docs: add run-level API endpoints to CLAUDE.md routers table

feat(api): retrofit cursor pagination onto GET /threads/{tid}/runs/{rid}/messages
Replace bare list[dict] response with {data: [...], has_more: bool} envelope, forwarding limit/before_seq/after_seq query params to the event store. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-12 16:17:09 +08:00 · 2026-04-12 16:14:14 +08:00 · 2026-04-12 16:11:21 +08:00 · 2026-04-12 16:09:34 +08:00 · 2026-04-12 15:58:33 +08:00 · 2026-04-12 15:32:02 +08:00
366 changed files with 8081 additions and 16692 deletions
@@ -24,7 +24,6 @@ INFOQUEST_API_KEY=your-infoquest-api-key
 # SLACK_BOT_TOKEN=your-slack-bot-token
 # SLACK_APP_TOKEN=your-slack-app-token
 # TELEGRAM_BOT_TOKEN=your-telegram-bot-token
-# DISCORD_BOT_TOKEN=your-discord-bot-token

 # Enable LangSmith to monitor and debug your LLM calls, agent runs, and tool executions.
 # LANGSMITH_TRACING=true
@@ -1,63 +0,0 @@
-name: E2E Tests
-
-on:
-  push:
-    branches: [ 'main' ]
-    paths:
-      - 'frontend/**'
-      - '.github/workflows/e2e-tests.yml'
-  pull_request:
-    types: [opened, synchronize, reopened, ready_for_review]
-    paths:
-      - 'frontend/**'
-      - '.github/workflows/e2e-tests.yml'
-
-concurrency:
-  group: e2e-tests-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  e2e-tests:
-    if: ${{ github.event_name != 'pull_request' || github.event.pull_request.draft == false }}
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '22'
-
-      - name: Enable Corepack
-        run: corepack enable
-
-      - name: Use pinned pnpm version
-        run: corepack prepare pnpm@10.26.2 --activate
-
-      - name: Install frontend dependencies
-        working-directory: frontend
-        run: pnpm install --frozen-lockfile
-
-      - name: Install Playwright Chromium
-        working-directory: frontend
-        run: npx playwright install chromium --with-deps
-
-      - name: Run E2E tests
-        working-directory: frontend
-        run: pnpm exec playwright test
-        env:
-          SKIP_ENV_VALIDATION: '1'
-
-      - name: Upload Playwright report
-        uses: actions/upload-artifact@v4
-        if: ${{ !cancelled() }}
-        with:
-          name: playwright-report
-          path: frontend/playwright-report/
-          retention-days: 7
@@ -1,43 +0,0 @@
-name: Frontend Unit Tests
-
-on:
-  push:
-    branches: [ 'main' ]
-  pull_request:
-    types: [opened, synchronize, reopened, ready_for_review]
-
-concurrency:
-  group: frontend-unit-tests-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  frontend-unit-tests:
-    if: github.event.pull_request.draft == false
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '22'
-
-      - name: Enable Corepack
-        run: corepack enable
-
-      - name: Use pinned pnpm version
-        run: corepack prepare pnpm@10.26.2 --activate
-
-      - name: Install frontend dependencies
-        working-directory: frontend
-        run: pnpm install --frozen-lockfile
-
-      - name: Run unit tests of frontend
-        working-directory: frontend
-        run: make test
@@ -40,7 +40,6 @@ coverage/
 skills/custom/*
 logs/
 log/
-debug.log

 # Local git hooks (keep only on this machine, do not push)
 .githooks/
@@ -56,7 +55,5 @@ web/
 backend/Dockerfile.langgraph
 config.yaml.bak
 .playwright-mcp
-/frontend/test-results/
-/frontend/playwright-report/
 .gstack/
 .worktrees
@@ -1,33 +0,0 @@
-repos:
-  # Backend: ruff lint + format via uv (uses the same ruff version as backend deps)
-  - repo: local
-    hooks:
-      - id: ruff
-        name: ruff lint
-        entry: bash -c 'cd backend && uv run ruff check --fix "${@/#backend\//}"' --
-        language: system
-        types_or: [python]
-        files: ^backend/
-      - id: ruff-format
-        name: ruff format
-        entry: bash -c 'cd backend && uv run ruff format "${@/#backend\//}"' --
-        language: system
-        types_or: [python]
-        files: ^backend/
-
-  # Frontend: eslint + prettier (must run from frontend/ for node_modules resolution)
-  - repo: local
-    hooks:
-      - id: frontend-eslint
-        name: eslint (frontend)
-        entry: bash -c 'cd frontend && npx eslint --fix "${@/#frontend\//}"' --
-        language: system
-        types_or: [javascript, tsx, ts]
-        files: ^frontend/
-
-      - id: frontend-prettier
-        name: prettier (frontend)
-        entry: bash -c 'cd frontend && npx prettier --write "${@/#frontend\//}"' --
-        language: system
-        files: ^frontend/
-        types_or: [javascript, tsx, ts, json, css]
@@ -166,7 +166,7 @@ Required tools:

 1. **Configure the application** (same as Docker setup above)

-2. **Install dependencies** (this also sets up pre-commit hooks):
+2. **Install dependencies**:
   ```bash
   make install
   ```
@@ -298,24 +298,19 @@ Nginx (port 2026) ← Unified entry point
 ```bash
 # Backend tests
 cd backend
-make test
+uv run pytest

-# Frontend unit tests
+# Frontend checks
 cd frontend
-make test
-
-# Frontend E2E tests (requires Chromium; builds and auto-starts the Next.js production server)
-cd frontend
-make test-e2e
+pnpm check
 ```

 ### PR Regression Checks

-Every pull request triggers the following CI workflows:
+Every pull request runs the backend regression workflow at [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml), including:

- **Backend unit tests** — [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml)
- **Frontend unit tests** — [.github/workflows/frontend-unit-tests.yml](.github/workflows/frontend-unit-tests.yml)
- **Frontend E2E tests** — [.github/workflows/e2e-tests.yml](.github/workflows/e2e-tests.yml) (triggered only when `frontend/` files change)
+- `tests/test_provisioner_kubeconfig.py`
+- `tests/test_docker_sandbox_mode_detection.py`

 ## Code Style

@@ -1,6 +1,6 @@
 # DeerFlow - Unified Development Environment

-.PHONY: help config config-upgrade check install setup doctor 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
@@ -23,22 +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 install         - Install all dependencies (frontend + backend + pre-commit hooks)"
+	@echo "  make install         - Install all dependencies (frontend + backend)"
 	@echo "  make setup-sandbox   - Pre-pull sandbox container image (recommended)"
 	@echo "  make dev             - Start all services in development mode (with hot-reloading)"
+	@echo "  make dev-pro         - Start in dev + Gateway mode (experimental, no LangGraph server)"
 	@echo "  make dev-daemon      - Start dev services in background (daemon mode)"
+	@echo "  make dev-daemon-pro  - Start dev daemon + Gateway mode (experimental)"
 	@echo "  make start           - Start all services in production mode (optimized, no hot-reloading)"
+	@echo "  make start-pro       - Start in prod + Gateway mode (experimental)"
 	@echo "  make start-daemon    - Start prod services in background (daemon mode)"
+	@echo "  make start-daemon-pro - Start prod daemon + Gateway mode (experimental)"
 	@echo "  make stop            - Stop all running services"
 	@echo "  make clean           - Clean up processes and temporary files"
 	@echo ""
 	@echo "Docker Production Commands:"
 	@echo "  make up              - Build and start production Docker services (localhost:2026)"
+	@echo "  make up-pro          - Build and start production Docker in Gateway mode (experimental)"
 	@echo "  make down            - Stop and remove production Docker containers"
 	@echo ""
 	@echo "Docker Development Commands:"
 	@echo "  make docker-init     - Pull the sandbox image"
 	@echo "  make docker-start    - Start Docker services (mode-aware from config.yaml, localhost:2026)"
+	@echo "  make docker-start-pro - Start Docker in Gateway mode (experimental, no LangGraph container)"
 	@echo "  make docker-stop     - Stop Docker development services"
 	@echo "  make docker-logs     - View Docker development logs"
 	@echo "  make docker-logs-frontend - View Docker frontend logs"
@@ -67,8 +73,6 @@ install:
 	@cd backend && uv sync
 	@echo "Installing frontend dependencies..."
 	@cd frontend && pnpm install
-	@echo "Installing pre-commit hooks..."
-	@$(BACKEND_UV_RUN) --with pre-commit pre-commit install
 	@echo "✓ All dependencies installed"
 	@echo ""
 	@echo "=========================================="
@@ -95,7 +99,7 @@ setup-sandbox:
 	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"; \
+		container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
 	fi; \
 	if command -v docker >/dev/null 2>&1; then \
 		echo "Pulling image using Docker..."; \
@@ -117,21 +121,41 @@ 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:
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --stop
@@ -156,6 +180,10 @@ docker-init:
 docker-start:
 	@$(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
@@ -178,6 +206,10 @@ 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
@@ -243,6 +243,9 @@ make up     # Build images and start all production services
 make down   # Stop and remove containers
 ```

+> [!NOTE]
+> The LangGraph agent server currently runs via `langgraph dev` (the open-source CLI server).
+
 Access: http://localhost:2026

 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
@@ -261,7 +264,7 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P

 2. **Install dependencies**:
   ```bash
-   make install  # Install backend + frontend dependencies + pre-commit hooks
+   make install  # Install backend + frontend dependencies
   ```

 3. **(Optional) Pre-pull sandbox image**:
@@ -286,31 +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
@@ -350,8 +375,8 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when

 ```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

@@ -419,7 +444,6 @@ channels:
 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:

@@ -480,7 +504,7 @@ WECOM_BOT_SECRET=your_bot_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.

-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**

@@ -634,8 +658,6 @@ This is the difference between a chatbot with tool access and an agent with an a

 **Summarization**: Within a session, DeerFlow manages context aggressively — summarizing completed sub-tasks, offloading intermediate results to the filesystem, compressing what's no longer immediately relevant. This lets it stay sharp across long, multi-step tasks without blowing the context window.

-**Strict Tool-Call Recovery**: When a provider or middleware interrupts a tool-call loop, DeerFlow now strips provider-level raw tool-call metadata on forced-stop assistant messages and injects placeholder tool results for dangling calls before the next model invocation. This keeps OpenAI-compatible reasoning models that strictly validate `tool_call_id` sequences from failing with malformed history errors.
-
 ### Long-Term Memory

 Most agents forget everything the moment a conversation ends. DeerFlow remembers.
@@ -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
@@ -81,15 +83,16 @@ 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 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
@@ -127,7 +130,7 @@ from app.gateway.app import app
 from app.channels.service import start_channel_service

 # App → Harness (allowed)
-from deerflow.config.app_config import AppConfig
+from deerflow.config import get_app_config

 # Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
 # from app.gateway.routers.uploads import ...  # ← will fail CI
@@ -153,26 +156,20 @@ from deerflow.config.app_config import AppConfig

 ### Middleware Chain

-Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`_build_middlewares`):
+Middlewares execute in strict order in `packages/harness/deerflow/agents/lead_agent/agent.py`:

 1. **ThreadDataMiddleware** - Creates per-thread directories under the user's isolation scope (`backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); resolves `user_id` via `get_effective_user_id()` (falls back to `"default"` in no-auth mode); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local thread directory
 2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
 3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
-4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption), including raw provider tool-call payloads preserved only in `additional_kwargs["tool_calls"]`
-5. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later middleware/tool stages run
-6. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
-7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
-8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
-9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
-10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
-12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
-13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
-14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
-15. **DeferredToolFilterMiddleware** - Hides deferred tool schemas from the bound model until tool search is enabled (optional)
-16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
-17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
-18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
+4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
+5. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
+6. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
+7. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
+8. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+9. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+10. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+11. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
+12. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)

 ### Configuration System

@@ -182,16 +179,7 @@ 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 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:
-
- **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.
-
-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.
+**Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.

 Configuration priority:
 1. Explicit `config_path` argument
@@ -321,9 +309,9 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →

 ### IM Channels System (`app/channels/`)

-Bridges external messaging platforms (Feishu, Slack, Telegram) 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)
@@ -336,7 +324,7 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
 **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)
@@ -344,9 +332,9 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
 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`.
+- 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/`)
@@ -416,9 +404,9 @@ 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
@@ -481,15 +469,20 @@ 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)

@@ -498,11 +491,15 @@ 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
@@ -2,7 +2,7 @@ install:
 	uv sync

 dev:
-	PYTHONPATH=. 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=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
@@ -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
@@ -1,273 +0,0 @@
-"""Discord channel integration using discord.py."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import threading
-from typing import Any
-
-from app.channels.base import Channel
-from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
-
-logger = logging.getLogger(__name__)
-
-_DISCORD_MAX_MESSAGE_LEN = 2000
-
-
-class DiscordChannel(Channel):
-    """Discord bot channel.
-
-    Configuration keys (in ``config.yaml`` under ``channels.discord``):
-        - ``bot_token``: Discord Bot token.
-        - ``allowed_guilds``: (optional) List of allowed Discord guild IDs. Empty = allow all.
-    """
-
-    def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
-        super().__init__(name="discord", bus=bus, config=config)
-        self._bot_token = str(config.get("bot_token", "")).strip()
-        self._allowed_guilds: set[int] = set()
-        for guild_id in config.get("allowed_guilds", []):
-            try:
-                self._allowed_guilds.add(int(guild_id))
-            except (TypeError, ValueError):
-                continue
-
-        self._client = None
-        self._thread: threading.Thread | None = None
-        self._discord_loop: asyncio.AbstractEventLoop | None = None
-        self._main_loop: asyncio.AbstractEventLoop | None = None
-        self._discord_module = None
-
-    async def start(self) -> None:
-        if self._running:
-            return
-
-        try:
-            import discord
-        except ImportError:
-            logger.error("discord.py is not installed. Install it with: uv add discord.py")
-            return
-
-        if not self._bot_token:
-            logger.error("Discord channel requires bot_token")
-            return
-
-        intents = discord.Intents.default()
-        intents.messages = True
-        intents.guilds = True
-        intents.message_content = True
-
-        client = discord.Client(
-            intents=intents,
-            allowed_mentions=discord.AllowedMentions.none(),
-        )
-        self._client = client
-        self._discord_module = discord
-        self._main_loop = asyncio.get_event_loop()
-
-        @client.event
-        async def on_message(message) -> None:
-            await self._on_message(message)
-
-        self._running = True
-        self.bus.subscribe_outbound(self._on_outbound)
-
-        self._thread = threading.Thread(target=self._run_client, daemon=True)
-        self._thread.start()
-        logger.info("Discord channel started")
-
-    async def stop(self) -> None:
-        self._running = False
-        self.bus.unsubscribe_outbound(self._on_outbound)
-
-        if self._client and self._discord_loop and self._discord_loop.is_running():
-            close_future = asyncio.run_coroutine_threadsafe(self._client.close(), self._discord_loop)
-            try:
-                await asyncio.wait_for(asyncio.wrap_future(close_future), timeout=10)
-            except TimeoutError:
-                logger.warning("[Discord] client close timed out after 10s")
-            except Exception:
-                logger.exception("[Discord] error while closing client")
-
-        if self._thread:
-            self._thread.join(timeout=10)
-            self._thread = None
-
-        self._client = None
-        self._discord_loop = None
-        self._discord_module = None
-        logger.info("Discord channel stopped")
-
-    async def send(self, msg: OutboundMessage) -> None:
-        target = await self._resolve_target(msg)
-        if target is None:
-            logger.error("[Discord] target not found for chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
-            return
-
-        text = msg.text or ""
-        for chunk in self._split_text(text):
-            send_future = asyncio.run_coroutine_threadsafe(target.send(chunk), self._discord_loop)
-            await asyncio.wrap_future(send_future)
-
-    async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
-        target = await self._resolve_target(msg)
-        if target is None:
-            logger.error("[Discord] target not found for file upload chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
-            return False
-
-        if self._discord_module is None:
-            return False
-
-        try:
-            fp = open(str(attachment.actual_path), "rb")  # noqa: SIM115
-            file = self._discord_module.File(fp, filename=attachment.filename)
-            send_future = asyncio.run_coroutine_threadsafe(target.send(file=file), self._discord_loop)
-            await asyncio.wrap_future(send_future)
-            logger.info("[Discord] file uploaded: %s", attachment.filename)
-            return True
-        except Exception:
-            logger.exception("[Discord] failed to upload file: %s", attachment.filename)
-            return False
-
-    async def _on_message(self, message) -> None:
-        if not self._running or not self._client:
-            return
-
-        if message.author.bot:
-            return
-
-        if self._client.user and message.author.id == self._client.user.id:
-            return
-
-        guild = message.guild
-        if self._allowed_guilds:
-            if guild is None or guild.id not in self._allowed_guilds:
-                return
-
-        text = (message.content or "").strip()
-        if not text:
-            return
-
-        if self._discord_module is None:
-            return
-
-        if isinstance(message.channel, self._discord_module.Thread):
-            chat_id = str(message.channel.parent_id or message.channel.id)
-            thread_id = str(message.channel.id)
-        else:
-            thread = await self._create_thread(message)
-            if thread is None:
-                return
-            chat_id = str(message.channel.id)
-            thread_id = str(thread.id)
-
-        msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
-        inbound = self._make_inbound(
-            chat_id=chat_id,
-            user_id=str(message.author.id),
-            text=text,
-            msg_type=msg_type,
-            thread_ts=thread_id,
-            metadata={
-                "guild_id": str(guild.id) if guild else None,
-                "channel_id": str(message.channel.id),
-                "message_id": str(message.id),
-            },
-        )
-        inbound.topic_id = thread_id
-
-        if self._main_loop and self._main_loop.is_running():
-            future = asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._main_loop)
-            future.add_done_callback(lambda f: logger.exception("[Discord] publish_inbound failed", exc_info=f.exception()) if f.exception() else None)
-
-    def _run_client(self) -> None:
-        self._discord_loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(self._discord_loop)
-        try:
-            self._discord_loop.run_until_complete(self._client.start(self._bot_token))
-        except Exception:
-            if self._running:
-                logger.exception("Discord client error")
-        finally:
-            try:
-                if self._client and not self._client.is_closed():
-                    self._discord_loop.run_until_complete(self._client.close())
-            except Exception:
-                logger.exception("Error during Discord shutdown")
-
-    async def _create_thread(self, message):
-        try:
-            thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
-            return await message.create_thread(name=thread_name)
-        except Exception:
-            logger.exception("[Discord] failed to create thread for message=%s (threads may be disabled or missing permissions)", message.id)
-            try:
-                await message.channel.send("Could not create a thread for your message. Please check that threads are enabled in this channel.")
-            except Exception:
-                pass
-            return None
-
-    async def _resolve_target(self, msg: OutboundMessage):
-        if not self._client or not self._discord_loop:
-            return None
-
-        target_ids: list[str] = []
-        if msg.thread_ts:
-            target_ids.append(msg.thread_ts)
-        if msg.chat_id and msg.chat_id not in target_ids:
-            target_ids.append(msg.chat_id)
-
-        for raw_id in target_ids:
-            target = await self._get_channel_or_thread(raw_id)
-            if target is not None:
-                return target
-        return None
-
-    async def _get_channel_or_thread(self, raw_id: str):
-        if not self._client or not self._discord_loop:
-            return None
-
-        try:
-            target_id = int(raw_id)
-        except (TypeError, ValueError):
-            return None
-
-        get_future = asyncio.run_coroutine_threadsafe(self._fetch_channel(target_id), self._discord_loop)
-        try:
-            return await asyncio.wrap_future(get_future)
-        except Exception:
-            logger.exception("[Discord] failed to resolve target id=%s", raw_id)
-            return None
-
-    async def _fetch_channel(self, target_id: int):
-        if not self._client:
-            return None
-
-        channel = self._client.get_channel(target_id)
-        if channel is not None:
-            return channel
-
-        try:
-            return await self._client.fetch_channel(target_id)
-        except Exception:
-            return None
-
-    @staticmethod
-    def _split_text(text: str) -> list[str]:
-        if not text:
-            return [""]
-
-        chunks: list[str] = []
-        remaining = text
-        while len(remaining) > _DISCORD_MAX_MESSAGE_LEN:
-            split_at = remaining.rfind("\n", 0, _DISCORD_MAX_MESSAGE_LEN)
-            if split_at <= 0:
-                split_at = _DISCORD_MAX_MESSAGE_LEN
-            chunks.append(remaining[:split_at])
-            remaining = remaining[split_at:].lstrip("\n")
-
-        if remaining:
-            chunks.append(remaining)
-
-        return chunks
@@ -375,9 +375,7 @@ class FeishuChannel(Channel):
        virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"

        try:
-            from deerflow.config.app_config import AppConfig
-
-            sandbox_provider = get_sandbox_provider(AppConfig.from_file())
+            sandbox_provider = get_sandbox_provider()
            sandbox_id = sandbox_provider.acquire(thread_id)
            if sandbox_id != "local":
                sandbox = sandbox_provider.get(sandbox_id)
@@ -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

@@ -21,7 +21,7 @@ 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-]+$")
@@ -36,7 +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 = {
-    "discord": {"supports_streaming": False},
    "feishu": {"supports_streaming": True},
    "slack": {"supports_streaming": False},
    "telegram": {"supports_streaming": False},
@@ -509,7 +508,7 @@ 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.
    """

@@ -534,7 +533,6 @@ 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
@@ -587,14 +585,7 @@ 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 ---------------------------------------------------------
@@ -677,7 +668,7 @@ 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(
@@ -687,7 +678,7 @@ 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:
@@ -894,7 +885,7 @@ 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"]
@@ -4,21 +4,17 @@ from __future__ import annotations

 import logging
 import os
-from typing import TYPE_CHECKING, Any
+from typing import Any

 from app.channels.base import Channel
 from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
 from app.channels.message_bus import MessageBus
 from app.channels.store import ChannelStore

-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] = {
-    "discord": "app.channels.discord:DiscordChannel",
    "feishu": "app.channels.feishu:FeishuChannel",
    "slack": "app.channels.slack:SlackChannel",
    "telegram": "app.channels.telegram:TelegramChannel",
@@ -26,16 +22,6 @@ _CHANNEL_REGISTRY: dict[str, str] = {
    "wecom": "app.channels.wecom:WeComChannel",
 }

-# Keys that indicate a user has configured credentials for a channel.
-_CHANNEL_CREDENTIAL_KEYS: dict[str, list[str]] = {
-    "discord": ["bot_token"],
-    "feishu": ["app_id", "app_secret"],
-    "slack": ["bot_token", "app_token"],
-    "telegram": ["bot_token"],
-    "wecom": ["bot_id", "bot_secret"],
-    "wechat": ["bot_token"],
-}
-
 _CHANNELS_LANGGRAPH_URL_ENV = "DEER_FLOW_CHANNELS_LANGGRAPH_URL"
 _CHANNELS_GATEWAY_URL_ENV = "DEER_FLOW_CHANNELS_GATEWAY_URL"

@@ -78,11 +64,14 @@ class ChannelService:
        self._running = False

    @classmethod
-    def from_app_config(cls, app_config: AppConfig) -> ChannelService:
-        """Create a ChannelService from an explicit application config."""
+    def from_app_config(cls) -> ChannelService:
+        """Create a ChannelService from the application config."""
+        from deerflow.config.app_config import get_app_config
+
+        config = get_app_config()
        channels_config = {}
        # extra fields are allowed by AppConfig (extra="allow")
-        extra = app_config.model_extra or {}
+        extra = config.model_extra or {}
        if "channels" in extra:
            channels_config = extra["channels"]
        return cls(channels_config=channels_config)
@@ -98,16 +87,7 @@ class ChannelService:
            if not isinstance(channel_config, dict):
                continue
            if not channel_config.get("enabled", False):
-                cred_keys = _CHANNEL_CREDENTIAL_KEYS.get(name, [])
-                has_creds = any(not isinstance(channel_config.get(k), bool) and channel_config.get(k) is not None and str(channel_config[k]).strip() for k in cred_keys)
-                if has_creds:
-                    logger.warning(
-                        "Channel '%s' has credentials configured but is disabled. Set enabled: true under channels.%s in config.yaml to activate it.",
-                        name,
-                        name,
-                    )
-                else:
-                    logger.info("Channel %s is disabled, skipping", name)
+                logger.info("Channel %s is disabled, skipping", name)
                continue

            await self._start_channel(name, channel_config)
@@ -201,12 +181,12 @@ def get_channel_service() -> ChannelService | None:
    return _channel_service


-async def start_channel_service(app_config: AppConfig) -> ChannelService:
+async def start_channel_service() -> ChannelService:
    """Create and start the global ChannelService from app config."""
    global _channel_service
    if _channel_service is not None:
        return _channel_service
-    _channel_service = ChannelService.from_app_config(app_config)
+    _channel_service = ChannelService.from_app_config()
    await _channel_service.start()
    return _channel_service

@@ -16,31 +16,13 @@ logger = logging.getLogger(__name__)
 _slack_md_converter = SlackMarkdownConverter()


-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(
-            "Slack allowed_users should be a list of Slack user IDs or a single Slack user ID string; treating %s as one string value",
-            type(allowed_users).__name__,
-        )
-        values = [allowed_users]
-    return {str(user_id) for user_id in values if str(user_id)}
-
-
 class SlackChannel(Channel):
    """Slack IM channel using Socket Mode (WebSocket, no public IP).

