
-
-### 注册
-
-`POST /api/v1/auth/register` 创建普通 `user`，并自动登录。
-
-当前实现允许在没有 admin 时注册普通用户，但 `setup-status` 仍会返回 `needs_setup=true`，因为 admin 仍不存在。这是当前产品策略边界：如果后续要求“必须先初始化 admin 才能注册普通用户”，需要在 `/register` 增加 admin-exists gate。
-
-### 改密码与 reset setup
-
-`POST /api/v1/auth/change-password` 需要当前密码和新密码：
-
-- 校验当前密码。
-- 更新 bcrypt hash。
-- `token_version += 1`，使旧 JWT 立即失效。
-- 重新签发 cookie。
-- 如果 `needs_setup=true` 且传了 `new_email`，则更新邮箱并清除 `needs_setup`。
-
-`python -m app.gateway.auth.reset_admin` 会：
-
-- 找到 admin 或指定邮箱用户。
-- 生成随机密码。
-- 更新密码 hash。
-- `token_version += 1`。
-- 设置 `needs_setup=true`。
-- 写入 `.deer-flow/admin_initial_credentials.txt`，权限 `0600`。
-
-命令行只输出凭据文件路径，不输出明文密码。
-
-## HTTP 认证边界
-
-`AuthMiddleware` 是 fail-closed（默认拒绝）的全局认证门。
-
-公开路径：
-
-- `/health`
-- `/docs`
-- `/redoc`
-- `/openapi.json`
-- `/api/v1/auth/login/local`
-- `/api/v1/auth/register`
-- `/api/v1/auth/logout`
-- `/api/v1/auth/setup-status`
-- `/api/v1/auth/initialize`
-
-其余路径都要求有效 `access_token` cookie。存在 cookie 但 JWT 无效、过期、用户不存在或 `token_version` 不匹配时，直接返回 401，而不是让请求穿透到业务路由。
-
-路由级别的 owner check 由 `require_permission(..., owner_check=True)` 完成：
-
-- 读类请求允许旧的未追踪 legacy thread 兼容读取。
-- 写 / 删除类请求使用 `require_existing=True`，要求 thread row 存在且属于当前用户，避免删除后缺 row 导致其他用户误通过。
-
-## CSRF 设计
-
-DeerFlow 使用 Double Submit Cookie：
-
-- 服务端设置 `csrf_token` cookie。
-- 前端 state-changing 请求发送同值 `X-CSRF-Token` header。
-- 服务端用 `secrets.compare_digest` 比较 cookie/header。
-
-需要 CSRF 的方法：
-
-- `POST`
-- `PUT`
-- `DELETE`
-- `PATCH`
-
-auth bootstrap 端点（login/register/initialize/logout）不要求 double-submit token，因为首次调用时浏览器还没有 token；但这些端点会校验 browser `Origin`，拒绝 hostile Origin，避免 login CSRF / session fixation。
-
-## 用户隔离
-
-### Thread metadata
-
-Thread metadata 存在 `threads_meta`，关键隔离字段是 `user_id`。
-
-创建 thread 时：
-
-- 客户端传入的 `metadata.user_id` 和 `metadata.owner_id` 会被剥离。
-- `ThreadMetaRepository.create(..., user_id=AUTO)` 从 `ContextVar` 解析真实用户。
-- `/api/threads/search` 默认只返回当前用户的 thread。
-
-读取 / 修改 / 删除时：
-
-- `get()` 默认按当前用户过滤。
-- `check_access()` 用于路由 owner check。
-- 对其他用户的 thread 返回 404，避免泄露资源存在性。
-
-### 文件系统
-
-当前线程文件布局：
-
-```text
-{base_dir}/users/{user_id}/threads/{thread_id}/user-data/
-├── workspace/
-├── uploads/
-└── outputs/
-```
-
-agent 在 sandbox 内看到统一虚拟路径：
-
-```text
-/mnt/user-data/workspace
-/mnt/user-data/uploads
-/mnt/user-data/outputs
-```
-
-`ThreadDataMiddleware` 使用 `get_effective_user_id()` 解析当前用户并生成线程路径。没有认证上下文时会落到 `default` 用户桶，主要用于内部调用、嵌入式 client 或无 HTTP 的本地执行路径。
-
-### Memory
-
-默认 memory 存储：
-
-```text
-{base_dir}/users/{user_id}/memory.json
-{base_dir}/users/{user_id}/agents/{agent_name}/memory.json
-```
-
-有用户上下文时，空或相对 `memory.storage_path` 都使用上述 per-user 默认路径；只有绝对 `memory.storage_path` 会视为显式 opt-out（退出） per-user isolation，所有用户共享该路径。无用户上下文的 legacy 路径仍会把相对 `storage_path` 解析到 `Paths.base_dir` 下。
-
-### 自定义 agent
-
-用户自定义 agent 写入：
-
-```text
-{base_dir}/users/{user_id}/agents/{agent_name}/
-├── config.yaml
-├── SOUL.md
-└── memory.json
-```
-
-旧布局 `{base_dir}/agents/{agent_name}/` 只作为只读兼容回退。更新或删除旧共享 agent 会要求先运行迁移脚本。
-
-## 内部调用与 IM 渠道
-
-IM channel worker 不是浏览器用户，不持有浏览器 cookie。它们通过 Gateway 内部认证：
-
-- 请求带 `X-DeerFlow-Internal-Token`。
-- 同时带匹配的 CSRF cookie/header。
-- 服务端识别为内部用户，`id="default"`、`system_role="internal"`。
-
-这意味着 channel 产生的数据默认进入 `default` 用户桶。这个选择适合“平台级 bot 身份”，但不是“每个 IM 用户单独隔离”。如果后续要做到外部 IM 用户隔离，需要把外部 platform user 映射到 DeerFlow user，并让 channel manager 设置对应的 scoped identity。
-
-## LangGraph-compatible 认证
-
-Gateway 内嵌 runtime 路径由 `AuthMiddleware` 和 `CSRFMiddleware` 保护。
-
-仓库仍保留 `app.gateway.langgraph_auth`，用于 LangGraph Server 直连模式：
-
-- `@auth.authenticate` 校验 JWT cookie、CSRF、用户存在性和 `token_version`。
-- `@auth.on` 在写入 metadata 时注入 `user_id`，并在读路径返回 `{"user_id": current_user}` 过滤条件。
-
-这保证 Gateway 路由和 LangGraph-compatible 直连模式使用同一 JWT 语义。
-
-## 升级与迁移
-
-从无认证版本升级时，可能存在没有 `user_id` 的历史 thread。
-
-当前策略：
-
-1. 首次启动如果没有 admin，只提示访问 `/setup`，不迁移。
-2. 操作者创建 admin。
-3. 后续启动时，`_ensure_admin_user()` 找到 admin，并把 LangGraph store 中缺少 `metadata.user_id` 的 thread 迁移到 admin。
-
-文件系统旧布局迁移由脚本处理：
-
-```bash
-cd backend
-PYTHONPATH=. python scripts/migrate_user_isolation.py --dry-run
-PYTHONPATH=. python scripts/migrate_user_isolation.py --user-id <target-user-id>
-```
-
-迁移脚本覆盖 legacy `memory.json`、`threads/` 和 `agents/` 到 per-user layout。
-
-## 安全不变量
-
-必须长期保持的不变量：
-
-- JWT 只在 HttpOnly cookie 中传输，不出现在响应 JSON。
-- 任何非 public HTTP 路由都不能只靠“cookie 存在”放行，必须严格验证 JWT。
-- `token_version` 不匹配必须拒绝，保证改密码 / reset 后旧 session 失效。
-- 客户端 metadata 中的 `user_id` / `owner_id` 必须剥离。
-- repository 默认 `AUTO` 必须从当前用户上下文解析，不能静默退化成全局查询。
-- 只有迁移脚本和 admin CLI 可以显式传 `user_id=None` 绕过隔离。
-- 本地文件路径必须通过 `Paths` 和 sandbox path validation 解析，不能拼接未校验的用户输入。
-- 捕获认证、迁移、后台任务异常必须记录日志；不能空 catch。
-
-## 已知边界
-
-| 边界 | 当前行为 | 后续方向 |
-|---|---|---|
-| 无 admin 时注册普通用户 | 允许注册普通 `user` | 如产品要求先初始化 admin，给 `/register` 加 gate |
-| 登录限速 | 进程内 dict，单 worker 精确，多 worker 近似 | Redis / DB-backed rate limiter |
-| OAuth | 端点占位，未实现 | 接入 provider 并统一 `token_version` / role 语义 |
-| IM 用户隔离 | channel 使用 `default` 内部用户 | 建立外部用户到 DeerFlow user 的映射 |
-| 绝对 memory path | 显式共享 memory | UI / docs 明确提示 opt-out 风险 |
-
-## 相关文件
-
-| 文件 | 职责 |
-|---|---|
-| `app/gateway/auth_middleware.py` | 全局认证门、JWT 严格验证、写入 user context |
-| `app/gateway/csrf_middleware.py` | CSRF double-submit 和 auth Origin 校验 |
-| `app/gateway/routers/auth.py` | initialize/login/register/logout/me/change-password |
-| `app/gateway/auth/jwt.py` | JWT 创建与解析 |
-| `app/gateway/auth/reset_admin.py` | 密码 reset CLI |
-| `app/gateway/auth/credential_file.py` | 0600 凭据文件写入 |
-| `app/gateway/authz.py` | 路由权限与 owner check |
-| `deerflow/runtime/user_context.py` | 当前用户 ContextVar 与 `AUTO` sentinel |
-| `deerflow/persistence/thread_meta/` | thread metadata owner filter |
-| `deerflow/config/paths.py` | per-user filesystem layout |
-| `deerflow/agents/middlewares/thread_data_middleware.py` | run 时解析用户线程目录 |
-| `deerflow/agents/memory/storage.py` | per-user memory storage |
-| `deerflow/config/agents_config.py` | per-user custom agents |
-| `app/channels/manager.py` | IM channel 内部认证调用 |
-| `scripts/migrate_user_isolation.py` | legacy 数据迁移到 per-user layout |
-| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库，包含 users / threads_meta / runs / feedback 等表 |
-| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
-| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件（0600，读完应删除） |

backend/docs/AUTH_TEST_DOCKER_GAP.md

@@ -24,12 +24,12 @@ All other test plan sections were executed against either:
 
 | Case | Title | What it covers | Why not run |
 |---|---|---|---|
-| TC-DOCKER-01 | `deerflow.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
+| TC-DOCKER-01 | `users.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
 | TC-DOCKER-02 | Session persistence across container restart | `AUTH_JWT_SECRET` env var keeps cookies valid after `docker compose down && up` | needs `docker compose down/up` |
 | TC-DOCKER-03 | Per-worker rate limiter divergence | Confirms in-process `_login_attempts` dict doesn't share state across `gunicorn` workers (4 by default in the compose file); known limitation, documented | needs multi-worker container |
-| TC-DOCKER-04 | IM channels use internal Gateway auth | Verify Feishu/Slack/Telegram dispatchers attach the process-local internal auth header plus CSRF cookie/header when calling Gateway-compatible LangGraph APIs | needs `docker logs` |
-| TC-DOCKER-05 | Reset credentials surfacing | `reset_admin` writes a 0600 credential file in `DEER_FLOW_HOME` instead of logging plaintext. The file-based behavior is validated by non-Docker reset tests, so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
-| TC-DOCKER-06 | Docker deploy uses Gateway embedded runtime | `./scripts/deploy.sh` produces a Gateway + frontend + nginx topology (no `langgraph` container); same auth flow as local `make dev` | needs `docker compose up` |
+| TC-DOCKER-04 | IM channels skip AuthMiddleware | Verify Feishu/Slack/Telegram dispatchers run in-container against `http://langgraph:2024` without going through nginx | needs `docker logs` |
+| TC-DOCKER-05 | Admin credentials surfacing | **Updated post-simplify** — was "log scrape", now "0600 credential file in `DEER_FLOW_HOME`". The file-based behavior is already validated by TC-1.1 + TC-UPG-13 on sg_dev (non-Docker), so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
+| TC-DOCKER-06 | Gateway-mode Docker deploy | `./scripts/deploy.sh --gateway` produces a 3-container topology (no `langgraph` container); same auth flow as standard mode | needs `docker compose --profile gateway` |
 
 ## Coverage already provided by non-Docker tests
 
@@ -41,9 +41,9 @@ the test cases that ran on sg_dev or local:
 | TC-DOCKER-01 (volume persistence) | TC-REENT-01 on sg_dev (admin row survives gateway restart) — same SQLite file, just no container layer between |
 | TC-DOCKER-02 (session persistence) | TC-API-02/03/06 (cookie roundtrip), plus TC-REENT-04 (multi-cookie) — JWT verification is process-state-free, container restart is equivalent to `pkill uvicorn && uv run uvicorn` |
 | TC-DOCKER-03 (per-worker rate limit) | TC-GW-04 + TC-REENT-09 (single-worker rate limit + 5min expiry). The cross-worker divergence is an architectural property of the in-memory dict; no auth code path differs |
-| TC-DOCKER-04 (IM channels use internal auth) | Code-level: `app/channels/manager.py` creates the `langgraph_sdk` client with `create_internal_auth_headers()` plus CSRF cookie/header, so channel workers do not rely on browser cookies |
-| TC-DOCKER-05 (credential surfacing) | `reset_admin` writes `.deer-flow/admin_initial_credentials.txt` with mode 0600 and logs only the path — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
-| TC-DOCKER-06 (Gateway embedded runtime container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (Gateway auth flow on sg_dev) — same Gateway code, container is just a packaging change |
+| TC-DOCKER-04 (IM channels skip auth) | Code-level only: `app/channels/manager.py` uses `langgraph_sdk` directly with no cookie handling. The langgraph_auth handler is bypassed by going through SDK, not HTTP |
+| TC-DOCKER-05 (credential surfacing) | TC-1.1 on sg_dev (file at `~/deer-flow/backend/.deer-flow/admin_initial_credentials.txt`, mode 0600, password 22 chars) — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
+| TC-DOCKER-06 (gateway-mode container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (gateway-mode auth flow on sg_dev) — same Gateway code, container is just a packaging change |
 
 ## Reproduction steps when Docker becomes available
 
@@ -72,6 +72,6 @@ Then run TC-DOCKER-01..06 from the test plan as written.
   about *container packaging* details (bind mounts, multi-worker, log
   collection), not about whether the auth code paths work.
 - **TC-DOCKER-05 was updated in place** in `AUTH_TEST_PLAN.md` to reflect
-  the current reset flow (`reset_admin` → 0600 credentials file, no log leak).
+  the post-simplify reality (credentials file → 0600 file, no log leak).
   The old "grep 'Password:' in docker logs" expectation would have failed
   silently and given a false sense of coverage.

backend/docs/AUTH_TEST_PLAN.md

@@ -4,12 +4,10 @@
 
 | 模式 | 启动命令 | Auth 层 | 端口 |
 |------|---------|---------|------|
-| 标准模式 | `make dev` | Gateway AuthMiddleware（全量） | 2026 (nginx) |
+| 标准模式 | `make dev` | Gateway AuthMiddleware + LangGraph auth | 2026 (nginx) |
+| Gateway 模式 | `make dev-pro` | Gateway AuthMiddleware（全量） | 2026 (nginx) |
 | 直连 Gateway | `cd backend && make gateway` | Gateway AuthMiddleware | 8001 |
-| 直连 LangGraph 兼容性 | 手动运行 LangGraph 工具链时使用 | LangGraph auth | 2024 |
-
-`make dev`、Docker dev 和生产部署默认都运行 Gateway embedded runtime。
-`app.gateway.langgraph_auth` 仅用于保留的直连 LangGraph 工具链 / Studio 兼容性测试，不是标准服务启动路径。
+| 直连 LangGraph | `cd backend && make dev` | LangGraph auth | 2024 |
 
 每种模式下都需执行以下测试。
 
@@ -21,18 +19,19 @@
 
 ```bash
 # 清除已有数据
-rm -f backend/.deer-flow/data/deerflow.db
+rm -f backend/.deer-flow/users.db
 
-# 启动标准模式（Gateway embedded runtime）
-make dev
+# 选择模式启动
+make dev          # 标准模式
+# 或
+make dev-pro      # Gateway 模式
 ```
 
 **验证点：**
-- [ ] 控制台不输出 admin 邮箱或明文密码
-- [ ] 控制台提示 `First boot detected — no admin account exists.`
-- [ ] 控制台提示访问 `/setup` 完成 admin 创建
-- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": true}`
-- [ ] 前端访问 `/login` 会跳转 `/setup`
+- [ ] 控制台输出 admin 邮箱和随机密码
+- [ ] 密码格式为 `secrets.token_urlsafe(16)` 的 22 字符字符串
+- [ ] 邮箱为 `admin@deerflow.dev`
+- [ ] 提示 `Change it after login: Settings -> Account`
 
 ### 1.2 非首次启动
 
@@ -43,8 +42,7 @@ make dev
 
 **验证点：**
 - [ ] 控制台不输出密码
-- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": false}`
-- [ ] 已登录用户如果 `needs_setup=True`，访问 workspace 会被引导到 `/setup` 完成改邮箱 / 改密码流程
+- [ ] 如果 admin 仍 `needs_setup=True`，控制台有 warning 提示
 
 ### 1.3 环境变量配置
 
@@ -57,7 +55,7 @@ make dev
 
 ## 二、接口流程测试
 
-> 以下用 `BASE=http://localhost:2026` 为例。标准模式经 nginx 暴露此地址。
+> 以下用 `BASE=http://localhost:2026` 为例。标准模式和 Gateway 模式都用此地址。
 > 直连测试替换为对应端口。
 >
 > **CSRF token 提取**：多处用到从 cookie jar 提取 CSRF token，统一使用：
@@ -78,22 +76,19 @@ make dev
 curl -s $BASE/api/v1/auth/setup-status | jq .
 ```
 
-**预期：**
-- 干净数据库且尚未初始化 admin：返回 `{"needs_setup": true}`
-- 已存在 admin：返回 `{"needs_setup": false}`
+**预期：** 返回 `{"needs_setup": false}`（admin 在启动时已自动创建，`count_users() > 0`）。仅在启动完成前的极短窗口内可能返回 `true`。
 
-#### TC-API-02: 首次初始化 Admin
+#### TC-API-02: Admin 首次登录
 
 ```bash
-curl -s -X POST $BASE/api/v1/auth/initialize \
-  -H "Content-Type: application/json" \
-  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
+curl -s -X POST $BASE/api/v1/auth/login/local \
+  -d "username=admin@deerflow.dev&password=<控制台密码>" \
   -c cookies.txt | jq .
 ```
 
 **预期：**
-- 状态码 201
-- Body: `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`
+- 状态码 200
+- Body: `{"expires_in": 604800, "needs_setup": true}`
 - `cookies.txt` 包含 `access_token`（HttpOnly）和 `csrf_token`（非 HttpOnly）
 
 #### TC-API-03: 获取当前用户
@@ -102,9 +97,9 @@ curl -s -X POST $BASE/api/v1/auth/initialize \
 curl -s $BASE/api/v1/auth/me -b cookies.txt | jq .
 ```
 
-**预期：** `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`
+**预期：** `{"id": "...", "email": "admin@deerflow.dev", "system_role": "admin", "needs_setup": true}`
 
-#### TC-API-04: 改密码流程
+#### TC-API-04: Setup 流程（改邮箱 + 改密码）
 
 ```bash
 CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
@@ -112,36 +107,13 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b cookies.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"AdminPass1!","new_password":"NewPass123!"}' | jq .
+  -d '{"current_password":"<控制台密码>","new_password":"NewPass123!","new_email":"admin@example.com"}' | jq .
 ```
 
 **预期：**
 - 状态码 200
 - `{"message": "Password changed successfully"}`
-- 再调 `/auth/me` 仍为 `admin@example.com`，`needs_setup` 仍为 `false`
-
-#### TC-API-04a: reset_admin 后的 Setup 流程（改邮箱 + 改密码）
-
-```bash
-cd backend
-python -m app.gateway.auth.reset_admin --email admin@example.com
-# 从 .deer-flow/admin_initial_credentials.txt 读取 reset 后密码
-
-curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@example.com&password=<凭据文件密码>" \
-  -c cookies.txt | jq .
-
-CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
-curl -s -X POST $BASE/api/v1/auth/change-password \
-  -b cookies.txt \
-  -H "Content-Type: application/json" \
-  -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<凭据文件密码>","new_password":"AdminPass2!","new_email":"admin2@example.com"}' | jq .
-```
-
-**预期：**
-- 登录返回 `{"expires_in": 604800, "needs_setup": true}`
-- `change-password` 后 `/auth/me` 邮箱变为 `admin2@example.com`，`needs_setup` 变为 `false`
+- 再调 `/auth/me` 邮箱变为 `admin@example.com`，`needs_setup` 变为 `false`
 
 #### TC-API-05: 普通用户注册
 
@@ -211,18 +183,20 @@ curl -s -X POST $BASE/api/threads/search \
 
 **预期：** 返回 0 或仅包含 user2 自己的 thread
 
-### 2.3 LangGraph-compatible Gateway 路由隔离
+### 2.3 标准模式 LangGraph Server 隔离
 
-#### TC-API-10: LangGraph-compatible 端点需要 cookie
+> 仅在标准模式下测试。Gateway 模式不跑 LangGraph Server。
+
+#### TC-API-10: LangGraph 端点需要 cookie
 
 ```bash
-# 不带 cookie 访问 LangGraph-compatible 接口
+# 不带 cookie 访问 LangGraph 接口
 curl -s -w "%{http_code}" $BASE/api/langgraph/threads
 ```
 
 **预期：** 401
 
-#### TC-API-11: LangGraph-compatible 路由带 cookie 可访问
+#### TC-API-11: LangGraph 带 cookie 可访问
 
 ```bash
 curl -s $BASE/api/langgraph/threads -b user1.txt | jq length
@@ -230,10 +204,10 @@ curl -s $BASE/api/langgraph/threads -b user1.txt | jq length
 
 **预期：** 200，返回 user1 的 thread 列表
 
-#### TC-API-12: LangGraph-compatible 路由隔离 — 用户只看到自己的
+#### TC-API-12: LangGraph 隔离 — 用户只看到自己的
 
 ```bash
-# user2 查 threads
+# user2 查 LangGraph threads
 curl -s $BASE/api/langgraph/threads -b user2.txt | jq length
 ```
 
@@ -519,7 +493,7 @@ curl -s -X POST $BASE/api/v1/auth/register \
 