    Configuration keys (in ``config.yaml`` under ``channels.slack``):
        - ``bot_token``: Slack Bot User OAuth Token (xoxb-...).
        - ``app_token``: Slack App-Level Token (xapp-...) for Socket Mode.
-        - ``allowed_users``: (optional) List of allowed Slack user IDs, or a
-          single Slack user ID string as shorthand. Empty = allow all. Other
-          scalar values are treated as a single string with a warning.
+        - ``allowed_users``: (optional) List of allowed Slack user IDs. Empty = allow all.
    """

    def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -48,7 +30,7 @@ class SlackChannel(Channel):
        self._socket_client = None
        self._web_client = None
        self._loop: asyncio.AbstractEventLoop | None = None
-        self._allowed_users = _normalize_allowed_users(config.get("allowed_users", []))
+        self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}

    async def start(self) -> None:
        if self._running:
@@ -1,4 +1,3 @@
-import asyncio
 import logging
 import os
 from collections.abc import AsyncGenerator
@@ -28,7 +27,7 @@ from app.gateway.routers import (
    threads,
    uploads,
 )
-from deerflow.config.app_config import AppConfig
+from deerflow.config.app_config import get_app_config

 # Configure logging
 logging.basicConfig(
@@ -39,23 +38,20 @@ logging.basicConfig(

 logger = logging.getLogger(__name__)

-# Upper bound (seconds) each lifespan shutdown hook is allowed to run.
-# Bounds worker exit time so uvicorn's reload supervisor does not keep
-# firing signals into a worker that is stuck waiting for shutdown cleanup.
-_SHUTDOWN_HOOK_TIMEOUT_SECONDS = 5.0
-

 async def _ensure_admin_user(app: FastAPI) -> None:
-    """Startup hook: handle first boot and migrate orphan threads otherwise.
+    """Startup hook: generate init token on first boot; migrate orphan threads otherwise.

    After admin creation, migrate orphan threads from the LangGraph
    store (metadata.user_id unset) to the admin account. This is the
    "no-auth → with-auth" upgrade path: users who ran DeerFlow without
    authentication have existing LangGraph thread data that needs an
    owner assigned.
-        First boot (no admin exists):
-            - Does NOT create any user accounts automatically.
-            - The operator must visit ``/setup`` to create the first admin.
+    First boot (no admin exists):
+      - Generates a one-time ``init_token`` stored in ``app.state.init_token``
+      - Logs the token to stdout so the operator can copy-paste it into the
+        ``/setup`` form to create the first admin account interactively.
+      - Does NOT create any user accounts automatically.

    Subsequent boots (admin already exists):
      - Runs the one-time "no-auth → with-auth" orphan thread migration for
@@ -66,6 +62,8 @@ async def _ensure_admin_user(app: FastAPI) -> None:
    alongside the auth module via create_all, so freshly created tables
    never contain NULL-owner rows.
    """
+    import secrets
+
    from sqlalchemy import select

    from app.gateway.deps import get_local_provider
@@ -76,8 +74,13 @@ async def _ensure_admin_user(app: FastAPI) -> None:
    admin_count = await provider.count_admin_users()

    if admin_count == 0:
+        init_token = secrets.token_urlsafe(32)
+        app.state.init_token = init_token
        logger.info("=" * 60)
        logger.info("  First boot detected — no admin account exists.")
+        logger.info("  Use the one-time token below to create the admin account.")
+        logger.info("  Copy it into the /setup form when prompted.")
+        logger.info("  INIT TOKEN: %s", init_token)
        logger.info("  Visit /setup to complete admin account creation.")
        logger.info("=" * 60)
        return
@@ -151,11 +154,9 @@ 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
    try:
-        # ``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()
+        get_app_config()
        logger.info("Configuration loaded successfully")
    except Exception as e:
        error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -176,26 +177,18 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
        try:
            from app.channels.service import start_channel_service

-            channel_service = await start_channel_service(app.state.config)
+            channel_service = await start_channel_service()
            logger.info("Channel service started: %s", channel_service.get_status())
        except Exception:
            logger.exception("No IM channels configured or channel service failed to start")

        yield

-        # Stop channel service on shutdown (bounded to prevent worker hang)
+        # Stop channel service on shutdown
        try:
            from app.channels.service import stop_channel_service

-            await asyncio.wait_for(
-                stop_channel_service(),
-                timeout=_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
-            )
-        except TimeoutError:
-            logger.warning(
-                "Channel service shutdown exceeded %.1fs; proceeding with worker exit.",
-                _SHUTDOWN_HOOK_TIMEOUT_SECONDS,
-            )
+            await stop_channel_service()
        except Exception:
            logger.exception("Failed to stop channel service")

@@ -372,6 +365,11 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
        """
        return {"status": "healthy", "service": "deer-flow-gateway"}

+    # Ensure init_token always exists on app.state (None until lifespan sets it
+    # if no admin is found).  This prevents AttributeError in tests that don't
+    # run the full lifespan.
+    app.state.init_token = None
+
    return app


@@ -21,6 +21,7 @@ class AuthErrorCode(StrEnum):
    PROVIDER_NOT_FOUND = "provider_not_found"
    NOT_AUTHENTICATED = "not_authenticated"
    SYSTEM_ALREADY_INITIALIZED = "system_already_initialized"
+    INVALID_INIT_TOKEN = "invalid_init_token"


 class TokenError(StrEnum):
@@ -25,15 +25,14 @@ from deerflow.persistence.user.model import UserRow


 async def _run(email: str | None) -> int:
-    from deerflow.config import AppConfig
+    from deerflow.config import get_app_config
    from deerflow.persistence.engine import (
        close_engine,
        get_session_factory,
        init_engine_from_config,
    )

-    # CLI entry: load config explicitly at the top, pass down through the closure.
-    config = AppConfig.from_file()
+    config = get_app_config()
    await init_engine_from_config(config.database)
    try:
        sf = get_session_factory()
@@ -8,14 +8,12 @@ Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.

 from __future__ import annotations

-from collections.abc import AsyncGenerator, Callable
+from collections.abc import AsyncGenerator
 from contextlib import AsyncExitStack, asynccontextmanager
 from typing import TYPE_CHECKING

 from fastapi import FastAPI, HTTPException, Request
-from langgraph.types import Checkpointer

-from deerflow.config.app_config import AppConfig
 from deerflow.runtime import RunContext, RunManager

 if TYPE_CHECKING:
@@ -24,19 +22,6 @@ if TYPE_CHECKING:
    from deerflow.persistence.thread_meta.base import ThreadMetaStore


-def get_config(request: Request) -> AppConfig:
-    """FastAPI dependency returning the app-scoped ``AppConfig``.
-
-    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``).
-    """
-    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) -> AsyncGenerator[None, None]:
    """Bootstrap and tear down all LangGraph runtime singletons.
@@ -46,24 +31,22 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
        async with langgraph_runtime(app):
            yield
    """
+    from deerflow.config import get_app_config
    from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
    from deerflow.runtime import make_store, make_stream_bridge
    from deerflow.runtime.checkpointer.async_provider import make_checkpointer
    from deerflow.runtime.events.store import make_run_event_store

    async with AsyncExitStack() as stack:
-        # 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))
+        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())

        # Initialize persistence engine BEFORE checkpointer so that
        # auto-create-database logic runs first (postgres backend).
+        config = get_app_config()
        await init_engine_from_config(config.database)

-        app.state.checkpointer = await stack.enter_async_context(make_checkpointer(config))
-        app.state.store = await stack.enter_async_context(make_store(config))
+        app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
+        app.state.store = await stack.enter_async_context(make_store())

        # Initialize repositories — one get_session_factory() call for all.
        sf = get_session_factory()
@@ -143,14 +126,14 @@ def get_run_context(request: Request) -> RunContext:
    ``dataclasses.replace(ctx, follow_up_to_run_id=...)`` before passing it
    to :func:`run_agent`.
    """
-    config = get_config(request)
+    from deerflow.config import get_app_config
+
    return RunContext(
        checkpointer=get_checkpointer(request),
        store=get_store(request),
        event_store=get_run_event_store(request),
-        run_events_config=getattr(config, "run_events", None),
+        run_events_config=getattr(get_app_config(), "run_events", None),
        thread_store=get_thread_store(request),
-        app_config=config,
    )


@@ -1,26 +0,0 @@
-"""Process-local authentication for Gateway internal callers."""
-
-from __future__ import annotations
-
-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_TOKEN = secrets.token_urlsafe(32)
-
-
-def create_internal_auth_headers() -> dict[str, str]:
-    """Return headers that authenticate same-process 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 the process-local 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")
@@ -5,12 +5,10 @@ import re
 import shutil

 import yaml
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field

-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

 logger = logging.getLogger(__name__)
@@ -26,7 +24,6 @@ class AgentResponse(BaseModel):
    description: str = Field(default="", description="Agent description")
    model: str | None = Field(default=None, description="Optional model override")
    tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
-    skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all, []=none)")
    soul: str | None = Field(default=None, description="SOUL.md content")


@@ -43,7 +40,6 @@ class AgentCreateRequest(BaseModel):
    description: str = Field(default="", description="Agent description")
    model: str | None = Field(default=None, description="Optional model override")
    tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
-    skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all enabled, []=none)")
    soul: str = Field(default="", description="SOUL.md content — agent personality and behavioral guardrails")


@@ -53,7 +49,6 @@ class AgentUpdateRequest(BaseModel):
    description: str | None = Field(default=None, description="Updated description")
    model: str | None = Field(default=None, description="Updated model override")
    tool_groups: list[str] | None = Field(default=None, description="Updated tool group whitelist")
-    skills: list[str] | None = Field(default=None, description="Updated skill whitelist (None=all, []=none)")
    soul: str | None = Field(default=None, description="Updated SOUL.md content")


@@ -78,15 +73,6 @@ def _normalize_agent_name(name: str) -> str:
    return name.lower()


-def _require_agents_api_enabled(app_config: AppConfig) -> None:
-    """Reject access unless the custom-agent management API is explicitly 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) -> AgentResponse:
    """Convert AgentConfig to AgentResponse."""
    soul: str | None = None
@@ -98,7 +84,6 @@ def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False
        description=agent_cfg.description,
        model=agent_cfg.model,
        tool_groups=agent_cfg.tool_groups,
-        skills=agent_cfg.skills,
        soul=soul,
    )

@@ -109,14 +94,12 @@ 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(app_config: AppConfig = Depends(get_config)) -> AgentsListResponse:
+async def list_agents() -> AgentsListResponse:
    """List all custom agents.

    Returns:
        List of all custom agents with their metadata and soul content.
    """
-    _require_agents_api_enabled(app_config)
-
    try:
        agents = list_custom_agents()
        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
@@ -142,7 +125,6 @@ async def check_agent_name(name: str) -> dict:
    Raises:
        HTTPException: 422 if the name is invalid.
    """
-    _require_agents_api_enabled(app_config)
    _validate_agent_name(name)
    normalized = _normalize_agent_name(name)
    available = not get_paths().agent_dir(normalized).exists()
@@ -155,7 +137,7 @@ 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, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
+async def get_agent(name: str) -> AgentResponse:
    """Get a specific custom agent by name.

    Args:
@@ -167,7 +149,6 @@ async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> A
    Raises:
        HTTPException: 404 if agent not found.
    """
-    _require_agents_api_enabled(app_config)
    _validate_agent_name(name)
    name = _normalize_agent_name(name)

@@ -188,7 +169,7 @@ async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> A
    summary="Create Custom Agent",
    description="Create a new custom agent with its config and SOUL.md.",
 )
-async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
+async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
    """Create a new custom agent.

    Args:
@@ -200,7 +181,6 @@ async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConf
    Raises:
        HTTPException: 409 if agent already exists, 422 if name is invalid.
    """
-    _require_agents_api_enabled(app_config)
    _validate_agent_name(request.name)
    normalized_name = _normalize_agent_name(request.name)

@@ -220,8 +200,6 @@ async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConf
            config_data["model"] = request.model
        if request.tool_groups is not None:
            config_data["tool_groups"] = request.tool_groups
-        if request.skills is not None:
-            config_data["skills"] = request.skills

        config_file = agent_dir / "config.yaml"
        with open(config_file, "w", encoding="utf-8") as f:
@@ -252,7 +230,7 @@ async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConf
    summary="Update Custom Agent",
    description="Update an existing custom agent's config and/or SOUL.md.",
 )
-async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
+async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
    """Update an existing custom agent.

    Args:
@@ -265,7 +243,6 @@ async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppCo
    Raises:
        HTTPException: 404 if agent not found.
    """
-    _require_agents_api_enabled(app_config)
    _validate_agent_name(name)
    name = _normalize_agent_name(name)

@@ -278,32 +255,21 @@ async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppCo

    try:
        # Update config if any config fields changed
-        # Use model_fields_set to distinguish "field omitted" from "explicitly set to null".
-        # This is critical for skills where None means "inherit all" (not "don't change").
-        fields_set = request.model_fields_set
-        config_changed = bool(fields_set & {"description", "model", "tool_groups", "skills"})
+        config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])

        if config_changed:
            updated: dict = {
                "name": agent_cfg.name,
-                "description": request.description if "description" in fields_set else agent_cfg.description,
+                "description": request.description if request.description is not None else agent_cfg.description,
            }
-            new_model = request.model if "model" in fields_set else agent_cfg.model
+            new_model = request.model if request.model is not None else agent_cfg.model
            if new_model is not None:
                updated["model"] = new_model

-            new_tool_groups = request.tool_groups if "tool_groups" in fields_set else agent_cfg.tool_groups
+            new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
            if new_tool_groups is not None:
                updated["tool_groups"] = new_tool_groups

-            # skills: None = inherit all, [] = no skills, ["a","b"] = whitelist
-            if "skills" in fields_set:
-                new_skills = request.skills
-            else:
-                new_skills = agent_cfg.skills
-            if new_skills is not None:
-                updated["skills"] = new_skills
-
            config_file = agent_dir / "config.yaml"
            with open(config_file, "w", encoding="utf-8") as f:
                yaml.dump(updated, f, default_flow_style=False, allow_unicode=True)
@@ -343,14 +309,12 @@ 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(app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
+async def get_user_profile() -> UserProfileResponse:
    """Return the current USER.md content.

    Returns:
        UserProfileResponse with content=None if USER.md does not exist yet.
    """
-    _require_agents_api_enabled(app_config)
-
    try:
        user_md_path = get_paths().user_md_file
        if not user_md_path.exists():
@@ -368,7 +332,7 @@ async def get_user_profile(app_config: AppConfig = Depends(get_config)) -> UserP
    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, app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
+async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileResponse:
    """Create or overwrite the global USER.md.

    Args:
@@ -377,8 +341,6 @@ async def update_user_profile(request: UserProfileUpdateRequest, app_config: App
    Returns:
        UserProfileResponse with the saved content.
    """
-    _require_agents_api_enabled(app_config)
-
    try:
        paths = get_paths()
        paths.base_dir.mkdir(parents=True, exist_ok=True)
@@ -396,7 +358,7 @@ async def update_user_profile(request: UserProfileUpdateRequest, app_config: App
    summary="Delete Custom Agent",
    description="Delete a custom agent and all its files (config, SOUL.md, memory).",
 )
-async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -> None:
+async def delete_agent(name: str) -> None:
    """Delete a custom agent.

    Args:
@@ -405,7 +367,6 @@ async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -
    Raises:
        HTTPException: 404 if agent not found.
    """
-    _require_agents_api_enabled(app_config)
    _validate_agent_name(name)
    name = _normalize_agent_name(name)

@@ -2,6 +2,7 @@

 import logging
 import os
+import secrets
 import time
 from ipaddress import ip_address, ip_network

@@ -388,6 +389,7 @@ class InitializeAdminRequest(BaseModel):

    email: EmailStr
    password: str = Field(..., min_length=8)
+    init_token: str | None = Field(default=None, description="One-time initialization token printed to server logs on first boot")

    _strong_password = field_validator("password")(classmethod(lambda cls, v: _validate_strong_password(v)))

@@ -397,13 +399,31 @@ async def initialize_admin(request: Request, response: Response, body: Initializ
    """Create the first admin account on initial system setup.

    Only callable when no admin exists. Returns 409 Conflict if an admin
-    already exists.
+    already exists. Requires the one-time ``init_token`` that is logged to
+    stdout at startup whenever the system has no admin account.

-    On success, the admin account is created with ``needs_setup=False`` and
-    the session cookie is set.
+    On success the token is consumed (one-time use), the admin account is
+    created with ``needs_setup=False``, and the session cookie is set.
    """
+    # Validate the one-time initialization token.  The token is generated
+    # at startup and stored in app.state.init_token; it is consumed here on
+    # the first successful call so it cannot be replayed.
+    # Using str | None allows a missing/null token to return 403 (not 422),
+    # giving a consistent error response regardless of whether the token is
+    # absent or incorrect.
+    stored_token: str | None = getattr(request.app.state, "init_token", None)
+    provided_token: str = body.init_token or ""
+    if stored_token is None or not secrets.compare_digest(stored_token, provided_token):
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail=AuthErrorResponse(code=AuthErrorCode.INVALID_INIT_TOKEN, message="Invalid or expired initialization token").model_dump(),
+        )
+
    admin_count = await get_local_provider().count_admin_users()
    if admin_count > 0:
+        # Do NOT consume the token on this error path — consuming it here
+        # would allow an attacker to exhaust the token by calling with the
+        # correct token when admin already exists (denial-of-service).
        raise HTTPException(
            status_code=status.HTTP_409_CONFLICT,
            detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
@@ -413,11 +433,16 @@ async def initialize_admin(request: Request, response: Response, body: Initializ
        user = await get_local_provider().create_user(email=body.email, password=body.password, system_role="admin", needs_setup=False)
    except ValueError:
        # DB unique-constraint race: another concurrent request beat us.
+        # Do NOT consume the token here for the same reason as above.
        raise HTTPException(
            status_code=status.HTTP_409_CONFLICT,
            detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
        )

+    # Consume the token only after successful initialization — this is the
+    # single place where one-time use is enforced.
+    request.app.state.init_token = None
+
    token = create_access_token(str(user.id), token_version=user.token_version)
    _set_session_cookie(response, token, request)

@@ -3,12 +3,10 @@ import logging
 from pathlib import Path
 from typing import Literal

-from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field

-from app.gateway.deps import get_config
-from deerflow.config.app_config import AppConfig
-from deerflow.config.extensions_config import ExtensionsConfig
+from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config

 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["mcp"])
@@ -71,7 +69,7 @@ class McpConfigUpdateRequest(BaseModel):
    summary="Get MCP Configuration",
    description="Retrieve the current Model Context Protocol (MCP) server configurations.",
 )
-async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpConfigResponse:
+async def get_mcp_configuration() -> McpConfigResponse:
    """Get the current MCP configuration.

    Returns:
@@ -92,9 +90,9 @@ async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpC
        }
        ```
    """
-    ext = config.extensions
+    config = get_extensions_config()

-    return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in ext.mcp_servers.items()})
+    return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})


@router.put(
@@ -103,11 +101,7 @@ async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpC
    summary="Update MCP Configuration",
    description="Update Model Context Protocol (MCP) server configurations and save to file.",
 )
-async def update_mcp_configuration(
-    request: McpConfigUpdateRequest,
-    http_request: Request,
-    config: AppConfig = Depends(get_config),
-) -> McpConfigResponse:
+async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
    """Update the MCP configuration.

    This will:
@@ -148,13 +142,13 @@ async def update_mcp_configuration(
            config_path = Path.cwd().parent / "extensions_config.json"
            logger.info(f"No existing extensions config found. Creating new config at: {config_path}")

-        # Use injected config to preserve skills configuration
-        current_ext = config.extensions
+        # Load current config to preserve skills configuration
+        current_config = get_extensions_config()

        # Convert request to dict format for JSON serialization
        config_data = {
            "mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
-            "skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
+            "skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
        }

        # Write the configuration to file
@@ -166,11 +160,9 @@ async def update_mcp_configuration(
        # 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()})
+        # Reload the configuration and update the global cache
+        reloaded_config = reload_extensions_config()
+        return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})

    except Exception as e:
        logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
@@ -1,9 +1,8 @@
 """Memory API router for retrieving and managing global memory data."""

-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, 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,
@@ -13,7 +12,7 @@ from deerflow.agents.memory.updater import (
    reload_memory_data,
    update_memory_fact,
 )
-from deerflow.config.app_config import AppConfig
+from deerflow.config.memory_config import get_memory_config
 from deerflow.runtime.user_context import get_effective_user_id

 router = APIRouter(prefix="/api", tags=["memory"])
@@ -115,7 +114,7 @@ class MemoryStatusResponse(BaseModel):
    summary="Get Memory Data",
    description="Retrieve the current global memory data including user context, history, and facts.",
 )
-async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def get_memory() -> MemoryResponse:
    """Get the current global memory data.

    Returns:
@@ -149,7 +148,7 @@ async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRespo
        }
        ```
    """
-    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
+    memory_data = get_memory_data(user_id=get_effective_user_id())
    return MemoryResponse(**memory_data)


@@ -160,7 +159,7 @@ async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRespo
    summary="Reload Memory Data",
    description="Reload memory data from the storage file, refreshing the in-memory cache.",
 )
-async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def reload_memory() -> MemoryResponse:
    """Reload memory data from file.

    This forces a reload of the memory data from the storage file,
@@ -169,7 +168,7 @@ async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
    Returns:
        The reloaded memory data.
    """
-    memory_data = reload_memory_data(app_config.memory, user_id=get_effective_user_id())
+    memory_data = reload_memory_data(user_id=get_effective_user_id())
    return MemoryResponse(**memory_data)


@@ -180,10 +179,10 @@ async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
    summary="Clear All Memory Data",
    description="Delete all saved memory data and reset the memory structure to an empty state.",
 )
-async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def clear_memory() -> MemoryResponse:
    """Clear all persisted memory data."""
    try:
-        memory_data = clear_memory_data(app_config.memory, user_id=get_effective_user_id())
+        memory_data = clear_memory_data(user_id=get_effective_user_id())
    except OSError as exc:
        raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc

@@ -197,11 +196,10 @@ async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRes
    summary="Create Memory Fact",
    description="Create a single saved memory fact manually.",
 )
-async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
    """Create a single fact manually."""
    try:
        memory_data = create_memory_fact(
-            app_config.memory,
            content=request.content,
            category=request.category,
            confidence=request.confidence,
@@ -222,10 +220,10 @@ async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: Ap
    summary="Delete Memory Fact",
    description="Delete a single saved memory fact by its fact id.",
 )
-async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
    """Delete a single fact from memory by fact id."""
    try:
-        memory_data = delete_memory_fact(app_config.memory, fact_id, user_id=get_effective_user_id())
+        memory_data = delete_memory_fact(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:
@@ -241,11 +239,10 @@ async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depe
    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, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -> 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,
@@ -269,9 +266,9 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest, a
    summary="Export Memory Data",
    description="Export the current global memory data as JSON for backup or transfer.",
 )
-async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def export_memory() -> MemoryResponse:
    """Export the current memory data."""
-    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
+    memory_data = get_memory_data(user_id=get_effective_user_id())
    return MemoryResponse(**memory_data)


@@ -282,10 +279,10 @@ async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
    summary="Import Memory Data",
    description="Import and overwrite the current global memory data from a JSON payload.",
 )
-async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
+async def import_memory(request: MemoryResponse) -> MemoryResponse:
    """Import and persist memory data."""
    try:
-        memory_data = import_memory_data(app_config.memory, request.model_dump(), user_id=get_effective_user_id())
+        memory_data = import_memory_data(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

@@ -298,9 +295,7 @@ async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends
    summary="Get Memory Configuration",
    description="Retrieve the current memory system configuration.",
 )
-async def get_memory_config_endpoint(
-    app_config: AppConfig = Depends(get_config),
-) -> MemoryConfigResponse:
+async def get_memory_config_endpoint() -> MemoryConfigResponse:
    """Get the memory system configuration.

    Returns:
@@ -319,7 +314,7 @@ async def get_memory_config_endpoint(
        }
        ```
    """
-    config = app_config.memory
+    config = get_memory_config()
    return MemoryConfigResponse(
        enabled=config.enabled,
        storage_path=config.storage_path,
@@ -338,16 +333,14 @@ async def get_memory_config_endpoint(
    summary="Get Memory Status",
    description="Retrieve both memory configuration and current data in a single request.",
 )
-async def get_memory_status(
-    app_config: AppConfig = Depends(get_config),
-) -> MemoryStatusResponse:
+async def get_memory_status() -> MemoryStatusResponse:
    """Get the memory system status including configuration and data.

    Returns:
        Combined memory configuration and current data.
    """
-    config = app_config.memory
-    memory_data = get_memory_data(config, user_id=get_effective_user_id())
+    config = get_memory_config()
+    memory_data = get_memory_data(user_id=get_effective_user_id())

    return MemoryStatusResponse(
        config=MemoryConfigResponse(
@@ -1,8 +1,7 @@
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field

-from app.gateway.deps import get_config
-from deerflow.config.app_config import AppConfig
+from deerflow.config import get_app_config

 router = APIRouter(prefix="/api", tags=["models"])

@@ -18,17 +17,10 @@ class ModelResponse(BaseModel):
    supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")


-class TokenUsageResponse(BaseModel):
-    """Token usage display configuration."""
-
-    enabled: bool = Field(default=False, description="Whether token usage display is enabled")
-
-
 class ModelsListResponse(BaseModel):
    """Response model for listing all models."""

    models: list[ModelResponse]
-    token_usage: TokenUsageResponse


@router.get(
@@ -37,14 +29,14 @@ class ModelsListResponse(BaseModel):
    summary="List All Models",
    description="Retrieve a list of all available AI models configured in the system.",
 )
-async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResponse:
+async def list_models() -> ModelsListResponse:
    """List all available models from configuration.

    Returns model information suitable for frontend display,
    excluding sensitive fields like API keys and internal configuration.

    Returns:
-        A list of all configured models with their metadata and token usage display settings.
+        A list of all configured models with their metadata.

    Example Response:
        ```json
@@ -52,27 +44,21 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
            "models": [
                {
                    "name": "gpt-4",
-                    "model": "gpt-4",
                    "display_name": "GPT-4",
                    "description": "OpenAI GPT-4 model",
-                    "supports_thinking": false,
-                    "supports_reasoning_effort": false
+                    "supports_thinking": false
                },
                {
                    "name": "claude-3-opus",
-                    "model": "claude-3-opus",
                    "display_name": "Claude 3 Opus",
                    "description": "Anthropic Claude 3 Opus model",
-                    "supports_thinking": true,
-                    "supports_reasoning_effort": false
+                    "supports_thinking": true
                }
-            ],
-            "token_usage": {
-                "enabled": true
-            }
+            ]
        }
        ```
    """
+    config = get_app_config()
    models = [
        ModelResponse(
            name=model.name,
@@ -84,10 +70,7 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
        )
        for model in config.models
    ]
-    return ModelsListResponse(
-        models=models,
-        token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
-    )
+    return ModelsListResponse(models=models)


@router.get(
@@ -96,7 +79,7 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
    summary="Get Model Details",
    description="Retrieve detailed information about a specific AI model by its name.",
 )
-async def get_model(model_name: str, config: AppConfig = Depends(get_config)) -> ModelResponse:
+async def get_model(model_name: str) -> ModelResponse:
    """Get a specific model by name.

    Args:
@@ -118,6 +101,7 @@ async def get_model(model_name: str, config: AppConfig = Depends(get_config)) ->
        }
        ```
    """
+    config = get_app_config()
    model = config.get_model_config(model_name)
    if model is None:
        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
@@ -1,17 +1,14 @@
-import errno
 import json
 import logging
 import shutil
 from pathlib import Path

-from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi import APIRouter, HTTPException
 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
+from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
 from deerflow.skills import Skill, load_skills
 from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
 from deerflow.skills.manager import (
@@ -103,9 +100,9 @@ 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(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
+async def list_skills() -> SkillsListResponse:
    try:
-        skills = load_skills(app_config, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
    except Exception as e:
        logger.error(f"Failed to load skills: {e}", exc_info=True)
@@ -118,11 +115,11 @@ async def list_skills(app_config: AppConfig = Depends(get_config)) -> SkillsList
    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, app_config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
+async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
    try:
        skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
        result = install_skill_from_archive(skill_file_path)
-        await refresh_skills_system_prompt_cache_async(app_config)
+        await refresh_skills_system_prompt_cache_async()
        return SkillInstallResponse(**result)
    except FileNotFoundError as e:
        raise HTTPException(status_code=404, detail=str(e))
@@ -138,9 +135,9 @@ async def install_skill(request: SkillInstallRequest, app_config: AppConfig = De


@router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
-async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
+async def list_custom_skills() -> SkillsListResponse:
    try:
-        skills = [skill for skill in load_skills(app_config, enabled_only=False) if skill.category == "custom"]
+        skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
        return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
    except Exception as e:
        logger.error("Failed to list custom skills: %s", e, exc_info=True)
@@ -148,13 +145,13 @@ async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> Ski


@router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
-async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
+async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
    try:
-        skills = load_skills(app_config, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
        if skill is None:
            raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name, app_config))
+        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
    except HTTPException:
        raise
    except Exception as e:
@@ -163,18 +160,14 @@ async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_


@router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
-async def update_custom_skill(
-    skill_name: str,
-    request: CustomSkillUpdateRequest,
-    app_config: AppConfig = Depends(get_config),
-) -> CustomSkillContentResponse:
+async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
    try:
-        ensure_custom_skill_is_editable(skill_name, app_config)
+        ensure_custom_skill_is_editable(skill_name)
        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")
+        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
        if scan.decision == "block":
            raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
-        skill_file = get_custom_skill_dir(skill_name, app_config) / "SKILL.md"
+        skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
        prev_content = skill_file.read_text(encoding="utf-8")
        atomic_write(skill_file, request.content)
        append_history(
@@ -188,10 +181,9 @@ async def update_custom_skill(
                "new_content": request.content,
                "scanner": {"decision": scan.decision, "reason": scan.reason},
            },
-            app_config,
        )
-        await refresh_skills_system_prompt_cache_async(app_config)
-        return await get_custom_skill(skill_name, app_config)
+        await refresh_skills_system_prompt_cache_async()
+        return await get_custom_skill(skill_name)
    except HTTPException:
        raise
    except FileNotFoundError as e:
@@ -204,31 +196,25 @@ async def update_custom_skill(


@router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
-async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> dict[str, bool]:
+async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
    try:
-        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)
+        ensure_custom_skill_is_editable(skill_name)
+        skill_dir = get_custom_skill_dir(skill_name)
+        prev_content = read_custom_skill_content(skill_name)
+        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."},
+            },
+        )
        shutil.rmtree(skill_dir)
-        await refresh_skills_system_prompt_cache_async(app_config)
+        await refresh_skills_system_prompt_cache_async()
        return {"success": True}
    except FileNotFoundError as e:
        raise HTTPException(status_code=404, detail=str(e))
@@ -240,11 +226,11 @@ async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(g


@router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
-async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
+async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
    try:
-        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
+        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
            raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillHistoryResponse(history=read_history(skill_name, app_config))
+        return CustomSkillHistoryResponse(history=read_history(skill_name))
    except HTTPException:
        raise
    except Exception as e:
@@ -253,15 +239,11 @@ async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depe


@router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
-async def rollback_custom_skill(
-    skill_name: str,
-    request: SkillRollbackRequest,
-    app_config: AppConfig = Depends(get_config),
-) -> CustomSkillContentResponse:
+async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
    try:
-        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
+        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
            raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        history = read_history(skill_name, app_config)
+        history = read_history(skill_name)
        if not history:
            raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
        record = history[request.history_index]
@@ -269,8 +251,8 @@ async def rollback_custom_skill(
        if target_content is None:
            raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
        validate_skill_markdown_content(skill_name, target_content)
-        scan = await scan_skill_content(app_config, target_content, executable=False, location=f"{skill_name}/SKILL.md")
-        skill_file = get_custom_skill_file(skill_name, app_config)
+        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
+        skill_file = get_custom_skill_file(skill_name)
        current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
        history_entry = {
            "action": "rollback",
@@ -283,12 +265,12 @@ async def rollback_custom_skill(
            "scanner": {"decision": scan.decision, "reason": scan.reason},
        }
        if scan.decision == "block":
-            append_history(skill_name, history_entry, app_config)
+            append_history(skill_name, history_entry)
            raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
        atomic_write(skill_file, target_content)
-        append_history(skill_name, history_entry, app_config)
-        await refresh_skills_system_prompt_cache_async(app_config)
-        return await get_custom_skill(skill_name, app_config)
+        append_history(skill_name, history_entry)
+        await refresh_skills_system_prompt_cache_async()
+        return await get_custom_skill(skill_name)
    except HTTPException:
        raise
    except IndexError:
@@ -308,9 +290,9 @@ async def rollback_custom_skill(
    summary="Get Skill Details",
    description="Retrieve detailed information about a specific skill by its name.",
 )
-async def get_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> SkillResponse:
+async def get_skill(skill_name: str) -> SkillResponse:
    try:
-        skills = load_skills(app_config, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        skill = next((s for s in skills if s.name == skill_name), None)

        if skill is None:
@@ -330,14 +312,9 @@ async def get_skill(skill_name: str, app_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,
-    http_request: Request,
-    app_config: AppConfig = Depends(get_config),
-) -> SkillResponse:
+async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
    try:
-        skills = load_skills(app_config, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        skill = next((s for s in skills if s.name == skill_name), None)

        if skill is None:
@@ -348,29 +325,22 @@ async def update_skill(
            config_path = Path.cwd().parent / "extensions_config.json"
            logger.info(f"No existing extensions config found. Creating new config at: {config_path}")

-        # 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}
+        extensions_config = get_extensions_config()
+        extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)

        config_data = {
-            "mcpServers": {name: server.model_dump() for name, server in ext.mcp_servers.items()},
-            "skills": updated_skills,
+            "mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
+            "skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
        }

        with open(config_path, "w", encoding="utf-8") as f:
            json.dump(config_data, f, indent=2)

        logger.info(f"Skills configuration updated and saved to: {config_path}")
-        # Reload 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)
+        reload_extensions_config()
+        await refresh_skills_system_prompt_cache_async()

-        skills = load_skills(reloaded, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        updated_skill = next((s for s in skills if s.name == skill_name), None)

        if updated_skill is None:
@@ -1,13 +1,11 @@
 import json
 import logging

-from fastapi import APIRouter, Depends, Request
+from fastapi import APIRouter, Request
 from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import BaseModel, Field

 from app.gateway.authz import require_permission
-from app.gateway.deps import get_config
-from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model

 logger = logging.getLogger(__name__)
@@ -102,7 +100,7 @@ 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, app_config: AppConfig = Depends(get_config)) -> SuggestionsResponse:
+async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request) -> SuggestionsResponse:
    if not body.messages:
        return SuggestionsResponse(suggestions=[])

@@ -124,8 +122,8 @@ async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request
    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=app_config)
-        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
+        model = create_chat_model(name=body.model_name, thinking_enabled=False)
+        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
        raw = _extract_response_text(response.content)
        suggestions = _parse_json_string_list(raw) or []
        cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]
@@ -22,7 +22,7 @@ from fastapi import APIRouter, HTTPException, Request
 from pydantic import BaseModel, Field, field_validator

 from app.gateway.authz import require_permission
-from app.gateway.deps import get_checkpointer
+from app.gateway.deps import get_checkpointer, get_current_user, get_feedback_repo, get_run_event_store
 from app.gateway.utils import sanitize_log_param
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime import serialize_channel_values
@@ -405,6 +405,164 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:


 # ---------------------------------------------------------------------------
+# Event-store-backed message loader
+# ---------------------------------------------------------------------------
+
+_LEGACY_CMD_INNER_CONTENT_RE = re.compile(
+    r"ToolMessage\(content=(?P<q>['\"])(?P<inner>.*?)(?P=q)",
+    re.DOTALL,
+)
+
+
+def _sanitize_legacy_command_repr(content_field: Any) -> Any:
+    """Recover the inner ToolMessage text from a legacy ``str(Command(...))`` repr.
+
+    Runs captured before the ``on_tool_end`` fix in ``journal.py`` stored
+    ``str(Command(update={'messages':[ToolMessage(content='X', ...)]}))`` as the
+    tool_result content. New runs store ``'X'`` directly. For legacy rows, try
+    to extract ``'X'`` defensively; return the original string if extraction
+    fails (still no worse than the checkpoint fallback for summarized threads).
+    """
+    if not isinstance(content_field, str) or not content_field.startswith("Command(update="):
+        return content_field
+    match = _LEGACY_CMD_INNER_CONTENT_RE.search(content_field)
+    return match.group("inner") if match else content_field
+
+
+async def _get_event_store_messages(request: Request, thread_id: str) -> list[dict] | None:
+    """Load the full message stream for ``thread_id`` from the event store.
+
+    The event store is append-only and unaffected by summarization — the
+    checkpoint's ``channel_values["messages"]`` is rewritten in-place when the
+    SummarizationMiddleware runs, which drops all pre-summarize messages. The
+    event store retains the full transcript, so callers in Gateway mode should
+    prefer it for rendering the conversation history.
+
+    In addition to the core message content, this helper attaches two extra
+    fields to every returned dict:
+
+    - ``run_id``: the ``run_id`` of the event that produced this message.
+      Always present.
+    - ``feedback``: thumbs-up/down data. Present only on the **final
+      ``ai_message`` of each run** (matching the per-run feedback semantics
+      of ``POST /api/threads/{id}/runs/{run_id}/feedback``). The frontend uses
+      the presence of this field to decide whether to render the feedback
+      button, which sidesteps the positional-index mapping bug that an
+      out-of-band ``/messages`` fetch exhibited.
+
+    Behaviour contract:
+
+    - **Full pagination.** ``RunEventStore.list_messages`` returns the newest
+      ``limit`` records when no cursor is given, so a fixed limit silently
+      drops older messages on long threads. We size the read from
+      ``count_messages()`` and then page forward with ``after_seq`` cursors.
+    - **Copy-on-read.** Each content dict is copied before ``id`` is patched
+      so the live store object is never mutated; ``MemoryRunEventStore``
+      returns live references.
+    - **Stable ids.** Messages with ``id=None`` (human + tool_result) receive
+      a deterministic ``uuid5(NAMESPACE_URL, f"{thread_id}:{seq}")`` so React
+      keys are stable across requests without altering stored data. AI messages
+      retain their LLM-assigned ``lc_run--*`` ids.
+    - **Legacy Command repr.** Rows captured before the ``journal.py``
+      ``on_tool_end`` fix stored ``str(Command(update={...}))`` as the tool
+      result content. ``_sanitize_legacy_command_repr`` extracts the inner
+      ToolMessage text.
+    - **User context.** ``DbRunEventStore`` is user-scoped by default via
+      ``resolve_user_id(AUTO)`` in ``runtime/user_context.py``. This helper
+      must run inside a request where ``@require_permission`` has populated
+      the user contextvar. Both callers below are decorated appropriately.
+      Do not call this helper from CLI or migration scripts without passing
+      ``user_id=None`` explicitly to the underlying store methods.
+
+    Returns ``None`` when the event store is not configured or has no message
+    events for this thread, so callers fall back to checkpoint messages.
+    """
+    try:
+        event_store = get_run_event_store(request)
+    except Exception:
+        return None
+
+    try:
+        total = await event_store.count_messages(thread_id)
+    except Exception:
+        logger.exception("count_messages failed for thread %s", sanitize_log_param(thread_id))
+        return None
+    if not total:
+        return None
+
+    # Batch by page_size to keep memory bounded for very long threads.
+    page_size = 500
+    collected: list[dict] = []
+    after_seq: int | None = None
+    while True:
+        try:
+            page = await event_store.list_messages(thread_id, limit=page_size, after_seq=after_seq)
+        except Exception:
+            logger.exception("list_messages failed for thread %s", sanitize_log_param(thread_id))
+            return None
+        if not page:
+            break
+        collected.extend(page)
+        if len(page) < page_size:
+            break
+        next_cursor = page[-1].get("seq")
+        if next_cursor is None or (after_seq is not None and next_cursor <= after_seq):
+            break
+        after_seq = next_cursor
+
+    # Build the message list; track the final ``ai_message`` index per run so
+    # feedback can be attached at the right position (matches thread_runs.py).
+    messages: list[dict] = []
+    last_ai_per_run: dict[str, int] = {}
+    for evt in collected:
+        raw = evt.get("content")
+        if not isinstance(raw, dict) or "type" not in raw:
+            continue
+        content = dict(raw)
+        if content.get("id") is None:
+            content["id"] = str(uuid.uuid5(uuid.NAMESPACE_URL, f"{thread_id}:{evt['seq']}"))
+        if content.get("type") == "tool":
+            content["content"] = _sanitize_legacy_command_repr(content.get("content"))
+        run_id = evt.get("run_id")
+        if run_id:
+            content["run_id"] = run_id
+        if evt.get("event_type") == "ai_message" and run_id:
+            last_ai_per_run[run_id] = len(messages)
+        messages.append(content)
+
+    if not messages:
+        return None
+
+    # Attach feedback to the final ai_message of each run. If the feedback
+    # subsystem is unavailable, leave the ``feedback`` field absent entirely
+    # so the frontend hides the button rather than showing it over a broken
+    # write path.
+    feedback_available = False
+    feedback_map: dict[str, dict] = {}
+    try:
+        feedback_repo = get_feedback_repo(request)
+        user_id = await get_current_user(request)
+        feedback_map = await feedback_repo.list_by_thread_grouped(thread_id, user_id=user_id)
+        feedback_available = True
+    except Exception:
+        logger.exception("feedback lookup failed for thread %s", sanitize_log_param(thread_id))
+
+    if feedback_available:
+        for run_id, idx in last_ai_per_run.items():
+            fb = feedback_map.get(run_id)
+            messages[idx]["feedback"] = (
+                {
+                    "feedback_id": fb["feedback_id"],
+                    "rating": fb["rating"],
+                    "comment": fb.get("comment"),
+                }
+                if fb
+                else None
+            )
+
+    return messages
+
+
@router.get("/{thread_id}/state", response_model=ThreadStateResponse)
@require_permission("threads", "read", owner_check=True)
 async def get_thread_state(thread_id: str, request: Request) -> ThreadStateResponse:
@@ -445,6 +603,11 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo

    values = serialize_channel_values(channel_values)

+    # Prefer event-store messages: append-only, immune to summarization.
+    es_messages = await _get_event_store_messages(request, thread_id)
+    if es_messages is not None:
+        values["messages"] = es_messages
+
    return ThreadStateResponse(
        values=values,
        next=next_tasks,
@@ -564,6 +727,11 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
    if body.before:
        config["configurable"]["checkpoint_id"] = body.before

+    # Load the full event-store message stream once; attach to the latest
+    # checkpoint entry only (matching the prior semantics). The event store
+    # is append-only and immune to summarization.
+    es_messages = await _get_event_store_messages(request, thread_id)
+
    entries: list[HistoryEntry] = []
    is_latest_checkpoint = True
    try:
@@ -587,11 +755,17 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
            if thread_data := channel_values.get("thread_data"):
                values["thread_data"] = thread_data

-            # Attach messages only to the latest checkpoint entry.
+            # Attach messages only to the latest checkpoint. Prefer the
+            # event-store stream (complete and unaffected by summarization);
+            # fall back to checkpoint channel_values when the event store is
+            # unavailable or empty.
            if is_latest_checkpoint:
-                messages = channel_values.get("messages")
-                if messages:
-                    values["messages"] = serialize_channel_values({"messages": messages}).get("messages", [])
+                if es_messages is not None:
+                    values["messages"] = es_messages
+                else:
+                    messages = channel_values.get("messages")
+                    if messages:
+                        values["messages"] = serialize_channel_values({"messages": messages}).get("messages", [])
            is_latest_checkpoint = False

            # Derive next tasks
@@ -4,15 +4,13 @@ import logging
 import os
 import stat

-from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
+from fastapi import APIRouter, File, HTTPException, Request, UploadFile
 from pydantic import BaseModel

 from app.gateway.authz import require_permission
-from app.gateway.deps import get_config
-from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
 from deerflow.runtime.user_context import get_effective_user_id
-from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
+from deerflow.sandbox.sandbox_provider import get_sandbox_provider
 from deerflow.uploads.manager import (
    PathTraversalError,
    delete_file_safe,
@@ -57,40 +55,12 @@ def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
    os.chmod(file_path, writable_mode, **chmod_kwargs)


-def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
-    return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
-
-
-def _get_uploads_config_value(app_config: AppConfig, key: str, default: object) -> object:
-    """Read a value from the uploads config, supporting dict and attribute access."""
-    uploads_cfg = getattr(app_config, "uploads", None)
-    if isinstance(uploads_cfg, dict):
-        return uploads_cfg.get(key, default)
-    return getattr(uploads_cfg, key, default)
-
-
-def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
-    """Return whether automatic host-side document conversion is enabled.
-
-    The secure default is disabled unless an operator explicitly opts in via
-    uploads.auto_convert_documents in config.yaml.
-    """
-    try:
-        raw = _get_uploads_config_value(app_config, "auto_convert_documents", False)
-        if isinstance(raw, str):
-            return raw.strip().lower() in {"1", "true", "yes", "on"}
-        return bool(raw)
-    except Exception:
-        return False
-
-
@router.post("", response_model=UploadResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
 async def upload_files(
    thread_id: str,
    request: Request,
    files: list[UploadFile] = File(...),
-    app_config: AppConfig = Depends(get_config),
 ) -> UploadResponse:
    """Upload multiple files to a thread's uploads directory."""
    if not files:
@@ -103,13 +73,9 @@ async def upload_files(
    sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
    uploaded_files = []

-    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)
-    auto_convert_documents = _auto_convert_documents_enabled(app_config)
+    sandbox_provider = get_sandbox_provider()
+    sandbox_id = sandbox_provider.acquire(thread_id)
+    sandbox = sandbox_provider.get(sandbox_id)

    for file in files:
        if not file.filename:
@@ -128,7 +94,7 @@ async def upload_files(

            virtual_path = upload_virtual_path(safe_filename)

-            if sync_to_sandbox and sandbox is not None:
+            if sandbox_id != "local":
                _make_file_sandbox_writable(file_path)
                sandbox.update_file(virtual_path, content)

@@ -143,12 +109,12 @@ async def upload_files(
            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:
+            if file_ext in CONVERTIBLE_EXTENSIONS:
                md_path = await convert_file_to_markdown(file_path)
                if md_path:
                    md_virtual_path = upload_virtual_path(md_path.name)

-                    if sync_to_sandbox and sandbox is not None:
+                    if sandbox_id != "local":
                        _make_file_sandbox_writable(md_path)
                        sandbox.update_file(md_virtual_path, md_path.read_bytes())

@@ -12,7 +12,6 @@ import dataclasses
 import json
 import logging
 import re
-from collections.abc import Mapping
 from typing import Any

 from fastapi import HTTPException, Request
@@ -103,10 +102,9 @@ def resolve_agent_factory(assistant_id: str | None):
    """Resolve the agent factory callable from config.