 ```bash
 # 检查数据库
-sqlite3 backend/.deer-flow/data/deerflow.db "SELECT email, password_hash FROM users LIMIT 3;"
+sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMIT 3;"
 ```
 
 **预期：** `password_hash` 以 `$2b$` 开头（bcrypt 格式）
@@ -532,25 +506,24 @@ sqlite3 backend/.deer-flow/data/deerflow.db "SELECT email, password_hash FROM us
 
 ### 4.1 首次登录流程
 
-#### TC-UI-01: 无 admin 时访问 workspace 跳转 setup
+#### TC-UI-01: 访问首页跳转登录
 
 1. 打开 `http://localhost:2026/workspace`
-2. **预期：** 自动跳转到 `/setup`
+2. **预期：** 自动跳转到 `/login`
 
-#### TC-UI-02: Setup 页面创建 admin
+#### TC-UI-02: Login 页面
 
-1. 输入 admin 邮箱、密码、确认密码
-2. 点击 Create Admin Account
+1. 输入 admin 邮箱和控制台密码
+2. 点击 Login
+3. **预期：** 跳转到 `/setup`（因为 `needs_setup=true`）
+
+#### TC-UI-03: Setup 页面
+
+1. 输入新邮箱、控制台密码（current）、新密码、确认密码
+2. 点击 Complete Setup
 3. **预期：** 跳转到 `/workspace`
 4. 刷新页面不跳回 `/setup`
 
-#### TC-UI-03: 已初始化后 Login 页面
-
-1. 退出登录后访问 `/login`
-2. 输入 admin 邮箱和密码
-3. 点击 Login
-4. **预期：** 跳转到 `/workspace`
-
 #### TC-UI-04: Setup 密码不匹配
 
 1. 新密码和确认密码不一致
@@ -629,7 +602,7 @@ sqlite3 backend/.deer-flow/data/deerflow.db "SELECT email, password_hash FROM us
 #### TC-UI-15: reset_admin 后重新登录
 
 1. 执行 `cd backend && python -m app.gateway.auth.reset_admin`
-2. 从 `.deer-flow/admin_initial_credentials.txt` 读取新密码并登录
+2. 使用新密码登录
 3. **预期：** 跳转到 `/setup` 页面（`needs_setup` 被重置为 true）
 4. 旧 session 已失效
 
@@ -672,28 +645,18 @@ make install
 make dev
 ```
 
-#### TC-UPG-01: 首次启动等待 admin 初始化
+#### TC-UPG-01: 首次启动创建 admin
 
 **预期：**
-- [ ] 控制台不输出 admin 邮箱或随机密码
-- [ ] 访问 `/setup` 可创建第一个 admin
+- [ ] 控制台输出 admin 邮箱（`admin@deerflow.dev`）和随机密码
 - [ ] 无报错，正常启动
 
 #### TC-UPG-02: 旧 Thread 迁移到 admin
 
 ```bash
-# 创建第一个 admin
-curl -s -X POST http://localhost:2026/api/v1/auth/initialize \
-  -H "Content-Type: application/json" \
-  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
-  -c cookies.txt
-
-# 重启一次：启动迁移只在已有 admin 的启动路径执行
-make stop && make dev
-
 # 登录 admin
 curl -s -X POST http://localhost:2026/api/v1/auth/login/local \
-  -d "username=admin@example.com&password=AdminPass1!" \
+  -d "username=admin@deerflow.dev&password=<控制台密码>" \
   -c cookies.txt
 
 # 查看 thread 列表
@@ -707,8 +670,8 @@ curl -s -X POST http://localhost:2026/api/threads/search \
 
 **预期：**
 - [ ] 返回的 thread 数量 ≥ 旧版创建的数量
-- [ ] 控制台日志有 `Migrated N orphan LangGraph thread(s) to admin`
-- [ ] 旧 thread 只对 admin 可见
+- [ ] 控制台日志有 `Migrated N orphaned thread(s) to admin`
+- [ ] 每个 thread 的 `metadata.owner_id` 都已被设为 admin 的 ID
 
 #### TC-UPG-03: 旧 Thread 内容完整
 
@@ -720,7 +683,7 @@ curl -s http://localhost:2026/api/threads/<old-thread-id> \
 
 **预期：**
 - [ ] `metadata.title` 保留原值（如 `old-thread-1`）
-- [ ] 响应不回显服务端保留的 `user_id` / `owner_id`
+- [ ] `metadata.owner_id` 已填充
 
 #### TC-UPG-04: 新用户看不到旧 Thread
 
@@ -743,19 +706,18 @@ curl -s -X POST http://localhost:2026/api/threads/search \
 
 ### 5.3 数据库 Schema 兼容
 
-#### TC-UPG-05: 无 deerflow.db 时创建 schema 但不创建默认用户
+#### TC-UPG-05: 无 users.db 时自动创建
 
 ```bash
-ls -la backend/.deer-flow/data/deerflow.db
-sqlite3 backend/.deer-flow/data/deerflow.db "SELECT COUNT(*) FROM users;"
+ls -la backend/.deer-flow/users.db
 ```
 
-**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列；未调用 `/initialize` 前用户数为 0
+**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列
 
-#### TC-UPG-06: deerflow.db WAL 模式
+#### TC-UPG-06: users.db WAL 模式
 
 ```bash
-sqlite3 backend/.deer-flow/data/deerflow.db "PRAGMA journal_mode;"
+sqlite3 backend/.deer-flow/users.db "PRAGMA journal_mode;"
 ```
 
 **预期：** 返回 `wal`
@@ -806,9 +768,9 @@ make dev
 ```
 
 **预期：**
-- [ ] 服务正常启动（忽略 `deerflow.db`，无 auth 相关代码不报错）
+- [ ] 服务正常启动（忽略 `users.db`，无 auth 相关代码不报错）
 - [ ] 旧对话数据仍然可访问
-- [ ] `deerflow.db` 文件残留但不影响运行
+- [ ] `users.db` 文件残留但不影响运行
 
 #### TC-UPG-12: 再次升级到 auth 分支
 
@@ -819,47 +781,51 @@ make dev
 ```
 
 **预期：**
-- [ ] 识别已有 `deerflow.db`，不重新创建 admin
-- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `deerflow.db`）
+- [ ] 识别已有 `users.db`，不重新创建 admin
+- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `users.db`）
 
-### 5.7 Admin 初始化与 reset_admin
+### 5.7 休眠 Admin（初始密码未使用/未更改）
 
-> 首次启动不生成默认 admin，也不在日志输出密码。忘记密码时走 `reset_admin`，新密码写入 0600 凭据文件。
+> 首次启动生成 admin + 随机密码，但运维未登录、未改密码。
+> 密码只在首次启动的控制台闪过一次，后续启动不再显示。
 
-#### TC-UPG-13: 未初始化 admin 时重启不创建默认账号
+#### TC-UPG-13: 重启后自动重置密码并打印
 
 ```bash
-rm -f backend/.deer-flow/data/deerflow.db
+# 首次启动，记录密码
+rm -f backend/.deer-flow/users.db
 make dev
+# 控制台输出密码 P0，不登录
 make stop
 
+# 隔了几天，再次启动
 make dev
-curl -s $BASE/api/v1/auth/setup-status | jq .
+# 控制台输出新密码 P1
 ```
 
 **预期：**
-- [ ] 控制台不输出密码
-- [ ] `setup-status` 仍为 `{"needs_setup": true}`
-- [ ] 访问 `/setup` 仍可创建第一个 admin
+- [ ] 控制台输出 `Admin account setup incomplete — password reset`
+- [ ] 输出新密码 P1（P0 已失效）
+- [ ] 用 P1 可以登录，P0 不可以
+- [ ] 登录后 `needs_setup=true`，跳转 `/setup`
+- [ ] `token_version` 递增（旧 session 如有也失效）
 
-#### TC-UPG-14: 密码丢失 — reset_admin 写入凭据文件
+#### TC-UPG-14: 密码丢失 — 无需 CLI，重启即可
 
 ```bash
-python -m app.gateway.auth.reset_admin --email admin@example.com
-ls -la backend/.deer-flow/admin_initial_credentials.txt
-cat backend/.deer-flow/admin_initial_credentials.txt
+# 忘记了控制台密码 → 直接重启服务
+make stop && make dev
+# 控制台自动输出新密码
 ```
 
 **预期：**
-- [ ] 命令行只输出凭据文件路径，不输出明文密码
-- [ ] 凭据文件权限为 `0600`
-- [ ] 凭据文件包含 email + password 行
-- [ ] 该用户下次登录返回 `needs_setup=true`
+- [ ] 无需 `reset_admin`，重启服务即可拿到新密码
+- [ ] `reset_admin` CLI 仍然可用作手动备选方案
 
-#### TC-UPG-15: 未初始化 admin 期间普通用户注册策略边界
+#### TC-UPG-15: 休眠 admin 期间普通用户注册
 
 ```bash
-# admin 尚不存在，普通用户尝试注册
+# admin 存在但从未登录，普通用户先注册
 curl -s -X POST $BASE/api/v1/auth/register \
   -H "Content-Type: application/json" \
   -d '{"email":"earlybird@example.com","password":"EarlyPass1!"}' \
@@ -867,11 +833,11 @@ curl -s -X POST $BASE/api/v1/auth/register \
 ```
 
 **预期：**
-- [ ] 当前代码允许注册普通用户并自动登录（201，角色为 `user`）
-- [ ] 但 `setup-status` 仍为 `{"needs_setup": true}`，因为 admin 仍不存在
-- [ ] 这是一个产品策略边界：若要求“必须先有 admin”，需要在 `/register` 增加 admin-exists gate
+- [ ] 注册成功（201），角色为 `user`
+- [ ] 无法提权为 admin
+- [ ] 普通用户的数据与 admin 隔离
 
-#### TC-UPG-16: 普通用户数据与后续 admin 隔离
+#### TC-UPG-16: 休眠 admin 不影响后续操作
 
 ```bash
 # 普通用户正常创建 thread、发消息
@@ -883,13 +849,14 @@ curl -s -X POST $BASE/api/threads \
   -d '{"metadata":{}}' | jq .thread_id
 ```
 
-**预期：** 普通用户正常创建 thread；后续 admin 创建后，搜索不到该普通用户 thread
+**预期：** 正常创建，不受休眠 admin 影响
 
-#### TC-UPG-17: reset_admin 后完成 Setup
+#### TC-UPG-17: 休眠 admin 最终完成 Setup
 
 ```bash
+# 运维终于登录
 curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@example.com&password=<凭据文件密码>" \
+  -d "username=admin@deerflow.dev&password=<P0或P1>" \
   -c admin.txt | jq .needs_setup
 # 预期: true
 
@@ -899,7 +866,7 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b admin.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<凭据文件密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
+  -d '{"current_password":"<密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
   -c admin.txt
 
 # 验证
@@ -909,7 +876,7 @@ curl -s $BASE/api/v1/auth/me -b admin.txt | jq '{email, needs_setup}'
 **预期：**
 - [ ] `email` 变为 `admin@real.com`
 - [ ] `needs_setup` 变为 `false`
-- [ ] 后续登录使用新密码
+- [ ] 后续重启控制台不再有 warning
 
 #### TC-UPG-18: 长期未用后 JWT 密钥轮换
 
@@ -923,8 +890,8 @@ make stop && make dev
 
 **预期：**
 - [ ] 服务正常启动
-- [ ] 账号密码仍可登录（密码存在 DB，与 JWT 密钥无关）
-- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）
+- [ ] 旧密码仍可登录（密码存在 DB，与 JWT 密钥无关）
+- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）— 但因为从未登录过也没有旧 token
 
 ---
 
@@ -943,7 +910,7 @@ for i in 1 2 3; do
 done
 
 # 检查 admin 数量
-sqlite3 backend/.deer-flow/data/deerflow.db \
+sqlite3 backend/.deer-flow/users.db \
   "SELECT COUNT(*) FROM users WHERE system_role='admin';"
 ```
 
@@ -1088,7 +1055,7 @@ curl -s -X POST $BASE/api/v1/auth/register \
 wait
 
 # 检查用户数
-sqlite3 backend/.deer-flow/data/deerflow.db \
+sqlite3 backend/.deer-flow/users.db \
   "SELECT COUNT(*) FROM users WHERE email='race@example.com';"
 ```
 
@@ -1198,16 +1165,13 @@ curl -s -w "%{http_code}" -X DELETE "$BASE/api/threads/$TID" \
 ```bash
 cd backend
 python -m app.gateway.auth.reset_admin
-cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p1.txt
-P1=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p1.txt)
+# 记录密码 P1
 
 python -m app.gateway.auth.reset_admin
-cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p2.txt
-P2=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p2.txt)
+# 记录密码 P2
 ```
 
 **预期：**
-- [ ] `.deer-flow/admin_initial_credentials.txt` 每次都会被重写，文件权限为 `0600`
 - [ ] P1 ≠ P2（每次生成新随机密码）
 - [ ] P1 不可用，只有 P2 有效
 - [ ] `token_version` 递增了 2
@@ -1232,11 +1196,21 @@ P2=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p2.txt)
 ## 七、模式差异测试
 
 > 以下用 `GW=http://localhost:8001` 表示直连 Gateway，`BASE=http://localhost:2026` 表示经 nginx。
-> 标准启动命令：`make dev`（或 `./scripts/serve.sh --dev`）。
+> Gateway 模式启动命令：`make dev-pro`（或 `./scripts/serve.sh --dev --gateway`）。
 
-### 7.1 标准启动模式
+### 7.1 标准模式独有
 
-#### TC-MODE-01: Gateway AuthMiddleware 的 token_version 检查
+> 启动命令：`make dev`（或 `./scripts/serve.sh --dev`）
+
+#### TC-MODE-01: LangGraph Server 独立运行，需 cookie
+
+```bash
+# 无 cookie 访问 LangGraph
+curl -s -w "%{http_code}" -o /dev/null $BASE/api/langgraph/threads/search
+# 预期: 403（LangGraph auth handler 拒绝）
+```
+
+#### TC-MODE-02: LangGraph auth 的 token_version 检查
 
 ```bash
 # 登录拿 cookie
@@ -1249,9 +1223,9 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b cookies.txt -H "Content-Type: application/json" -H "X-CSRF-Token: $CSRF" \
   -d '{"current_password":"正确密码","new_password":"NewPass1!"}' -c new_cookies.txt
 
-# 用旧 cookie 访问 LangGraph-compatible 路由
+# 用旧 cookie 访问 LangGraph
 curl -s -w "%{http_code}" $BASE/api/langgraph/threads/search -b cookies.txt
-# 预期: 401（token_version 不匹配）
+# 预期: 403（token_version 不匹配）
 
 # 用新 cookie 访问
 CSRF2=$(grep csrf_token new_cookies.txt | awk '{print $NF}')
@@ -1260,7 +1234,7 @@ curl -s -w "%{http_code}" -X POST $BASE/api/langgraph/threads/search \
 # 预期: 200
 ```
 
-#### TC-MODE-02: Gateway owner filter 隔离
+#### TC-MODE-03: LangGraph auth 的 owner filter 隔离
 
 ```bash
 # user1 创建 thread
@@ -1285,9 +1259,18 @@ print('OK: user2 sees', len(threads), 'threads, none belong to user1')
 "
 ```
 
-#### TC-MODE-03: 所有请求经 AuthMiddleware
+### 7.2 Gateway 模式独有
+
+> 启动命令：`make dev-pro`（或 `./scripts/serve.sh --dev --gateway`）
+> 无 LangGraph Server 进程，agent runtime 嵌入 Gateway。
+
+#### TC-MODE-04: 所有请求经 AuthMiddleware
 
 ```bash
+# 确认 LangGraph Server 未运行
+curl -s -w "%{http_code}" -o /dev/null http://localhost:2024/ok
+# 预期: 000（连接被拒）
+
 # Gateway API 受保护
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models
 # 预期: 401
@@ -1298,7 +1281,7 @@ curl -s -w "%{http_code}" -o /dev/null -X POST $BASE/api/langgraph/threads/searc
 # 预期: 401
 ```
 
-#### TC-MODE-04: 标准模式下完整 auth 流程
+#### TC-MODE-05: Gateway 模式下完整 auth 流程
 
 ```bash
 # 登录
@@ -1313,7 +1296,7 @@ curl -s -X POST $BASE/api/langgraph/threads \
   -d '{"metadata":{}}' | python3 -c "import sys,json; print(json.load(sys.stdin)['thread_id'])"
 # 预期: 返回 thread_id
 
-# CSRF 保护（CSRFMiddleware 覆盖所有 Gateway 路由）
+# CSRF 保护（Gateway 模式下 CSRFMiddleware 直接覆盖所有路由）
 curl -s -w "%{http_code}" -o /dev/null -X POST $BASE/api/langgraph/threads \
   -b cookies.txt -H "Content-Type: application/json" -d '{"metadata":{}}'
 # 预期: 403（CSRF token missing）
@@ -1341,8 +1324,7 @@ done
 ```bash
 GW=http://localhost:8001
 
-for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local \
-            /api/v1/auth/register /api/v1/auth/initialize /api/v1/auth/logout; do
+for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local /api/v1/auth/register; do
   echo "$path: $(curl -s -w '%{http_code}' -o /dev/null $GW$path)"
 done
 # 预期: 200 或 405/422（方法不对但不是 401）
@@ -1412,14 +1394,14 @@ done
 
 ### 7.4 Docker 部署
 
-> 启动命令：`./scripts/deploy.sh`
+> 启动命令：`./scripts/deploy.sh`（标准）或 `./scripts/deploy.sh --gateway`（Gateway 模式）
 > Docker Compose 文件：`docker/docker-compose.yaml`
 >
 > 前置条件：
 > - `.env` 中设置 `AUTH_JWT_SECRET`（否则每次容器重启 session 全部失效）
-> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `deerflow.db`）
+> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `users.db`）
 
-#### TC-DOCKER-01: deerflow.db 通过 volume 持久化
+#### TC-DOCKER-01: users.db 通过 volume 持久化
 
 ```bash
 # 启动容器
@@ -1434,13 +1416,13 @@ curl -s -X POST $BASE/api/v1/auth/register \
   -H "Content-Type: application/json" \
   -d '{"email":"docker-test@example.com","password":"DockerTest1!"}' -w "\nHTTP %{http_code}"
 
-# 检查宿主机上的 deerflow.db
-ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db
-sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db \
+# 检查宿主机上的 users.db
+ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db
+sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db \
   "SELECT email FROM users WHERE email='docker-test@example.com';"
 ```
 
-**预期：** deerflow.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。
+**预期：** users.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。
 
 #### TC-DOCKER-02: 重启容器后 session 保持
 
@@ -1484,24 +1466,22 @@ done
 
 **已知限制：** In-process rate limiter 不跨 worker 共享。生产环境如需精确限速，需要 Redis 等外部存储。
 
-#### TC-DOCKER-04: IM 渠道使用内部认证
+#### TC-DOCKER-04: IM 渠道不经过 auth
 
 ```bash
-# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 调 Gateway
-# 请求携带 process-local internal auth header，并带匹配的 CSRF cookie/header
+# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 通信
+# 不走 nginx，不经过 AuthMiddleware
 
 # 验证方式：检查 gateway 日志中 channel manager 的请求不包含 auth 错误
 docker logs deer-flow-gateway 2>&1 | grep -E "ChannelManager|channel" | head -10
 ```
 
-**预期：** 无 auth 相关错误。渠道不依赖浏览器 cookie；服务端通过内部认证头把请求归入 `default` 用户桶。
+**预期：** 无 auth 相关错误。渠道通过 `langgraph-sdk` 直连 LangGraph Server（`http://langgraph:2024`），不走 auth 层。
 
-#### TC-DOCKER-05: reset_admin 密码写入 0600 凭证文件（不再走日志）
+#### TC-DOCKER-05: admin 密码写入 0600 凭证文件（不再走日志）
 
 ```bash
-# 首次启动不会自动生成 admin 密码。先重置已有 admin，凭据文件写在挂载到宿主机的 DEER_FLOW_HOME 下。
-docker exec deer-flow-gateway python -m app.gateway.auth.reset_admin --email docker-test@example.com
-
+# 凭证文件写在挂载到宿主机的 DEER_FLOW_HOME 下
 ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/admin_initial_credentials.txt
 # 预期文件权限: -rw------- (0600)
 
@@ -1521,26 +1501,25 @@ docker logs deer-flow-gateway 2>&1 | grep -iE "Password: .{15,}" && echo "FAIL:
 - 容器日志输出**路径**（不是密码本身），符合 CodeQL `py/clear-text-logging-sensitive-data` 规则
 - `grep "Password:"` 在日志中**应当无匹配**（旧行为已废弃，simplify pass 移除了日志泄露路径）
 
-#### TC-DOCKER-06: Docker 部署
+#### TC-DOCKER-06: Gateway 模式 Docker 部署
 
 ```bash
-# 标准 Docker 模式：runtime 嵌入 gateway 容器
-./scripts/deploy.sh
+# Gateway 模式：无 langgraph 容器
+./scripts/deploy.sh --gateway
 sleep 15
 
-# 确认 gateway 容器存在
-docker ps --filter name=deer-flow-gateway --format '{{.Names}}'
-# 预期: deer-flow-gateway
+# 确认 langgraph 容器不存在
+docker ps --filter name=deer-flow-langgraph --format '{{.Names}}' | wc -l
+# 预期: 0
 
-# auth 流程正常：未登录受保护接口返回 401
+# auth 流程正常
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models
 # 预期: 401
 
-curl -s -X POST $BASE/api/v1/auth/initialize \
-  -H "Content-Type: application/json" \
-  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
+curl -s -X POST $BASE/api/v1/auth/login/local \
+  -d "username=admin@deerflow.dev&password=<日志密码>" \
   -c cookies.txt -w "\nHTTP %{http_code}"
-# 预期: 201
+# 预期: 200
 ```
 
 ### 7.4 补充边界用例
@@ -1608,15 +1587,13 @@ curl -s -D - -X POST $BASE/api/v1/auth/login/local \
 #### TC-EDGE-05: HTTP 无 max_age / HTTPS 有 max_age
 
 ```bash
-GW=http://localhost:8001
-
 # HTTP
-curl -s -D - -X POST $GW/api/v1/auth/login/local \
+curl -s -D - -X POST $BASE/api/v1/auth/login/local \
   -d "username=admin@example.com&password=正确密码" 2>/dev/null \
   | grep "access_token=" | grep -oi "max-age=[0-9]*" || echo "NO max-age (HTTP session cookie)"
 
-# HTTPS：直连 Gateway 才能用 X-Forwarded-Proto 模拟 HTTPS；nginx 会覆盖该 header
-curl -s -D - -X POST $GW/api/v1/auth/login/local \
+# HTTPS
+curl -s -D - -X POST $BASE/api/v1/auth/login/local \
   -H "X-Forwarded-Proto: https" \
   -d "username=admin@example.com&password=正确密码" 2>/dev/null \
   | grep "access_token=" | grep -oi "max-age=[0-9]*"
@@ -1735,10 +1712,10 @@ curl -s -X POST $BASE/api/threads \
   -b cookies.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"metadata":{"owner_id":"victim-user-id","user_id":"victim-user-id"}}' | jq .metadata
+  -d '{"metadata":{"owner_id":"victim-user-id"}}' | jq .metadata.owner_id
 ```
 
-**预期：** 返回的 `metadata` 不包含 `owner_id` 或 `user_id`。真实所有权写入 `threads_meta.user_id`，不从客户端 metadata 接收，也不通过 metadata 回显。
+**预期：** 返回的 `metadata.owner_id` 应为当前登录用户的 ID，不是请求中注入的 `victim-user-id`。服务端应覆盖客户端提供的 `user_id`。
 
 #### 7.5.6 HTTP Method 探测
 
@@ -1819,6 +1796,6 @@ cd backend && PYTHONPATH=. uv run pytest \
 # 核心接口冒烟
 curl -s $BASE/health                              # 200
 curl -s $BASE/api/models                          # 401 (无 cookie)
-curl -s $BASE/api/v1/auth/setup-status            # 200
+curl -s -X POST $BASE/api/v1/auth/setup-status    # 200
 curl -s $BASE/api/v1/auth/me -b cookies.txt       # 200 (有 cookie)
 ```

backend/docs/AUTH_UPGRADE.md

@@ -2,16 +2,13 @@
 
 DeerFlow 内置了认证模块。本文档面向从无认证版本升级的用户。
 
-完整设计见 [AUTH_DESIGN.md](AUTH_DESIGN.md)。
-
 ## 核心概念
 
 认证模块采用**始终强制**策略：
 
-- 首次启动时不会自动创建账号；首次访问 `/setup` 时由操作者创建第一个 admin 账号
+- 首次启动时自动创建 admin 账号，随机密码打印到控制台日志
 - 认证从一开始就是强制的，无竞争窗口
-- 已有 admin 后，服务启动时会把历史对话（升级前创建且缺少 `user_id` 的 thread）迁移到 admin 名下
-- 新数据按用户隔离：thread、workspace/uploads/outputs、memory、自定义 agent 都归属当前用户
+- 历史对话（升级前创建的 thread）自动迁移到 admin 名下
 
 ## 升级步骤
 
@@ -28,41 +25,39 @@ cd backend && make install
 make dev
 ```
 
-如果没有 admin 账号，控制台只会提示：
+控制台会输出：
 
 ```
 ============================================================
-  First boot detected — no admin account exists.
-  Visit /setup to complete admin account creation.
+  Admin account created on first boot
+  Email:    admin@deerflow.dev
+  Password: aB3xK9mN_pQ7rT2w
+  Change it after login: Settings → Account
 ============================================================
 ```
 
-首次启动不会在日志里打印随机密码，也不会写入默认 admin。这样避免启动日志泄露凭据，也避免在操作者创建账号前出现可被猜测的默认身份。
+如果未登录就重启了服务，不用担心——只要 setup 未完成，每次启动都会重置密码并重新打印到控制台。
 
-### 3. 创建 admin
+### 3. 登录
 
-访问 `http://localhost:2026/setup`，填写邮箱和密码创建第一个 admin 账号。创建成功后会自动登录并进入 workspace。
+访问 `http://localhost:2026/login`，使用控制台输出的邮箱和密码登录。
 
-如果这是从无认证版本升级，创建 admin 后重启一次服务，让启动迁移把缺少 `user_id` 的历史 thread 归属到 admin。
+### 4. 修改密码
 
-### 4. 登录
-
-后续访问 `http://localhost:2026/login`，使用已创建的邮箱和密码登录。
+登录后进入 Settings → Account → Change Password。
 
 ### 5. 添加用户（可选）
 
-其他用户通过 `/login` 页面注册，自动获得 **user** 角色。每个用户只能看到自己的对话、上传文件、输出文件、memory 和自定义 agent。
+其他用户通过 `/login` 页面注册，自动获得 **user** 角色。每个用户只能看到自己的对话。
 
 ## 安全机制
 
 | 机制 | 说明 |
 |------|------|
 | JWT HttpOnly Cookie | Token 不暴露给 JavaScript，防止 XSS 窃取 |
-| CSRF Double Submit Cookie | 受保护的 POST/PUT/PATCH/DELETE 请求需携带 `X-CSRF-Token`；登录/注册/初始化/登出走 auth 端点 Origin 校验 |
+| CSRF Double Submit Cookie | 所有 POST/PUT/DELETE 请求需携带 `X-CSRF-Token` |
 | bcrypt 密码哈希 | 密码不以明文存储 |
-| Thread owner filter | `threads_meta.user_id` 由服务端认证上下文写入，搜索、读取、更新、删除默认按当前用户过滤 |
-| 文件系统隔离 | 线程数据写入 `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/`，sandbox 内统一映射为 `/mnt/user-data/` |
-| Memory / agent 隔离 | 用户 memory 和自定义 agent 写入 `{base_dir}/users/{user_id}/...`；旧共享 agent 只作为只读兼容回退 |
+| 多租户隔离 | 用户只能访问自己的 thread |
 | HTTPS 自适应 | 检测 `x-forwarded-proto`，自动设置 `Secure` cookie 标志 |
 
 ## 常见操作
@@ -79,27 +74,23 @@ python -m app.gateway.auth.reset_admin
 python -m app.gateway.auth.reset_admin --email user@example.com
 ```
 
-会把新的随机密码写入 `.deer-flow/admin_initial_credentials.txt`，文件权限为 `0600`。命令行只输出文件路径，不输出明文密码。
+会输出新的随机密码。
 
 ### 完全重置
 
-删除统一 SQLite 数据库，重启后重新访问 `/setup` 创建新 admin：
+删除用户数据库，重启后自动创建新 admin：
 
 ```bash
-rm -f backend/.deer-flow/data/deerflow.db
-# 重启服务后访问 http://localhost:2026/setup
+rm -f backend/.deer-flow/users.db
+# 重启服务，控制台输出新密码
 ```
 
 ## 数据存储
 
 | 文件 | 内容 |
 |------|------|
-| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库（users、threads_meta、runs、feedback 等应用数据） |
-| `.deer-flow/users/{user_id}/threads/{thread_id}/user-data/` | 用户线程的 workspace、uploads、outputs |
-| `.deer-flow/users/{user_id}/memory.json` | 用户级 memory |
-| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
-| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件（0600，读完应删除） |
-| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥（未设置时自动生成并持久化到 `.deer-flow/.jwt_secret`，重启后 session 保持） |
+| `.deer-flow/users.db` | SQLite 用户数据库（密码哈希、角色） |
+| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥（未设置时自动生成临时密钥，重启后 session 失效） |
 
 ### 生产环境建议
 
@@ -120,21 +111,19 @@ python -c "import secrets; print(secrets.token_urlsafe(32))"
 | `/api/v1/auth/me` | GET | 获取当前用户信息 |
 | `/api/v1/auth/change-password` | POST | 修改密码 |
 | `/api/v1/auth/setup-status` | GET | 检查 admin 是否存在 |
-| `/api/v1/auth/initialize` | POST | 首次初始化第一个 admin（仅无 admin 时可调用） |
 
 ## 兼容性
 
-- **本地开发**（`make dev`）：Gateway embedded runtime 完全兼容；无 admin 时访问 `/setup` 初始化
-- **Gateway embedded runtime**：标准脚本、Docker dev 和生产部署均通过 Gateway 提供认证与 LangGraph-compatible API
-- **Docker 部署**：完全兼容，`.deer-flow/data/deerflow.db` 需持久化卷挂载
-- **IM 渠道**（Feishu/Slack/Telegram）：通过 Gateway 内部认证通信，使用 `default` 用户桶
+- **标准模式**（`make dev`）：完全兼容，admin 自动创建
+- **Gateway 模式**（`make dev-pro`）：完全兼容
+- **Docker 部署**：完全兼容，`.deer-flow/users.db` 需持久化卷挂载
+- **IM 渠道**（Feishu/Slack/Telegram）：通过 LangGraph SDK 通信，不经过认证层
 - **DeerFlowClient**（嵌入式）：不经过 HTTP，不受认证影响
 
 ## 故障排查
 
 | 症状 | 原因 | 解决 |
 |------|------|------|
-| 启动后没看到密码 | 当前实现不在启动日志输出密码 | 首次安装访问 `/setup`；忘记密码用 `reset_admin` |
-| `/login` 自动跳到 `/setup` | 系统还没有 admin | 在 `/setup` 创建第一个 admin |
+| 启动后没看到密码 | admin 已存在（非首次启动） | 用 `reset_admin` 重置，或删 `users.db` |
 | 登录后 POST 返回 403 | CSRF token 缺失 | 确认前端已更新 |
-| 重启后需要重新登录 | `.jwt_secret` 文件被删除且 `.env` 未设置 `AUTH_JWT_SECRET` | 在 `.env` 中设置固定密钥 |
+| 重启后需要重新登录 | `AUTH_JWT_SECRET` 未持久化 | 在 `.env` 中设置固定密钥 |

backend/docs/BLOCKING_IO_DETECTION.md

@@ -1,154 +0,0 @@
-# Blocking IO detection usage and maintenance
-
-This document describes how to use and maintain DeerFlow backend blocking-IO
-detection for async event-loop safety.
-
-The goal is narrow: find and prevent synchronous IO from blocking backend
-async event-loop paths. Static and runtime detection are complementary, but
-they have different jobs.
-
-## Static detector
-
-The static detector is the discovery tool. It scans backend source code and
-reports candidate blocking-IO call sites that may need human review.
-
-Run it from the repository root:
-
-```bash
-make detect-blocking-io
-```
-
-Or from `backend/`:
-
-```bash
-make detect-blocking-io
-```
-
-The report is written to:
-
-```text
-.deer-flow/blocking-io-findings.json
-```
-
-Use this output for review and triage. A static finding is a candidate, not
-proof that production blocks the event loop at runtime. The current static
-rules are intentionally broad; prefer triaging existing output before adding
-new static rules.
-
-Add a static rule only when review finds a recurring high-risk blocking
-pattern that is invisible to the current detector.
-
-## Runtime detector
-
-The runtime detector is the CI regression guard. It uses Blockbuster to fail a
-focused test when code under `app.*` or `deerflow.*` performs blocking IO on
-the asyncio event-loop thread.
-
-Run it from `backend/`:
-
-```bash
-make test-blocking-io
-```
-
-The runtime gate starts from confirmed production bugs and protects those
-paths from regressing. It does not prove that the entire backend is free of
-blocking IO; it only covers the production paths exercised by
-`backend/tests/blocking_io/`.
-
-## Maintenance workflow
-
-Use the static detector to find candidates, then use review to decide which
-async production paths are worth protecting in CI.
-
-The normal workflow is:
-
-1. Run the static detector to find backend blocking-IO candidates.
-2. Use human review to pick high-risk production async paths.
-3. Add or update a focused runtime anchor in `backend/tests/blocking_io/`.
-4. Let CI prevent that path from regressing.
-
-Runtime detection has two maintenance paths.
-
-### Add a runtime rule
-
-Add a runtime rule when Blockbuster's default rules do not cover a generic
-blocking primitive used by production code.
-
-Rules belong in:
-
-```text
-backend/tests/support/detectors/blocking_io_runtime.py
-```
-
-Add them to `_PROJECT_BLOCKING_RULES`, not directly inside individual tests.
-Keeping rules centralized makes it clear which extra primitives DeerFlow
-expects Blockbuster to catch.
-
-Example shape:
-
-```python
-import subprocess
-
-from blockbuster import BlockBusterFunction
-
-_PROJECT_BLOCKING_RULES = (
-    (
-        "subprocess.Popen.__init__",
-        BlockBusterFunction(
-            subprocess.Popen,
-            "__init__",
-            scanned_modules=["app", "deerflow"],
-        ),
-    ),
-)
-```
-
-Do not add a runtime rule just because a business path is not tested. A rule
-only expands what Blockbuster can intercept after code runs.
-
-### Add a runtime anchor
-
-Add a runtime anchor when a high-risk async production path should be protected
-by CI but no existing `backend/tests/blocking_io/` test executes it.
-
-Anchors belong in:
-
-```text
-backend/tests/blocking_io/
-```
-
-A good anchor should:
-
-- Call the real production async entry point.
-- Avoid bypassing the blocking surface with test-only `asyncio.to_thread`
-  wrappers.
-- Use real local filesystem inputs when the bug shape is filesystem IO.
-- Mock only the external dependency boundary, such as a network service or
-  third-party saver class.
-- Fail if a future change moves the blocking operation back onto the event
-  loop.
-
-Avoid testing only the low-level helper unless that helper is the production
-async entry point. The runtime gate is most useful when it protects the caller
-that production actually executes.
-
-## Current runtime coverage
-
-The runtime anchors protect confirmed blocking-IO bug shapes:
-
-- SQLite checkpointer setup, including path resolution and parent-directory
-  creation.
-- Subagent skill metadata loading through `SubagentExecutor._load_skills()`.
-- `JsonlRunEventStore` async API (`put` / `list_*` / `delete_*`): the JSONL
-  run-event backend offloads its synchronous file IO via `asyncio.to_thread`
-  (fix #3084); this anchor drives the real async API under the gate so any
-  blocking IO reintroduced on the loop fails, not only removal of one
-  `to_thread` call.
-- `UploadsMiddleware.before_agent` uploads-directory scan: a sync-only middleware
-  hook runs on the event loop under async graph execution, so the scan is
-  offloaded via `abefore_agent` + `run_in_executor`.
-- Gate health checks: Blockbuster catches unoffloaded calls, opt-out works, and
-  patches are restored after exceptions.
-
-As static detection and review identify more high-risk async paths, add new
-runtime anchors incrementally.

backend/docs/CONFIGURATION.md

@@ -36,7 +36,6 @@ models:
 - OpenAI (`langchain_openai:ChatOpenAI`)
 - Anthropic (`langchain_anthropic:ChatAnthropic`)
 - DeepSeek (`langchain_deepseek:ChatDeepSeek`)
-- Xiaomi MiMo (`deerflow.models.patched_mimo:PatchedChatMiMo`)
 - Claude Code OAuth (`deerflow.models.claude_provider:ClaudeChatModel`)
 - Codex CLI (`deerflow.models.openai_codex_provider:CodexChatModel`)
 - Any LangChain-compatible provider
@@ -95,30 +94,20 @@ models:
         thinking:
           type: enabled
 
-  - name: minimax-m3
-    display_name: MiniMax M3
+  - name: minimax-m2.5
+    display_name: MiniMax M2.5
     use: langchain_openai:ChatOpenAI
-    model: MiniMax-M3
+    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-m2.7
-    display_name: MiniMax M2.7
+  - name: minimax-m2.5-highspeed
+    display_name: MiniMax M2.5 Highspeed
     use: langchain_openai:ChatOpenAI
-    model: MiniMax-M2.7
-    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-m2.7-highspeed
-    display_name: MiniMax M2.7 Highspeed
-    use: langchain_openai:ChatOpenAI
-    model: MiniMax-M2.7-highspeed
+    model: MiniMax-M2.5-highspeed
     api_key: $MINIMAX_API_KEY
     base_url: https://api.minimax.io/v1
     max_tokens: 4096
@@ -177,37 +166,6 @@ models:
 
 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.
 
-**MiMo with thinking via OpenAI-compatible API**:
-
-MiMo returns `reasoning_content` on assistant messages in thinking mode. In multi-turn agent conversations with tool calls, subsequent requests must preserve that historical `reasoning_content` on assistant messages or the MiMo API can return HTTP 400. Standard `langchain_openai:ChatOpenAI` drops this provider-specific field, so use `deerflow.models.patched_mimo:PatchedChatMiMo`:
-
-For pay-as-you-go API keys (`sk-...`), use `https://api.xiaomimimo.com/v1`. For Token Plan keys (`tp-...`), use the regional Token Plan Base URL shown in the MiMo console, such as `https://token-plan-cn.xiaomimimo.com/v1`. MiMo documents these key types as separate and non-interchangeable.
-
-`PatchedChatMiMo` is model-id agnostic. Use it for every MiMo thinking model entry you configure, including model entries referenced by `subagents.*.model` overrides (for example `mimo-v2.5-pro`, `mimo-v2.5`, `mimo-v2-pro`, `mimo-v2-omni`, or `mimo-v2-flash`).
-
-```yaml
-models:
-  - name: mimo-v2.5-pro
-    display_name: MiMo V2.5 Pro
-    use: deerflow.models.patched_mimo:PatchedChatMiMo
-    model: mimo-v2.5-pro
-    api_key: $MIMO_API_KEY
-    base_url: https://api.xiaomimimo.com/v1
-    max_tokens: 8192
-    supports_thinking: true
-    supports_vision: false
-    when_thinking_enabled:
-      extra_body:
-        thinking:
-          type: enabled
-    when_thinking_disabled:
-      extra_body:
-        thinking:
-          type: disabled
-```
-
-`PatchedChatMiMo` preserves MiMo's `choices[].message.reasoning_content`, streaming `delta.reasoning_content`, and request-history assistant `reasoning_content` fields. It does not reuse the DeepSeek provider.
-
 ### Tool Groups
 
 Organize tools into logical groups:
@@ -301,8 +259,6 @@ sandbox:
 
 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`.
 
-For bare-metal Docker sandbox runs that use localhost, DeerFlow binds the sandbox HTTP port to `127.0.0.1` by default so it is not exposed on every host interface. Docker-outside-of-Docker deployments that connect through `host.docker.internal` keep the broad legacy bind for compatibility. Set `DEER_FLOW_SANDBOX_BIND_HOST` explicitly if your deployment needs a different bind address.
-
 ### Skills
 
 Configure the skills directory for specialized workflows:
@@ -361,19 +317,13 @@ models:
 - `OPENAI_API_KEY` - OpenAI API key
 - `ANTHROPIC_API_KEY` - Anthropic API key
 - `DEEPSEEK_API_KEY` - DeepSeek API key
-- `MIMO_API_KEY` - Xiaomi MiMo API key
 - `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
 - `TAVILY_API_KEY` - Tavily search API key
-- `DEER_FLOW_PROJECT_ROOT` - Project root for relative runtime paths
 - `DEER_FLOW_CONFIG_PATH` - Custom config file path
-- `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Custom extensions config file path
-- `DEER_FLOW_HOME` - Runtime state directory (defaults to `.deer-flow` under the project root)
-- `DEER_FLOW_SKILLS_PATH` - Skills directory when `skills.path` is omitted
-- `GATEWAY_ENABLE_DOCS` - Set to `false` to disable Swagger UI (`/docs`), ReDoc (`/redoc`), and OpenAPI schema (`/openapi.json`) endpoints (default: `true`)
 
 ## Configuration Location
 
-The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`). Set `DEER_FLOW_PROJECT_ROOT` when the process may start from another working directory, or set `DEER_FLOW_CONFIG_PATH` to point at a specific file.
+The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
 
 ## Configuration Priority
 
@@ -381,12 +331,12 @@ 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` under `DEER_FLOW_PROJECT_ROOT`, or under the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
-4. Legacy backend/repository-root locations for monorepo compatibility
+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** - Set `DEER_FLOW_PROJECT_ROOT` if the runtime starts elsewhere
+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
@@ -397,7 +347,7 @@ DeerFlow searches for configuration in this order:
 
 ### "Config file not found"
 - Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
-- If the runtime starts outside the project root, set `DEER_FLOW_PROJECT_ROOT`
+- 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"
@@ -407,7 +357,7 @@ DeerFlow searches for configuration in this order:
 ### "Skills not loading"
 - Check that `deer-flow/skills/` directory exists
 - Verify skills have valid `SKILL.md` files
-- Check `skills.path` or `DEER_FLOW_SKILLS_PATH` if using a custom path
+- Check `skills.path` configuration if using custom path
 
 ### "Docker sandbox fails to start"
 - Ensure Docker is running

backend/docs/FILE_UPLOAD.md

@@ -22,8 +22,6 @@ POST /api/threads/{thread_id}/uploads
 **请求体：** `multipart/form-data`
 - `files`: 一个或多个文件
 
-网关会在应用层限制上传规模，默认最多 10 个文件、单文件 50 MiB、单次请求总计 100 MiB。可通过 `config.yaml` 的 `uploads.max_files`、`uploads.max_file_size`、`uploads.max_total_size` 调整；前端会读取同一组限制并在选择文件时提示，超过限制时后端返回 `413 Payload Too Large`。
-
 **响应：**
 ```json
 {
@@ -50,23 +48,7 @@ POST /api/threads/{thread_id}/uploads
 - `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. 查询上传限制
-```
-GET /api/threads/{thread_id}/uploads/limits
-```
-
-返回网关当前生效的上传限制，供前端在用户选择文件前提示和拦截。
-
-**响应：**
-```json
-{
-  "max_files": 10,
-  "max_file_size": 52428800,
-  "max_total_size": 104857600
-}
-```
-
-### 3. 列出已上传文件
+### 2. 列出已上传文件
 ```
 GET /api/threads/{thread_id}/uploads/list
 ```
@@ -89,7 +71,7 @@ GET /api/threads/{thread_id}/uploads/list
 }
 ```
 
-### 4. 删除文件
+### 3. 删除文件
 ```
 DELETE /api/threads/{thread_id}/uploads/{filename}
 ```

backend/docs/HARNESS_APP_SPLIT.md

@@ -0,0 +1,343 @@
+# 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`）

backend/docs/MCP_SERVER.md

@@ -14,19 +14,6 @@ DeerFlow supports configurable MCP servers and skills to extend its capabilities
 3. Configure each server’s command, arguments, and environment variables as needed.
 4. Restart the application to load and register MCP tools.
 