    Custom agents are implemented as ``lead_agent`` + an ``agent_name``
-    injected into ``configurable`` or ``context`` — see
-    :func:`build_run_config`.  All ``assistant_id`` values therefore map to the
-    same factory; the routing happens inside ``make_lead_agent`` when it reads
-    ``cfg["agent_name"]``.
+    injected into ``configurable`` — see :func:`build_run_config`.  All
+    ``assistant_id`` values therefore map to the same factory; the routing
+    happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
    """
    from deerflow.agents.lead_agent.agent import make_lead_agent

@@ -123,12 +121,10 @@ def build_run_config(
    """Build a RunnableConfig dict for the agent.

    When *assistant_id* refers to a custom agent (anything other than
-    ``"lead_agent"`` / ``None``), the name is forwarded as ``agent_name`` in
-    whichever runtime options container is active: ``context`` for
-    LangGraph >= 0.6.0 requests, otherwise ``configurable``.
-    ``make_lead_agent`` reads this key to load the matching
-    ``agents/<name>/SOUL.md`` and per-agent config — without it the agent
-    silently runs as the default lead agent.
+    ``"lead_agent"`` / ``None``), the name is forwarded as
+    ``configurable["agent_name"]``.  ``make_lead_agent`` reads this key to
+    load the matching ``agents/<name>/SOUL.md`` and per-agent config —
+    without it the agent silently runs as the default lead agent.

    This mirrors the channel manager's ``_resolve_run_params`` logic so that
    the LangGraph Platform-compatible HTTP API and the IM channel path behave
@@ -147,14 +143,7 @@ def build_run_config(
                    thread_id,
                    list(request_config.get("configurable", {}).keys()),
                )
-            context_value = request_config["context"]
-            if context_value is None:
-                context = {}
-            elif isinstance(context_value, Mapping):
-                context = dict(context_value)
-            else:
-                raise ValueError("request config 'context' must be a mapping or null.")
-            config["context"] = context
+            config["context"] = request_config["context"]
        else:
            configurable = {"thread_id": thread_id}
            configurable.update(request_config.get("configurable", {}))
@@ -166,19 +155,13 @@ def build_run_config(
        config["configurable"] = {"thread_id": thread_id}

    # Inject custom agent name when the caller specified a non-default assistant.
-    # Honour an explicit agent_name in the active runtime options container.
-    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID:
-        normalized = assistant_id.strip().lower().replace("_", "-")
-        if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
-            raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
-        if "configurable" in config:
-            target = config["configurable"]
-        elif "context" in config:
-            target = config["context"]
-        else:
-            target = config.setdefault("configurable", {})
-        if target is not None and "agent_name" not in target:
-            target["agent_name"] = normalized
+    # Honour an explicit configurable["agent_name"] in the request if already set.
+    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
+        if "agent_name" not in config["configurable"]:
+            normalized = assistant_id.strip().lower().replace("_", "-")
+            if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
+                raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
+            config["configurable"]["agent_name"] = normalized
    if metadata:
        config.setdefault("metadata", {}).update(metadata)
    return config
@@ -276,8 +259,6 @@ async def start_run(
            "is_plan_mode",
            "subagent_enabled",
            "max_concurrent_subagents",
-            "agent_name",
-            "is_bootstrap",
        }
        configurable = config.setdefault("configurable", {})
        for key in _CONTEXT_CONFIGURABLE_KEYS:
@@ -19,78 +19,24 @@ import asyncio
 import logging

 from dotenv import load_dotenv
+from langchain_core.messages import HumanMessage

-try:
-    from prompt_toolkit import PromptSession
-    from prompt_toolkit.history import InMemoryHistory
-
-    _HAS_PROMPT_TOOLKIT = True
-except ImportError:
-    _HAS_PROMPT_TOOLKIT = False
+from deerflow.agents import make_lead_agent

 load_dotenv()

-_LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-_LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
-
-
-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)
-
-
-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(level)
-
-    file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
-    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)
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)


 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("info")
-
-    from deerflow.config import get_app_config
-
-    app_config = get_app_config()
-    _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
-    # background skill-loader thread on import) emit logs to debug.log instead
-    # of leaking onto the interactive terminal via Python's lastResort handler.
-    from langchain_core.messages import HumanMessage
-    from langgraph.runtime import Runtime
-
-    from deerflow.agents import make_lead_agent
-    from deerflow.mcp import initialize_mcp_tools
-
    # Initialize MCP tools at startup
    try:
+        from deerflow.mcp import initialize_mcp_tools
+
        await initialize_mcp_tools()
    except Exception as e:
        print(f"Warning: Failed to initialize MCP tools: {e}")
@@ -106,27 +52,16 @@ async def main():
        }
    }

-    runtime = Runtime(context={"thread_id": config["configurable"]["thread_id"]})
-    config["configurable"]["__pregel_runtime"] = runtime
-
    agent = make_lead_agent(config)

-    session = PromptSession(history=InMemoryHistory()) if _HAS_PROMPT_TOOLKIT else None
-
    print("=" * 50)
    print("Lead Agent Debug Mode")
    print("Type 'quit' or 'exit' to stop")
-    print(f"Logs: debug.log (log_level={app_config.log_level})")
-    if not _HAS_PROMPT_TOOLKIT:
-        print("Tip: `uv sync --group dev` to enable arrow-key & history support")
    print("=" * 50)

    while True:
        try:
-            if session:
-                user_input = (await session.prompt_async("\nYou: ")).strip()
-            else:
-                user_input = input("\nYou: ").strip()
+            user_input = input("\nYou: ").strip()
            if not user_input:
                continue
            if user_input.lower() in ("quit", "exit"):
@@ -135,15 +70,15 @@ async def main():

            # Invoke the agent
            state = {"messages": [HumanMessage(content=user_input)]}
-            result = await agent.ainvoke(state, config=config)
+            result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})

            # Print the response
            if result.get("messages"):
                last_message = result["messages"][-1]
                print(f"\nAgent: {last_message.content}")

-        except (KeyboardInterrupt, EOFError):
-            print("\nGoodbye!")
+        except KeyboardInterrupt:
+            print("\nInterrupted. Goodbye!")
            break
        except Exception as e:
            print(f"\nError: {e}")
@@ -199,7 +199,7 @@ class ThreadState(AgentState):
 │   Built-in Tools    │  │  Configured Tools   │  │     MCP Tools       │
 │  (packages/harness/deerflow/tools/)       │  │  (config.yaml)      │  │  (extensions.json)  │
 ├─────────────────────┤  ├─────────────────────┤  ├─────────────────────┤
-│ - present_files     │  │ - web_search        │  │ - github            │
+│ - present_file      │  │ - web_search        │  │ - github            │
 │ - ask_clarification │  │ - web_fetch         │  │ - filesystem        │
 │ - view_image        │  │ - bash              │  │ - postgres          │
 │                     │  │ - read_file         │  │ - brave-search      │
@@ -2,12 +2,12 @@

 ## 概述

-DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并可选地将 Office 文档和 PDF 转换为 Markdown 格式。
+DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并自动将 Office 文档和 PDF 转换为 Markdown 格式。

 ## 功能特性

 - ✅ 支持多文件同时上传
- ✅ 可选地转换文档为 Markdown（PDF、PPT、Excel、Word）
+- ✅ 自动转换文档为 Markdown（PDF、PPT、Excel、Word）
 - ✅ 文件存储在线程隔离的目录中
 - ✅ Agent 自动感知已上传的文件
 - ✅ 支持文件列表查询和删除
@@ -86,7 +86,7 @@ DELETE /api/threads/{thread_id}/uploads/{filename}

 ## 支持的文档格式

-以下格式在显式启用 `uploads.auto_convert_documents: true` 时会自动转换为 Markdown：
+以下格式会自动转换为 Markdown：
 - PDF (`.pdf`)
 - PowerPoint (`.ppt`, `.pptx`)
 - Excel (`.xls`, `.xlsx`)
@@ -94,8 +94,6 @@ DELETE /api/threads/{thread_id}/uploads/{filename}

 转换后的 Markdown 文件会保存在同一目录下，文件名为原文件名 + `.md` 扩展名。

-默认情况下，自动转换是关闭的，以避免在网关主机上对不受信任的 Office/PDF 上传执行解析。只有在受信任部署中明确接受此风险时，才应将 `uploads.auto_convert_documents` 设置为 `true`。
-
 ## Agent 集成

 ### 自动文件列举
@@ -209,7 +207,6 @@ backend/.deer-flow/threads/
 - 最大文件大小：100MB（可在 nginx.conf 中配置 `client_max_body_size`）
 - 文件名安全性：系统会自动验证文件路径，防止目录遍历攻击
 - 线程隔离：每个线程的上传文件相互隔离，无法跨线程访问
- 自动文档转换默认关闭；如需启用，需在 `config.yaml` 中显式设置 `uploads.auto_convert_documents: true`

 ## 技术实现

@@ -296,7 +296,7 @@ These are the tool names your provider will see in `request.tool_name`:
 | `web_search` | Web search query |
 | `web_fetch` | Fetch URL content |
 | `image_search` | Image search |
-| `present_files` | Present file to user |
+| `present_file` | Present file to user |
 | `view_image` | Display image |
 | `ask_clarification` | Ask user a question |
 | `task` | Delegate to subagent |
@@ -277,7 +277,7 @@ LangGraph Server 只需要 harness 包。`langgraph.json` 更新：
    "lead_agent": "deerflow.agents:make_lead_agent"
  },
  "checkpointer": {
-    "path": "./packages/harness/deerflow/runtime/checkpointer/async_provider.py:make_checkpointer"
+    "path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
  }
 }
 ```
@@ -45,41 +45,6 @@ Example:
 }
 ```

-## Custom Tool Interceptors
-
-You can register custom interceptors that run before every MCP tool call. This is useful for injecting per-request headers (e.g., user auth tokens from the LangGraph execution context), logging, or metrics.
-
-Declare interceptors in `extensions_config.json` using the `mcpInterceptors` field:
-
-```json
-{
-  "mcpInterceptors": [
-    "my_package.mcp.auth:build_auth_interceptor"
-  ],
-  "mcpServers": { ... }
-}
-```
-
-Each entry is a Python import path in `module:variable` format (resolved via `resolve_variable`). The variable must be a **no-arg builder function** that returns an async interceptor compatible with `MultiServerMCPClient`’s `tool_interceptors` interface, or `None` to skip.
-
-Example interceptor that injects auth headers from LangGraph metadata:
-
-```python
-def build_auth_interceptor():
-    async def interceptor(request, handler):
-        from langgraph.config import get_config
-        metadata = get_config().get("metadata", {})
-        headers = dict(request.headers or {})
-        if token := metadata.get("auth_token"):
-            headers["X-Auth-Token"] = token
-        return await handler(request.override(headers=headers))
-    return interceptor
-```
-
- A single string value is accepted and normalized to a one-element list.
- Invalid paths or builder failures are logged as warnings without blocking other interceptors.
- The builder return value must be `callable`; non-callable values are skipped with a warning.
-
 ## How It Works

 MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.
@@ -11,7 +11,6 @@
 - [x] Add Plan Mode with TodoList middleware
 - [x] Add vision model support with ViewImageMiddleware
 - [x] Skills system with SKILL.md format
- [x] Replace `time.sleep(5)` with `asyncio.sleep()` in `packages/harness/deerflow/tools/builtins/task_tool.py` (subagent polling)

 ## Planned Features

@@ -22,9 +21,10 @@
 - [ ] Support for more document formats in upload
 - [ ] Skill marketplace / remote skill installation
 - [ ] Optimize async concurrency in agent hot path (IM channels multi-task scenario)
- [ ] Replace `subprocess.run()` with `asyncio.create_subprocess_shell()` in `packages/harness/deerflow/sandbox/local/local_sandbox.py`
+  - Replace `time.sleep(5)` with `asyncio.sleep()` in `packages/harness/deerflow/tools/builtins/task_tool.py` (subagent polling)
+  - Replace `subprocess.run()` with `asyncio.create_subprocess_shell()` in `packages/harness/deerflow/sandbox/local/local_sandbox.py`
  - 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
+  - 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: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)

@@ -41,13 +41,6 @@ summarization:

  # Custom summary prompt (optional)
  summary_prompt: null
-
-  # Tool names treated as skill file reads for skill rescue
-  skill_file_read_tool_names:
-    - read_file
-    - read
-    - view
-    - cat
 ```

 ### Configuration Options
@@ -132,26 +125,6 @@ keep:
 - **Default**: `null` (uses LangChain's default prompt)
 - **Description**: Custom prompt template for generating summaries. The prompt should guide the model to extract the most important context.

-#### `preserve_recent_skill_count`
- **Type**: Integer (≥ 0)
- **Default**: `5`
- **Description**: Number of most-recently-loaded skill files (tool results whose tool name is in `skill_file_read_tool_names` and whose target path is under `skills.container_path`, e.g. `/mnt/skills/...`) that are rescued from summarization. Prevents the agent from losing skill instructions after compression. Set to `0` to disable skill rescue entirely.
-
-#### `preserve_recent_skill_tokens`
- **Type**: Integer (≥ 0)
- **Default**: `25000`
- **Description**: Total token budget reserved for rescued skill reads. Once this budget is exhausted, older skill bundles are allowed to be summarized.
-
-#### `preserve_recent_skill_tokens_per_skill`
- **Type**: Integer (≥ 0)
- **Default**: `5000`
- **Description**: Per-skill token cap. Any individual skill read whose tool result exceeds this size is not rescued (it falls through to the summarizer like ordinary content).
-
-#### `skill_file_read_tool_names`
- **Type**: List of strings
- **Default**: `["read_file", "read", "view", "cat"]`
- **Description**: Tool names treated as skill file reads during summarization rescue. A tool call is only eligible for skill rescue when its name appears in this list and its target path is under `skills.container_path`.
-
 **Default Prompt Behavior:**
 The default LangChain prompt instructs the model to:
 - Extract highest quality/most relevant context
@@ -174,7 +147,6 @@ The default LangChain prompt instructs the model to:
   - A single summary message is added
   - Recent messages are preserved
 6. **AI/Tool Pair Protection**: The system ensures AI messages and their corresponding tool messages stay together
-7. **Skill Rescue**: Before the summary is generated, the most recently loaded skill files (tool results whose tool name is in `skill_file_read_tool_names` and whose target path is under `skills.container_path`) are lifted out of the summarization set and prepended to the preserved tail. Selection walks newest-first under three budgets: `preserve_recent_skill_count`, `preserve_recent_skill_tokens`, and `preserve_recent_skill_tokens_per_skill`. The triggering AIMessage and all of its paired ToolMessages move together so tool_call ↔ tool_result pairing stays intact.

 ### Token Counting

@@ -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"
  }
 }
@@ -1,42 +1,31 @@
 import logging

 from langchain.agents import create_agent
-from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware import AgentMiddleware, SummarizationMiddleware
 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.subagent_limit_middleware import SubagentLimitMiddleware
-from deerflow.agents.middlewares.summarization_middleware import BeforeSummarizationHook, DeerFlowSummarizationMiddleware
 from deerflow.agents.middlewares.title_middleware import TitleMiddleware
 from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
 from deerflow.agents.middlewares.token_usage_middleware import TokenUsageMiddleware
 from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares
 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
-from deerflow.config.deer_flow_context import DeerFlowContext
+from deerflow.config.agents_config import load_agent_config
+from deerflow.config.app_config import get_app_config
+from deerflow.config.summarization_config import get_summarization_config
 from deerflow.models import create_chat_model

 logger = logging.getLogger(__name__)


-def _get_runtime_config(config: RunnableConfig) -> dict:
-    """Merge legacy configurable options with LangGraph runtime context."""
-    cfg = dict(config.get("configurable", {}) or {})
-    context = config.get("context", {}) or {}
-    if isinstance(context, dict):
-        cfg.update(context)
-    return cfg
-
-
-def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None = None) -> str:
+def _resolve_model_name(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 = 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.")
@@ -49,9 +38,9 @@ def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None
    return default_model_name


-def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummarizationMiddleware | None:
+def _create_summarization_middleware() -> SummarizationMiddleware | None:
    """Create and configure the summarization middleware from config."""
-    config = app_config.summarization
+    config = get_summarization_config()

    if not config.enabled:
        return None
@@ -72,9 +61,9 @@ def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummariza
    # as middleware rather than lead_agent (SummarizationMiddleware is a
    # LangChain built-in, so we tag the model at creation time).
    if config.model_name:
-        model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
+        model = create_chat_model(name=config.model_name, thinking_enabled=False)
    else:
-        model = create_chat_model(thinking_enabled=False, app_config=app_config)
+        model = create_chat_model(thinking_enabled=False)
    model = model.with_config(tags=["middleware:summarize"])

    # Prepare kwargs
@@ -90,28 +79,7 @@ def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummariza
    if config.summary_prompt is not None:
        kwargs["summary_prompt"] = config.summary_prompt

-    hooks: list[BeforeSummarizationHook] = []
-    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.
-    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,
-        skills_container_path=skills_container_path,
-        skill_file_read_tool_names=config.skill_file_read_tool_names,
-        before_summarization=hooks,
-        preserve_recent_skill_count=config.preserve_recent_skill_count,
-        preserve_recent_skill_tokens=config.preserve_recent_skill_tokens,
-        preserve_recent_skill_tokens_per_skill=config.preserve_recent_skill_tokens_per_skill,
-    )
+    return SummarizationMiddleware(**kwargs)


 def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
@@ -239,18 +207,10 @@ Being proactive with task management demonstrates thoroughness and ensures all r
 # ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
 # 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,
-):
+def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = 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.
@@ -258,22 +218,21 @@ def _build_middlewares(
    Returns:
        List of middleware instances.
    """
-    middlewares = build_lead_runtime_middlewares(app_config=app_config, lazy_init=True)
+    middlewares = build_lead_runtime_middlewares(lazy_init=True)

    # Add summarization middleware if enabled
-    summarization_middleware = _create_summarization_middleware(app_config)
+    summarization_middleware = _create_summarization_middleware()
    if summarization_middleware is not None:
        middlewares.append(summarization_middleware)

    # Add TodoList middleware if plan mode is enabled
-    cfg = _get_runtime_config(config)
-    is_plan_mode = cfg.get("is_plan_mode", False)
+    is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
    todo_list_middleware = _create_todo_list_middleware(is_plan_mode)
    if todo_list_middleware is not None:
        middlewares.append(todo_list_middleware)

    # Add TokenUsageMiddleware when token_usage tracking is enabled
-    if app_config.token_usage.enabled:
+    if get_app_config().token_usage.enabled:
        middlewares.append(TokenUsageMiddleware())

    # Add TitleMiddleware
@@ -284,6 +243,7 @@ def _build_middlewares(

    # Add ViewImageMiddleware only if the current model supports vision.
    # Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
+    app_config = get_app_config()
    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())
@@ -295,9 +255,9 @@ def _build_middlewares(
        middlewares.append(DeferredToolFilterMiddleware())

    # Add SubagentLimitMiddleware to truncate excess parallel task calls
-    subagent_enabled = cfg.get("subagent_enabled", False)
+    subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
    if subagent_enabled:
-        max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
+        max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
        middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))

    # LoopDetectionMiddleware — detect and break repetitive tool call loops
@@ -312,33 +272,12 @@ def _build_middlewares(
    return middlewares


-def make_lead_agent(
-    config: RunnableConfig,
-    app_config: AppConfig | None = None,
-) -> CompiledStateGraph:
-    """Build the lead agent from runtime config.
-
-    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.
-    """
+def make_lead_agent(config: RunnableConfig):
    # Lazy import to avoid circular dependency
    from deerflow.tools import get_available_tools
    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)
+    cfg = config.get("configurable", {})

    thinking_enabled = cfg.get("thinking_enabled", True)
    reasoning_effort = cfg.get("reasoning_effort", None)
@@ -347,15 +286,16 @@ def make_lead_agent(
    subagent_enabled = cfg.get("subagent_enabled", False)
    max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
    is_bootstrap = cfg.get("is_bootstrap", False)
-    agent_name = validate_agent_name(cfg.get("agent_name"))
+    agent_name = cfg.get("agent_name")

    agent_config = load_agent_config(agent_name) if not is_bootstrap else None
    # 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(app_config, requested_model_name or agent_model_name)
+    model_name = _resolve_model_name(requested_model_name or agent_model_name)

+    app_config = get_app_config()
    model_config = app_config.get_model_config(model_name)

    if model_config is None:
@@ -387,30 +327,26 @@ def make_lead_agent(
            "reasoning_effort": reasoning_effort,
            "is_plan_mode": is_plan_mode,
            "subagent_enabled": subagent_enabled,
-            "tool_groups": agent_config.tool_groups if agent_config else None,
-            "available_skills": ["bootstrap"] if is_bootstrap else (agent_config.skills if agent_config and agent_config.skills is not None else None),
        }
    )

    if is_bootstrap:
        # Special bootstrap agent with minimal prompt for initial custom agent creation flow
        return create_agent(
-            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"])),
+            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
+            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
+            middleware=_build_middlewares(config, model_name=model_name),
+            system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
            state_schema=ThreadState,
-            context_schema=DeerFlowContext,
        )

    # Default lead agent (unchanged behavior)
    return create_agent(
-        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),
+        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
+        tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
+        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
        system_prompt=apply_prompt_template(
-            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
+            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,
    )
@@ -5,7 +5,6 @@ from datetime import datetime
 from functools import lru_cache

 from deerflow.config.agents_config import load_agent_soul
-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
@@ -20,20 +19,19 @@ _enabled_skills_refresh_version = 0
 _enabled_skills_refresh_event = threading.Event()


-def _load_enabled_skills_sync(app_config: AppConfig | None) -> list[Skill]:
-    return list(load_skills(app_config, enabled_only=True))
+def _load_enabled_skills_sync() -> list[Skill]:
+    return list(load_skills(enabled_only=True))


-def _start_enabled_skills_refresh_thread(app_config: AppConfig | None) -> None:
+def _start_enabled_skills_refresh_thread() -> 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(app_config: AppConfig | None) -> None:
+def _refresh_enabled_skills_cache_worker() -> None:
    global _enabled_skills_cache, _enabled_skills_refresh_active

    while True:
@@ -41,8 +39,8 @@ def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
            target_version = _enabled_skills_refresh_version

        try:
-            skills = _load_enabled_skills_sync(app_config)
-        except (OSError, ImportError):
+            skills = _load_enabled_skills_sync()
+        except Exception:
            logger.exception("Failed to load enabled skills for prompt injection")
            skills = []

@@ -58,7 +56,7 @@ def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
            _enabled_skills_cache = None


-def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
+def _ensure_enabled_skills_cache() -> threading.Event:
    global _enabled_skills_refresh_active

    with _enabled_skills_lock:
@@ -70,11 +68,11 @@ def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Even
        _enabled_skills_refresh_active = True
        _enabled_skills_refresh_event.clear()

-    _start_enabled_skills_refresh_thread(app_config)
+    _start_enabled_skills_refresh_thread()
    return _enabled_skills_refresh_event


-def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
+def _invalidate_enabled_skills_cache() -> threading.Event:
    global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version

    _get_cached_skills_prompt_section.cache_clear()
@@ -86,30 +84,30 @@ def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.
            return _enabled_skills_refresh_event
        _enabled_skills_refresh_active = True

-    _start_enabled_skills_refresh_thread(app_config)
+    _start_enabled_skills_refresh_thread()
    return _enabled_skills_refresh_event


-def prime_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
-    _ensure_enabled_skills_cache(app_config)
+def prime_enabled_skills_cache() -> None:
+    _ensure_enabled_skills_cache()


-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):
+def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
+    if _ensure_enabled_skills_cache().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(app_config: AppConfig | None = None):
+def _get_enabled_skills():
    with _enabled_skills_lock:
        cached = _enabled_skills_cache

    if cached is not None:
        return list(cached)

-    _ensure_enabled_skills_cache(app_config)
+    _ensure_enabled_skills_cache()
    return []


@@ -117,12 +115,12 @@ def _skill_mutability_label(category: str) -> str:
    return "[custom, editable]" if category == "custom" else "[built-in]"


-def clear_skills_system_prompt_cache(app_config: AppConfig | None = None) -> None:
-    _invalidate_enabled_skills_cache(app_config)
+def clear_skills_system_prompt_cache() -> None:
+    _invalidate_enabled_skills_cache()


-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)
+async def refresh_skills_system_prompt_cache_async() -> None:
+    await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)


 def _reset_skills_system_prompt_cache_state() -> None:
@@ -136,10 +134,10 @@ def _reset_skills_system_prompt_cache_state() -> None:
        _enabled_skills_refresh_event.clear()


-def _refresh_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
+def _refresh_enabled_skills_cache() -> None:
    """Backward-compatible test helper for direct synchronous reload."""
    try:
-        skills = _load_enabled_skills_sync(app_config)
+        skills = _load_enabled_skills_sync()
    except Exception:
        logger.exception("Failed to load enabled skills for prompt injection")
        skills = []
@@ -166,53 +164,23 @@ Skip simple one-off tasks.
 """


-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
-    from all registered roles, so the LLM knows about every available type.
-    """
-    # Built-in descriptions (kept for backward compatibility with existing prompt quality)
-    builtin_descriptions = {
-        "general-purpose": "For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.",
-        "bash": (
-            "For command execution (git, build, test, deploy operations)" if bash_available else "Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
-        ),
-    }
-
-    # Lazy import moved outside loop to avoid repeated import overhead
-    from deerflow.subagents.registry import get_subagent_config
-
-    lines = []
-    for name in available_names:
-        if name in builtin_descriptions:
-            lines.append(f"- **{name}**: {builtin_descriptions[name]}")
-        else:
-            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}")
-
-    return "\n".join(lines)
-
-
-def _build_subagent_section(max_concurrent: int, app_config: AppConfig) -> str:
+def _build_subagent_section(max_concurrent: int) -> 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)
-    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)
+    bash_available = "bash" in get_available_subagent_names()
+    available_subagents = (
+        "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n- **bash**: For command execution (git, build, test, deploy operations)"
+        if bash_available
+        else "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n"
+        "- **bash**: Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
+    )
    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()'
@@ -452,7 +420,7 @@ You: "Deploying to staging..." [proceed]
 - Treat `/mnt/user-data/workspace` as your default current working directory for coding and file-editing tasks
 - When writing scripts or commands that create/read files from the workspace, prefer relative paths such as `hello.txt`, `../uploads/data.csv`, and `../outputs/report.md`
 - Avoid hardcoding `/mnt/user-data/...` inside generated scripts when a relative path from the workspace is enough
- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_files` tool
+- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_file` tool
 {acp_section}
 </working_directory>

@@ -539,34 +507,37 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 """


-def _get_memory_context(app_config: AppConfig, agent_name: str | None = None) -> str:
+def _get_memory_context(agent_name: str | None = None) -> str:
    """Get memory context for injection into system prompt.

-    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.
+    Args:
+        agent_name: If provided, loads per-agent memory. If None, loads global memory.
+
+    Returns:
+        Formatted memory context string wrapped in XML tags, or empty string if disabled.
    """
-    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:
-        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 ""
+        from deerflow.agents.memory import format_memory_for_injection, get_memory_data
+        from deerflow.config.memory_config import get_memory_config
+        from deerflow.runtime.user_context import get_effective_user_id

-    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()
+        if not config.enabled or not config.injection_enabled:
+            return ""

-    return f"""<memory>
+        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>
 {memory_content}
 </memory>
 """
+    except Exception as e:
+        logger.error("Failed to load memory context: %s", e)
+        return ""


@lru_cache(maxsize=32)
@@ -601,12 +572,19 @@ You have access to skills that provide optimized workflows for specific tasks. E
 </skill_system>"""


-def get_skills_prompt_section(app_config: AppConfig, available_skills: set[str] | None = None) -> str:
+def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
    """Generate the skills prompt section with available skills list."""
-    skills = _get_enabled_skills(app_config)
+    skills = _get_enabled_skills()

-    container_base_path = app_config.skills.container_path
-    skill_evolution_enabled = app_config.skill_evolution.enabled
+    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

    if not skills and not skill_evolution_enabled:
        return ""
@@ -630,7 +608,7 @@ def get_agent_soul(agent_name: str | None) -> str:
    return ""


-def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
+def get_deferred_tools_prompt_section() -> str:
    """Generate <available-deferred-tools> block for the system prompt.

    Lists only deferred tool names so the agent knows what exists
@@ -639,7 +617,12 @@ def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
    """
    from deerflow.tools.builtins.tool_search import get_deferred_registry

-    if not app_config.tool_search.enabled:
+    try:
+        from deerflow.config import get_app_config
+
+        if not get_app_config().tool_search.enabled:
+            return ""
+    except Exception:
        return ""

    registry = get_deferred_registry()
@@ -650,9 +633,15 @@ def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
    return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"


-def _build_acp_section(app_config: AppConfig) -> str:
+def _build_acp_section() -> str:
    """Build the ACP agent prompt section, only if ACP agents are configured."""
-    if not app_config.acp_agents:
+    try:
+        from deerflow.config.acp_config import get_acp_agents
+
+        agents = get_acp_agents()
+        if not agents:
+            return ""
+    except Exception:
        return ""

    return (
@@ -660,13 +649,19 @@ def _build_acp_section(app_config: AppConfig) -> str:
        "- ACP agents (e.g. codex, claude_code) run in their own independent workspace — NOT in `/mnt/user-data/`\n"
        "- When writing prompts for ACP agents, describe the task only — do NOT reference `/mnt/user-data` paths\n"
        "- ACP agent results are accessible at `/mnt/acp-workspace/` (read-only) — use `ls`, `read_file`, or `bash cp` to retrieve output files\n"
-        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_files`"
+        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_file`"
    )


-def _build_custom_mounts_section(app_config: AppConfig) -> str:
+def _build_custom_mounts_section() -> str:
    """Build a prompt section for explicitly configured sandbox mounts."""
-    mounts = app_config.sandbox.mounts or []
+    try:
+        from deerflow.config import get_app_config
+
+        mounts = get_app_config().sandbox.mounts or []
+    except Exception:
+        logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
+        return ""

    if not mounts:
        return ""
@@ -680,20 +675,13 @@ def _build_custom_mounts_section(app_config: AppConfig) -> str:
    return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"


-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,
-) -> str:
+def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
    # Get memory context
-    memory_context = _get_memory_context(app_config, agent_name)
+    memory_context = _get_memory_context(agent_name)

    # Include subagent section only if enabled (from runtime parameter)
    n = max_concurrent_subagents
-    subagent_section = _build_subagent_section(n, app_config) if subagent_enabled else ""
+    subagent_section = _build_subagent_section(n) if subagent_enabled else ""

    # Add subagent reminder to critical_reminders if enabled
    subagent_reminder = (
@@ -714,14 +702,14 @@ def apply_prompt_template(
    )

    # Get skills section
-    skills_section = get_skills_prompt_section(app_config, available_skills)
+    skills_section = get_skills_prompt_section(available_skills)

    # Get deferred tools section (tool_search)
-    deferred_tools_section = get_deferred_tools_prompt_section(app_config)
+    deferred_tools_section = get_deferred_tools_prompt_section()

    # Build ACP agent section only if ACP agents are configured
-    acp_section = _build_acp_section(app_config)
-    custom_mounts_section = _build_custom_mounts_section(app_config)
+    acp_section = _build_acp_section()
+    custom_mounts_section = _build_custom_mounts_section()
    acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)

    # Format the prompt with dynamic skills and memory
@@ -1,109 +0,0 @@
-"""Shared helpers for turning conversations into memory update inputs."""
-
-from __future__ import annotations
-
-import re
-from copy import copy
-from typing import Any
-
-_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
-_CORRECTION_PATTERNS = (
-    re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
-    re.compile(r"\byou misunderstood\b", re.IGNORECASE),
-    re.compile(r"\btry again\b", re.IGNORECASE),
-    re.compile(r"\bredo\b", re.IGNORECASE),
-    re.compile(r"不对"),
-    re.compile(r"你理解错了"),
-    re.compile(r"你理解有误"),
-    re.compile(r"重试"),
-    re.compile(r"重新来"),
-    re.compile(r"换一种"),
-    re.compile(r"改用"),
-)
-_REINFORCEMENT_PATTERNS = (
-    re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
-    re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
-    re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
-    re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
-    re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
-    re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
-    re.compile(r"完全正确(?:[。！？!?.]|$)"),
-    re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
-    re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
-    re.compile(r"继续保持(?:[。！？!?.]|$)"),
-)
-
-
-def extract_message_text(message: Any) -> str:
-    """Extract plain text from message content for filtering and signal detection."""
-    content = getattr(message, "content", "")
-    if isinstance(content, list):
-        text_parts: list[str] = []
-        for part in content:
-            if isinstance(part, str):
-                text_parts.append(part)
-            elif isinstance(part, dict):
-                text_val = part.get("text")
-                if isinstance(text_val, str):
-                    text_parts.append(text_val)
-        return " ".join(text_parts)
-    return str(content)
-
-
-def filter_messages_for_memory(messages: list[Any]) -> list[Any]:
-    """Keep only user inputs and final assistant responses for memory updates."""
-    filtered = []
-    skip_next_ai = False
-    for msg in messages:
-        msg_type = getattr(msg, "type", None)
-
-        if msg_type == "human":
-            content_str = extract_message_text(msg)
-            if "<uploaded_files>" in content_str:
-                stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
-                if not stripped:
-                    skip_next_ai = True
-                    continue
-                clean_msg = copy(msg)
-                clean_msg.content = stripped
-                filtered.append(clean_msg)
-                skip_next_ai = False
-            else:
-                filtered.append(msg)
-                skip_next_ai = False
-        elif msg_type == "ai":
-            tool_calls = getattr(msg, "tool_calls", None)
-            if not tool_calls:
-                if skip_next_ai:
-                    skip_next_ai = False
-                    continue
-                filtered.append(msg)
-
-    return filtered
-
-
-def detect_correction(messages: list[Any]) -> bool:
-    """Detect explicit user corrections in recent conversation turns."""
-    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
-
-    for msg in recent_user_msgs:
-        content = extract_message_text(msg).strip()
-        if content and any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
-            return True
-
-    return False
-
-
-def detect_reinforcement(messages: list[Any]) -> bool:
-    """Detect explicit positive reinforcement signals in recent conversation turns."""
-    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
-
-    for msg in recent_user_msgs:
-        content = extract_message_text(msg).strip()
-        if content and any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
-            return True
-
-    return False
@@ -7,17 +7,11 @@ from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from typing import Any

-from deerflow.config.app_config import AppConfig
+from deerflow.config.memory_config import get_memory_config

 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."""
@@ -37,21 +31,10 @@ 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, 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
+    def __init__(self):
+        """Initialize the memory update queue."""
        self._queue: list[ConversationContext] = []
        self._lock = threading.Lock()
        self._timer: threading.Timer | None = None
@@ -66,99 +49,66 @@ class MemoryUpdateQueue:
        correction_detected: bool = False,
        reinforcement_detected: bool = False,
    ) -> None:
-        """Add a conversation to the update queue."""
-        config = self._app_config.memory
+        """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()
        if not config.enabled:
            return

        with self._lock:
-            self._enqueue_locked(
+            existing_context = next(
+                (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)
+            merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
+            context = ConversationContext(
                thread_id=thread_id,
                messages=messages,
                agent_name=agent_name,
                user_id=user_id,
-                correction_detected=correction_detected,
-                reinforcement_detected=reinforcement_detected,
+                correction_detected=merged_correction_detected,
+                reinforcement_detected=merged_reinforcement_detected,
            )
+
+            # Check if this thread already has a pending update
+            # If so, replace it with the newer one
+            self._queue = [c for c in self._queue if c.thread_id != thread_id]
+            self._queue.append(context)
+
+            # Reset or start the debounce timer
            self._reset_timer()

        logger.info("Memory update queued for thread %s, queue size: %d", thread_id, len(self._queue))

-    def add_nowait(
-        self,
-        thread_id: str,
-        messages: list[Any],
-        agent_name: str | None = None,
-        user_id: str | None = None,
-        correction_detected: bool = False,
-        reinforcement_detected: bool = False,
-    ) -> None:
-        """Add a conversation and start processing immediately in the background."""
-        config = self._app_config.memory
-        if not config.enabled:
-            return
-
-        with self._lock:
-            self._enqueue_locked(
-                thread_id=thread_id,
-                messages=messages,
-                agent_name=agent_name,
-                user_id=user_id,
-                correction_detected=correction_detected,
-                reinforcement_detected=reinforcement_detected,
-            )
-            self._schedule_timer(0)
-
-        logger.info("Memory update queued for immediate processing on thread %s, queue size: %d", thread_id, len(self._queue))
-
-    def _enqueue_locked(
-        self,
-        *,
-        thread_id: str,
-        messages: list[Any],
-        agent_name: str | None,
-        user_id: str | None = None,
-        correction_detected: bool,
-        reinforcement_detected: bool,
-    ) -> None:
-        existing_context = next(
-            (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)
-        merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
-        context = ConversationContext(
-            thread_id=thread_id,
-            messages=messages,
-            agent_name=agent_name,
-            user_id=user_id,
-            correction_detected=merged_correction_detected,
-            reinforcement_detected=merged_reinforcement_detected,
-        )
-
-        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 = self._app_config.memory
-        self._schedule_timer(config.debounce_seconds)
+        config = get_memory_config()

-        logger.debug("Memory update timer set for %ss", config.debounce_seconds)
-
-    def _schedule_timer(self, delay_seconds: float) -> None:
-        """Schedule queue processing after the provided delay."""
        # Cancel existing timer if any
        if self._timer is not None:
            self._timer.cancel()

+        # Start new timer
        self._timer = threading.Timer(
-            delay_seconds,
+            config.debounce_seconds,
            self._process_queue,
        )
        self._timer.daemon = True
        self._timer.start()

+        logger.debug("Memory update timer set for %ss", config.debounce_seconds)
+
    def _process_queue(self) -> None:
        """Process all queued conversation contexts."""
        # Import here to avoid circular dependency
@@ -166,8 +116,8 @@ class MemoryUpdateQueue:

        with self._lock:
            if self._processing:
-                # Preserve immediate flush semantics even if another worker is active.