-## Filesystem MCP Servers
-
-DeerFlow already provides built-in file tools for thread-scoped workspace access.
-Do not add an MCP filesystem server for the same DeerFlow workspace. The
-overlapping file tools use different path semantics, which can make LLM tool
-selection and file access behavior unstable.
-
-DeerFlow does not currently adapt the MCP Roots mode for filesystem servers. In
-particular, it does not publish per-thread MCP roots or map DeerFlow sandbox
-paths such as `/mnt/user-data/...` to paths accepted by
-`@modelcontextprotocol/server-filesystem`. Use DeerFlow's built-in file tools
-for DeerFlow workspace files.
-
 ## OAuth Support (HTTP/SSE MCP Servers)
 
 For `http` and `sse` MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.
@@ -101,6 +88,7 @@ MCP servers expose tools that are automatically discovered and integrated into D
 
 MCP servers can provide access to:
 
+- **File systems**
 - **Databases** (e.g., PostgreSQL)
 - **External APIs** (e.g., GitHub, Brave Search)
 - **Browser automation** (e.g., Puppeteer)
@@ -109,4 +97,4 @@ MCP servers can provide access to:
 ## Learn More
 
 For detailed documentation about the Model Context Protocol, visit:  
-https://modelcontextprotocol.io
+https://modelcontextprotocol.io

backend/docs/README.md

@@ -8,7 +8,6 @@ This directory contains detailed documentation for the DeerFlow backend.
 |----------|-------------|
 | [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
 | [API.md](API.md) | Complete API reference |
-| [AUTH_DESIGN.md](AUTH_DESIGN.md) | User authentication, CSRF, and per-user isolation design |
 | [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
 | [SETUP.md](SETUP.md) | Quick setup guide |
 
@@ -19,7 +18,6 @@ This directory contains detailed documentation for the DeerFlow backend.
 | [STREAMING.md](STREAMING.md) | Token-level streaming design: Gateway vs DeerFlowClient paths, `stream_mode` semantics, per-id dedup |
 | [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
 | [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
-| [SANDBOX_MEMORY_PROFILING.md](SANDBOX_MEMORY_PROFILING.md) | Sandbox memory baseline and runtime comparison guide |
 | [summarization.md](summarization.md) | Context summarization feature |
 | [plan_mode_usage.md](plan_mode_usage.md) | Plan mode with TodoList |
 | [AUTO_TITLE_GENERATION.md](AUTO_TITLE_GENERATION.md) | Automatic title generation |
@@ -44,7 +42,6 @@ docs/
 ├── README.md                  # This file
 ├── ARCHITECTURE.md            # System architecture
 ├── API.md                     # API reference
-├── AUTH_DESIGN.md             # User authentication and isolation design
 ├── CONFIGURATION.md           # Configuration guide
 ├── SETUP.md                   # Setup instructions
 ├── FILE_UPLOAD.md             # File upload feature

backend/docs/SANDBOX_MEMORY_PROFILING.md

@@ -1,81 +0,0 @@
-# Sandbox Memory Profiling
-
-This guide records a repeatable baseline before changing the sandbox runtime.
-Issue #3213 reports per-sandbox memory near 1 GiB in Kubernetes. Before adding
-or recommending a new provider, capture the current AIO sandbox baseline and
-compare candidates with the same DeerFlow workload.
-
-## What to Measure
-
-Measure at least these samples:
-
-1. Empty sandbox after it becomes ready.
-2. After a simple bash command.
-3. After a Python task that imports common packages.
-4. After a Node task when Node-based workloads are expected.
-5. After generating files under `/mnt/user-data/outputs`.
-6. After release and warm reuse.
-7. At the target concurrency level, for example 10, 50, or 100 sandboxes.
-
-`kubectl top` reports Kubernetes/container working set memory. Treat it as a
-capacity signal, not exclusive RSS/PSS. Pod-level memory includes every
-container in the Pod and may include cache charged to the cgroup. If a result
-looks surprising, inspect the sandbox processes and cgroup metrics on the node
-before drawing conclusions.
-
-## Capture a Snapshot
-
-Run this from the repository root:
-
-```bash
-python scripts/sandbox_memory_profile.py \
-  --namespace deer-flow \
-  --selector app=deer-flow-sandbox \
-  --sample empty \
-  --include-processes \
-  --format markdown
-```
-
-Use a descriptive `--sample` value for each phase:
-
-```bash
-python scripts/sandbox_memory_profile.py --sample after-bash --format json
-python scripts/sandbox_memory_profile.py --sample after-python --format json
-python scripts/sandbox_memory_profile.py --sample after-artifact --format json
-```
-
-`--include-processes` runs `kubectl exec ... ps` in each sandbox Pod and adds
-the highest-RSS processes to the report. This helps distinguish Pod-level cgroup
-memory from process RSS. The two numbers will not match exactly because cgroup
-memory can include cache and other kernel-accounted memory.
-
-Save the raw JSON when comparing backends so totals, pod names, images,
-requests, limits, and timestamps can be audited later.
-
-## Candidate Runtime Matrix
-
-For AIO, CubeSandbox, OpenSandbox, gVisor, Kata, or another candidate, compare
-the same workload and record:
-
-| Area | Required Evidence |
-| --- | --- |
-| Capacity | Pod or instance count, total memory, average memory, max memory |
-| Startup | Ready latency at 1, 10, 50, and 100 concurrent sandboxes |
-| Commands | Bash output, timeout behavior, failure shape |
-| Files | `read_file`, `write_file`, binary `update_file`, `list_dir`, `glob`, `grep` |
-| Uploads | Files uploaded by the gateway are visible inside the sandbox |
-| Artifacts | Files written to `/mnt/user-data/outputs` are readable by the backend artifact API |
-| Paths | `/mnt/user-data/workspace`, `/mnt/user-data/uploads`, `/mnt/user-data/outputs`, `/mnt/acp-workspace`, and skills paths keep their expected semantics |
-| Isolation | Different users and threads cannot read each other's data |
-| Cleanup | Release, idle timeout, process restart, and orphan cleanup free resources |
-| Operations | Deployment prerequisites, privileged components, networking, storage, and upgrade path |
-
-## PR Guidance
-
-Do not claim that a new provider fixes high-concurrency memory usage until the
-same DeerFlow workload has been measured on both the current AIO sandbox and the
-candidate backend.
-
-For an experimental provider PR, prefer `Related to #3213` unless the PR also
-includes reproducible DeerFlow workload data that demonstrates the target memory
-reduction and preserves uploads, outputs, artifacts, and isolation behavior.

backend/docs/SETUP.md

@@ -23,9 +23,6 @@ DeerFlow uses a YAML configuration file that should be placed in the **project r
    # Option A: Set environment variables (recommended)
    export OPENAI_API_KEY="your-key-here"
 
-   # Optional: pin the project root when running from another directory
-   export DEER_FLOW_PROJECT_ROOT="/path/to/deer-flow"
-
    # Option B: Edit config.yaml directly
    vim config.yaml  # or your preferred editor
    ```
@@ -38,20 +35,17 @@ DeerFlow uses a YAML configuration file that should be placed in the **project r
 
 ## Important Notes
 
-- **Location**: `config.yaml` should be in `deer-flow/` (project root)
+- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
 - **Git**: `config.yaml` is automatically ignored by git (contains secrets)
-- **Runtime root**: Set `DEER_FLOW_PROJECT_ROOT` if DeerFlow may start from outside the project root
-- **Runtime data**: State defaults to `.deer-flow` under the project root; set `DEER_FLOW_HOME` to move it
-- **Skills**: Skills default to `skills/` under the project root; set `DEER_FLOW_SKILLS_PATH` or `skills.path` to move them
+- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
 
 ## Configuration File Locations
 
 The backend searches for `config.yaml` in this order:
 
-1. Explicit `config_path` argument from code
-2. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
-3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
-4. Legacy backend/repository-root locations for monorepo compatibility
+1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
+2. `backend/config.yaml` (current directory when running from backend/)
+3. `deer-flow/config.yaml` (parent directory - **recommended location**)
 
 **Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
 
@@ -83,8 +77,8 @@ python -c "from deerflow.config.app_config import AppConfig; print(AppConfig.res
 
 If it can't find the config:
 1. Ensure you've copied `config.example.yaml` to `config.yaml`
-2. Verify you're in the project root, or set `DEER_FLOW_PROJECT_ROOT`
-3. Check the file exists: `ls -la config.yaml`
+2. Verify you're in the correct directory
+3. Check the file exists: `ls -la ../config.yaml`
 
 ### Permission denied
 
@@ -95,4 +89,4 @@ chmod 600 ../config.yaml  # Protect sensitive configuration
 ## See Also
 
 - [Configuration Guide](CONFIGURATION.md) - Detailed configuration options
-- [Architecture Overview](../CLAUDE.md) - System architecture
+- [Architecture Overview](../CLAUDE.md) - System architecture

backend/docs/TODO.md

@@ -26,7 +26,7 @@
   - Replace sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
   - [x] Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
   - Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
-  - For production: tune Gateway worker/runtime settings for long-running agent workloads
+  - For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
 
 ## Resolved Issues
 

backend/docs/middleware-execution-flow.md

@@ -4,22 +4,22 @@
 
 `create_deerflow_agent` 通过 `RuntimeFeatures` 组装的完整 middleware 链（默认全开时）：
 
-| # | Middleware | `before_agent` | `before_model` | `after_model` | `after_agent` | `wrap_model_call` | `wrap_tool_call` | 主 Agent | Subagent | 来源 |
-|---|-----------|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|------|
-| 0 | ThreadDataMiddleware | ✓ | | | | | | ✓ | ✓ | `sandbox` |
-| 1 | UploadsMiddleware | ✓ | | | | | | ✓ | ✗ | `sandbox` |
-| 2 | SandboxMiddleware | ✓ | | | ✓ | | | ✓ | ✓ | `sandbox` |
-| 3 | DanglingToolCallMiddleware | | | | | ✓ | | ✓ | ✗ | 始终开启 |
-| 4 | GuardrailMiddleware | | | | | | ✓ | ✓ | ✓ | *Phase 2 纳入* |
-| 5 | ToolErrorHandlingMiddleware | | | | | | ✓ | ✓ | ✓ | 始终开启 |
-| 6 | SummarizationMiddleware | | ✓ | | | | | ✓ | ✗ | `summarization` |
-| 7 | TodoMiddleware | | ✓ | ✓ | | ✓ | | ✓ | ✗ | `plan_mode` 参数 |
-| 8 | TitleMiddleware | | | ✓ | | | | ✓ | ✗ | `auto_title` |
-| 9 | MemoryMiddleware | | | | ✓ | | | ✓ | ✗ | `memory` |
-| 10 | ViewImageMiddleware | | ✓ | | | | | ✓ | ✗ | `vision` |
-| 11 | SubagentLimitMiddleware | | | ✓ | | | | ✓ | ✗ | `subagent` |
-| 12 | LoopDetectionMiddleware | ✓ | | ✓ | ✓ | ✓ | | ✓ | ✗ | 始终开启 |
-| 13 | ClarificationMiddleware | | | | | | ✓ | ✓ | ✗ | 始终最后 |
+| # | Middleware | `before_agent` | `before_model` | `after_model` | `after_agent` | `wrap_tool_call` | 主 Agent | Subagent | 来源 |
+|---|-----------|:-:|:-:|:-:|:-:|:-:|:-:|:-:|------|
+| 0 | ThreadDataMiddleware | ✓ | | | | | ✓ | ✓ | `sandbox` |
+| 1 | UploadsMiddleware | ✓ | | | | | ✓ | ✗ | `sandbox` |
+| 2 | SandboxMiddleware | ✓ | | | ✓ | | ✓ | ✓ | `sandbox` |
+| 3 | DanglingToolCallMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
+| 4 | GuardrailMiddleware | | | | | ✓ | ✓ | ✓ | *Phase 2 纳入* |
+| 5 | ToolErrorHandlingMiddleware | | | | | ✓ | ✓ | ✓ | 始终开启 |
+| 6 | SummarizationMiddleware | | | ✓ | | | ✓ | ✗ | `summarization` |
+| 7 | TodoMiddleware | | | ✓ | | | ✓ | ✗ | `plan_mode` 参数 |
+| 8 | TitleMiddleware | | | ✓ | | | ✓ | ✗ | `auto_title` |
+| 9 | MemoryMiddleware | | | | ✓ | | ✓ | ✗ | `memory` |
+| 10 | ViewImageMiddleware | | ✓ | | | | ✓ | ✗ | `vision` |
+| 11 | SubagentLimitMiddleware | | | ✓ | | | ✓ | ✗ | `subagent` |
+| 12 | LoopDetectionMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
+| 13 | ClarificationMiddleware | | | ✓ | | | ✓ | ✗ | 始终最后 |
 
 主 agent **14 个** middleware（`make_lead_agent`），subagent **4 个**（ThreadData、Sandbox、Guardrail、ToolErrorHandling）。`create_deerflow_agent` Phase 1 实现 **13 个**（Guardrail 仅支持自定义实例，无内置默认）。
 
@@ -35,7 +35,7 @@ graph TB
 
     subgraph BA ["<b>before_agent</b> 正序 0→N"]
         direction TB
-        TD["[0] ThreadData<br/>创建线程目录"] --> UL["[1] Uploads<br/>扫描上传文件"] --> SB["[2] Sandbox<br/>获取沙箱"] --> LD_BA["[12] LoopDetection<br/>清理 stale warning"]
+        TD["[0] ThreadData<br/>创建线程目录"] --> UL["[1] Uploads<br/>扫描上传文件"] --> SB["[2] Sandbox<br/>获取沙箱"]
     end
 
     subgraph BM ["<b>before_model</b> 正序 0→N"]
@@ -43,42 +43,34 @@ graph TB
         VI["[10] ViewImage<br/>注入图片 base64"]
     end
 
-    subgraph WM ["<b>wrap_model_call</b>"]
-        direction TB
-        DTC_WM["[3] DanglingToolCall<br/>补悬空 ToolMessage"] --> LD_WM["[12] LoopDetection<br/>注入当前 run warning"]
-    end
-
-    LD_BA --> VI
-    VI --> DTC_WM
-    LD_WM --> M["<b>MODEL</b>"]
+    SB --> VI
+    VI --> M["<b>MODEL</b>"]
 
     subgraph AM ["<b>after_model</b> 反序 N→0"]
         direction TB
-        LD["[12] LoopDetection<br/>检测循环/排队 warning"] --> SL["[11] SubagentLimit<br/>截断多余 task"] --> TI["[8] Title<br/>生成标题"]
+        CL["[13] Clarification<br/>拦截 ask_clarification"] --> LD["[12] LoopDetection<br/>检测循环"] --> SL["[11] SubagentLimit<br/>截断多余 task"] --> TI["[8] Title<br/>生成标题"] --> SM["[6] Summarization<br/>上下文压缩"] --> DTC["[3] DanglingToolCall<br/>补缺失 ToolMessage"]
     end
 
-    M --> LD
+    M --> CL
 
     subgraph AA ["<b>after_agent</b> 反序 N→0"]
         direction TB
-        LD_CLEAN["[12] LoopDetection<br/>清理 pending warning"] --> MEM["[9] Memory<br/>入队记忆"] --> SBR["[2] Sandbox<br/>释放沙箱"]
+        SBR["[2] Sandbox<br/>释放沙箱"] --> MEM["[9] Memory<br/>入队记忆"]
     end
 
-    TI --> LD_CLEAN
-    SBR --> END(["response"])
+    DTC --> SBR
+    MEM --> END(["response"])
 
     classDef beforeNode fill:#a0a8b5,stroke:#636b7a,color:#2d3239
     classDef modelNode fill:#b5a8a0,stroke:#7a6b63,color:#2d3239
-    classDef wrapModelNode fill:#a8a0b5,stroke:#6b637a,color:#2d3239
     classDef afterModelNode fill:#b5a0a8,stroke:#7a636b,color:#2d3239
     classDef afterAgentNode fill:#a0b5a8,stroke:#637a6b,color:#2d3239
     classDef terminalNode fill:#a8b5a0,stroke:#6b7a63,color:#2d3239
 
-    class TD,UL,SB,LD_BA,VI beforeNode
-    class DTC_WM,LD_WM wrapModelNode
+    class TD,UL,SB,VI beforeNode
     class M modelNode
-    class LD,SL,TI afterModelNode
-    class LD_CLEAN,SBR,MEM afterAgentNode
+    class CL,LD,SL,TI,SM,DTC afterModelNode
+    class SBR,MEM afterAgentNode
     class START,END terminalNode
 ```
 
@@ -90,12 +82,13 @@ sequenceDiagram
     participant TD as ThreadDataMiddleware
     participant UL as UploadsMiddleware
     participant SB as SandboxMiddleware
-    participant LD as LoopDetectionMiddleware
     participant VI as ViewImageMiddleware
-    participant DTC as DanglingToolCallMiddleware
     participant M as MODEL
+    participant CL as ClarificationMiddleware
     participant SL as SubagentLimitMiddleware
     participant TI as TitleMiddleware
+    participant SM as SummarizationMiddleware
+    participant DTC as DanglingToolCallMiddleware
     participant MEM as MemoryMiddleware
 
     U ->> TD: invoke
@@ -110,26 +103,19 @@ sequenceDiagram
     activate SB
     Note right of SB: before_agent 获取沙箱
 
-    SB ->> LD: before_agent
-    activate LD
-    Note right of LD: before_agent 清理同 thread 旧 run 的 pending warning
-    LD ->> VI: before_model
+    SB ->> VI: before_model
     activate VI
     Note right of VI: before_model 注入图片 base64
 
-    VI ->> DTC: wrap_model_call
-    activate DTC
-    Note right of DTC: wrap_model_call 补悬空 ToolMessage
-    DTC ->> LD: wrap_model_call
-    Note right of LD: wrap_model_call drain 当前 run warning 并追加到末尾
-    LD ->> M: messages + tools
+    VI ->> M: messages + tools
     activate M
-    M -->> LD: AI response
+    M -->> CL: AI response
     deactivate M
 
-    Note right of LD: after_model 检测循环；warning 入队，hard-stop 清 tool_calls
-    LD -->> SL: after_model
-    deactivate LD
+    activate CL
+    Note right of CL: after_model 拦截 ask_clarification
+    CL -->> SL: after_model
+    deactivate CL
 
     activate SL
     Note right of SL: after_model 截断多余 task
@@ -138,18 +124,22 @@ sequenceDiagram
 
     activate TI
     Note right of TI: after_model 生成标题
-    TI -->> DTC: done
+    TI -->> SM: after_model
     deactivate TI
 
+    activate SM
+    Note right of SM: after_model 上下文压缩
+    SM -->> DTC: after_model
+    deactivate SM
+
+    activate DTC
+    Note right of DTC: after_model 补缺失 ToolMessage
+    DTC -->> VI: done
     deactivate DTC
 
     VI -->> SB: done
     deactivate VI
 
-    Note right of LD: after_agent 清理当前 run 未消费 warning
-
-    Note right of MEM: after_agent 入队记忆
-
     Note right of SB: after_agent 释放沙箱
     SB -->> UL: done
     deactivate SB
@@ -157,6 +147,8 @@ sequenceDiagram
     UL -->> TD: done
     deactivate UL
 
+    Note right of MEM: after_agent 入队记忆
+
     TD -->> U: response
     deactivate TD
 ```
@@ -232,12 +224,12 @@ sequenceDiagram
     participant TD as ThreadData
     participant UL as Uploads
     participant SB as Sandbox
-    participant LD as LoopDetection
     participant VI as ViewImage
-    participant DTC as DanglingToolCall
     participant M as MODEL
+    participant CL as Clarification
     participant SL as SubagentLimit
     participant TI as Title
+    participant SM as Summarization
     participant MEM as Memory
 
     U ->> TD: invoke
@@ -246,40 +238,34 @@ sequenceDiagram
     Note right of UL: before_agent 扫描文件
     UL ->> SB: .
     Note right of SB: before_agent 获取沙箱
-    SB ->> LD: .
-    Note right of LD: before_agent 清理 stale pending warning
 
     loop 每轮对话（tool call 循环）
         SB ->> VI: .
         Note right of VI: before_model 注入图片
-        VI ->> DTC: .
-        Note right of DTC: wrap_model_call 补悬空工具结果
-        DTC ->> LD: .
-        Note right of LD: wrap_model_call 注入当前 run warning
-        LD ->> M: messages + tools
-        M -->> LD: AI response
-        Note right of LD: after_model 检测循环/排队 warning
-        LD -->> SL: .
+        VI ->> M: messages + tools
+        M -->> CL: AI response
+        Note right of CL: after_model 拦截 ask_clarification
+        CL -->> SL: .
         Note right of SL: after_model 截断多余 task
         SL -->> TI: .
         Note right of TI: after_model 生成标题
+        TI -->> SM: .
+        Note right of SM: after_model 上下文压缩
     end
 