-                self._schedule_timer(0)
+                # Already processing, reschedule
+                self._reset_timer()
                return

            if not self._queue:
@@ -181,7 +131,7 @@ class MemoryUpdateQueue:
        logger.info("Processing %d queued memory updates", len(contexts_to_process))

        try:
-            updater = MemoryUpdater(self._app_config)
+            updater = MemoryUpdater()

            for context in contexts_to_process:
                try:
@@ -221,13 +171,6 @@ class MemoryUpdateQueue:

        self._process_queue()

-    def flush_nowait(self) -> None:
-        """Start queue processing immediately in a background thread."""
-        with self._lock:
-            # Daemon thread: queued messages may be lost if the process exits
-            # before _process_queue completes. Acceptable for best-effort memory updates.
-            self._schedule_timer(0)
-
    def clear(self) -> None:
        """Clear the queue without processing.

@@ -253,35 +196,31 @@ class MemoryUpdateQueue:
            return self._processing


-# 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] = {}
+# Global singleton instance
+_memory_queue: MemoryUpdateQueue | None = None
 _queue_lock = threading.Lock()


-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:
-        queue = _memory_queues.get(key)
-        if queue is None:
-            queue = MemoryUpdateQueue(app_config)
-            _memory_queues[key] = queue
-        return queue
+def get_memory_queue() -> MemoryUpdateQueue:
+    """Get the global memory update queue singleton.

-
-def reset_memory_queue(app_config: AppConfig | None = None) -> None:
-    """Reset memory queue(s).
-
-    Pass an ``app_config`` to reset only its queue, or omit to reset all
-    (useful at test teardown).
+    Returns:
+        The memory update queue instance.
    """
+    global _memory_queue
    with _queue_lock:
-        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()
+        if _memory_queue is None:
+            _memory_queue = MemoryUpdateQueue()
+        return _memory_queue
+
+
+def reset_memory_queue() -> None:
+    """Reset the global memory queue.
+
+    This is useful for testing.
+    """
+    global _memory_queue
+    with _queue_lock:
+        if _memory_queue is not None:
+            _memory_queue.clear()
+        _memory_queue = None
@@ -4,13 +4,12 @@ import abc
 import json
 import logging
 import threading
-import uuid
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any

 from deerflow.config.agents_config import AGENT_NAME_PATTERN
-from deerflow.config.memory_config import MemoryConfig
+from deerflow.config.memory_config import get_memory_config
 from deerflow.config.paths import get_paths

 logger = logging.getLogger(__name__)
@@ -62,20 +61,11 @@ class MemoryStorage(abc.ABC):
 class FileMemoryStorage(MemoryStorage):
    """File-based memory storage provider."""

-    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
+    def __init__(self):
+        """Initialize the file memory storage."""
        # 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]] = {}
-        # Guards all reads and writes to _memory_cache across concurrent callers.
-        self._cache_lock = threading.Lock()

    def _validate_agent_name(self, agent_name: str) -> None:
        """Validate that the agent name is safe to use in filesystem paths.
@@ -90,11 +80,11 @@ 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)
@@ -102,6 +92,7 @@ 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
@@ -132,17 +123,14 @@ class FileMemoryStorage(MemoryStorage):
            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:
-                return cached[0]
+        cached = self._memory_cache.get(cache_key)

-        memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
-
-        with self._cache_lock:
+        if cached is None or cached[1] != current_mtime:
+            memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
            self._memory_cache[cache_key] = (memory_data, current_mtime)
+            return memory_data

-        return memory_data
+        return cached[0]

    def reload(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
        """Reload memory data from file, forcing cache invalidation."""
@@ -155,8 +143,7 @@ class FileMemoryStorage(MemoryStorage):
            mtime = None

        cache_key = (user_id, agent_name)
-        with self._cache_lock:
-            self._memory_cache[cache_key] = (memory_data, mtime)
+        self._memory_cache[cache_key] = (memory_data, mtime)
        return memory_data

    def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
@@ -165,12 +152,9 @@ class FileMemoryStorage(MemoryStorage):

        try:
            file_path.parent.mkdir(parents=True, exist_ok=True)
-            # Shallow-copy before adding lastUpdated so the caller's dict is not
-            # mutated as a side-effect, and the cache reference is not silently
-            # updated before the file write succeeds.
-            memory_data = {**memory_data, "lastUpdated": utc_now_iso_z()}
+            memory_data["lastUpdated"] = utc_now_iso_z()

-            temp_path = file_path.with_suffix(f".{uuid.uuid4().hex}.tmp")
+            temp_path = file_path.with_suffix(".tmp")
            with open(temp_path, "w", encoding="utf-8") as f:
                json.dump(memory_data, f, indent=2, ensure_ascii=False)

@@ -182,8 +166,7 @@ class FileMemoryStorage(MemoryStorage):
                mtime = None

            cache_key = (user_id, agent_name)
-            with self._cache_lock:
-                self._memory_cache[cache_key] = (memory_data, mtime)
+            self._memory_cache[cache_key] = (memory_data, mtime)
            logger.info("Memory saved to %s", file_path)
            return True
        except OSError as e:
@@ -191,31 +174,23 @@ class FileMemoryStorage(MemoryStorage):
            return False


-# 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_instance: MemoryStorage | None = None
 _storage_lock = threading.Lock()


-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
+def get_memory_storage() -> MemoryStorage:
+    """Get the configured memory storage instance."""
+    global _storage_instance
+    if _storage_instance is not None:
+        return _storage_instance

    with _storage_lock:
-        existing = _storage_instances.get(key)
-        if existing is not None:
-            return existing
+        if _storage_instance is not None:
+            return _storage_instance
+
+        config = get_memory_config()
+        storage_class_path = config.storage_class

-        storage_class_path = memory_config.storage_class
        try:
            module_path, class_name = storage_class_path.rsplit(".", 1)
            import importlib
@@ -229,14 +204,13 @@ def get_memory_storage(memory_config: MemoryConfig) -> MemoryStorage:
            if not issubclass(storage_class, MemoryStorage):
                raise TypeError(f"Configured memory storage '{storage_class_path}' is not a subclass of MemoryStorage")

-            instance = storage_class(memory_config)
+            _storage_instance = storage_class()
        except Exception as e:
            logger.error(
                "Failed to load memory storage %s, falling back to FileMemoryStorage: %s",
                storage_class_path,
                e,
            )
-            instance = FileMemoryStorage(memory_config)
+            _storage_instance = FileMemoryStorage()

-        _storage_instances[key] = instance
-        return instance
+    return _storage_instance
@@ -1,38 +0,0 @@
-"""Hooks fired before summarization removes messages from state."""
-
-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.app_config import AppConfig
-
-
-def memory_flush_hook(event: SummarizationEvent) -> None:
-    """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))
-    user_messages = [message for message in filtered_messages if getattr(message, "type", None) == "human"]
-    assistant_messages = [message for message in filtered_messages if getattr(message, "type", None) == "ai"]
-    if not user_messages or not assistant_messages:
-        return
-
-    correction_detected = detect_correction(filtered_messages)
-    reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
-    queue = get_memory_queue(app_config)
-    queue.add_nowait(
-        thread_id=event.thread_id,
-        messages=filtered_messages,
-        agent_name=event.agent_name,
-        correction_detected=correction_detected,
-        reinforcement_detected=reinforcement_detected,
-    )
@@ -1,15 +1,10 @@
 """Memory updater for reading, writing, and updating memory data."""

-import asyncio
-import atexit
-import concurrent.futures
-import copy
 import json
 import logging
 import math
 import re
 import uuid
-from collections.abc import Awaitable
 from typing import Any

 from deerflow.agents.memory.prompt import (
@@ -21,51 +16,56 @@ from deerflow.agents.memory.storage import (
    get_memory_storage,
    utc_now_iso_z,
 )
-from deerflow.config.app_config import AppConfig
-from deerflow.config.memory_config import MemoryConfig
+from deerflow.config.memory_config import get_memory_config
 from deerflow.models import create_chat_model

 logger = logging.getLogger(__name__)

-_SYNC_MEMORY_UPDATER_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
-    max_workers=4,
-    thread_name_prefix="memory-updater-sync",
-)
-atexit.register(lambda: _SYNC_MEMORY_UPDATER_EXECUTOR.shutdown(wait=False))
-

 def _create_empty_memory() -> dict[str, Any]:
    """Backward-compatible wrapper around the storage-layer empty-memory factory."""
    return create_empty_memory()


-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 _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 get_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def get_memory_data(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(memory_config).load(agent_name, user_id=user_id)
+    return get_memory_storage().load(agent_name, user_id=user_id)


-def reload_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def reload_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
    """Reload memory data via storage provider."""
-    return get_memory_storage(memory_config).reload(agent_name, user_id=user_id)
+    return get_memory_storage().reload(agent_name, user_id=user_id)


-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)
+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()
    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(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def clear_memory_data(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(memory_config, cleared_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(cleared_memory, agent_name, user_id=user_id):
        raise OSError("Failed to save cleared memory data")
    return cleared_memory

@@ -78,7 +78,6 @@ def _validate_confidence(confidence: float) -> float:


 def create_memory_fact(
-    memory_config: MemoryConfig,
    content: str,
    category: str = "context",
    confidence: float = 0.5,
@@ -94,7 +93,7 @@ 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(memory_config, agent_name, user_id=user_id)
+    memory_data = get_memory_data(agent_name, user_id=user_id)
    updated_memory = dict(memory_data)
    facts = list(memory_data.get("facts", []))
    facts.append(
@@ -109,15 +108,15 @@ def create_memory_fact(
    )
    updated_memory["facts"] = facts

-    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(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(memory_config: MemoryConfig, fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def delete_memory_fact(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(memory_config, agent_name, user_id=user_id)
+    memory_data = get_memory_data(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):
@@ -126,14 +125,13 @@ def delete_memory_fact(memory_config: MemoryConfig, fact_id: str, agent_name: st
    updated_memory = dict(memory_data)
    updated_memory["facts"] = updated_facts

-    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(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,
@@ -143,7 +141,7 @@ 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(memory_config, agent_name, user_id=user_id)
+    memory_data = get_memory_data(agent_name, user_id=user_id)
    updated_memory = dict(memory_data)
    updated_facts: list[dict[str, Any]] = []
    found = False
@@ -170,7 +168,7 @@ def update_memory_fact(

    updated_memory["facts"] = updated_facts

-    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
        raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")

    return updated_memory
@@ -213,39 +211,6 @@ def _extract_text(content: Any) -> str:
    return str(content)


-def _run_async_update_sync(coro: Awaitable[bool]) -> bool:
-    """Run an async memory update from sync code, including nested-loop contexts."""
-    handed_off = False
-
-    try:
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = 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()
-
-        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,
-                    )
-
-        logger.exception("Failed to run async memory update from sync context")
-        return False
-
-
 # Matches sentences that describe a file-upload *event* rather than general
 # file-related work.  Deliberately narrow to avoid removing legitimate facts
 # such as "User works with CSV files" or "prefers PDF export".
@@ -295,141 +260,19 @@ def _fact_content_key(content: Any) -> str | None:
 class MemoryUpdater:
    """Updates memory using LLM based on conversation context."""

-    def __init__(self, app_config: AppConfig, model_name: str | None = None):
+    def __init__(self, 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."""
-        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,
-        correction_detected: bool,
-        reinforcement_detected: bool,
-    ) -> str:
-        """Build optional prompt hints for correction and reinforcement signals."""
-        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
-
-        return correction_hint
-
-    def _prepare_update_prompt(
-        self,
-        messages: list[Any],
-        agent_name: str | None,
-        correction_detected: bool,
-        reinforcement_detected: bool,
-        user_id: str | None = None,
-    ) -> tuple[dict[str, Any], str] | None:
-        """Load memory and build the update prompt for a conversation."""
-        config = self._memory_config
-        if not config.enabled or not messages:
-            return None
-
-        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
-
-        correction_hint = self._build_correction_hint(
-            correction_detected=correction_detected,
-            reinforcement_detected=reinforcement_detected,
-        )
-        prompt = MEMORY_UPDATE_PROMPT.format(
-            current_memory=json.dumps(current_memory, indent=2),
-            conversation=conversation_text,
-            correction_hint=correction_hint,
-        )
-        return current_memory, prompt
-
-    def _finalize_update(
-        self,
-        current_memory: dict[str, Any],
-        response_content: Any,
-        thread_id: str | None,
-        agent_name: str | None,
-        user_id: str | None = None,
-    ) -> bool:
-        """Parse the model response, apply updates, and persist memory."""
-        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(self._memory_config).save(updated_memory, agent_name, user_id=user_id)
-
-    async def aupdate_memory(
-        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:
-        """Update memory asynchronously based on conversation messages."""
-        try:
-            prepared = await asyncio.to_thread(
-                self._prepare_update_prompt,
-                messages=messages,
-                agent_name=agent_name,
-                correction_detected=correction_detected,
-                reinforcement_detected=reinforcement_detected,
-                user_id=user_id,
-            )
-            if prepared is None:
-                return False
-
-            current_memory, prompt = prepared
-            model = self._get_model()
-            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,
-                agent_name=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
+        config = get_memory_config()
+        model_name = self._model_name or config.model_name
+        return create_chat_model(name=model_name, thinking_enabled=False)

    def update_memory(
        self,
@@ -440,7 +283,7 @@ class MemoryUpdater:
        reinforcement_detected: bool = False,
        user_id: str | None = None,
    ) -> bool:
-        """Synchronously update memory via the async updater path.
+        """Update memory based on conversation messages.

        Args:
            messages: List of conversation messages.
@@ -453,7 +296,7 @@ class MemoryUpdater:
        Returns:
            True if update was successful, False otherwise.
        """
-        config = self._memory_config
+        config = get_memory_config()
        if not config.enabled:
            return False

@@ -462,7 +305,7 @@ class MemoryUpdater:

        try:
            # Get current memory
-            current_memory = get_memory_data(config, agent_name, user_id=user_id)
+            current_memory = get_memory_data(agent_name, user_id=user_id)

            # Format conversation for prompt
            conversation_text = format_conversation_for_update(messages)
@@ -517,7 +360,7 @@ class MemoryUpdater:
            updated_memory = _strip_upload_mentions_from_memory(updated_memory)

            # Save
-            return get_memory_storage(config).save(updated_memory, agent_name, user_id=user_id)
+            return get_memory_storage().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)
@@ -542,7 +385,7 @@ class MemoryUpdater:
        Returns:
            Updated memory data.
        """
-        config = self._memory_config
+        config = get_memory_config()
        now = utc_now_iso_z()

        # Update user sections
@@ -3,7 +3,6 @@
 import json
 import logging
 from collections.abc import Callable
-from hashlib import sha256
 from typing import override

 from langchain.agents import AgentState
@@ -37,13 +36,6 @@ class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):

    state_schema = ClarificationMiddlewareState

-    def _stable_message_id(self, tool_call_id: str, formatted_message: str) -> str:
-        """Build a deterministic message ID so retried clarification calls replace, not append."""
-        if tool_call_id:
-            return f"clarification:{tool_call_id}"
-        digest = sha256(formatted_message.encode("utf-8")).hexdigest()[:16]
-        return f"clarification:{digest}"
-
    def _is_chinese(self, text: str) -> bool:
        """Check if text contains Chinese characters.

@@ -139,7 +131,6 @@ class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
        # Create a ToolMessage with the formatted question
        # This will be added to the message history
        tool_message = ToolMessage(
-            id=self._stable_message_id(tool_call_id, formatted_message),
            content=formatted_message,
            tool_call_id=tool_call_id,
            name="ask_clarification",
@@ -13,7 +13,6 @@ at the correct positions (immediately after each dangling AIMessage), not append
 to the end of the message list as before_model + add_messages reducer would do.
 """

-import json
 import logging
 from collections.abc import Awaitable, Callable
 from typing import override
@@ -34,44 +33,6 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
    offending AIMessage so the LLM receives a well-formed conversation.
    """

-    @staticmethod
-    def _message_tool_calls(msg) -> list[dict]:
-        """Return normalized tool calls from structured fields or raw provider payloads."""
-        tool_calls = getattr(msg, "tool_calls", None) or []
-        if tool_calls:
-            return list(tool_calls)
-
-        raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
-        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": raw_tc.get("id"),
-                    "name": name or "unknown",
-                    "args": args if isinstance(args, dict) else {},
-                }
-            )
-
-        return normalized
-
    def _build_patched_messages(self, messages: list) -> list | None:
        """Return a new message list with patches inserted at the correct positions.

@@ -90,7 +51,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
        for msg in messages:
            if getattr(msg, "type", None) != "ai":
                continue
-            for tc in self._message_tool_calls(msg):
+            for tc in getattr(msg, "tool_calls", None) or []:
                tc_id = tc.get("id")
                if tc_id and tc_id not in existing_tool_msg_ids:
                    needs_patch = True
@@ -109,7 +70,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
            patched.append(msg)
            if getattr(msg, "type", None) != "ai":
                continue
-            for tc in self._message_tool_calls(msg):
+            for tc in getattr(msg, "tool_calls", None) or []:
                tc_id = tc.get("id")
                if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
                    patched.append(
@@ -16,9 +16,6 @@ from typing import override
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
-from langchain_core.messages import ToolMessage
-from langgraph.prebuilt.tool_node import ToolCallRequest
-from langgraph.types import Command

 logger = logging.getLogger(__name__)

@@ -38,7 +35,7 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
        if not registry:
            return request

-        deferred_names = registry.deferred_names
+        deferred_names = {e.name for e in registry.entries}
        active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]

        if len(active_tools) < len(request.tools):
@@ -46,28 +43,6 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):

        return request.override(tools=active_tools)

-    def _blocked_tool_message(self, request: ToolCallRequest) -> ToolMessage | None:
-        from deerflow.tools.builtins.tool_search import get_deferred_registry
-
-        registry = get_deferred_registry()
-        if not registry:
-            return None
-
-        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 '{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=tool_name,
-            status="error",
-        )
-
    @override
    def wrap_model_call(
        self,
@@ -76,17 +51,6 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
    ) -> ModelCallResult:
        return handler(self._filter_tools(request))

-    @override
-    def wrap_tool_call(
-        self,
-        request: ToolCallRequest,
-        handler: Callable[[ToolCallRequest], ToolMessage | Command],
-    ) -> ToolMessage | Command:
-        blocked = self._blocked_tool_message(request)
-        if blocked is not None:
-            return blocked
-        return handler(request)
-
    @override
    async def awrap_model_call(
        self,
@@ -94,14 +58,3 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
    ) -> ModelCallResult:
        return await handler(self._filter_tools(request))
-
-    @override
-    async def awrap_tool_call(
-        self,
-        request: ToolCallRequest,
-        handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
-    ) -> ToolMessage | Command:
-        blocked = self._blocked_tool_message(request)
-        if blocked is not None:
-            return blocked
-        return await handler(request)
@@ -4,7 +4,6 @@ from __future__ import annotations

 import asyncio
 import logging
-import threading
 import time
 from collections.abc import Awaitable, Callable
 from email.utils import parsedate_to_datetime
@@ -20,8 +19,6 @@ from langchain.agents.middleware.types import (
 from langchain_core.messages import AIMessage
 from langgraph.errors import GraphBubbleUp

-from deerflow.config.app_config import AppConfig
-
 logger = logging.getLogger(__name__)

 _RETRIABLE_STATUS_CODES = {408, 409, 425, 429, 500, 502, 503, 504}
@@ -70,80 +67,6 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
    retry_base_delay_ms: int = 1000
    retry_cap_delay_ms: int = 8000

-    circuit_failure_threshold: int = 5
-    circuit_recovery_timeout_sec: int = 60
-
-    def __init__(self, **kwargs: Any) -> None:
-        super().__init__(**kwargs)
-
-        # Load Circuit Breaker configs from app config if available, fall back to defaults
-        try:
-            app_config = AppConfig.from_file()
-            self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
-            self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
-        except (FileNotFoundError, RuntimeError):
-            # Gracefully fall back to class defaults in test environments
-            pass
-
-        # Circuit Breaker state
-        self._circuit_lock = threading.Lock()
-        self._circuit_failure_count = 0
-        self._circuit_open_until = 0.0
-        self._circuit_state = "closed"
-        self._circuit_probe_in_flight = False
-
-    def _check_circuit(self) -> bool:
-        """Returns True if circuit is OPEN (fast fail), False otherwise."""
-        with self._circuit_lock:
-            now = time.time()
-
-            if self._circuit_state == "open":
-                if now < self._circuit_open_until:
-                    return True
-                self._circuit_state = "half_open"
-                self._circuit_probe_in_flight = False
-
-            if self._circuit_state == "half_open":
-                if self._circuit_probe_in_flight:
-                    return True
-                self._circuit_probe_in_flight = True
-                return False
-
-            return False
-
-    def _record_success(self) -> None:
-        with self._circuit_lock:
-            if self._circuit_state != "closed" or self._circuit_failure_count > 0:
-                logger.info("Circuit breaker reset (Closed). LLM service recovered.")
-            self._circuit_failure_count = 0
-            self._circuit_open_until = 0.0
-            self._circuit_state = "closed"
-            self._circuit_probe_in_flight = False
-
-    def _record_failure(self) -> None:
-        with self._circuit_lock:
-            if self._circuit_state == "half_open":
-                self._circuit_open_until = time.time() + self.circuit_recovery_timeout_sec
-                self._circuit_state = "open"
-                self._circuit_probe_in_flight = False
-                logger.error(
-                    "Circuit breaker probe failed (Open). Will probe again after %ds.",
-                    self.circuit_recovery_timeout_sec,
-                )
-                return
-
-            self._circuit_failure_count += 1
-            if self._circuit_failure_count >= self.circuit_failure_threshold:
-                self._circuit_open_until = time.time() + self.circuit_recovery_timeout_sec
-                if self._circuit_state != "open":
-                    self._circuit_state = "open"
-                    self._circuit_probe_in_flight = False
-                    logger.error(
-                        "Circuit breaker tripped (Open). Threshold reached (%d). Will probe after %ds.",
-                        self.circuit_failure_threshold,
-                        self.circuit_recovery_timeout_sec,
-                    )
-
    def _classify_error(self, exc: BaseException) -> tuple[bool, str]:
        detail = _extract_error_detail(exc)
        lowered = detail.lower()
@@ -160,8 +83,6 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
            "APITimeoutError",
            "APIConnectionError",
            "InternalServerError",
-            "ReadError",  # httpx.ReadError: connection dropped mid-stream
-            "RemoteProtocolError",  # httpx: server closed connection unexpectedly
        }:
            return True, "transient"
        if status_code in _RETRIABLE_STATUS_CODES:
@@ -183,9 +104,6 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
        reason_text = "provider is busy" if reason == "busy" else "provider request failed temporarily"
        return f"LLM request retry {attempt}/{self.retry_max_attempts}: {reason_text}. Retrying in {seconds}s."

-    def _build_circuit_breaker_message(self) -> str:
-        return "The configured LLM provider is currently unavailable due to continuous failures. Circuit breaker is engaged to protect the system. Please wait a moment before trying again."
-
    def _build_user_message(self, exc: BaseException, reason: str) -> str:
        detail = _extract_error_detail(exc)
        if reason == "quota":
@@ -220,20 +138,12 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
        request: ModelRequest,
        handler: Callable[[ModelRequest], ModelResponse],
    ) -> ModelCallResult:
-        if self._check_circuit():
-            return AIMessage(content=self._build_circuit_breaker_message())
-
        attempt = 1
        while True:
            try:
-                response = handler(request)
-                self._record_success()
-                return response
+                return handler(request)
            except GraphBubbleUp:
                # Preserve LangGraph control-flow signals (interrupt/pause/resume).
-                with self._circuit_lock:
-                    if self._circuit_state == "half_open":
-                        self._circuit_probe_in_flight = False
                raise
            except Exception as exc:
                retriable, reason = self._classify_error(exc)
@@ -256,8 +166,6 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                    _extract_error_detail(exc),
                    exc_info=exc,
                )
-                if retriable:
-                    self._record_failure()
                return AIMessage(content=self._build_user_message(exc, reason))

    @override
@@ -266,20 +174,12 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
        request: ModelRequest,
        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
    ) -> ModelCallResult:
-        if self._check_circuit():
-            return AIMessage(content=self._build_circuit_breaker_message())
-
        attempt = 1
        while True:
            try:
-                response = await handler(request)
-                self._record_success()
-                return response
+                return await handler(request)
            except GraphBubbleUp:
                # Preserve LangGraph control-flow signals (interrupt/pause/resume).
-                with self._circuit_lock:
-                    if self._circuit_state == "half_open":
-                        self._circuit_probe_in_flight = False
                raise
            except Exception as exc:
                retriable, reason = self._classify_error(exc)
@@ -302,8 +202,6 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                    _extract_error_detail(exc),
                    exc_info=exc,
                )
-                if retriable:
-                    self._record_failure()
                return AIMessage(content=self._build_user_message(exc, reason))


@@ -17,7 +17,6 @@ import json
 import logging
 import threading
 from collections import OrderedDict, defaultdict
-from copy import deepcopy
 from typing import override

 from langchain.agents import AgentState
@@ -25,8 +24,6 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime

-from deerflow.config.deer_flow_context import DeerFlowContext
-
 logger = logging.getLogger(__name__)

 # Defaults — can be overridden via constructor
@@ -34,8 +31,6 @@ _DEFAULT_WARN_THRESHOLD = 3  # inject warning after 3 identical calls
 _DEFAULT_HARD_LIMIT = 5  # force-stop after 5 identical calls
 _DEFAULT_WINDOW_SIZE = 20  # track last N tool calls
 _DEFAULT_MAX_TRACKED_THREADS = 100  # LRU eviction limit
-_DEFAULT_TOOL_FREQ_WARN = 30  # warn after 30 calls to the same tool type
-_DEFAULT_TOOL_FREQ_HARD_LIMIT = 50  # force-stop after 50 calls to the same tool type


 def _normalize_tool_call_args(raw_args: object) -> tuple[dict, str | None]:
@@ -130,14 +125,8 @@ def _hash_tool_calls(tool_calls: list[dict]) -> str:

 _WARNING_MSG = "[LOOP DETECTED] You are repeating the same tool calls. Stop calling tools and produce your final answer now. If you cannot complete the task, summarize what you accomplished so far."

-_TOOL_FREQ_WARNING_MSG = (
-    "[LOOP DETECTED] You have called {tool_name} {count} times without producing a final answer. Stop calling tools and produce your final answer now. If you cannot complete the task, summarize what you accomplished so far."
-)
-
 _HARD_STOP_MSG = "[FORCED STOP] Repeated tool calls exceeded the safety limit. Producing final answer with results collected so far."

-_TOOL_FREQ_HARD_STOP_MSG = "[FORCED STOP] Tool {tool_name} called {count} times — exceeded the per-tool safety limit. Producing final answer with results collected so far."
-

 class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
    """Detects and breaks repetitive tool call loops.
@@ -151,12 +140,6 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            Default: 20.