-    Note right of LD: after_agent 清理当前 run pending warning
-    LD -->> MEM: .
-    Note right of MEM: after_agent 入队记忆
-    MEM -->> SB: .
     Note right of SB: after_agent 释放沙箱
-    SB -->> U: response
+    SB -->> MEM: .
+    Note right of MEM: after_agent 入队记忆
+    MEM -->> U: response
 ```
 
 > [!warning] 不是洋葱
-> 大部分 middleware 只用一个阶段。SandboxMiddleware 使用 `before_agent`/`after_agent` 做资源获取/释放；LoopDetectionMiddleware 也使用这两个钩子，但用途是清理 run-scoped pending warnings，不是资源生命周期对称。`before_agent` / `after_agent` 只跑一次，`before_model` / `after_model` / `wrap_model_call` 每轮循环都跑。
+> 14 个 middleware 中只有 SandboxMiddleware 有 before/after 对称（获取/释放）。其余都是单向的：要么只在 `before_*` 做事，要么只在 `after_*` 做事。`before_agent` / `after_agent` 只跑一次，`before_model` / `after_model` 每轮循环都跑。
 
 硬依赖只有 2 处：
 
 1. **ThreadData 在 Sandbox 之前** — sandbox 需要线程目录
-2. **Clarification 在列表最后** — `wrap_tool_call` 处理 `ask_clarification` 时优先拦截，并通过 `Command(goto=END)` 中断执行
+2. **Clarification 在列表最后** — `after_model` 反序时最先执行，第一个拦截 `ask_clarification`
 
 ### 结论
 
@@ -287,19 +273,19 @@ sequenceDiagram
 |---|---|---|
 | 每个 middleware | before + after 对称 | 大多只用一个钩子 |
 | 激活条 | 嵌套（外长内短） | 不嵌套（串行） |
-| 反序的意义 | 清理与初始化配对 | 影响 `after_model` / `after_agent` 的执行优先级 |
+| 反序的意义 | 清理与初始化配对 | 仅影响 after_model 的执行优先级 |
 | 典型例子 | Auth: 校验 token / 清理上下文 | ThreadData: 只创建目录，没有清理 |
 
 ## 关键设计点
 
 ### ClarificationMiddleware 为什么在列表最后？
 
-位置最后使它在工具调用包装链中优先拦截 `ask_clarification`。如果命中，它返回 `Command(goto=END)`，把格式化后的澄清问题写成 `ToolMessage` 并中断执行。
+位置最后 = `after_model` 最先执行。它需要**第一个**看到 model 输出，检查是否有 `ask_clarification` tool call。如果有，立即中断（`Command(goto=END)`），后续 middleware 的 `after_model` 不再执行。
 
 ### SandboxMiddleware 的对称性
 
 `before_agent`（正序第 3 个）获取沙箱，`after_agent`（反序第 1 个）释放沙箱。外层进入 → 外层退出，天然的洋葱对称。
 
-### LoopDetectionMiddleware 为什么同时用多个钩子？
+### 大部分 middleware 只用一个钩子
 
-`after_model` 只做检测：重复工具调用达到 warning 阈值时，把 warning 放入 `(thread_id, run_id)` 作用域的 pending 队列。真正注入发生在下一次 `wrap_model_call`：此时上一轮 `AIMessage(tool_calls)` 对应的 `ToolMessage` 已经在请求里，warning 追加在末尾，不会破坏 OpenAI/Moonshot 的 tool-call pairing。`before_agent` 清理同一 thread 下旧 run 的残留 warning，`after_agent` 清理当前 run 没被消费的 warning。
+14 个 middleware 中，只有 SandboxMiddleware 同时用了 `before_agent` + `after_agent`（获取/释放）。其余都只在一个阶段执行。洋葱模型的反序特性主要影响 `after_model` 阶段的执行顺序。

backend/langgraph.json

@@ -12,6 +12,6 @@
     "path": "./app/gateway/langgraph_auth.py:auth"
   },
   "checkpointer": {
-    "path": "./packages/harness/deerflow/runtime/checkpointer/async_provider.py:make_checkpointer"
+    "path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
   }
 }

backend/packages/harness/deerflow/agents/factory.py

@@ -173,7 +173,7 @@ def _assemble_from_features(
       9.   MemoryMiddleware (memory feature)
       10.  ViewImageMiddleware (vision feature)
       11.  SubagentLimitMiddleware (subagent feature)
-      12.  LoopDetectionMiddleware (loop_detection feature)
+      12.  LoopDetectionMiddleware (always)
       13.  ClarificationMiddleware (always last)
 
     Two-phase ordering:
@@ -254,11 +254,9 @@ def _assemble_from_features(
             from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 
             chain.append(ViewImageMiddleware())
+        from deerflow.tools.builtins import view_image_tool
 
-        if feat.sandbox is not False:
-            from deerflow.tools.builtins import view_image_tool
-
-            extra_tools.append(view_image_tool)
+        extra_tools.append(view_image_tool)
 
     # --- [11] Subagent ---
     if feat.subagent is not False:
@@ -272,15 +270,10 @@ def _assemble_from_features(
 
         extra_tools.append(task_tool)
 
-    # --- [12] LoopDetection ---
-    if feat.loop_detection is not False:
-        if isinstance(feat.loop_detection, AgentMiddleware):
-            chain.append(feat.loop_detection)
-        else:
-            from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
-            from deerflow.config.loop_detection_config import LoopDetectionConfig
+    # --- [12] LoopDetection (always) ---
+    from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
 
-            chain.append(LoopDetectionMiddleware.from_config(LoopDetectionConfig()))
+    chain.append(LoopDetectionMiddleware())
 
     # --- [13] Clarification (always last among built-ins) ---
     chain.append(ClarificationMiddleware())

backend/packages/harness/deerflow/agents/features.py

@@ -31,7 +31,6 @@ class RuntimeFeatures:
     vision: bool | AgentMiddleware = False
     auto_title: bool | AgentMiddleware = False
     guardrail: Literal[False] | AgentMiddleware = False
-    loop_detection: bool | AgentMiddleware = True
 
 
 # ---------------------------------------------------------------------------

backend/packages/harness/deerflow/agents/lead_agent/agent.py

@@ -1,38 +1,15 @@
-"""Lead agent factory.
-
-INVARIANT — tracing callback placement
-======================================
-
-Tracing callbacks (Langfuse, LangSmith) are attached at the **graph
-invocation root** in :func:`_make_lead_agent` (see the
-``build_tracing_callbacks()`` block that appends to ``config["callbacks"]``).
-Every ``create_chat_model(...)`` call inside this module — and inside any
-middleware reachable from this graph (e.g. ``TitleMiddleware``) — MUST pass
-``attach_tracing=False``.
-
-Forgetting that flag emits duplicate spans (one rooted at the graph, one at
-the model) AND prevents the Langfuse handler's ``propagate_attributes``
-path from firing, so ``session_id`` / ``user_id`` never reach the trace.
-The four current sites are: bootstrap agent, default agent, summarization
-middleware, and the async path inside ``TitleMiddleware``. Any new in-graph
-``create_chat_model`` call must add to this list and pass the flag.
-"""
-
-from __future__ import annotations
-
 import logging
-from typing import TYPE_CHECKING
 
 from langchain.agents import create_agent
 from langchain.agents.middleware import AgentMiddleware
 from langchain_core.runnables import RunnableConfig
+from langgraph.graph.state import CompiledStateGraph
 
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
 from deerflow.agents.memory.summarization_hook import memory_flush_hook
 from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
 from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
 from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
-from deerflow.agents.middlewares.safety_finish_reason_middleware import SafetyFinishReasonMiddleware
 from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
 from deerflow.agents.middlewares.summarization_middleware import BeforeSummarizationHook, DeerFlowSummarizationMiddleware
 from deerflow.agents.middlewares.title_middleware import TitleMiddleware
@@ -42,16 +19,9 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
 from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import load_agent_config, validate_agent_name
-from deerflow.config.app_config import AppConfig, get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.models import create_chat_model
-from deerflow.skills.tool_policy import filter_tools_by_skill_allowed_tools
-from deerflow.skills.types import Skill
-from deerflow.tracing import build_tracing_callbacks
-
-if TYPE_CHECKING:
-    from langchain.tools import BaseTool
-
-    from deerflow.tools.builtins.tool_search import DeferredToolSetup
 
 logger = logging.getLogger(__name__)
 
@@ -65,9 +35,8 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
     return cfg
 
 
-def _resolve_model_name(requested_model_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
+def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None = None) -> str:
     """Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
-    app_config = app_config or get_app_config()
     default_model_name = app_config.models[0].name if app_config.models else None
     if default_model_name is None:
         raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
@@ -80,10 +49,9 @@ def _resolve_model_name(requested_model_name: str | None = None, *, app_config:
     return default_model_name
 
 
-def _create_summarization_middleware(*, app_config: AppConfig | None = None) -> DeerFlowSummarizationMiddleware | None:
+def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummarizationMiddleware | None:
     """Create and configure the summarization middleware from config."""
-    resolved_app_config = app_config or get_app_config()
-    config = resolved_app_config.summarization
+    config = app_config.summarization
 
     if not config.enabled:
         return None
@@ -103,14 +71,10 @@ def _create_summarization_middleware(*, app_config: AppConfig | None = None) ->
     # Bind "middleware:summarize" tag so RunJournal identifies these LLM calls
     # as middleware rather than lead_agent (SummarizationMiddleware is a
     # LangChain built-in, so we tag the model at creation time).
-    # attach_tracing=False because the graph-level RunnableConfig (set in
-    # ``_make_lead_agent``) already carries tracing callbacks; binding them
-    # again at the model level would emit duplicate spans and break
-    # ``session_id`` / ``user_id`` propagation.
     if config.model_name:
-        model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=resolved_app_config, attach_tracing=False)
+        model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
     else:
-        model = create_chat_model(thinking_enabled=False, app_config=resolved_app_config, attach_tracing=False)
+        model = create_chat_model(thinking_enabled=False, app_config=app_config)
     model = model.with_config(tags=["middleware:summarize"])
 
     # Prepare kwargs
@@ -127,13 +91,17 @@ def _create_summarization_middleware(*, app_config: AppConfig | None = None) ->
         kwargs["summary_prompt"] = config.summary_prompt
 
     hooks: list[BeforeSummarizationHook] = []
-    if resolved_app_config.memory.enabled:
+    if app_config.memory.enabled:
         hooks.append(memory_flush_hook)
 
     # The logic below relies on two assumptions holding true: this factory is
     # the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
     # config is not expected to change after startup.
-    skills_container_path = resolved_app_config.skills.container_path or "/mnt/skills"
+    try:
+        skills_container_path = app_config.skills.container_path or "/mnt/skills"
+    except Exception:
+        logger.exception("Failed to resolve skills container path; falling back to default")
+        skills_container_path = "/mnt/skills"
 
     return DeerFlowSummarizationMiddleware(
         **kwargs,
@@ -272,17 +240,17 @@ Being proactive with task management demonstrates thoroughness and ensures all r
 # ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
 # ClarificationMiddleware should be last to intercept clarification requests after model calls
 def _build_middlewares(
+    app_config: AppConfig,
     config: RunnableConfig,
+    *,
     model_name: str | None,
     agent_name: str | None = None,
     custom_middlewares: list[AgentMiddleware] | None = None,
-    *,
-    app_config: AppConfig | None = None,
-    deferred_setup=None,
 ):
     """Build middleware chain based on runtime configuration.
 
     Args:
+        app_config: Resolved application config.
         config: Runtime configuration containing configurable options like is_plan_mode.
         agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
         custom_middlewares: Optional list of custom middlewares to inject into the chain.
@@ -290,17 +258,10 @@ def _build_middlewares(
     Returns:
         List of middleware instances.
     """
-    resolved_app_config = app_config or get_app_config()
-    middlewares = build_lead_runtime_middlewares(app_config=resolved_app_config, lazy_init=True)
-
-    # Always inject current date (and optionally memory) as <system-reminder> into the
-    # first HumanMessage to keep the system prompt fully static for prefix-cache reuse.
-    from deerflow.agents.middlewares.dynamic_context_middleware import DynamicContextMiddleware
-
-    middlewares.append(DynamicContextMiddleware(agent_name=agent_name, app_config=resolved_app_config))
+    middlewares = build_lead_runtime_middlewares(app_config=app_config, lazy_init=True)
 
     # Add summarization middleware if enabled
-    summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
+    summarization_middleware = _create_summarization_middleware(app_config)
     if summarization_middleware is not None:
         middlewares.append(summarization_middleware)
 
@@ -312,28 +273,26 @@ def _build_middlewares(
         middlewares.append(todo_list_middleware)
 
     # Add TokenUsageMiddleware when token_usage tracking is enabled
-    if resolved_app_config.token_usage.enabled:
+    if app_config.token_usage.enabled:
         middlewares.append(TokenUsageMiddleware())
 
     # Add TitleMiddleware
-    middlewares.append(TitleMiddleware(app_config=resolved_app_config))
+    middlewares.append(TitleMiddleware())
 
     # Add MemoryMiddleware (after TitleMiddleware)
-    middlewares.append(MemoryMiddleware(agent_name=agent_name, memory_config=resolved_app_config.memory))
+    middlewares.append(MemoryMiddleware(agent_name=agent_name))
 
     # Add ViewImageMiddleware only if the current model supports vision.
     # Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
-    model_config = resolved_app_config.get_model_config(model_name) if model_name else None
+    model_config = app_config.get_model_config(model_name) if model_name else None
     if model_config is not None and model_config.supports_vision:
         middlewares.append(ViewImageMiddleware())
 
-    # Hide deferred tool schemas from model binding until tool_search promotes them.
-    # The deferred set + catalog hash come from the build-time setup (assembled
-    # after tool-policy filtering); promotion is read from graph state.
-    if deferred_setup is not None and deferred_setup.deferred_names:
+    # Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
+    if app_config.tool_search.enabled:
         from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
 
-        middlewares.append(DeferredToolFilterMiddleware(deferred_setup.deferred_names, deferred_setup.catalog_hash))
+        middlewares.append(DeferredToolFilterMiddleware())
 
     # Add SubagentLimitMiddleware to truncate excess parallel task calls
     subagent_enabled = cfg.get("subagent_enabled", False)
@@ -342,84 +301,44 @@ def _build_middlewares(
         middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
 
     # LoopDetectionMiddleware — detect and break repetitive tool call loops
-    loop_detection_config = resolved_app_config.loop_detection
-    if loop_detection_config.enabled:
-        middlewares.append(LoopDetectionMiddleware.from_config(loop_detection_config))
+    middlewares.append(LoopDetectionMiddleware())
 
     # Inject custom middlewares before ClarificationMiddleware
     if custom_middlewares:
         middlewares.extend(custom_middlewares)
 
-    # SafetyFinishReasonMiddleware — suppress tool execution when the provider
-    # safety-terminated the response. Registered after custom middlewares so
-    # that LangChain's reverse-order after_model dispatch runs Safety first;
-    # cleared tool_calls then flow through Loop/Subagent accounting without
-    # firing extra alarms. See safety_finish_reason_middleware.py docstring.
-    safety_config = resolved_app_config.safety_finish_reason
-    if safety_config.enabled:
-        middlewares.append(SafetyFinishReasonMiddleware.from_config(safety_config))
-
     # ClarificationMiddleware should always be last
     middlewares.append(ClarificationMiddleware())
     return middlewares
 
 
-def _assemble_deferred(filtered_tools: list[BaseTool], *, enabled: bool) -> tuple[list[BaseTool], DeferredToolSetup]:
-    """Build the final tool list + deferred setup from a policy-filtered list.
+def make_lead_agent(
+    config: RunnableConfig,
+    app_config: AppConfig | None = None,
+) -> CompiledStateGraph:
+    """Build the lead agent from runtime config.
 
-    Call AFTER tool-policy filtering so the deferred catalog never exposes a
-    tool the agent is not allowed to use. Fail-closed: if tool_search is enabled
-    and MCP tools survived filtering but no deferred set was recovered, raise
-    rather than silently binding their full schemas to the model.
+    Args:
+        config: LangGraph ``RunnableConfig`` carrying per-invocation options
+            (``thinking_enabled``, ``model_name``, ``is_plan_mode``, etc.).
+        app_config: Resolved application config. Required for in-process
+            entry points (DeerFlowClient, Gateway Worker). When omitted we
+            are being called via ``langgraph.json`` registration and reload
+            from disk — the LangGraph Server bootstrap path has no other
+            way to thread the value.
     """
-    from deerflow.tools.builtins.tool_search import build_deferred_tool_setup
-    from deerflow.tools.mcp_metadata import is_mcp_tool
-
-    deferred_setup = build_deferred_tool_setup(filtered_tools, enabled=enabled)
-    if enabled and not deferred_setup.deferred_names and any(is_mcp_tool(t) for t in filtered_tools):
-        raise RuntimeError("tool_search enabled and MCP tools survived policy filtering, but no deferred set was recovered — refusing to bind MCP schemas (fail-closed).")
-    final_tools = list(filtered_tools)
-    if deferred_setup.tool_search_tool:
-        final_tools.append(deferred_setup.tool_search_tool)
-    return final_tools, deferred_setup
-
-
-def _available_skill_names(agent_config, is_bootstrap: bool) -> set[str] | None:
-    if is_bootstrap:
-        return {"bootstrap"}
-    if agent_config and agent_config.skills is not None:
-        return set(agent_config.skills)
-    return None
-
-
-def _load_enabled_skills_for_tool_policy(available_skills: set[str] | None, *, app_config: AppConfig) -> list[Skill]:
-    try:
-        from deerflow.agents.lead_agent.prompt import get_enabled_skills_for_config
-
-        skills = get_enabled_skills_for_config(app_config)
-    except Exception:
-        logger.exception("Failed to load skills for allowed-tools policy")
-        raise
-
-    if available_skills is None:
-        return skills
-    return [skill for skill in skills if skill.name in available_skills]
-
-
-def make_lead_agent(config: RunnableConfig):
-    """LangGraph graph factory; keep the signature compatible with LangGraph Server."""
-    runtime_config = _get_runtime_config(config)
-    runtime_app_config = runtime_config.get("app_config")
-    return _make_lead_agent(config, app_config=runtime_app_config or get_app_config())
-
-
-def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
-    from deerflow.tools.builtins import setup_agent, update_agent
+    from deerflow.tools.builtins import setup_agent
+
+    if app_config is None:
+        # LangGraph Server registers ``make_lead_agent`` via ``langgraph.json``
+        # and hands us only a ``RunnableConfig``. Reload config from disk
+        # here — it's a pure function, equivalent to the process-global the
+        # old code path would have read.
+        app_config = AppConfig.from_file()
 
     cfg = _get_runtime_config(config)
-    resolved_app_config = app_config
 
     thinking_enabled = cfg.get("thinking_enabled", True)
     reasoning_effort = cfg.get("reasoning_effort", None)
@@ -431,14 +350,13 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     agent_name = validate_agent_name(cfg.get("agent_name"))
 
     agent_config = load_agent_config(agent_name) if not is_bootstrap else None
-    available_skills = _available_skill_names(agent_config, is_bootstrap)
     # Custom agent model from agent config (if any), or None to let _resolve_model_name pick the default
     agent_model_name = agent_config.model if agent_config and agent_config.model else None
 
     # Final model name resolution: request → agent config → global default, with fallback for unknown names
-    model_name = _resolve_model_name(requested_model_name or agent_model_name, app_config=resolved_app_config)
+    model_name = _resolve_model_name(app_config, requested_model_name or agent_model_name)
 
-    model_config = resolved_app_config.get_model_config(model_name)
+    model_config = app_config.get_model_config(model_name)
 
     if model_config is None:
         raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
@@ -470,62 +388,29 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
             "is_plan_mode": is_plan_mode,
             "subagent_enabled": subagent_enabled,
             "tool_groups": agent_config.tool_groups if agent_config else None,
-            "available_skills": sorted(available_skills) if available_skills is not None else None,
+            "available_skills": ["bootstrap"] if is_bootstrap else (agent_config.skills if agent_config and agent_config.skills is not None else None),
         }
     )
 
-    # Inject tracing callbacks at the graph invocation root so a single LangGraph
-    # run produces one trace with all node / LLM / tool calls as child spans,
-    # AND so the Langfuse handler sees ``on_chain_start(parent_run_id=None)`` and
-    # actually propagates ``langfuse_session_id`` / ``langfuse_user_id`` from
-    # ``config["metadata"]`` onto the trace. Without root-level attachment the
-    # model is a nested observation and the handler strips ``langfuse_*`` keys.
-    tracing_callbacks = build_tracing_callbacks()
-    if tracing_callbacks:
-        existing = config.get("callbacks") or []
-        if not isinstance(existing, list):
-            existing = list(existing)
-        config["callbacks"] = [*existing, *tracing_callbacks]
-
-    skills_for_tool_policy = _load_enabled_skills_for_tool_policy(available_skills, app_config=resolved_app_config)
-
     if is_bootstrap:
         # Special bootstrap agent with minimal prompt for initial custom agent creation flow
-        raw_tools = get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent]
-        filtered = filter_tools_by_skill_allowed_tools(raw_tools, skills_for_tool_policy)
-        final_tools, setup = _assemble_deferred(filtered, enabled=resolved_app_config.tool_search.enabled)
         return create_agent(
-            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config, attach_tracing=False),
-            tools=final_tools,
-            middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config, deferred_setup=setup),
-            system_prompt=apply_prompt_template(
-                subagent_enabled=subagent_enabled,
-                max_concurrent_subagents=max_concurrent_subagents,
-                available_skills=set(["bootstrap"]),
-                app_config=resolved_app_config,
-                deferred_names=setup.deferred_names,
-            ),
+            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=app_config),
+            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=app_config) + [setup_agent],
+            middleware=_build_middlewares(app_config, config, model_name=model_name),
+            system_prompt=apply_prompt_template(app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
             state_schema=ThreadState,
+            context_schema=DeerFlowContext,
         )
 
-    # Custom agents can update their own SOUL.md / config via update_agent.
-    # The default agent (no agent_name) does not see this tool.
-    extra_tools = [update_agent] if agent_name else []
     # Default lead agent (unchanged behavior)
-    raw_tools = get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=resolved_app_config)
-    filtered = filter_tools_by_skill_allowed_tools(raw_tools + extra_tools, skills_for_tool_policy)
-    final_tools, setup = _assemble_deferred(filtered, enabled=resolved_app_config.tool_search.enabled)
     return create_agent(
-        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config, attach_tracing=False),
-        tools=final_tools,
-        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config, deferred_setup=setup),
+        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=app_config),
+        tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=app_config),
+        middleware=_build_middlewares(app_config, config, model_name=model_name, agent_name=agent_name),
         system_prompt=apply_prompt_template(
-            subagent_enabled=subagent_enabled,
-            max_concurrent_subagents=max_concurrent_subagents,
-            agent_name=agent_name,
-            available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None,
-            app_config=resolved_app_config,
-            deferred_names=setup.deferred_names,
+            app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
         ),
         state_schema=ThreadState,
+        context_schema=DeerFlowContext,
     )

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -1,43 +1,39 @@
-from __future__ import annotations
-
 import asyncio
 import logging
 import threading
+from datetime import datetime
 from functools import lru_cache
-from typing import TYPE_CHECKING
 
 from deerflow.config.agents_config import load_agent_soul
-from deerflow.skills.storage import get_or_new_skill_storage
-from deerflow.skills.types import Skill, SkillCategory
+from deerflow.config.app_config import AppConfig
+from deerflow.skills import load_skills
+from deerflow.skills.types import Skill
 from deerflow.subagents import get_available_subagent_names
 
-if TYPE_CHECKING:
-    from deerflow.config.app_config import AppConfig
-
 logger = logging.getLogger(__name__)
 
 _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
 _enabled_skills_lock = threading.Lock()
 _enabled_skills_cache: list[Skill] | None = None
-_enabled_skills_by_config_cache: dict[int, tuple[object, list[Skill]]] = {}
 _enabled_skills_refresh_active = False
 _enabled_skills_refresh_version = 0
 _enabled_skills_refresh_event = threading.Event()
 
 
-def _load_enabled_skills_sync() -> list[Skill]:
-    return list(get_or_new_skill_storage().load_skills(enabled_only=True))
+def _load_enabled_skills_sync(app_config: AppConfig | None) -> list[Skill]:
+    return list(load_skills(app_config, enabled_only=True))
 
 
-def _start_enabled_skills_refresh_thread() -> None:
+def _start_enabled_skills_refresh_thread(app_config: AppConfig | None) -> None:
     threading.Thread(
         target=_refresh_enabled_skills_cache_worker,
+        args=(app_config,),
         name="deerflow-enabled-skills-loader",
         daemon=True,
     ).start()
 
 
-def _refresh_enabled_skills_cache_worker() -> None:
+def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
     global _enabled_skills_cache, _enabled_skills_refresh_active
 
     while True:
@@ -45,8 +41,8 @@ def _refresh_enabled_skills_cache_worker() -> None:
             target_version = _enabled_skills_refresh_version
 
         try:
-            skills = _load_enabled_skills_sync()
-        except Exception:
+            skills = _load_enabled_skills_sync(app_config)
+        except (OSError, ImportError):
             logger.exception("Failed to load enabled skills for prompt injection")
             skills = []
 
@@ -62,7 +58,7 @@ def _refresh_enabled_skills_cache_worker() -> None:
             _enabled_skills_cache = None
 
 
-def _ensure_enabled_skills_cache() -> threading.Event:
+def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
     global _enabled_skills_refresh_active
 
     with _enabled_skills_lock:
@@ -74,94 +70,84 @@ def _ensure_enabled_skills_cache() -> threading.Event:
         _enabled_skills_refresh_active = True
         _enabled_skills_refresh_event.clear()
 
-    _start_enabled_skills_refresh_thread()
+    _start_enabled_skills_refresh_thread(app_config)
     return _enabled_skills_refresh_event
 
 
-def _invalidate_enabled_skills_cache() -> threading.Event:
+def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
     global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
 
     _get_cached_skills_prompt_section.cache_clear()
     with _enabled_skills_lock:
         _enabled_skills_cache = None
-        _enabled_skills_by_config_cache.clear()
         _enabled_skills_refresh_version += 1
         _enabled_skills_refresh_event.clear()
         if _enabled_skills_refresh_active:
             return _enabled_skills_refresh_event
         _enabled_skills_refresh_active = True
 
-    _start_enabled_skills_refresh_thread()
+    _start_enabled_skills_refresh_thread(app_config)
     return _enabled_skills_refresh_event
 
 
-def prime_enabled_skills_cache() -> None:
-    _ensure_enabled_skills_cache()
+def prime_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
+    _ensure_enabled_skills_cache(app_config)
 
 
-def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
-    if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
+def warm_enabled_skills_cache(app_config: AppConfig | None = None, timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
+    if _ensure_enabled_skills_cache(app_config).wait(timeout=timeout_seconds):
         return True
 
     logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
     return False
 
 
-def _get_enabled_skills():
-    return get_cached_enabled_skills()
-
-
-def get_cached_enabled_skills() -> list[Skill]:
-    """Return the cached enabled-skills list, kicking off a background refresh on miss.
-
-    Safe to call from request paths: never blocks on disk I/O. Returns an empty
-    list on cache miss; the next call will see the warmed result.
-    """
+def _get_enabled_skills(app_config: AppConfig | None = None):
     with _enabled_skills_lock:
         cached = _enabled_skills_cache
 
     if cached is not None:
         return list(cached)
 
-    _ensure_enabled_skills_cache()
+    _ensure_enabled_skills_cache(app_config)
     return []
 
 
-def get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
-    """Return enabled skills using the caller's config source.
+def _skill_mutability_label(category: str) -> str:
+    return "[custom, editable]" if category == "custom" else "[built-in]"
 
-    When a concrete ``app_config`` is supplied, cache the loaded skills by that
-    config object's identity so request-scoped config injection still resolves
-    skill paths from the matching config without rescanning storage on every
-    agent factory call.
-    """
-    if app_config is None:
-        return _get_enabled_skills()
 
-    cache_key = id(app_config)
+def clear_skills_system_prompt_cache(app_config: AppConfig | None = None) -> None:
+    _invalidate_enabled_skills_cache(app_config)
+
+
+async def refresh_skills_system_prompt_cache_async(app_config: AppConfig | None = None) -> None:
+    await asyncio.to_thread(_invalidate_enabled_skills_cache(app_config).wait)
+
+
+def _reset_skills_system_prompt_cache_state() -> None:
+    global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
+
+    _get_cached_skills_prompt_section.cache_clear()
     with _enabled_skills_lock:
-        cached = _enabled_skills_by_config_cache.get(cache_key)
-        if cached is not None:
-            cached_config, cached_skills = cached
-            if cached_config is app_config:
-                return list(cached_skills)
+        _enabled_skills_cache = None
+        _enabled_skills_refresh_active = False
+        _enabled_skills_refresh_version = 0
+        _enabled_skills_refresh_event.clear()
+
+
+def _refresh_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
+    """Backward-compatible test helper for direct synchronous reload."""
+    try:
+        skills = _load_enabled_skills_sync(app_config)
+    except Exception:
+        logger.exception("Failed to load enabled skills for prompt injection")
+        skills = []
 
-    skills = list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
     with _enabled_skills_lock:
-        _enabled_skills_by_config_cache[cache_key] = (app_config, skills)
-    return list(skills)
-
-
-def _skill_mutability_label(category: SkillCategory | str) -> str:
-    return "[custom, editable]" if category == SkillCategory.CUSTOM else "[built-in]"
-
-
-def clear_skills_system_prompt_cache() -> None:
-    _invalidate_enabled_skills_cache()
-
-
-async def refresh_skills_system_prompt_cache_async() -> None:
-    await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
+        _enabled_skills_cache = skills
+        _enabled_skills_refresh_active = False
+        _enabled_skills_refresh_event.set()
 
 
 def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
@@ -180,7 +166,7 @@ Skip simple one-off tasks.
 """
 
 
-def _build_available_subagents_description(available_names: list[str], bash_available: bool, *, app_config: AppConfig | None = None) -> str:
+def _build_available_subagents_description(available_names: list[str], bash_available: bool, app_config: AppConfig) -> str:
     """Dynamically build subagent type descriptions from registry.
 
     Mirrors Codex's pattern where agent_type_description is dynamically generated
@@ -202,7 +188,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
         if name in builtin_descriptions:
             lines.append(f"- **{name}**: {builtin_descriptions[name]}")
         else:
-            config = get_subagent_config(name, app_config=app_config)
+            config = get_subagent_config(name, app_config)
             if config is not None:
                 desc = config.description.split("\n")[0].strip()  # First line only for brevity
                 lines.append(f"- **{name}**: {desc}")
@@ -210,22 +196,23 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
     return "\n".join(lines)
 
 
-def _build_subagent_section(max_concurrent: int, *, app_config: AppConfig | None = None) -> str:
+def _build_subagent_section(max_concurrent: int, app_config: AppConfig) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 
     Args:
         max_concurrent: Maximum number of concurrent subagent calls allowed per response.
+        app_config: Application config used to gate bash availability.
 
     Returns:
         Formatted subagent section string.
     """
     n = max_concurrent
-    available_names = get_available_subagent_names(app_config=app_config) if app_config is not None else get_available_subagent_names()
+    available_names = get_available_subagent_names(app_config)
     bash_available = "bash" in available_names
 
     # Dynamically build subagent type descriptions from registry (aligned with Codex's
     # agent_type_description pattern where all registered roles are listed in the tool spec).
-    available_subagents = _build_available_subagents_description(available_names, bash_available, app_config=app_config)
+    available_subagents = _build_available_subagents_description(available_names, bash_available, app_config)
     direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
     direct_execution_example = (
         '# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test")  # Direct execution, not task()'
@@ -366,7 +353,8 @@ You are {agent_name}, an open-source super agent.
 </role>
 
 {soul}
-{self_update_section}
+{memory_context}
+
 <thinking_style>
 - Think concisely and strategically about the user's request BEFORE taking action
 - Break down the task: What is clear? What is ambiguous? What is missing?
@@ -542,14 +530,6 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 {subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
 - Progressive Loading: Load resources incrementally as referenced in skills
 - Output Files: Final deliverables must be in `/mnt/user-data/outputs`
-- File Editing Workflow: When revising an existing file, prefer
-  `str_replace` over `write_file` — it sends only the diff and avoids
-  re-emitting the whole file (mirrors Claude Code's Edit and Codex's
-  apply_patch). When writing long new content from scratch, split it
-  into sections: the first `write_file` call creates the file, then use
-  `write_file` with append=True to extend it section by section. This
-  keeps each tool call small and avoids mid-stream chunk-gap timeouts
-  on oversized single-shot writes. (See issue #3189.)  
 - Clarity: Be direct and helpful, avoid unnecessary meta-commentary
 - Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `![Image Description](image_path)\n\n` or "```mermaid" to display images in response or Markdown files
 - Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
@@ -559,44 +539,34 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 """
 
 
-def _get_memory_context(agent_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
+def _get_memory_context(app_config: AppConfig, agent_name: str | None = None) -> str:
     """Get memory context for injection into system prompt.
 
-    Args:
-        agent_name: If provided, loads per-agent memory. If None, loads global memory.
-        app_config: Explicit application config. When provided, memory options
-            are read from this value instead of the global config singleton.
-
-    Returns:
-        Formatted memory context string wrapped in XML tags, or empty string if disabled.
+    Returns an empty string when memory is disabled or the stored memory file
+    cannot be read/parsed. A corrupt memory.json degrades the prompt to
+    no-memory; it never kills the agent.
     """
+    from deerflow.agents.memory import format_memory_for_injection, get_memory_data
+    from deerflow.runtime.user_context import get_effective_user_id
+
+    memory_config = app_config.memory
+    if not memory_config.enabled or not memory_config.injection_enabled:
+        return ""
+
     try:
-        from deerflow.agents.memory import format_memory_for_injection, get_memory_data
-        from deerflow.runtime.user_context import get_effective_user_id
+        memory_data = get_memory_data(memory_config, agent_name, user_id=get_effective_user_id())
+    except (OSError, ValueError, UnicodeDecodeError):
+        logger.exception("Failed to load memory data for prompt injection")
+        return ""
 
-        if app_config is None:
-            from deerflow.config.memory_config import get_memory_config
+    memory_content = format_memory_for_injection(memory_data, max_tokens=memory_config.max_injection_tokens)
+    if not memory_content.strip():
+        return ""
 
-            config = get_memory_config()
-        else:
-            config = app_config.memory
-
-        if not config.enabled or not config.injection_enabled:
-            return ""
-
-        memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
-        memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
-
-        if not memory_content.strip():
-            return ""
-
-        return f"""<memory>
+    return f"""<memory>
 {memory_content}
 </memory>
 """
-    except Exception:
-        logger.exception("Failed to load memory context")
-        return ""
 
 
 @lru_cache(maxsize=32)
@@ -631,24 +601,12 @@ You have access to skills that provide optimized workflows for specific tasks. E
 </skill_system>"""
 
 
-def get_skills_prompt_section(available_skills: set[str] | None = None, *, app_config: AppConfig | None = None) -> str:
+def get_skills_prompt_section(app_config: AppConfig, available_skills: set[str] | None = None) -> str:
     """Generate the skills prompt section with available skills list."""
-    skills = get_enabled_skills_for_config(app_config)
+    skills = _get_enabled_skills(app_config)
 
-    if app_config is None:
-        try:
-            from deerflow.config import get_app_config
-
-            config = get_app_config()
-            container_base_path = config.skills.container_path
-            skill_evolution_enabled = config.skill_evolution.enabled
-        except Exception:
-            container_base_path = "/mnt/skills"
-            skill_evolution_enabled = False
-    else:
-        config = app_config
-        container_base_path = config.skills.container_path
-        skill_evolution_enabled = config.skill_evolution.enabled
+    container_base_path = app_config.skills.container_path
+    skill_evolution_enabled = app_config.skill_evolution.enabled
 
     if not skills and not skill_evolution_enabled:
         return ""
@@ -672,53 +630,29 @@ def get_agent_soul(agent_name: str | None) -> str:
     return ""
 
 
-def _build_self_update_section(agent_name: str | None) -> str:
-    """Prompt block that teaches the custom agent to persist self-updates via update_agent."""
-    if not agent_name:
-        return ""
-    return f"""<self_update>
-You are running as the custom agent **{agent_name}** with a persisted SOUL.md and config.yaml.
+def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
+    """Generate <available-deferred-tools> block for the system prompt.
 
-When the user asks you to update your own description, personality, behaviour, skill set, tool groups, or default model,
-you MUST persist the change with the `update_agent` tool. Do NOT use `bash`, `write_file`, or any sandbox tool to edit
-SOUL.md or config.yaml — those write into a temporary sandbox/tool workspace and the changes will be lost on the next turn.
-
-Rules:
-- Always pass the FULL replacement text for `soul` (no patch semantics). Start from your current SOUL above and apply the user's edits.
-- Only pass the fields that should change. Omit the others to preserve them.
-- Never pass literal strings like `"null"`, `"none"`, or `"undefined"` for unchanged fields.
-- Pass `skills=[]` to disable all skills, or omit `skills` to keep the existing whitelist.
-- After `update_agent` returns successfully, tell the user the change is persisted and will take effect on the next turn.
-</self_update>
-"""
-
-
-def get_deferred_tools_prompt_section(*, deferred_names: frozenset[str] = frozenset()) -> str:
-    """Generate <available-deferred-tools> from an explicit deferred-name set.
-
-    Lists only names so the agent knows what exists and can use tool_search to
-    load them. Returns empty string when there are no deferred tools. The set is
-    computed at agent build time (after tool-policy filtering) and passed in.
+    Lists only deferred tool names so the agent knows what exists
+    and can use tool_search to load them.
+    Returns empty string when tool_search is disabled or no tools are deferred.
     """
-    if not deferred_names:
+    from deerflow.tools.builtins.tool_search import get_deferred_registry
+
+    if not app_config.tool_search.enabled:
         return ""
-    names = "\n".join(sorted(deferred_names))
+
+    registry = get_deferred_registry()
+    if not registry:
+        return ""
+
+    names = "\n".join(e.name for e in registry.entries)
     return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
 
 
-def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
+def _build_acp_section(app_config: AppConfig) -> str:
     """Build the ACP agent prompt section, only if ACP agents are configured."""
-    if app_config is None:
-        try:
-            from deerflow.config.acp_config import get_acp_agents
-
-            agents = get_acp_agents()
-        except Exception:
-            return ""
-    else:
-        agents = getattr(app_config, "acp_agents", {}) or {}
-
-    if not agents:
+    if not app_config.acp_agents:
         return ""
 
     return (
@@ -730,20 +664,9 @@ def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
     )
 
 
-def _build_custom_mounts_section(*, app_config: AppConfig | None = None) -> str:
+def _build_custom_mounts_section(app_config: AppConfig) -> str:
     """Build a prompt section for explicitly configured sandbox mounts."""
-    if app_config is None:
-        try:
-            from deerflow.config import get_app_config
-
-            config = get_app_config()
-        except Exception:
-            logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
-            return ""
-    else:
-        config = app_config
-
-    mounts = config.sandbox.mounts or []
+    mounts = app_config.sandbox.mounts or []
 
     if not mounts:
         return ""
@@ -758,17 +681,19 @@ def _build_custom_mounts_section(*, app_config: AppConfig | None = None) -> str:
 
 
 def apply_prompt_template(
+    app_config: AppConfig,
     subagent_enabled: bool = False,
     max_concurrent_subagents: int = 3,
     *,
     agent_name: str | None = None,
     available_skills: set[str] | None = None,
-    app_config: AppConfig | None = None,
-    deferred_names: frozenset[str] = frozenset(),
 ) -> str:
+    # Get memory context
+    memory_context = _get_memory_context(app_config, agent_name)
+
     # Include subagent section only if enabled (from runtime parameter)
     n = max_concurrent_subagents
-    subagent_section = _build_subagent_section(n, app_config=app_config) if subagent_enabled else ""
+    subagent_section = _build_subagent_section(n, app_config) if subagent_enabled else ""
 
     # Add subagent reminder to critical_reminders if enabled
     subagent_reminder = (
@@ -789,28 +714,27 @@ def apply_prompt_template(
     )
 
     # Get skills section
-    skills_section = get_skills_prompt_section(available_skills, app_config=app_config)
+    skills_section = get_skills_prompt_section(app_config, available_skills)
 
     # Get deferred tools section (tool_search)
-    deferred_tools_section = get_deferred_tools_prompt_section(deferred_names=deferred_names)
+    deferred_tools_section = get_deferred_tools_prompt_section(app_config)
 
     # Build ACP agent section only if ACP agents are configured
-    acp_section = _build_acp_section(app_config=app_config)
-    custom_mounts_section = _build_custom_mounts_section(app_config=app_config)
+    acp_section = _build_acp_section(app_config)
+    custom_mounts_section = _build_custom_mounts_section(app_config)
     acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
 
-    # Build and return the fully static system prompt.
-    # Memory and current date are injected per-turn via DynamicContextMiddleware
-    # as a <system-reminder> in the first HumanMessage, keeping this prompt
-    # identical across users and sessions for maximum prefix-cache reuse.
-    return SYSTEM_PROMPT_TEMPLATE.format(
+    # Format the prompt with dynamic skills and memory
+    prompt = SYSTEM_PROMPT_TEMPLATE.format(
         agent_name=agent_name or "DeerFlow 2.0",
         soul=get_agent_soul(agent_name),
-        self_update_section=_build_self_update_section(agent_name),
         skills_section=skills_section,
         deferred_tools_section=deferred_tools_section,
+        memory_context=memory_context,
         subagent_section=subagent_section,
         subagent_reminder=subagent_reminder,
         subagent_thinking=subagent_thinking,
         acp_section=acp_and_mounts_section,
     )
+
+    return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"

backend/packages/harness/deerflow/agents/memory/queue.py

@@ -7,11 +7,17 @@ from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from typing import Any
 
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
 
 logger = logging.getLogger(__name__)
 
 
+# Module-level config pointer set by the middleware that owns the queue.
+# The queue runs on a background Timer thread where ``Runtime`` and FastAPI
+# request context are not accessible; the enqueuer (which does have runtime
+# context) is responsible for plumbing ``AppConfig`` through ``add()``.
+
+
 @dataclass
 class ConversationContext:
     """Context for a conversation to be processed for memory update."""
@@ -31,24 +37,26 @@ class MemoryUpdateQueue:
     This queue collects conversation contexts and processes them after
     a configurable debounce period. Multiple conversations received within
     the debounce window are batched together.
+
+    The queue captures an ``AppConfig`` reference at construction time and
+    reuses it for the MemoryUpdater it spawns. Callers must construct a
+    fresh queue when the config changes rather than reaching into a global.
     """
 
-    def __init__(self):
-        """Initialize the memory update queue."""
+    def __init__(self, app_config: AppConfig):
+        """Initialize the memory update queue.
+
+        Args:
+            app_config: Application config. The queue reads its own
+                ``memory`` section for debounce timing and hands the full
+                config to :class:`MemoryUpdater`.
+        """
+        self._app_config = app_config
         self._queue: list[ConversationContext] = []
         self._lock = threading.Lock()
         self._timer: threading.Timer | None = None
         self._processing = False
 
-    @staticmethod
-    def _queue_key(
-        thread_id: str,
-        user_id: str | None,
-        agent_name: str | None,
-    ) -> tuple[str, str | None, str | None]:
-        """Return the debounce identity for a memory update target."""
-        return (thread_id, user_id, agent_name)
-
     def add(
         self,
         thread_id: str,
@@ -58,19 +66,8 @@ class MemoryUpdateQueue:
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
     ) -> None:
-        """Add a conversation to the update queue.
-
-        Args:
-            thread_id: The thread ID.
-            messages: The conversation messages.
-            agent_name: If provided, memory is stored per-agent. If None, uses global memory.
-            user_id: The user ID captured at enqueue time. Stored in ConversationContext so it
-                survives the threading.Timer boundary (ContextVar does not propagate across
-                raw threads).
-            correction_detected: Whether recent turns include an explicit correction signal.
-            reinforcement_detected: Whether recent turns include a positive reinforcement signal.
-        """
-        config = get_memory_config()
+        """Add a conversation to the update queue."""
+        config = self._app_config.memory
         if not config.enabled:
             return
 
@@ -97,7 +94,7 @@ class MemoryUpdateQueue:
         reinforcement_detected: bool = False,
     ) -> None:
         """Add a conversation and start processing immediately in the background."""
-        config = get_memory_config()
+        config = self._app_config.memory
         if not config.enabled:
             return
 
@@ -120,13 +117,12 @@ class MemoryUpdateQueue:
         thread_id: str,
         messages: list[Any],
         agent_name: str | None,
-        user_id: str | None,
+        user_id: str | None = None,
         correction_detected: bool,
         reinforcement_detected: bool,
     ) -> None:
-        queue_key = self._queue_key(thread_id, user_id, agent_name)
         existing_context = next(
-            (context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) == queue_key),
+            (context for context in self._queue if context.thread_id == thread_id),
             None,
         )
         merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
@@ -140,12 +136,12 @@ class MemoryUpdateQueue:
             reinforcement_detected=merged_reinforcement_detected,
         )
 
-        self._queue = [context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) != queue_key]
+        self._queue = [c for c in self._queue if c.thread_id != thread_id]
         self._queue.append(context)
 
     def _reset_timer(self) -> None:
         """Reset the debounce timer."""
-        config = get_memory_config()
+        config = self._app_config.memory
         self._schedule_timer(config.debounce_seconds)
 
         logger.debug("Memory update timer set for %ss", config.debounce_seconds)
@@ -185,7 +181,7 @@ class MemoryUpdateQueue:
         logger.info("Processing %d queued memory updates", len(contexts_to_process))
 
         try:
-            updater = MemoryUpdater()
+            updater = MemoryUpdater(self._app_config)
 
             for context in contexts_to_process:
                 try:
@@ -257,31 +253,35 @@ class MemoryUpdateQueue:
             return self._processing
 
 
-# Global singleton instance
-_memory_queue: MemoryUpdateQueue | None = None
+# Queues keyed by ``id(AppConfig)`` so tests and multi-client setups with
+# distinct configs do not share a debounce queue.
+_memory_queues: dict[int, MemoryUpdateQueue] = {}
 _queue_lock = threading.Lock()
 
 
-def get_memory_queue() -> MemoryUpdateQueue:
-    """Get the global memory update queue singleton.
-
-    Returns:
-        The memory update queue instance.
-    """
-    global _memory_queue
+def get_memory_queue(app_config: AppConfig) -> MemoryUpdateQueue:
+    """Get or create the memory update queue for the given app config."""
+    key = id(app_config)
     with _queue_lock:
-        if _memory_queue is None:
-            _memory_queue = MemoryUpdateQueue()
-        return _memory_queue
+        queue = _memory_queues.get(key)
+        if queue is None:
+            queue = MemoryUpdateQueue(app_config)
+            _memory_queues[key] = queue
+        return queue
 
 
-def reset_memory_queue() -> None:
-    """Reset the global memory queue.
+def reset_memory_queue(app_config: AppConfig | None = None) -> None:
+    """Reset memory queue(s).
 
-    This is useful for testing.
+    Pass an ``app_config`` to reset only its queue, or omit to reset all
+    (useful at test teardown).
     """
-    global _memory_queue
     with _queue_lock:
-        if _memory_queue is not None:
-            _memory_queue.clear()
-        _memory_queue = None
+        if app_config is not None:
+            queue = _memory_queues.pop(id(app_config), None)
+            if queue is not None:
+                queue.clear()
+            return