        max_tracked_threads: Maximum number of threads to track before
            evicting the least recently used. Default: 100.
-        tool_freq_warn: Number of calls to the same tool *type* (regardless
-            of arguments) before injecting a frequency warning. Catches
-            cross-file read loops that hash-based detection misses.
-            Default: 30.
-        tool_freq_hard_limit: Number of calls to the same tool type before
-            forcing a stop. Default: 50.
    """

    def __init__(
@@ -165,27 +148,23 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
        hard_limit: int = _DEFAULT_HARD_LIMIT,
        window_size: int = _DEFAULT_WINDOW_SIZE,
        max_tracked_threads: int = _DEFAULT_MAX_TRACKED_THREADS,
-        tool_freq_warn: int = _DEFAULT_TOOL_FREQ_WARN,
-        tool_freq_hard_limit: int = _DEFAULT_TOOL_FREQ_HARD_LIMIT,
    ):
        super().__init__()
        self.warn_threshold = warn_threshold
        self.hard_limit = hard_limit
        self.window_size = window_size
        self.max_tracked_threads = max_tracked_threads
-        self.tool_freq_warn = tool_freq_warn
-        self.tool_freq_hard_limit = tool_freq_hard_limit
        self._lock = threading.Lock()
        # Per-thread tracking using OrderedDict for LRU eviction
        self._history: OrderedDict[str, list[str]] = OrderedDict()
        self._warned: dict[str, set[str]] = defaultdict(set)
-        # Per-thread, per-tool-type cumulative call counts
-        self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
-        self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)

-    def _get_thread_id(self, runtime: Runtime[DeerFlowContext]) -> str:
+    def _get_thread_id(self, runtime: Runtime) -> str:
        """Extract thread_id from runtime context for per-thread tracking."""
-        return runtime.context.thread_id or "default"
+        thread_id = runtime.context.get("thread_id") if runtime.context else None
+        if thread_id:
+            return thread_id
+        return "default"

    def _evict_if_needed(self) -> None:
        """Evict least recently used threads if over the limit.
@@ -195,19 +174,11 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
        while len(self._history) > self.max_tracked_threads:
            evicted_id, _ = self._history.popitem(last=False)
            self._warned.pop(evicted_id, None)
-            self._tool_freq.pop(evicted_id, None)
-            self._tool_freq_warned.pop(evicted_id, None)
            logger.debug("Evicted loop tracking for thread %s (LRU)", evicted_id)

    def _track_and_check(self, state: AgentState, runtime: Runtime) -> tuple[str | None, bool]:
        """Track tool calls and check for loops.

-        Two detection layers:
-          1. **Hash-based** (existing): catches identical tool call sets.
-          2. **Frequency-based** (new): catches the same *tool type* being
-             called many times with varying arguments (e.g. ``read_file``
-             on 40 different files).
-
        Returns:
            (warning_message_or_none, should_hard_stop)
        """
@@ -242,7 +213,6 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            count = history.count(call_hash)
            tool_names = [tc.get("name", "?") for tc in tool_calls]

-            # --- Layer 1: hash-based (identical call sets) ---
            if count >= self.hard_limit:
                logger.error(
                    "Loop hard limit reached — forcing stop",
@@ -269,40 +239,8 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
                        },
                    )
                    return _WARNING_MSG, False
-
-            # --- Layer 2: per-tool-type frequency ---
-            freq = self._tool_freq[thread_id]
-            for tc in tool_calls:
-                name = tc.get("name", "")
-                if not name:
-                    continue
-                freq[name] += 1
-                tc_count = freq[name]
-
-                if tc_count >= self.tool_freq_hard_limit:
-                    logger.error(
-                        "Tool frequency hard limit reached — forcing stop",
-                        extra={
-                            "thread_id": thread_id,
-                            "tool_name": name,
-                            "count": tc_count,
-                        },
-                    )
-                    return _TOOL_FREQ_HARD_STOP_MSG.format(tool_name=name, count=tc_count), True
-
-                if tc_count >= self.tool_freq_warn:
-                    warned = self._tool_freq_warned[thread_id]
-                    if name not in warned:
-                        warned.add(name)
-                        logger.warning(
-                            "Tool frequency warning — too many calls to same tool type",
-                            extra={
-                                "thread_id": thread_id,
-                                "tool_name": name,
-                                "count": tc_count,
-                            },
-                        )
-                        return _TOOL_FREQ_WARNING_MSG.format(tool_name=name, count=tc_count), False
+                # Warning already injected for this hash — suppress
+                return None, False

        return None, False

@@ -323,26 +261,6 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
        # Fallback: coerce unexpected types to str to avoid TypeError
        return str(content) + f"\n\n{text}"

-    @staticmethod
-    def _build_hard_stop_update(last_msg, content: str | list) -> dict:
-        """Clear tool-call metadata so forced-stop messages serialize as plain assistant text."""
-        update = {
-            "tool_calls": [],
-            "content": content,
-        }
-
-        additional_kwargs = dict(getattr(last_msg, "additional_kwargs", {}) or {})
-        for key in ("tool_calls", "function_call"):
-            additional_kwargs.pop(key, None)
-        update["additional_kwargs"] = additional_kwargs
-
-        response_metadata = deepcopy(getattr(last_msg, "response_metadata", {}) or {})
-        if response_metadata.get("finish_reason") == "tool_calls":
-            response_metadata["finish_reason"] = "stop"
-        update["response_metadata"] = response_metadata
-
-        return update
-
    def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
        warning, hard_stop = self._track_and_check(state, runtime)

@@ -350,8 +268,12 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            # Strip tool_calls from the last AIMessage to force text output
            messages = state.get("messages", [])
            last_msg = messages[-1]
-            content = self._append_text(last_msg.content, warning or _HARD_STOP_MSG)
-            stripped_msg = last_msg.model_copy(update=self._build_hard_stop_update(last_msg, content))
+            stripped_msg = last_msg.model_copy(
+                update={
+                    "tool_calls": [],
+                    "content": self._append_text(last_msg.content, _HARD_STOP_MSG),
+                }
+            )
            return {"messages": [stripped_msg]}

        if warning:
@@ -361,16 +283,16 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            # the conversation; injecting one mid-conversation crashes
            # langchain_anthropic's _format_messages(). HumanMessage works
            # with all providers. See #1299.
-            return {"messages": [HumanMessage(content=warning, name="loop_warning")]}
+            return {"messages": [HumanMessage(content=warning)]}

        return None

    @override
-    def after_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
        return self._apply(state, runtime)

    @override
-    async def aafter_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
        return self._apply(state, runtime)

    def reset(self, thread_id: str | None = None) -> None:
@@ -379,10 +301,6 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            if thread_id:
                self._history.pop(thread_id, None)
                self._warned.pop(thread_id, None)
-                self._tool_freq.pop(thread_id, None)
-                self._tool_freq_warned.pop(thread_id, None)
            else:
                self._history.clear()
                self._warned.clear()
-                self._tool_freq.clear()
-                self._tool_freq_warned.clear()
@@ -1,19 +1,51 @@
 """Middleware for memory mechanism."""

 import logging
-from typing import override
+import re
+from typing import Any, override

 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langgraph.config import get_config
 from langgraph.runtime import Runtime

-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.config.deer_flow_context import DeerFlowContext
+from deerflow.config.memory_config import get_memory_config
 from deerflow.runtime.user_context import get_effective_user_id

 logger = logging.getLogger(__name__)

+_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
+_CORRECTION_PATTERNS = (
+    re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
+    re.compile(r"\byou misunderstood\b", re.IGNORECASE),
+    re.compile(r"\btry again\b", re.IGNORECASE),
+    re.compile(r"\bredo\b", re.IGNORECASE),
+    re.compile(r"不对"),
+    re.compile(r"你理解错了"),
+    re.compile(r"你理解有误"),
+    re.compile(r"重试"),
+    re.compile(r"重新来"),
+    re.compile(r"换一种"),
+    re.compile(r"改用"),
+)
+
+_REINFORCEMENT_PATTERNS = (
+    re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
+    re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
+    re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
+    re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
+    re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
+    re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
+    re.compile(r"完全正确(?:[。！？!?.]|$)"),
+    re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
+    re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
+    re.compile(r"继续保持(?:[。！？!?.]|$)"),
+)
+

 class MemoryMiddlewareState(AgentState):
    """Compatible with the `ThreadState` schema."""
@@ -21,6 +53,125 @@ class MemoryMiddlewareState(AgentState):
    pass


+def _extract_message_text(message: Any) -> str:
+    """Extract plain text from message content for filtering and signal detection."""
+    content = getattr(message, "content", "")
+    if isinstance(content, list):
+        text_parts: list[str] = []
+        for part in content:
+            if isinstance(part, str):
+                text_parts.append(part)
+            elif isinstance(part, dict):
+                text_val = part.get("text")
+                if isinstance(text_val, str):
+                    text_parts.append(text_val)
+        return " ".join(text_parts)
+    return str(content)
+
+
+def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
+    """Filter messages to keep only user inputs and final assistant responses.
+
+    This filters out:
+    - Tool messages (intermediate tool call results)
+    - AI messages with tool_calls (intermediate steps, not final responses)
+    - The <uploaded_files> block injected by UploadsMiddleware into human messages
+      (file paths are session-scoped and must not persist in long-term memory).
+      The user's actual question is preserved; only turns whose content is entirely
+      the upload block (nothing remains after stripping) are dropped along with
+      their paired assistant response.
+
+    Only keeps:
+    - Human messages (with the ephemeral upload block removed)
+    - AI messages without tool_calls (final assistant responses), unless the
+      paired human turn was upload-only and had no real user text.
+
+    Args:
+        messages: List of all conversation messages.
+
+    Returns:
+        Filtered list containing only user inputs and final assistant responses.
+    """
+    filtered = []
+    skip_next_ai = False
+    for msg in messages:
+        msg_type = getattr(msg, "type", None)
+
+        if msg_type == "human":
+            content_str = _extract_message_text(msg)
+            if "<uploaded_files>" in content_str:
+                # Strip the ephemeral upload block; keep the user's real question.
+                stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
+                if not stripped:
+                    # Nothing left — the entire turn was upload bookkeeping;
+                    # skip it and the paired assistant response.
+                    skip_next_ai = True
+                    continue
+                # Rebuild the message with cleaned content so the user's question
+                # is still available for memory summarisation.
+                from copy import copy
+
+                clean_msg = copy(msg)
+                clean_msg.content = stripped
+                filtered.append(clean_msg)
+                skip_next_ai = False
+            else:
+                filtered.append(msg)
+                skip_next_ai = False
+        elif msg_type == "ai":
+            tool_calls = getattr(msg, "tool_calls", None)
+            if not tool_calls:
+                if skip_next_ai:
+                    skip_next_ai = False
+                    continue
+                filtered.append(msg)
+        # Skip tool messages and AI messages with tool_calls
+
+    return filtered
+
+
+def detect_correction(messages: list[Any]) -> bool:
+    """Detect explicit user corrections in recent conversation turns.
+
+    The queue keeps only one pending context per thread, so callers pass the
+    latest filtered message list. Checking only recent user turns keeps signal
+    detection conservative while avoiding stale corrections from long histories.
+    """
+    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
+
+    for msg in recent_user_msgs:
+        content = _extract_message_text(msg).strip()
+        if not content:
+            continue
+        if any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
+            return True
+
+    return False
+
+
+def detect_reinforcement(messages: list[Any]) -> bool:
+    """Detect explicit positive reinforcement signals in recent conversation turns.
+
+    Complements detect_correction() by identifying when the user confirms the
+    agent's approach was correct. This allows the memory system to record what
+    worked well, not just what went wrong.
+
+    The queue keeps only one pending context per thread, so callers pass the
+    latest filtered message list. Checking only recent user turns keeps signal
+    detection conservative while avoiding stale signals from long histories.
+    """
+    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
+
+    for msg in recent_user_msgs:
+        content = _extract_message_text(msg).strip()
+        if not content:
+            continue
+        if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
+            return True
+
+    return False
+
+
 class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
    """Middleware that queues conversation for memory update after agent execution.

@@ -43,7 +194,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
        self._agent_name = agent_name

    @override
-    def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+    def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
        """Queue conversation for memory update after agent completes.

        Args:
@@ -53,11 +204,15 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
        Returns:
            None (no state changes needed from this middleware).
        """
-        memory_config = runtime.context.app_config.memory
-        if not memory_config.enabled:
+        config = get_memory_config()
+        if not config.enabled:
            return None

-        thread_id = runtime.context.thread_id
+        # Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
+        thread_id = runtime.context.get("thread_id") if runtime.context else None
+        if thread_id is None:
+            config_data = get_config()
+            thread_id = config_data.get("configurable", {}).get("thread_id")
        if not thread_id:
            logger.debug("No thread_id in context, skipping memory update")
            return None
@@ -69,7 +224,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
            return None

        # Filter to only keep user inputs and final assistant responses
-        filtered_messages = filter_messages_for_memory(messages)
+        filtered_messages = _filter_messages_for_memory(messages)

        # Only queue if there's meaningful conversation
        # At minimum need one user message and one assistant response
@@ -86,7 +241,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
        # threading.Timer fires on a different thread where ContextVar values are not
        # propagated, so we must store user_id explicitly in ConversationContext.
        user_id = get_effective_user_id()
-        queue = get_memory_queue(runtime.context.app_config)
+        queue = get_memory_queue()
        queue.add(
            thread_id=thread_id,
            messages=filtered_messages,
@@ -1,354 +0,0 @@
-"""Summarization middleware extensions for DeerFlow."""
-
-from __future__ import annotations
-
-import logging
-from collections.abc import Collection
-from dataclasses import dataclass
-from typing import Any, Protocol, override, runtime_checkable
-
-from langchain.agents import AgentState
-from langchain.agents.middleware import SummarizationMiddleware
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage
-from langgraph.config import get_config
-from langgraph.graph.message import REMOVE_ALL_MESSAGES
-from langgraph.runtime import Runtime
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class SummarizationEvent:
-    """Context emitted before conversation history is summarized away."""
-
-    messages_to_summarize: tuple[AnyMessage, ...]
-    preserved_messages: tuple[AnyMessage, ...]
-    thread_id: str | None
-    agent_name: str | None
-    runtime: Runtime
-
-
-@runtime_checkable
-class BeforeSummarizationHook(Protocol):
-    """Hook invoked before summarization removes messages from state."""
-
-    def __call__(self, event: SummarizationEvent) -> None: ...
-
-
-def _resolve_thread_id(runtime: Runtime) -> str | None:
-    """Resolve the current thread ID from runtime context or LangGraph config."""
-    thread_id = runtime.context.get("thread_id") if runtime.context else None
-    if thread_id is None:
-        try:
-            config_data = get_config()
-        except RuntimeError:
-            return None
-        thread_id = config_data.get("configurable", {}).get("thread_id")
-    return thread_id
-
-
-def _resolve_agent_name(runtime: Runtime) -> str | None:
-    """Resolve the current agent name from runtime context or LangGraph config."""
-    agent_name = runtime.context.get("agent_name") if runtime.context else None
-    if agent_name is None:
-        try:
-            config_data = get_config()
-        except RuntimeError:
-            return None
-        agent_name = config_data.get("configurable", {}).get("agent_name")
-    return agent_name
-
-
-def _tool_call_path(tool_call: dict[str, Any]) -> str | None:
-    """Best-effort extraction of a file path argument from a read_file-like tool call."""
-    args = tool_call.get("args") or {}
-    if not isinstance(args, dict):
-        return None
-    for key in ("path", "file_path", "filepath"):
-        value = args.get(key)
-        if isinstance(value, str) and value:
-            return value
-    return None
-
-
-def _clone_ai_message(
-    message: AIMessage,
-    tool_calls: list[dict[str, Any]],
-    *,
-    content: Any | None = None,
-) -> AIMessage:
-    """Clone an AIMessage while replacing its tool_calls list and optional content."""
-    update: dict[str, Any] = {"tool_calls": tool_calls}
-    if content is not None:
-        update["content"] = content
-    return message.model_copy(update=update)
-
-
-@dataclass
-class _SkillBundle:
-    """Skill-related tool calls and tool results associated with one AIMessage."""
-
-    ai_index: int
-    skill_tool_indices: tuple[int, ...]
-    skill_tool_call_ids: frozenset[str]
-    skill_tool_tokens: int
-    skill_key: str
-
-
-class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
-    """Summarization middleware with pre-compression hook dispatch and skill rescue."""
-
-    def __init__(
-        self,
-        *args,
-        skills_container_path: str | None = None,
-        skill_file_read_tool_names: Collection[str] | None = None,
-        before_summarization: list[BeforeSummarizationHook] | None = None,
-        preserve_recent_skill_count: int = 5,
-        preserve_recent_skill_tokens: int = 25_000,
-        preserve_recent_skill_tokens_per_skill: int = 5_000,
-        **kwargs,
-    ) -> None:
-        super().__init__(*args, **kwargs)
-        self._skills_container_path = skills_container_path or "/mnt/skills"
-        self._skill_file_read_tool_names = frozenset(skill_file_read_tool_names or {"read_file", "read", "view", "cat"})
-        self._before_summarization_hooks = before_summarization or []
-        self._preserve_recent_skill_count = max(0, preserve_recent_skill_count)
-        self._preserve_recent_skill_tokens = max(0, preserve_recent_skill_tokens)
-        self._preserve_recent_skill_tokens_per_skill = max(0, preserve_recent_skill_tokens_per_skill)
-
-    def before_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return self._maybe_summarize(state, runtime)
-
-    async def abefore_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return await self._amaybe_summarize(state, runtime)
-
-    def _maybe_summarize(self, state: AgentState, runtime: Runtime) -> dict | None:
-        messages = state["messages"]
-        self._ensure_message_ids(messages)
-
-        total_tokens = self.token_counter(messages)
-        if not self._should_summarize(messages, total_tokens):
-            return None
-
-        cutoff_index = self._determine_cutoff_index(messages)
-        if cutoff_index <= 0:
-            return None
-
-        messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
-        self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
-        summary = self._create_summary(messages_to_summarize)
-        new_messages = self._build_new_messages(summary)
-
-        return {
-            "messages": [
-                RemoveMessage(id=REMOVE_ALL_MESSAGES),
-                *new_messages,
-                *preserved_messages,
-            ]
-        }
-
-    async def _amaybe_summarize(self, state: AgentState, runtime: Runtime) -> dict | None:
-        messages = state["messages"]
-        self._ensure_message_ids(messages)
-
-        total_tokens = self.token_counter(messages)
-        if not self._should_summarize(messages, total_tokens):
-            return None
-
-        cutoff_index = self._determine_cutoff_index(messages)
-        if cutoff_index <= 0:
-            return None
-
-        messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
-        self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
-        summary = await self._acreate_summary(messages_to_summarize)
-        new_messages = self._build_new_messages(summary)
-
-        return {
-            "messages": [
-                RemoveMessage(id=REMOVE_ALL_MESSAGES),
-                *new_messages,
-                *preserved_messages,
-            ]
-        }
-
-    @override
-    def _build_new_messages(self, summary: str) -> list[HumanMessage]:
-        """Override the base implementation to let the human message with the special name 'summary'.
-        And this message will be ignored to display in the frontend, but still can be used as context for the model.
-        """
-        return [HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}", name="summary")]
-
-    def _partition_with_skill_rescue(
-        self,
-        messages: list[AnyMessage],
-        cutoff_index: int,
-    ) -> tuple[list[AnyMessage], list[AnyMessage]]:
-        """Partition like the parent, then rescue recently-loaded skill bundles."""
-        to_summarize, preserved = self._partition_messages(messages, cutoff_index)
-
-        if self._preserve_recent_skill_count == 0 or self._preserve_recent_skill_tokens == 0 or not to_summarize:
-            return to_summarize, preserved
-
-        try:
-            bundles = self._find_skill_bundles(to_summarize, self._skills_container_path)
-        except Exception:
-            logger.exception("Skill-preserving summarization rescue failed; falling back to default partition")
-            return to_summarize, preserved
-
-        if not bundles:
-            return to_summarize, preserved
-
-        rescue_bundles = self._select_bundles_to_rescue(bundles)
-        if not rescue_bundles:
-            return to_summarize, preserved
-
-        bundles_by_ai_index = {bundle.ai_index: bundle for bundle in rescue_bundles}
-        rescue_tool_indices = {idx for bundle in rescue_bundles for idx in bundle.skill_tool_indices}
-        rescued: list[AnyMessage] = []
-        remaining: list[AnyMessage] = []
-        for i, msg in enumerate(to_summarize):
-            bundle = bundles_by_ai_index.get(i)
-            if bundle is not None and isinstance(msg, AIMessage):
-                rescued_tool_calls = [tc for tc in msg.tool_calls if tc.get("id") in bundle.skill_tool_call_ids]
-                remaining_tool_calls = [tc for tc in msg.tool_calls if tc.get("id") not in bundle.skill_tool_call_ids]
-
-                if rescued_tool_calls:
-                    rescued.append(_clone_ai_message(msg, rescued_tool_calls, content=""))
-                if remaining_tool_calls or msg.content:
-                    remaining.append(_clone_ai_message(msg, remaining_tool_calls))
-                continue
-
-            if i in rescue_tool_indices:
-                rescued.append(msg)
-                continue
-
-            remaining.append(msg)
-
-        return remaining, rescued + preserved
-
-    def _find_skill_bundles(
-        self,
-        messages: list[AnyMessage],
-        skills_root: str,
-    ) -> list[_SkillBundle]:
-        """Locate AIMessage + paired ToolMessage groups that load skill files."""
-        bundles: list[_SkillBundle] = []
-        n = len(messages)
-        i = 0
-        while i < n:
-            msg = messages[i]
-            if not (isinstance(msg, AIMessage) and msg.tool_calls):
-                i += 1
-                continue
-
-            tool_calls = list(msg.tool_calls)
-            skill_paths_by_id: dict[str, str] = {}
-            for tc in tool_calls:
-                if self._is_skill_tool_call(tc, skills_root):
-                    tc_id = tc.get("id")
-                    path = _tool_call_path(tc)
-                    if tc_id and path:
-                        skill_paths_by_id[tc_id] = path
-
-            if not skill_paths_by_id:
-                i += 1
-                continue
-
-            skill_tool_tokens = 0
-            skill_key_parts: list[str] = []
-            skill_tool_indices: list[int] = []
-            matched_skill_call_ids: set[str] = set()
-
-            j = i + 1
-            while j < n and isinstance(messages[j], ToolMessage):
-                j += 1
-
-            for k in range(i + 1, j):
-                tool_msg = messages[k]
-                if isinstance(tool_msg, ToolMessage) and tool_msg.tool_call_id in skill_paths_by_id:
-                    skill_tool_tokens += self.token_counter([tool_msg])
-                    skill_key_parts.append(skill_paths_by_id[tool_msg.tool_call_id])
-                    skill_tool_indices.append(k)
-                    matched_skill_call_ids.add(tool_msg.tool_call_id)
-
-            if not skill_tool_indices:
-                i = j
-                continue
-
-            bundles.append(
-                _SkillBundle(
-                    ai_index=i,
-                    skill_tool_indices=tuple(skill_tool_indices),
-                    skill_tool_call_ids=frozenset(matched_skill_call_ids),
-                    skill_tool_tokens=skill_tool_tokens,
-                    skill_key="|".join(sorted(skill_key_parts)),
-                )
-            )
-            i = j
-
-        return bundles
-
-    def _select_bundles_to_rescue(self, bundles: list[_SkillBundle]) -> list[_SkillBundle]:
-        """Pick bundles to keep, walking newest-first under count/token budgets."""
-        selected: list[_SkillBundle] = []
-        if not bundles:
-            return selected
-
-        seen_skill_keys: set[str] = set()
-        total_tokens = 0
-        kept = 0
-
-        for bundle in reversed(bundles):
-            if kept >= self._preserve_recent_skill_count:
-                break
-            if bundle.skill_key in seen_skill_keys:
-                continue
-            if bundle.skill_tool_tokens > self._preserve_recent_skill_tokens_per_skill:
-                continue
-            if total_tokens + bundle.skill_tool_tokens > self._preserve_recent_skill_tokens:
-                continue
-
-            selected.append(bundle)
-            total_tokens += bundle.skill_tool_tokens
-            kept += 1
-            seen_skill_keys.add(bundle.skill_key)
-
-        selected.reverse()
-        return selected
-
-    def _is_skill_tool_call(self, tool_call: dict[str, Any], skills_root: str) -> bool:
-        """Return True when ``tool_call`` reads a file under the configured skills root."""
-        name = tool_call.get("name") or ""
-        if name not in self._skill_file_read_tool_names:
-            return False
-        path = _tool_call_path(tool_call)
-        if not path:
-            return False
-        normalized_root = skills_root.rstrip("/")
-        return path == normalized_root or path.startswith(normalized_root + "/")
-
-    def _fire_hooks(
-        self,
-        messages_to_summarize: list[AnyMessage],
-        preserved_messages: list[AnyMessage],
-        runtime: Runtime,
-    ) -> None:
-        if not self._before_summarization_hooks:
-            return
-
-        event = SummarizationEvent(
-            messages_to_summarize=tuple(messages_to_summarize),
-            preserved_messages=tuple(preserved_messages),
-            thread_id=_resolve_thread_id(runtime),
-            agent_name=_resolve_agent_name(runtime),
-            runtime=runtime,
-        )
-
-        for hook in self._before_summarization_hooks:
-            try:
-                hook(event)
-            except Exception:
-                hook_name = getattr(hook, "__name__", None) or type(hook).__name__
-                logger.exception("before_summarization hook %s failed", hook_name)
@@ -1,13 +1,12 @@
 import logging
-from datetime import UTC, datetime
 from typing import NotRequired, override

 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langgraph.config import get_config
 from langgraph.runtime import Runtime

 from deerflow.agents.thread_state import ThreadDataState
-from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime.user_context import get_effective_user_id

@@ -78,10 +77,14 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
        return self._get_thread_paths(thread_id, user_id=user_id)

    @override
-    def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
-        thread_id = runtime.context.thread_id
+    def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
+        context = runtime.context or {}
+        thread_id = context.get("thread_id")
+        if thread_id is None:
+            config = get_config()
+            thread_id = config.get("configurable", {}).get("thread_id")

-        if not thread_id:
+        if thread_id is None:
            raise ValueError("Thread ID is required in runtime context or config.configurable")

        user_id = get_effective_user_id()
@@ -94,20 +97,8 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
            paths = self._create_thread_directories(thread_id, user_id=user_id)
            logger.debug("Created thread data directories for thread %s", thread_id)

-        messages = list(state.get("messages", []))
-        last_message = messages[-1] if messages else None
-
-        if last_message and isinstance(last_message, HumanMessage):
-            messages[-1] = HumanMessage(
-                content=last_message.content,
-                id=last_message.id,
-                name=last_message.name or "user-input",
-                additional_kwargs={**last_message.additional_kwargs, "run_id": runtime.context.get("run_id"), "timestamp": datetime.now(UTC).isoformat()},
-            )
-
        return {
            "thread_data": {
                **paths,
-            },
-            "messages": messages,
+            }
        }
@@ -1,7 +1,6 @@
 """Middleware for automatic thread title generation."""

 import logging
-import re
 from typing import Any, NotRequired, override

 from langchain.agents import AgentState
@@ -9,9 +8,7 @@ from langchain.agents.middleware import AgentMiddleware
 from langgraph.config import get_config
 from langgraph.runtime import Runtime

-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import DeerFlowContext
-from deerflow.config.title_config import TitleConfig