+        for queue in _memory_queues.values():
+            queue.clear()
+        _memory_queues.clear()

backend/packages/harness/deerflow/agents/memory/storage.py

@@ -10,7 +10,7 @@ from pathlib import Path
 from typing import Any
 
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.config.paths import get_paths
 
 logger = logging.getLogger(__name__)
@@ -62,8 +62,15 @@ class MemoryStorage(abc.ABC):
 class FileMemoryStorage(MemoryStorage):
     """File-based memory storage provider."""
 
-    def __init__(self):
-        """Initialize the file memory storage."""
+    def __init__(self, memory_config: MemoryConfig):
+        """Initialize the file memory storage.
+
+        Args:
+            memory_config: Memory configuration (storage_path etc.). Stored on
+                the instance so per-request lookups don't need to reach for
+                ambient state.
+        """
+        self._memory_config = memory_config
         # Per-user/agent memory cache: keyed by (user_id, agent_name) tuple (None = global)
         # Value: (memory_data, file_mtime)
         self._memory_cache: dict[tuple[str | None, str | None], tuple[dict[str, Any], float | None]] = {}
@@ -83,11 +90,11 @@ class FileMemoryStorage(MemoryStorage):
 
     def _get_memory_file_path(self, agent_name: str | None = None, *, user_id: str | None = None) -> Path:
         """Get the path to the memory file."""
+        config = self._memory_config
         if user_id is not None:
             if agent_name is not None:
                 self._validate_agent_name(agent_name)
                 return get_paths().user_agent_memory_file(user_id, agent_name)
-            config = get_memory_config()
             if config.storage_path and Path(config.storage_path).is_absolute():
                 return Path(config.storage_path)
             return get_paths().user_memory_file(user_id)
@@ -95,7 +102,6 @@ class FileMemoryStorage(MemoryStorage):
         if agent_name is not None:
             self._validate_agent_name(agent_name)
             return get_paths().agent_memory_file(agent_name)
-        config = get_memory_config()
         if config.storage_path:
             p = Path(config.storage_path)
             return p if p.is_absolute() else get_paths().base_dir / p
@@ -116,20 +122,16 @@ class FileMemoryStorage(MemoryStorage):
             logger.warning("Failed to load memory file: %s", e)
             return create_empty_memory()
 
-    @staticmethod
-    def _cache_key(agent_name: str | None = None, *, user_id: str | None = None) -> tuple[str | None, str | None]:
-        return (user_id, agent_name)
-
     def load(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Load memory data (cached with file modification time check)."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             current_mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             current_mtime = None
 
+        cache_key = (user_id, agent_name)
         with self._cache_lock:
             cached = self._memory_cache.get(cache_key)
             if cached is not None and cached[1] == current_mtime:
@@ -146,13 +148,13 @@ class FileMemoryStorage(MemoryStorage):
         """Reload memory data from file, forcing cache invalidation."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
         memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             mtime = None
 
+        cache_key = (user_id, agent_name)
         with self._cache_lock:
             self._memory_cache[cache_key] = (memory_data, mtime)
         return memory_data
@@ -160,7 +162,6 @@ class FileMemoryStorage(MemoryStorage):
     def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
         """Save memory data to file and update cache."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             file_path.parent.mkdir(parents=True, exist_ok=True)
@@ -180,6 +181,7 @@ class FileMemoryStorage(MemoryStorage):
             except OSError:
                 mtime = None
 
+            cache_key = (user_id, agent_name)
             with self._cache_lock:
                 self._memory_cache[cache_key] = (memory_data, mtime)
             logger.info("Memory saved to %s", file_path)
@@ -189,23 +191,31 @@ class FileMemoryStorage(MemoryStorage):
             return False
 
 
-_storage_instance: MemoryStorage | None = None
+# Instances keyed by (storage_class_path, id(memory_config)) so tests can
+# construct isolated storages and multi-client setups with different configs
+# don't collide on a single process-wide singleton.
+_storage_instances: dict[tuple[str, int], MemoryStorage] = {}
 _storage_lock = threading.Lock()
 
 
-def get_memory_storage() -> MemoryStorage:
-    """Get the configured memory storage instance."""
-    global _storage_instance
-    if _storage_instance is not None:
-        return _storage_instance
+def get_memory_storage(memory_config: MemoryConfig) -> MemoryStorage:
+    """Get the configured memory storage instance.
+
+    Caches one instance per ``(storage_class, memory_config)`` pair. In
+    single-config deployments this collapses to one instance; in multi-client
+    or test scenarios each config gets its own storage.
+    """
+    key = (memory_config.storage_class, id(memory_config))
+    existing = _storage_instances.get(key)
+    if existing is not None:
+        return existing
 
     with _storage_lock:
-        if _storage_instance is not None:
-            return _storage_instance
-
-        config = get_memory_config()
-        storage_class_path = config.storage_class
+        existing = _storage_instances.get(key)
+        if existing is not None:
+            return existing
 
+        storage_class_path = memory_config.storage_class
         try:
             module_path, class_name = storage_class_path.rsplit(".", 1)
             import importlib
@@ -219,13 +229,14 @@ def get_memory_storage() -> MemoryStorage:
             if not issubclass(storage_class, MemoryStorage):
                 raise TypeError(f"Configured memory storage '{storage_class_path}' is not a subclass of MemoryStorage")
 
-            _storage_instance = storage_class()
+            instance = storage_class(memory_config)
         except Exception as e:
             logger.error(
                 "Failed to load memory storage %s, falling back to FileMemoryStorage: %s",
                 storage_class_path,
                 e,
             )
-            _storage_instance = FileMemoryStorage()
+            instance = FileMemoryStorage(memory_config)
 
-    return _storage_instance
+        _storage_instances[key] = instance
+        return instance

backend/packages/harness/deerflow/agents/memory/summarization_hook.py

@@ -5,13 +5,19 @@ from __future__ import annotations
 from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 from deerflow.agents.memory.queue import get_memory_queue
 from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
-from deerflow.config.memory_config import get_memory_config
-from deerflow.runtime.user_context import resolve_runtime_user_id
+from deerflow.config.app_config import AppConfig
 
 
 def memory_flush_hook(event: SummarizationEvent) -> None:
-    """Flush messages about to be summarized into the memory queue."""
-    if not get_memory_config().enabled or not event.thread_id:
+    """Flush messages about to be summarized into the memory queue.
+
+    Reads ``AppConfig`` from disk on every invocation. This hook is fired by
+    ``SummarizationMiddleware`` which has no ergonomic way to thread an
+    explicit ``app_config`` through; ``AppConfig.from_file()`` is a pure load
+    so the cost is acceptable for this rare pre-summarization callback.
+    """
+    app_config = AppConfig.from_file()
+    if not app_config.memory.enabled or not event.thread_id:
         return
 
     filtered_messages = filter_messages_for_memory(list(event.messages_to_summarize))
@@ -22,13 +28,11 @@ def memory_flush_hook(event: SummarizationEvent) -> None:
 
     correction_detected = detect_correction(filtered_messages)
     reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
-    user_id = resolve_runtime_user_id(event.runtime)
-    queue = get_memory_queue()
+    queue = get_memory_queue(app_config)
     queue.add_nowait(
         thread_id=event.thread_id,
         messages=filtered_messages,
         agent_name=event.agent_name,
-        user_id=user_id,
         correction_detected=correction_detected,
         reinforcement_detected=reinforcement_detected,
     )

backend/packages/harness/deerflow/agents/memory/updater.py

@@ -9,6 +9,7 @@ import logging
 import math
 import re
 import uuid
+from collections.abc import Awaitable
 from typing import Any
 
 from deerflow.agents.memory.prompt import (
@@ -20,17 +21,12 @@ from deerflow.agents.memory.storage import (
     get_memory_storage,
     utc_now_iso_z,
 )
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
 
-
-# Thread pool for offloading sync memory updates when called from an async
-# context.  Unlike the previous asyncio.run() approach, this runs *sync*
-# model.invoke() calls — no event loop is created, so the langchain async
-# httpx client pool (globally cached via @lru_cache) is never touched and
-# cross-loop connection reuse is impossible.
 _SYNC_MEMORY_UPDATER_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
     max_workers=4,
     thread_name_prefix="memory-updater-sync",
@@ -43,45 +39,33 @@ def _create_empty_memory() -> dict[str, Any]:
     return create_empty_memory()
 
 
-def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
-    """Backward-compatible wrapper around the configured memory storage save path."""
-    return get_memory_storage().save(memory_data, agent_name, user_id=user_id)
+def _save_memory_to_file(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
+    """Save via the configured memory storage."""
+    return get_memory_storage(memory_config).save(memory_data, agent_name, user_id=user_id)
 
 
-def get_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def get_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Get the current memory data via storage provider."""
-    return get_memory_storage().load(agent_name, user_id=user_id)
+    return get_memory_storage(memory_config).load(agent_name, user_id=user_id)
 
 
-def reload_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def reload_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Reload memory data via storage provider."""
-    return get_memory_storage().reload(agent_name, user_id=user_id)
+    return get_memory_storage(memory_config).reload(agent_name, user_id=user_id)
 
 
-def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
-    """Persist imported memory data via storage provider.
-
-    Args:
-        memory_data: Full memory payload to persist.
-        agent_name: If provided, imports into per-agent memory.
-        user_id: If provided, scopes memory to a specific user.
-
-    Returns:
-        The saved memory data after storage normalization.
-
-    Raises:
-        OSError: If persisting the imported memory fails.
-    """
-    storage = get_memory_storage()
+def import_memory_data(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+    """Persist imported memory data via storage provider."""
+    storage = get_memory_storage(memory_config)
     if not storage.save(memory_data, agent_name, user_id=user_id):
         raise OSError("Failed to save imported memory data")
     return storage.load(agent_name, user_id=user_id)
 
 
-def clear_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def clear_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Clear all stored memory data and persist an empty structure."""
     cleared_memory = create_empty_memory()
-    if not _save_memory_to_file(cleared_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, cleared_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save cleared memory data")
     return cleared_memory
 
@@ -94,6 +78,7 @@ def _validate_confidence(confidence: float) -> float:
 
 
 def create_memory_fact(
+    memory_config: MemoryConfig,
     content: str,
     category: str = "context",
     confidence: float = 0.5,
@@ -109,7 +94,7 @@ def create_memory_fact(
     normalized_category = category.strip() or "context"
     validated_confidence = _validate_confidence(confidence)
     now = utc_now_iso_z()
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     facts = list(memory_data.get("facts", []))
     facts.append(
@@ -124,15 +109,15 @@ def create_memory_fact(
     )
     updated_memory["facts"] = facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save memory data after creating fact")
 
     return updated_memory
 
 
-def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def delete_memory_fact(memory_config: MemoryConfig, fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Delete a fact by its id and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     facts = memory_data.get("facts", [])
     updated_facts = [fact for fact in facts if fact.get("id") != fact_id]
     if len(updated_facts) == len(facts):
@@ -141,13 +126,14 @@ def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id:
     updated_memory = dict(memory_data)
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after deleting fact '{fact_id}'")
 
     return updated_memory
 
 
 def update_memory_fact(
+    memory_config: MemoryConfig,
     fact_id: str,
     content: str | None = None,
     category: str | None = None,
@@ -157,7 +143,7 @@ def update_memory_fact(
     user_id: str | None = None,
 ) -> dict[str, Any]:
     """Update an existing fact and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     updated_facts: list[dict[str, Any]] = []
     found = False
@@ -184,7 +170,7 @@ def update_memory_fact(
 
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")
 
     return updated_memory
@@ -227,108 +213,37 @@ def _extract_text(content: Any) -> str:
     return str(content)
 
 
-_REQUIRED_MEMORY_UPDATE_TOP_LEVEL_KEYS = frozenset({"user", "history", "newFacts", "factsToRemove"})
+def _run_async_update_sync(coro: Awaitable[bool]) -> bool:
+    """Run an async memory update from sync code, including nested-loop contexts."""
+    handed_off = False
 
-
-def _normalize_memory_update_fact(fact: Any) -> dict[str, Any] | None:
-    """Normalize a single fact entry from a model-produced memory update."""
-    if not isinstance(fact, dict):
-        return None
-
-    raw_content = fact.get("content")
-    if not isinstance(raw_content, str):
-        return None
-    content = raw_content.strip()
-    if not content:
-        return None
-
-    raw_category = fact.get("category")
-    category = raw_category.strip() if isinstance(raw_category, str) and raw_category.strip() else "context"
-
-    raw_confidence = fact.get("confidence", 0.5)
-    if isinstance(raw_confidence, bool):
-        return None
-    if isinstance(raw_confidence, str):
-        raw_confidence = raw_confidence.strip()
-        if not raw_confidence:
-            return None
+    try:
         try:
-            raw_confidence = float(raw_confidence)
-        except ValueError:
-            return None
-    elif isinstance(raw_confidence, (int, float)):
-        raw_confidence = float(raw_confidence)
-    else:
-        return None
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = None
 
-    if not math.isfinite(raw_confidence):
-        return None
+        if loop is not None and loop.is_running():
+            future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(asyncio.run, coro)
+            handed_off = True
+            return future.result()
 
-    normalized_fact = {
-        "content": content,
-        "category": category,
-        "confidence": raw_confidence,
-    }
-    source_error = fact.get("sourceError")
-    if isinstance(source_error, str):
-        normalized_source_error = source_error.strip()
-        if normalized_source_error:
-            normalized_fact["sourceError"] = normalized_source_error
+        handed_off = True
+        return asyncio.run(coro)
+    except Exception:
+        if not handed_off:
+            close = getattr(coro, "close", None)
+            if callable(close):
+                try:
+                    close()
+                except Exception:
+                    logger.debug(
+                        "Failed to close un-awaited memory update coroutine",
+                        exc_info=True,
+                    )
 
-    return normalized_fact
-
-
-def _normalize_memory_update_data(update_data: dict[str, Any]) -> dict[str, Any]:
-    """Coerce parsed memory update data into the shape consumed by _apply_updates."""
-    user = update_data.get("user")
-    history = update_data.get("history")
-    new_facts = update_data.get("newFacts")
-    facts_to_remove = update_data.get("factsToRemove")
-    normalized_facts_to_remove = [fact_id for fact_id in facts_to_remove if isinstance(fact_id, str)] if isinstance(facts_to_remove, list) else []
-    normalized_new_facts = []
-    dropped_new_fact = not isinstance(new_facts, list)
-    if isinstance(new_facts, list):
-        for fact in new_facts:
-            normalized_fact = _normalize_memory_update_fact(fact)
-            if normalized_fact is not None:
-                normalized_new_facts.append(normalized_fact)
-            else:
-                dropped_new_fact = True
-
-    if normalized_facts_to_remove and dropped_new_fact:
-        raise json.JSONDecodeError(
-            "Unsafe partial memory update: factsToRemove with malformed newFacts",
-            json.dumps(update_data, ensure_ascii=False),
-            0,
-        )
-
-    return {
-        "user": user if isinstance(user, dict) else {},
-        "history": history if isinstance(history, dict) else {},
-        "newFacts": normalized_new_facts,
-        "factsToRemove": normalized_facts_to_remove,
-    }
-
-
-def _parse_memory_update_response(response_content: Any) -> dict[str, Any]:
-    """Parse the first valid memory-update JSON object from an LLM response.
-
-    Some providers may wrap JSON in thinking traces, prose, or markdown fences
-    even when prompted to return JSON only. This parser accepts safely
-    extractable JSON objects but does not repair truncated or malformed JSON.
-    """
-    response_text = _extract_text(response_content).strip()
-    decoder = json.JSONDecoder()
-
-    for match in re.finditer(r"\{", response_text):
-        try:
-            parsed, _end = decoder.raw_decode(response_text[match.start() :])
-        except json.JSONDecodeError:
-            continue
-        if isinstance(parsed, dict) and _REQUIRED_MEMORY_UPDATE_TOP_LEVEL_KEYS.issubset(parsed):
-            return _normalize_memory_update_data(parsed)
-
-    raise json.JSONDecodeError("No valid memory update JSON object found", response_text, 0)
+        logger.exception("Failed to run async memory update from sync context")
+        return False
 
 
 # Matches sentences that describe a file-upload *event* rather than general
@@ -380,19 +295,25 @@ def _fact_content_key(content: Any) -> str | None:
 class MemoryUpdater:
     """Updates memory using LLM based on conversation context."""
 
-    def __init__(self, model_name: str | None = None):
+    def __init__(self, app_config: AppConfig, model_name: str | None = None):
         """Initialize the memory updater.
 
         Args:
+            app_config: Application config (the updater needs both ``memory``
+                section for behavior and the full config for ``create_chat_model``).
             model_name: Optional model name to use. If None, uses config or default.
         """
+        self._app_config = app_config
         self._model_name = model_name
 
+    @property
+    def _memory_config(self) -> MemoryConfig:
+        return self._app_config.memory
+
     def _get_model(self):
         """Get the model for memory updates."""
-        config = get_memory_config()
-        model_name = self._model_name or config.model_name
-        return create_chat_model(name=model_name, thinking_enabled=False)
+        model_name = self._model_name or self._memory_config.model_name
+        return create_chat_model(name=model_name, thinking_enabled=False, app_config=self._app_config)
 
     def _build_correction_hint(
         self,
@@ -428,11 +349,11 @@ class MemoryUpdater:
         user_id: str | None = None,
     ) -> tuple[dict[str, Any], str] | None:
         """Load memory and build the update prompt for a conversation."""
-        config = get_memory_config()
+        config = self._memory_config
         if not config.enabled or not messages:
             return None
 
-        current_memory = get_memory_data(agent_name, user_id=user_id)
+        current_memory = get_memory_data(config, agent_name, user_id=user_id)
         conversation_text = format_conversation_for_update(messages)
         if not conversation_text.strip():
             return None
@@ -442,7 +363,7 @@ class MemoryUpdater:
             reinforcement_detected=reinforcement_detected,
         )
         prompt = MEMORY_UPDATE_PROMPT.format(
-            current_memory=json.dumps(current_memory, indent=2, ensure_ascii=False),
+            current_memory=json.dumps(current_memory, indent=2),
             conversation=conversation_text,
             correction_hint=correction_hint,
         )
@@ -457,12 +378,18 @@ class MemoryUpdater:
         user_id: str | None = None,
     ) -> bool:
         """Parse the model response, apply updates, and persist memory."""
-        update_data = _parse_memory_update_response(response_content)
+        response_text = _extract_text(response_content).strip()
+
+        if response_text.startswith("```"):
+            lines = response_text.split("\n")
+            response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
+
+        update_data = json.loads(response_text)
         # Deep-copy before in-place mutation so a subsequent save() failure
         # cannot corrupt the still-cached original object reference.
         updated_memory = self._apply_updates(copy.deepcopy(current_memory), update_data, thread_id)
         updated_memory = _strip_upload_mentions_from_memory(updated_memory)
-        return get_memory_storage().save(updated_memory, agent_name, user_id=user_id)
+        return get_memory_storage(self._memory_config).save(updated_memory, agent_name, user_id=user_id)
 
     async def aupdate_memory(
         self,
@@ -473,43 +400,10 @@ class MemoryUpdater:
         reinforcement_detected: bool = False,
         user_id: str | None = None,
     ) -> bool:
-        """Update memory asynchronously by delegating to the sync path.
-
-        Uses ``asyncio.to_thread`` to run the *sync* ``model.invoke()`` path
-        in a worker thread so no second event loop is created and the
-        langchain async httpx client pool (shared with the lead agent) is
-        never touched.  This eliminates the cross-loop connection-reuse bug
-        described in issue #2615.
-        """
-        return await asyncio.to_thread(
-            self._do_update_memory_sync,
-            messages=messages,
-            thread_id=thread_id,
-            agent_name=agent_name,
-            correction_detected=correction_detected,
-            reinforcement_detected=reinforcement_detected,
-            user_id=user_id,
-        )
-
-    def _do_update_memory_sync(
-        self,
-        messages: list[Any],
-        thread_id: str | None = None,
-        agent_name: str | None = None,
-        correction_detected: bool = False,
-        reinforcement_detected: bool = False,
-        user_id: str | None = None,
-    ) -> bool:
-        """Pure-sync memory update using ``model.invoke()``.
-
-        Uses the *sync* LLM call path so no event loop is created.  This
-        guarantees that the langchain provider's globally cached async
-        httpx ``AsyncClient`` / connection pool (the one shared with the
-        lead agent) is never touched — no cross-loop connection reuse is
-        possible.
-        """
+        """Update memory asynchronously based on conversation messages."""
         try:
-            prepared = self._prepare_update_prompt(
+            prepared = await asyncio.to_thread(
+                self._prepare_update_prompt,
                 messages=messages,
                 agent_name=agent_name,
                 correction_detected=correction_detected,
@@ -521,8 +415,9 @@ class MemoryUpdater:
 
             current_memory, prompt = prepared
             model = self._get_model()
-            response = model.invoke(prompt, config={"run_name": "memory_agent"})
-            return self._finalize_update(
+            response = await model.ainvoke(prompt, config={"run_name": "memory_agent"})
+            return await asyncio.to_thread(
+                self._finalize_update,
                 current_memory=current_memory,
                 response_content=response.content,
                 thread_id=thread_id,
@@ -545,16 +440,7 @@ class MemoryUpdater:
         reinforcement_detected: bool = False,
         user_id: str | None = None,
     ) -> bool:
-        """Synchronously update memory using the sync LLM path.
-
-        Uses ``model.invoke()`` (sync HTTP) which operates on a completely
-        separate connection pool from the async ``AsyncClient`` shared by
-        the lead agent.  This eliminates the cross-loop connection-reuse
-        bug described in issue #2615.
-
-        When called from within a running event loop (e.g. from a LangGraph
-        node), the blocking sync call is offloaded to a thread pool so the
-        caller's loop is not blocked.
+        """Synchronously update memory via the async updater path.
 
         Args:
             messages: List of conversation messages.
@@ -567,35 +453,78 @@ class MemoryUpdater:
         Returns:
             True if update was successful, False otherwise.
         """
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = None
+        config = self._memory_config
+        if not config.enabled:
+            return False
 
-        if loop is not None and loop.is_running():
-            try:
-                future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(
-                    self._do_update_memory_sync,
-                    messages=messages,
-                    thread_id=thread_id,
-                    agent_name=agent_name,
-                    correction_detected=correction_detected,
-                    reinforcement_detected=reinforcement_detected,
-                    user_id=user_id,
-                )
-                return future.result()
-            except Exception:
-                logger.exception("Failed to offload memory update to executor")
+        if not messages:
+            return False
+
+        try:
+            # Get current memory
+            current_memory = get_memory_data(config, agent_name, user_id=user_id)
+
+            # Format conversation for prompt
+            conversation_text = format_conversation_for_update(messages)
+
+            if not conversation_text.strip():
                 return False
 
-        return self._do_update_memory_sync(
-            messages=messages,
-            thread_id=thread_id,
-            agent_name=agent_name,
-            correction_detected=correction_detected,
-            reinforcement_detected=reinforcement_detected,
-            user_id=user_id,
-        )
+            # Build prompt
+            correction_hint = ""
+            if correction_detected:
+                correction_hint = (
+                    "IMPORTANT: Explicit correction signals were detected in this conversation. "
+                    "Pay special attention to what the agent got wrong, what the user corrected, "
+                    "and record the correct approach as a fact with category "
+                    '"correction" and confidence >= 0.95 when appropriate.'
+                )
+            if reinforcement_detected:
+                reinforcement_hint = (
+                    "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
+                    "The user explicitly confirmed the agent's approach was correct or helpful. "
+                    "Record the confirmed approach, style, or preference as a fact with category "
+                    '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
+                )
+                correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
+
+            prompt = MEMORY_UPDATE_PROMPT.format(
+                current_memory=json.dumps(current_memory, indent=2),
+                conversation=conversation_text,
+                correction_hint=correction_hint,
+            )
+
+            # Call LLM
+            model = self._get_model()
+            response = model.invoke(prompt)
+            response_text = _extract_text(response.content).strip()
+
+            # Parse response
+            # Remove markdown code blocks if present
+            if response_text.startswith("```"):
+                lines = response_text.split("\n")
+                response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
+
+            update_data = json.loads(response_text)
+
+            # Apply updates
+            updated_memory = self._apply_updates(current_memory, update_data, thread_id)
+
+            # Strip file-upload mentions from all summaries before saving.
+            # Uploaded files are session-scoped and won't exist in future sessions,
+            # so recording upload events in long-term memory causes the agent to
+            # try (and fail) to locate those files in subsequent conversations.
+            updated_memory = _strip_upload_mentions_from_memory(updated_memory)
+
+            # Save
+            return get_memory_storage(config).save(updated_memory, agent_name, user_id=user_id)
+
+        except json.JSONDecodeError as e:
+            logger.warning("Failed to parse LLM response for memory update: %s", e)
+            return False
+        except Exception as e:
+            logger.exception("Memory update failed: %s", e)
+            return False
 
     def _apply_updates(
         self,
@@ -613,7 +542,7 @@ class MemoryUpdater:
         Returns:
             Updated memory data.
         """
-        config = get_memory_config()
+        config = self._memory_config
         now = utc_now_iso_z()
 
         # Update user sections

backend/packages/harness/deerflow/agents/middlewares/dangling_tool_call_middleware.py

@@ -15,7 +15,6 @@ to the end of the message list as before_model + add_messages reducer would do.
 
 import json
 import logging
-from collections import defaultdict, deque
 from collections.abc import Awaitable, Callable
 from typing import override
 
@@ -26,11 +25,6 @@ from langchain_core.messages import ToolMessage
 
 logger = logging.getLogger(__name__)
 
-# Workaround for issue #2894: malformed write_file calls can carry huge Markdown
-# payloads in invalid tool-call args. Keep recovery error details short so the
-# synthetic ToolMessage does not echo large or malformed content back to the model.
-_MAX_RECOVERY_ERROR_DETAIL_LEN = 500
-
 
 class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
     """Inserts placeholder ToolMessages for dangling tool calls before model invocation.
@@ -42,144 +36,94 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
 
     @staticmethod
     def _message_tool_calls(msg) -> list[dict]:
-        """Return normalized tool calls from structured fields or raw provider payloads.
-
-        LangChain stores malformed provider function calls in ``invalid_tool_calls``.
-        They do not execute, but provider adapters may still serialize enough of
-        the call id/name back into the next request that strict OpenAI-compatible
-        validators expect a matching ToolMessage. Treat them as dangling calls so
-        the next model request stays well-formed and the model sees a recoverable
-        tool error instead of another provider 400.
-        """
-        normalized: list[dict] = []
-
+        """Return normalized tool calls from structured fields or raw provider payloads."""
         tool_calls = getattr(msg, "tool_calls", None) or []
-        normalized.extend(list(tool_calls))
+        if tool_calls:
+            return list(tool_calls)
 
         raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
-        if not tool_calls:
-            for raw_tc in raw_tool_calls:
-                if not isinstance(raw_tc, dict):
-                    continue
-
-                function = raw_tc.get("function")
-                name = raw_tc.get("name")
-                if not name and isinstance(function, dict):
-                    name = function.get("name")
-
-                args = raw_tc.get("args", {})
-                if not args and isinstance(function, dict):
-                    raw_args = function.get("arguments")
-                    if isinstance(raw_args, str):
-                        try:
-                            parsed_args = json.loads(raw_args)
-                        except (TypeError, ValueError, json.JSONDecodeError):
-                            parsed_args = {}
-                        args = parsed_args if isinstance(parsed_args, dict) else {}
-
-                normalized.append(
-                    {
-                        "id": raw_tc.get("id"),
-                        "name": name or "unknown",
-                        "args": args if isinstance(args, dict) else {},
-                    }
-                )
-
-        for invalid_tc in getattr(msg, "invalid_tool_calls", None) or []:
-            if not isinstance(invalid_tc, dict):
+        normalized: list[dict] = []
+        for raw_tc in raw_tool_calls:
+            if not isinstance(raw_tc, dict):
                 continue
+
+            function = raw_tc.get("function")
+            name = raw_tc.get("name")
+            if not name and isinstance(function, dict):
+                name = function.get("name")
+
+            args = raw_tc.get("args", {})
+            if not args and isinstance(function, dict):
+                raw_args = function.get("arguments")
+                if isinstance(raw_args, str):
+                    try:
+                        parsed_args = json.loads(raw_args)
+                    except (TypeError, ValueError, json.JSONDecodeError):
+                        parsed_args = {}
+                    args = parsed_args if isinstance(parsed_args, dict) else {}
+
             normalized.append(
                 {
-                    "id": invalid_tc.get("id"),
-                    "name": invalid_tc.get("name") or "unknown",
-                    "args": {},
-                    "invalid": True,
-                    "error": invalid_tc.get("error"),
+                    "id": raw_tc.get("id"),
+                    "name": name or "unknown",
+                    "args": args if isinstance(args, dict) else {},
                 }
             )
 
         return normalized
 
-    @staticmethod
-    def _synthetic_tool_message_content(tool_call: dict) -> str:
-        if tool_call.get("invalid"):
-            name = tool_call.get("name")
-            error = tool_call.get("error")
-            error_text = error[:_MAX_RECOVERY_ERROR_DETAIL_LEN] if isinstance(error, str) and error else ""
-            # Workaround for issue #2894: malformed write_file calls can carry huge Markdown
-            # payloads in invalid tool-call args. Keep recovery guidance actionable without
-            # echoing large or malformed content back to the model.
-            if name == "write_file":
-                details = f" Parser error: {error_text}" if error_text else ""
-                return (
-                    "[write_file failed before execution: the tool-call arguments were not valid JSON, "
-                    "so no file was written. This often happens when the model tries to write a very "
-                    "large Markdown file in a single tool call, especially when `content` contains "
-                    "unescaped quotes, inline JSON, backslashes, or code fences. Do not retry the same "
-                    "large `write_file` payload for this artifact; provide the report/content directly "
-                    "as normal assistant text in your next response. If a file write is still needed "
-                    f"later, split the file into smaller sections instead of one large payload.{details}]"
-                )
-            if error_text:
-                return f"[Tool call could not be executed because its arguments were invalid: {error_text}]"
-            return "[Tool call could not be executed because its arguments were invalid.]"
-        return "[Tool call was interrupted and did not return a result.]"
-
     def _build_patched_messages(self, messages: list) -> list | None:
-        """Return messages with tool results grouped after their tool-call AIMessage.
+        """Return a new message list with patches inserted at the correct positions.
 
-        This normalizes model-bound causal order before provider serialization while
-        preserving already-valid transcripts unchanged.
+        For each AIMessage with dangling tool_calls (no corresponding ToolMessage),
+        a synthetic ToolMessage is inserted immediately after that AIMessage.
+        Returns None if no patches are needed.
         """
-        tool_messages_by_id: dict[str, deque[ToolMessage]] = defaultdict(deque)
+        # Collect IDs of all existing ToolMessages
+        existing_tool_msg_ids: set[str] = set()
         for msg in messages:
             if isinstance(msg, ToolMessage):
-                tool_messages_by_id[msg.tool_call_id].append(msg)
+                existing_tool_msg_ids.add(msg.tool_call_id)
 
-        tool_call_ids: set[str] = set()
+        # Check if any patching is needed
+        needs_patch = False
         for msg in messages:
             if getattr(msg, "type", None) != "ai":
                 continue
             for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
-                if tc_id:
-                    tool_call_ids.add(tc_id)
+                if tc_id and tc_id not in existing_tool_msg_ids:
+                    needs_patch = True
+                    break
+            if needs_patch:
+                break
 
+        if not needs_patch:
+            return None
+
+        # Build new list with patches inserted right after each dangling AIMessage
         patched: list = []
+        patched_ids: set[str] = set()
         patch_count = 0
         for msg in messages:
-            if isinstance(msg, ToolMessage) and msg.tool_call_id in tool_call_ids:
-                continue
-
             patched.append(msg)
             if getattr(msg, "type", None) != "ai":
                 continue
-
             for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
-                if not tc_id:
-                    continue
-
-                tool_msg_queue = tool_messages_by_id.get(tc_id)
-                existing_tool_msg = tool_msg_queue.popleft() if tool_msg_queue else None
-                if existing_tool_msg is not None:
-                    patched.append(existing_tool_msg)
-                else:
+                if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
                     patched.append(
                         ToolMessage(
-                            content=self._synthetic_tool_message_content(tc),
+                            content="[Tool call was interrupted and did not return a result.]",
                             tool_call_id=tc_id,
                             name=tc.get("name", "unknown"),
                             status="error",
                         )
                     )
+                    patched_ids.add(tc_id)
                     patch_count += 1
 
-        if patched == messages:
-            return None
-
-        if patch_count:
-            logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
+        logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
         return patched
 
     @override

backend/packages/harness/deerflow/agents/middlewares/deferred_tool_filter_middleware.py

@@ -1,15 +1,12 @@
 """Middleware to filter deferred tool schemas from model binding.
 
-When tool_search is enabled, MCP tools are still passed to ToolNode for
-execution, but their schemas must NOT be sent to the LLM via bind_tools until
-the model has discovered them via tool_search. This middleware removes the
-still-deferred tools from request.tools before model binding, and blocks tool
-calls to tools that have not been promoted yet.
+When tool_search is enabled, MCP tools are registered in the DeferredToolRegistry
+and passed to ToolNode for execution, but their schemas should NOT be sent to the
+LLM via bind_tools (that's the whole point of deferral — saving context tokens).
 
-The deferred name set and the catalog hash are injected at construction time
-(no ContextVar). Promotion state is read from graph state (``state["promoted"]``),
-scoped by catalog hash so a stale persisted promotion cannot expose a renamed
-or drifted tool.
+This middleware intercepts wrap_model_call and removes deferred tools from
+request.tools so that model.bind_tools only receives active tool schemas.
+The agent discovers deferred tools at runtime via the tool_search tool.
 """
 
 import logging
@@ -27,49 +24,47 @@ logger = logging.getLogger(__name__)
 
 
 class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
-    """Hide deferred tool schemas from the bound model until promoted.
+    """Remove deferred tools from request.tools before model binding.
 
     ToolNode still holds all tools (including deferred) for execution routing,
-    but the LLM only sees active tool schemas plus tools that have already been
-    promoted (recorded in ``state["promoted"]`` under the current catalog hash).
+    but the LLM only sees active tool schemas — deferred tools are discoverable
+    via tool_search at runtime.
     """
 
-    def __init__(self, deferred_names: frozenset[str], catalog_hash: str | None):
-        super().__init__()
-        self._deferred = deferred_names
-        self._catalog_hash = catalog_hash
-
-    def _promoted(self, state) -> set[str]:
-        promoted = (state or {}).get("promoted")
-        if promoted and promoted.get("catalog_hash") == self._catalog_hash:
-            return set(promoted.get("names") or [])
-        return set()
-
-    def _hidden(self, state) -> set[str]:
-        return set(self._deferred) - self._promoted(state)
-
     def _filter_tools(self, request: ModelRequest) -> ModelRequest:
-        if not self._deferred:
+        from deerflow.tools.builtins.tool_search import get_deferred_registry
+
+        registry = get_deferred_registry()
+        if not registry:
             return request
-        hide = self._hidden(request.state)
-        if not hide:
-            return request
-        active = [t for t in request.tools if getattr(t, "name", None) not in hide]
-        if len(active) < len(request.tools):
-            logger.debug("Filtered %d deferred tool schema(s) from model binding", len(request.tools) - len(active))
-        return request.override(tools=active)
+
+        deferred_names = registry.deferred_names
+        active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
+
+        if len(active_tools) < len(request.tools):
+            logger.debug(f"Filtered {len(request.tools) - len(active_tools)} deferred tool schema(s) from model binding")
+
+        return request.override(tools=active_tools)
 
     def _blocked_tool_message(self, request: ToolCallRequest) -> ToolMessage | None:
-        if not self._deferred:
+        from deerflow.tools.builtins.tool_search import get_deferred_registry
+
+        registry = get_deferred_registry()
+        if not registry:
             return None
-        name = str(request.tool_call.get("name") or "")
-        if not name or name not in self._hidden(request.state):
+
+        tool_name = str(request.tool_call.get("name") or "")
+        if not tool_name:
             return None
+
+        if not registry.contains(tool_name):
+            return None
+
         tool_call_id = str(request.tool_call.get("id") or "missing_tool_call_id")
         return ToolMessage(
-            content=(f"Error: Tool '{name}' is deferred and has not been promoted yet. Call tool_search first to expose and promote this tool's schema, then retry."),
+            content=(f"Error: Tool '{tool_name}' is deferred and has not been promoted yet. Call tool_search first to expose and promote this tool's schema, then retry."),
             tool_call_id=tool_call_id,
-            name=name,
+            name=tool_name,
             status="error",
         )
 

backend/packages/harness/deerflow/agents/middlewares/dynamic_context_middleware.py

@@ -1,204 +0,0 @@
-"""Middleware to inject dynamic context (memory, current date) as a system-reminder.
-
-The system prompt is kept fully static for maximum prefix-cache reuse across users
-and sessions.  The current date is always injected.  Per-user memory is also injected
-when ``memory.injection_enabled`` is True in the app config.  Both are delivered once
-per conversation as a dedicated <system-reminder> HumanMessage inserted before the
-first user message (frozen-snapshot pattern).
-
-When a conversation spans midnight the middleware detects the date change and injects
-a lightweight date-update reminder as a separate HumanMessage before the current turn.
-This correction is persisted so subsequent turns on the new day see a consistent history
-and do not re-inject.
-
-Reminder format:
-
-    <system-reminder>
-    <memory>...</memory>
-
-    <current_date>2026-05-08, Friday</current_date>
-    </system-reminder>
-
-Date-update format:
-
-    <system-reminder>
-    <current_date>2026-05-09, Saturday</current_date>
-    </system-reminder>
-"""
-
-from __future__ import annotations
-
-import logging
-import re
-import uuid
-from datetime import datetime
-from typing import TYPE_CHECKING, override
-
-from langchain.agents.middleware import AgentMiddleware
-from langchain_core.messages import HumanMessage
-from langgraph.runtime import Runtime
-
-if TYPE_CHECKING:
-    from deerflow.config.app_config import AppConfig
-
-logger = logging.getLogger(__name__)
-
-_DATE_RE = re.compile(r"<current_date>([^<]+)</current_date>")
-_DYNAMIC_CONTEXT_REMINDER_KEY = "dynamic_context_reminder"
-_SUMMARY_MESSAGE_NAME = "summary"
-
-
-def _extract_date(content: str) -> str | None:
-    """Return the first <current_date> value found in *content*, or None."""
-    m = _DATE_RE.search(content)
-    return m.group(1) if m else None
-
-
-def is_dynamic_context_reminder(message: object) -> bool:
-    """Return whether *message* is a hidden dynamic-context reminder."""
-    return isinstance(message, HumanMessage) and bool(message.additional_kwargs.get(_DYNAMIC_CONTEXT_REMINDER_KEY))
-
-
-def _last_injected_date(messages: list) -> str | None:
-    """Scan messages in reverse and return the most recently injected date.
-
-    Detection uses the ``dynamic_context_reminder`` additional_kwargs flag rather
-    than content substring matching, so user messages containing ``<system-reminder>``
-    are not mistakenly treated as injected reminders.
-    """
-    for msg in reversed(messages):
-        if is_dynamic_context_reminder(msg):
-            content_str = msg.content if isinstance(msg.content, str) else str(msg.content)
-            return _extract_date(content_str)
-    return None
-
-
-def _is_user_injection_target(message: object) -> bool:
-    """Return whether *message* can receive a dynamic-context reminder."""
-    return isinstance(message, HumanMessage) and not is_dynamic_context_reminder(message) and message.name != _SUMMARY_MESSAGE_NAME
-
-
-class DynamicContextMiddleware(AgentMiddleware):
-    """Inject memory and current date into HumanMessages as a <system-reminder>.
-
-    First turn
-    ----------
-    Prepends a full system-reminder (memory + date) to the first HumanMessage and
-    persists it (same message ID).  The first message is then frozen for the whole
-    session — its content never changes again, so the prefix cache can hit on every
-    subsequent turn.
-
-    Midnight crossing
-    -----------------
-    If the conversation spans midnight, the current date differs from the date that
-    was injected earlier.  In that case a lightweight date-update reminder is prepended
-    to the **current** (last) HumanMessage and persisted.  Subsequent turns on the new
-    day see the corrected date in history and skip re-injection.
-    """
-
-    def __init__(self, agent_name: str | None = None, *, app_config: AppConfig | None = None):
-        super().__init__()
-        self._agent_name = agent_name
-        self._app_config = app_config
-
-    def _build_full_reminder(self) -> str:
-        from deerflow.agents.lead_agent.prompt import _get_memory_context
-
-        # Memory injection is gated by injection_enabled; date is always included.
-        injection_enabled = self._app_config.memory.injection_enabled if self._app_config else True
-        memory_context = _get_memory_context(self._agent_name, app_config=self._app_config) if injection_enabled else ""
-        current_date = datetime.now().strftime("%Y-%m-%d, %A")
-
-        lines: list[str] = ["<system-reminder>"]
-        if memory_context:
-            lines.append(memory_context.strip())
-            lines.append("")  # blank line separating memory from date
-        lines.append(f"<current_date>{current_date}</current_date>")
-        lines.append("</system-reminder>")
-
-        return "\n".join(lines)
-
-    def _build_date_update_reminder(self) -> str:
-        current_date = datetime.now().strftime("%Y-%m-%d, %A")
-        return "\n".join(
-            [
-                "<system-reminder>",
-                f"<current_date>{current_date}</current_date>",
-                "</system-reminder>",
-            ]
-        )
-
-    @staticmethod
-    def _make_reminder_and_user_messages(original: HumanMessage, reminder_content: str) -> tuple[HumanMessage, HumanMessage]:
-        """Return (reminder_msg, user_msg) using the ID-swap technique.
-
-        reminder_msg takes the original message's ID so that add_messages replaces it
-        in-place (preserving position).  user_msg carries the original content with a
-        derived ``{id}__user`` ID and is appended immediately after by add_messages.
-
-        If the original message has no ID a stable UUID is generated so the derived
-        ``{id}__user`` ID never collapses to the ambiguous ``None__user`` string.
-        """
-        stable_id = original.id or str(uuid.uuid4())
-        reminder_msg = HumanMessage(
-            content=reminder_content,
-            id=stable_id,
-            additional_kwargs={"hide_from_ui": True, _DYNAMIC_CONTEXT_REMINDER_KEY: True},
-        )
-        user_msg = HumanMessage(
-            content=original.content,
-            id=f"{stable_id}__user",
-            name=original.name,
-            additional_kwargs=original.additional_kwargs,
-        )
-        return reminder_msg, user_msg
-
-    def _inject(self, state) -> dict | None:
-        messages = list(state.get("messages", []))
-        if not messages:
-            return None
-
-        current_date = datetime.now().strftime("%Y-%m-%d, %A")
-        last_date = _last_injected_date(messages)
-        logger.debug(
-            "DynamicContextMiddleware._inject: msg_count=%d last_date=%r current_date=%r",
-            len(messages),
-            last_date,
-            current_date,
-        )
-
-        if last_date is None:
-            # ── First turn: inject full reminder as a separate HumanMessage ─────
-            first_idx = next((i for i, m in enumerate(messages) if _is_user_injection_target(m)), None)
-            if first_idx is None:
-                return None
-            full_reminder = self._build_full_reminder()
-            logger.info(
-                "DynamicContextMiddleware: injecting full reminder (len=%d, has_memory=%s) into first HumanMessage id=%r",
-                len(full_reminder),
-                "<memory>" in full_reminder,
-                messages[first_idx].id,
-            )
-            reminder_msg, user_msg = self._make_reminder_and_user_messages(messages[first_idx], full_reminder)
-            return {"messages": [reminder_msg, user_msg]}
-
-        if last_date == current_date:
-            # ── Same day: nothing to do ──────────────────────────────────────────
-            return None
-
-        # ── Midnight crossed: inject date-update reminder as a separate HumanMessage ──
-        last_human_idx = next((i for i in reversed(range(len(messages))) if _is_user_injection_target(messages[i])), None)
-        if last_human_idx is None:
-            return None
-
-        reminder_msg, user_msg = self._make_reminder_and_user_messages(messages[last_human_idx], self._build_date_update_reminder())
-        logger.info("DynamicContextMiddleware: midnight crossing detected — injected date update before current turn")
-        return {"messages": [reminder_msg, user_msg]}
-
-    @override
-    def before_agent(self, state, runtime: Runtime) -> dict | None:
-        return self._inject(state)
-
-    @override
-    async def abefore_agent(self, state, runtime: Runtime) -> dict | None:
-        return self._inject(state)