+from deerflow.config.title_config import get_title_config
 from deerflow.models import create_chat_model

 logger = logging.getLogger(__name__)
@@ -47,9 +44,10 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):

        return ""

-    def _should_generate_title(self, state: TitleMiddlewareState, title_config: TitleConfig) -> bool:
+    def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
        """Check if we should generate a title for this thread."""
-        if not title_config.enabled:
+        config = get_title_config()
+        if not config.enabled:
            return False

        # Check if thread already has a title in state
@@ -68,39 +66,37 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
        # Generate title after first complete exchange
        return len(user_messages) == 1 and len(assistant_messages) >= 1

-    def _build_title_prompt(self, state: TitleMiddlewareState, title_config: TitleConfig) -> tuple[str, str]:
+    def _build_title_prompt(self, state: TitleMiddlewareState) -> tuple[str, str]:
        """Extract user/assistant messages and build the title prompt.

        Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
        """
+        config = get_title_config()
        messages = state.get("messages", [])

        user_msg_content = next((m.content for m in messages if m.type == "human"), "")
        assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")

        user_msg = self._normalize_content(user_msg_content)
-        assistant_msg = self._strip_think_tags(self._normalize_content(assistant_msg_content))
+        assistant_msg = self._normalize_content(assistant_msg_content)

-        prompt = title_config.prompt_template.format(
-            max_words=title_config.max_words,
+        prompt = config.prompt_template.format(
+            max_words=config.max_words,
            user_msg=user_msg[:500],
            assistant_msg=assistant_msg[:500],
        )
        return prompt, user_msg

-    def _strip_think_tags(self, text: str) -> str:
-        """Remove <think>...</think> blocks emitted by reasoning models (e.g. minimax, DeepSeek-R1)."""
-        return re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.IGNORECASE).strip()
-
-    def _parse_title(self, content: object, title_config: TitleConfig) -> str:
+    def _parse_title(self, content: object) -> str:
        """Normalize model output into a clean title string."""
+        config = get_title_config()
        title_content = self._normalize_content(content)
-        title_content = self._strip_think_tags(title_content)
        title = title_content.strip().strip('"').strip("'")
-        return title[: title_config.max_chars] if len(title) > title_config.max_chars else title
+        return title[: config.max_chars] if len(title) > config.max_chars else title

-    def _fallback_title(self, user_msg: str, title_config: TitleConfig) -> str:
-        fallback_chars = min(title_config.max_chars, 50)
+    def _fallback_title(self, user_msg: str) -> str:
+        config = get_title_config()
+        fallback_chars = min(config.max_chars, 50)
        if len(user_msg) > fallback_chars:
            return user_msg[:fallback_chars].rstrip() + "..."
        return user_msg if user_msg else "New Conversation"
@@ -119,39 +115,39 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
        config["tags"] = [*(config.get("tags") or []), "middleware:title"]
        return config

-    def _generate_title_result(self, state: TitleMiddlewareState, title_config: TitleConfig) -> dict | None:
+    def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
        """Generate a local fallback title without blocking on an LLM call."""
-        if not self._should_generate_title(state, title_config):
+        if not self._should_generate_title(state):
            return None

-        _, user_msg = self._build_title_prompt(state, title_config)
-        return {"title": self._fallback_title(user_msg, title_config)}
+        _, user_msg = self._build_title_prompt(state)
+        return {"title": self._fallback_title(user_msg)}

-    async def _agenerate_title_result(self, state: TitleMiddlewareState, app_config: AppConfig) -> dict | None:
+    async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
        """Generate a title asynchronously and fall back locally on failure."""
-        title_config = app_config.title
-        if not self._should_generate_title(state, title_config):
+        if not self._should_generate_title(state):
            return None

-        prompt, user_msg = self._build_title_prompt(state, title_config)
+        config = get_title_config()
+        prompt, user_msg = self._build_title_prompt(state)

        try:
-            if title_config.model_name:
-                model = create_chat_model(name=title_config.model_name, thinking_enabled=False, app_config=app_config)
+            if config.model_name:
+                model = create_chat_model(name=config.model_name, thinking_enabled=False)
            else:
-                model = create_chat_model(thinking_enabled=False, app_config=app_config)
+                model = create_chat_model(thinking_enabled=False)
            response = await model.ainvoke(prompt, config=self._get_runnable_config())
-            title = self._parse_title(response.content, title_config)
+            title = self._parse_title(response.content)
            if title:
                return {"title": title}
        except Exception:
            logger.debug("Failed to generate async title; falling back to local title", exc_info=True)
-        return {"title": self._fallback_title(user_msg, title_config)}
+        return {"title": self._fallback_title(user_msg)}

    @override
-    def after_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
-        return self._generate_title_result(state, runtime.context.app_config.title)
+    def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
+        return self._generate_title_result(state)

    @override
-    async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
-        return await self._agenerate_title_result(state, runtime.context.app_config)
+    async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
+        return await self._agenerate_title_result(state)
@@ -1,14 +1,9 @@
-"""Middleware that extends TodoListMiddleware with context-loss detection and premature-exit prevention.
+"""Middleware that extends TodoListMiddleware with context-loss detection.

 When the message history is truncated (e.g., by SummarizationMiddleware), the
 original `write_todos` tool call and its ToolMessage can be scrolled out of the
 active context window. This middleware detects that situation and injects a
 reminder message so the model still knows about the outstanding todo list.
-
-Additionally, this middleware prevents the agent from exiting the loop while
-there are still incomplete todo items. When the model produces a final response
-(no tool calls) but todos are not yet complete, the middleware injects a reminder
-and jumps back to the model node to force continued engagement.
 """

 from __future__ import annotations
@@ -17,7 +12,6 @@ from typing import Any, override

 from langchain.agents.middleware import TodoListMiddleware
 from langchain.agents.middleware.todo import PlanningState, Todo
-from langchain.agents.middleware.types import hook_config
 from langchain_core.messages import AIMessage, HumanMessage
 from langgraph.runtime import Runtime

@@ -40,11 +34,6 @@ def _reminder_in_messages(messages: list[Any]) -> bool:
    return False


-def _completion_reminder_count(messages: list[Any]) -> int:
-    """Return the number of todo_completion_reminder HumanMessages in *messages*."""
-    return sum(1 for msg in messages if isinstance(msg, HumanMessage) and getattr(msg, "name", None) == "todo_completion_reminder")
-
-
 def _format_todos(todos: list[Todo]) -> str:
    """Format a list of Todo items into a human-readable string."""
    lines: list[str] = []
@@ -68,7 +57,7 @@ class TodoMiddleware(TodoListMiddleware):
    def before_model(
        self,
        state: PlanningState,
-        runtime: Runtime,
+        runtime: Runtime,  # noqa: ARG002
    ) -> dict[str, Any] | None:
        """Inject a todo-list reminder when write_todos has left the context window."""
        todos: list[Todo] = state.get("todos") or []  # type: ignore[assignment]
@@ -109,71 +98,3 @@ class TodoMiddleware(TodoListMiddleware):
    ) -> dict[str, Any] | None:
        """Async version of before_model."""
        return self.before_model(state, runtime)
-
-    # Maximum number of completion reminders before allowing the agent to exit.
-    # This prevents infinite loops when the agent cannot make further progress.
-    _MAX_COMPLETION_REMINDERS = 2
-
-    @hook_config(can_jump_to=["model"])
-    @override
-    def after_model(
-        self,
-        state: PlanningState,
-        runtime: Runtime,
-    ) -> dict[str, Any] | None:
-        """Prevent premature agent exit when todo items are still incomplete.
-
-        In addition to the base class check for parallel ``write_todos`` calls,
-        this override intercepts model responses that have no tool calls while
-        there are still incomplete todo items. It injects a reminder
-        ``HumanMessage`` and jumps back to the model node so the agent
-        continues working through the todo list.
-
-        A retry cap of ``_MAX_COMPLETION_REMINDERS`` (default 2) prevents
-        infinite loops when the agent cannot make further progress.
-        """
-        # 1. Preserve base class logic (parallel write_todos detection).
-        base_result = super().after_model(state, runtime)
-        if base_result is not None:
-            return base_result
-
-        # 2. Only intervene when the agent wants to exit (no tool calls).
-        messages = state.get("messages") or []
-        last_ai = next((m for m in reversed(messages) if isinstance(m, AIMessage)), None)
-        if not last_ai or last_ai.tool_calls:
-            return None
-
-        # 3. Allow exit when all todos are completed or there are no todos.
-        todos: list[Todo] = state.get("todos") or []  # type: ignore[assignment]
-        if not todos or all(t.get("status") == "completed" for t in todos):
-            return None
-
-        # 4. Enforce a reminder cap to prevent infinite re-engagement loops.
-        if _completion_reminder_count(messages) >= self._MAX_COMPLETION_REMINDERS:
-            return None
-
-        # 5. Inject a reminder and force the agent back to the model.
-        incomplete = [t for t in todos if t.get("status") != "completed"]
-        incomplete_text = "\n".join(f"- [{t.get('status', 'pending')}] {t.get('content', '')}" for t in incomplete)
-        reminder = HumanMessage(
-            name="todo_completion_reminder",
-            content=(
-                "<system_reminder>\n"
-                "You have incomplete todo items that must be finished before giving your final response:\n\n"
-                f"{incomplete_text}\n\n"
-                "Please continue working on these tasks. Call `write_todos` to mark items as completed "
-                "as you finish them, and only respond when all items are done.\n"
-                "</system_reminder>"
-            ),
-        )
-        return {"jump_to": "model", "messages": [reminder]}
-
-    @override
-    @hook_config(can_jump_to=["model"])
-    async def aafter_model(
-        self,
-        state: PlanningState,
-        runtime: Runtime,
-    ) -> dict[str, Any] | None:
-        """Async version of after_model."""
-        return self.after_model(state, runtime)
@@ -1,10 +1,8 @@
 """Tool error handling middleware and shared runtime middleware builders."""

-from __future__ import annotations
-
 import logging
 from collections.abc import Awaitable, Callable
-from typing import TYPE_CHECKING, override
+from typing import override

 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
@@ -13,9 +11,6 @@ from langgraph.errors import GraphBubbleUp
 from langgraph.prebuilt.tool_node import ToolCallRequest
 from langgraph.types import Command

-if TYPE_CHECKING:
-    from deerflow.config.app_config import AppConfig
-
 logger = logging.getLogger(__name__)

 _MISSING_TOOL_CALL_ID = "missing_tool_call_id"
@@ -72,7 +67,6 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):

 def _build_runtime_middlewares(
    *,
-    app_config: "AppConfig",
    include_uploads: bool,
    include_dangling_tool_call_patch: bool,
    lazy_init: bool = True,
@@ -100,7 +94,9 @@ def _build_runtime_middlewares(
    middlewares.append(LLMErrorHandlingMiddleware())

    # Guardrail middleware (if configured)
-    guardrails_config = app_config.guardrails
+    from deerflow.config.guardrails_config import get_guardrails_config
+
+    guardrails_config = get_guardrails_config()
    if guardrails_config.enabled and guardrails_config.provider:
        import inspect

@@ -129,10 +125,9 @@ def _build_runtime_middlewares(
    return middlewares


-def build_lead_runtime_middlewares(*, app_config: "AppConfig", lazy_init: bool = True) -> list[AgentMiddleware]:
+def build_lead_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
    """Middlewares shared by lead agent runtime before lead-only middlewares."""
    return _build_runtime_middlewares(
-        app_config=app_config,
        include_uploads=True,
        include_dangling_tool_call_patch=True,
        lazy_init=lazy_init,
@@ -9,7 +9,6 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime

-from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.utils.file_conversion import extract_outline
@@ -186,7 +185,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
        return files if files else None

    @override
-    def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+    def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
        """Inject uploaded files information before agent execution.

        New files come from the current message's additional_kwargs.files.
@@ -215,7 +214,14 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
            return None

        # Resolve uploads directory for existence checks
-        thread_id = runtime.context.thread_id
+        thread_id = (runtime.context or {}).get("thread_id")
+        if thread_id is None:
+            try:
+                from langgraph.config import get_config
+
+                thread_id = get_config().get("configurable", {}).get("thread_id")
+            except RuntimeError:
+                pass  # get_config() raises outside a runnable context (e.g. unit tests)
        uploads_dir = self._paths.sandbox_uploads_dir(thread_id, user_id=get_effective_user_id()) if thread_id else None

        # Get newly uploaded files from the current message's additional_kwargs.files
@@ -257,27 +263,22 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
        files_message = self._create_files_message(new_files, historical_files)

        # Extract original content - handle both string and list formats
-        original_content = last_message.content
-        if isinstance(original_content, str):
-            # Simple case: string content, just prepend files message
-            updated_content = f"{files_message}\n\n{original_content}"
-        elif isinstance(original_content, list):
-            # Complex case: list content (multimodal), preserve all blocks
-            # Prepend files message as the first text block
-            files_block = {"type": "text", "text": f"{files_message}\n\n"}
-            # Keep all original blocks (including images)
-            updated_content = [files_block, *original_content]
-        else:
-            # Other types, preserve as-is
-            updated_content = original_content
+        original_content = ""
+        if isinstance(last_message.content, str):
+            original_content = last_message.content
+        elif isinstance(last_message.content, list):
+            text_parts = []
+            for block in last_message.content:
+                if isinstance(block, dict) and block.get("type") == "text":
+                    text_parts.append(block.get("text", ""))
+            original_content = "\n".join(text_parts)

        # Create new message with combined content.
        # Preserve additional_kwargs (including files metadata) so the frontend
        # can read structured file info from the streamed message.
        updated_message = HumanMessage(
-            content=updated_content,
+            content=f"{files_message}\n\n{original_content}",
            id=last_message.id,
-            name=last_message.name,
            additional_kwargs=last_message.additional_kwargs,
        )

@@ -36,9 +36,8 @@ from deerflow.agents.lead_agent.agent import _build_middlewares
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import DeerFlowContext
-from deerflow.config.extensions_config import ExtensionsConfig
+from deerflow.config.app_config import get_app_config, reload_app_config
+from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
 from deerflow.config.paths import get_paths
 from deerflow.models import create_chat_model
 from deerflow.runtime.user_context import get_effective_user_id
@@ -117,7 +116,6 @@ class DeerFlowClient:
        config_path: str | None = None,
        checkpointer=None,
        *,
-        config: AppConfig | None = None,
        model_name: str | None = None,
        thinking_enabled: bool = True,
        subagent_enabled: bool = False,
@@ -132,14 +130,9 @@ class DeerFlowClient:

        Args:
            config_path: Path to config.yaml. Uses default resolution if None.
-                Ignored when ``config`` is provided.
            checkpointer: LangGraph checkpointer instance for state persistence.
                Required for multi-turn conversations on the same thread_id.
                Without a checkpointer, each call is stateless.
-            config: Optional pre-constructed AppConfig. When provided, it takes
-                precedence over ``config_path`` and no file is read. Enables
-                multi-client isolation: two clients with different configs can
-                coexist in the same process without touching process-global state.
            model_name: Override the default model name from config.
            thinking_enabled: Enable model's extended thinking.
            subagent_enabled: Enable subagent delegation.
@@ -148,18 +141,9 @@ class DeerFlowClient:
            available_skills: Optional set of skill names to make available. If None (default), all scanned skills are available.
            middlewares: Optional list of custom middlewares to inject into the agent.
        """
-        # Constructor-captured config: the client owns its AppConfig for its lifetime.
-        # Multiple clients with different configs do not contend.
-        #
-        # Priority: explicit ``config=`` > explicit ``config_path=`` > ``AppConfig.from_file()``
-        # with default path resolution. There is no ambient global fallback; if
-        # config.yaml cannot be located, ``from_file`` raises loudly.
-        if config is not None:
-            self._app_config = config
-        elif config_path is not None:
-            self._app_config = AppConfig.from_file(config_path)
-        else:
-            self._app_config = AppConfig.from_file()
+        if config_path is not None:
+            reload_app_config(config_path)
+        self._app_config = get_app_config()

        if agent_name is not None and not AGENT_NAME_PATTERN.match(agent_name):
            raise ValueError(f"Invalid agent name '{agent_name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
@@ -187,15 +171,6 @@ class DeerFlowClient:
        self._agent = None
        self._agent_config_key = None

-    def _reload_config(self) -> None:
-        """Reload config from file and refresh the cached reference.
-
-        Only the client's own ``_app_config`` is rebuilt. Other clients
-        and the process-global are untouched, so multi-client coexistence
-        survives reload.
-        """
-        self._app_config = AppConfig.from_file()
-
    # ------------------------------------------------------------------
    # Internal helpers
    # ------------------------------------------------------------------
@@ -253,11 +228,10 @@ class DeerFlowClient:
        max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)

        kwargs: dict[str, Any] = {
-            "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=self._app_config),
+            "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
            "tools": self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled),
-            "middleware": _build_middlewares(self._app_config, config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
+            "middleware": _build_middlewares(config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
            "system_prompt": apply_prompt_template(
-                self._app_config,
                subagent_enabled=subagent_enabled,
                max_concurrent_subagents=max_concurrent_subagents,
                agent_name=self._agent_name,
@@ -269,7 +243,7 @@ class DeerFlowClient:
        if checkpointer is None:
            from deerflow.runtime.checkpointer import get_checkpointer

-            checkpointer = get_checkpointer(self._app_config)
+            checkpointer = get_checkpointer()
        if checkpointer is not None:
            kwargs["checkpointer"] = checkpointer

@@ -277,11 +251,12 @@ class DeerFlowClient:
        self._agent_config_key = key
        logger.info("Agent created: agent_name=%s, model=%s, thinking=%s", self._agent_name, model_name, thinking_enabled)

-    def _get_tools(self, *, model_name: str | None, subagent_enabled: bool):
+    @staticmethod
+    def _get_tools(*, model_name: str | None, subagent_enabled: bool):
        """Lazy import to avoid circular dependency at module level."""
        from deerflow.tools import get_available_tools

-        return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=self._app_config)
+        return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled)

    @staticmethod
    def _serialize_tool_calls(tool_calls) -> list[dict]:
@@ -402,7 +377,7 @@ class DeerFlowClient:
        if checkpointer is None:
            from deerflow.runtime.checkpointer.provider import get_checkpointer

-            checkpointer = get_checkpointer(self._app_config)
+            checkpointer = get_checkpointer()

        thread_info_map = {}

@@ -457,7 +432,7 @@ class DeerFlowClient:
        if checkpointer is None:
            from deerflow.runtime.checkpointer.provider import get_checkpointer

-            checkpointer = get_checkpointer(self._app_config)
+            checkpointer = get_checkpointer()

        config = {"configurable": {"thread_id": thread_id}}
        checkpoints = []
@@ -577,7 +552,9 @@ class DeerFlowClient:
        self._ensure_agent(config)

        state: dict[str, Any] = {"messages": [HumanMessage(content=message)]}
-        context = DeerFlowContext(app_config=self._app_config, thread_id=thread_id, agent_name=self._agent_name)
+        context = {"thread_id": thread_id}
+        if self._agent_name:
+            context["agent_name"] = self._agent_name

        seen_ids: set[str] = set()
        # Cross-mode handoff: ids already streamed via LangGraph ``messages``
@@ -746,10 +723,6 @@ class DeerFlowClient:
            Dict with "models" key containing list of model info dicts,
            matching the Gateway API ``ModelsListResponse`` schema.
        """
-        token_usage_enabled = getattr(getattr(self._app_config, "token_usage", None), "enabled", False)
-        if not isinstance(token_usage_enabled, bool):
-            token_usage_enabled = False
-
        return {
            "models": [
                {
@@ -761,8 +734,7 @@ class DeerFlowClient:
                    "supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
                }
                for model in self._app_config.models
-            ],
-            "token_usage": {"enabled": token_usage_enabled},
+            ]
        }

    def list_skills(self, enabled_only: bool = False) -> dict:
@@ -786,7 +758,7 @@ class DeerFlowClient:
                    "category": s.category,
                    "enabled": s.enabled,
                }
-                for s in load_skills(self._app_config, enabled_only=enabled_only)
+                for s in load_skills(enabled_only=enabled_only)
            ]
        }

@@ -798,19 +770,19 @@ class DeerFlowClient:
        """
        from deerflow.agents.memory.updater import get_memory_data

-        return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
+        return get_memory_data(user_id=get_effective_user_id())

    def export_memory(self) -> dict:
        """Export current memory data for backup or transfer."""
        from deerflow.agents.memory.updater import get_memory_data

-        return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
+        return get_memory_data(user_id=get_effective_user_id())

    def import_memory(self, memory_data: dict) -> dict:
        """Import and persist full memory data."""
        from deerflow.agents.memory.updater import import_memory_data

-        return import_memory_data(self._app_config.memory, memory_data, user_id=get_effective_user_id())
+        return import_memory_data(memory_data, user_id=get_effective_user_id())

    def get_model(self, name: str) -> dict | None:
        """Get a specific model's configuration by name.
@@ -845,8 +817,8 @@ class DeerFlowClient:
            Dict with "mcp_servers" key mapping server name to config,
            matching the Gateway API ``McpConfigResponse`` schema.
        """
-        ext = self._app_config.extensions
-        return {"mcp_servers": {name: server.model_dump() for name, server in ext.mcp_servers.items()}}
+        config = get_extensions_config()
+        return {"mcp_servers": {name: server.model_dump() for name, server in config.mcp_servers.items()}}

    def update_mcp_config(self, mcp_servers: dict[str, dict]) -> dict:
        """Update MCP server configurations.
@@ -868,19 +840,18 @@ class DeerFlowClient:
        if config_path is None:
            raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")

-        current_ext = self._app_config.extensions
+        current_config = get_extensions_config()

        config_data = {
            "mcpServers": mcp_servers,
-            "skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
+            "skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
        }

        self._atomic_write_json(config_path, config_data)

        self._agent = None
        self._agent_config_key = None
-        self._reload_config()
-        reloaded = self._app_config.extensions
+        reloaded = reload_extensions_config()
        return {"mcp_servers": {name: server.model_dump() for name, server in reloaded.mcp_servers.items()}}

    # ------------------------------------------------------------------
@@ -898,7 +869,7 @@ class DeerFlowClient:
        """
        from deerflow.skills.loader import load_skills

-        skill = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
+        skill = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
        if skill is None:
            return None
        return {
@@ -925,7 +896,7 @@ class DeerFlowClient:
        """
        from deerflow.skills.loader import load_skills

-        skills = load_skills(self._app_config, enabled_only=False)
+        skills = load_skills(enabled_only=False)
        skill = next((s for s in skills if s.name == name), None)
        if skill is None:
            raise ValueError(f"Skill '{name}' not found")
@@ -934,25 +905,21 @@ class DeerFlowClient:
        if config_path is None:
            raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")

-        # Do not mutate self._app_config (frozen value). Compose the new
-        # skills state in a fresh dict, write it to disk, and let _reload_config()
-        # below rebuild AppConfig from the updated file.
-        ext = self._app_config.extensions
-        new_skills = {n: {"enabled": sc.enabled} for n, sc in ext.skills.items()}
-        new_skills[name] = {"enabled": enabled}
+        extensions_config = get_extensions_config()
+        extensions_config.skills[name] = SkillStateConfig(enabled=enabled)

        config_data = {
-            "mcpServers": {n: s.model_dump() for n, s in ext.mcp_servers.items()},
-            "skills": new_skills,
+            "mcpServers": {n: s.model_dump() for n, s in extensions_config.mcp_servers.items()},
+            "skills": {n: {"enabled": sc.enabled} for n, sc in extensions_config.skills.items()},
        }

        self._atomic_write_json(config_path, config_data)

        self._agent = None
        self._agent_config_key = None
-        self._reload_config()
+        reload_extensions_config()

-        updated = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
+        updated = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
        if updated is None:
            raise RuntimeError(f"Skill '{name}' disappeared after update")
        return {
@@ -990,25 +957,25 @@ class DeerFlowClient:
        """
        from deerflow.agents.memory.updater import reload_memory_data

-        return reload_memory_data(self._app_config.memory, user_id=get_effective_user_id())
+        return reload_memory_data(user_id=get_effective_user_id())

    def clear_memory(self) -> dict:
        """Clear all persisted memory data."""
        from deerflow.agents.memory.updater import clear_memory_data

-        return clear_memory_data(self._app_config.memory, user_id=get_effective_user_id())
+        return clear_memory_data(user_id=get_effective_user_id())

    def create_memory_fact(self, content: str, category: str = "context", confidence: float = 0.5) -> dict:
        """Create a single fact manually."""
        from deerflow.agents.memory.updater import create_memory_fact

-        return create_memory_fact(self._app_config.memory, content=content, category=category, confidence=confidence)
+        return create_memory_fact(content=content, category=category, confidence=confidence)

    def delete_memory_fact(self, fact_id: str) -> dict:
        """Delete a single fact from memory by fact id."""
        from deerflow.agents.memory.updater import delete_memory_fact

-        return delete_memory_fact(self._app_config.memory, fact_id)
+        return delete_memory_fact(fact_id)

    def update_memory_fact(
        self,
@@ -1021,7 +988,6 @@ class DeerFlowClient:
        from deerflow.agents.memory.updater import update_memory_fact

        return update_memory_fact(
-            self._app_config.memory,
            fact_id=fact_id,
            content=content,
            category=category,
@@ -1034,7 +1000,9 @@ class DeerFlowClient:
        Returns:
            Memory config dict.
        """
-        config = self._app_config.memory
+        from deerflow.config.memory_config import get_memory_config
+
+        config = get_memory_config()
        return {
            "enabled": config.enabled,
            "storage_path": config.storage_path,
@@ -25,7 +25,7 @@ except ImportError:  # pragma: no cover - Windows fallback
    fcntl = None  # type: ignore[assignment]
    import msvcrt

-from deerflow.config.app_config import AppConfig
+from deerflow.config import get_app_config
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox import Sandbox
@@ -90,8 +90,7 @@ class AioSandboxProvider(SandboxProvider):
          API_KEY: $MY_API_KEY
    """

-    def __init__(self, app_config: "AppConfig"):
-        self._app_config = app_config
+    def __init__(self):
        self._lock = threading.Lock()
        self._sandboxes: dict[str, AioSandbox] = {}  # sandbox_id -> AioSandbox instance
        self._sandbox_infos: dict[str, SandboxInfo] = {}  # sandbox_id -> SandboxInfo (for destroy)
@@ -121,16 +120,6 @@ class AioSandboxProvider(SandboxProvider):
        if self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT) > 0:
            self._start_idle_checker()

-    @property
-    def uses_thread_data_mounts(self) -> bool:
-        """Whether thread workspace/uploads/outputs are visible via mounts.
-
-        Local container backends bind-mount the thread data directories, so files
-        written by the gateway are already visible when the sandbox starts.
-        Remote backends may require explicit file sync.
-        """
-        return isinstance(self._backend, LocalContainerBackend)
-
    # ── Factory methods ──────────────────────────────────────────────────

    def _create_backend(self) -> SandboxBackend:
@@ -160,7 +149,8 @@ class AioSandboxProvider(SandboxProvider):

    def _load_config(self) -> dict:
        """Load sandbox configuration from app config."""
-        sandbox_config = self._app_config.sandbox
+        config = get_app_config()
+        sandbox_config = config.sandbox

        idle_timeout = getattr(sandbox_config, "idle_timeout", None)
        replicas = getattr(sandbox_config, "replicas", None)
@@ -283,15 +273,17 @@ class AioSandboxProvider(SandboxProvider):
            (paths.host_acp_workspace_dir(thread_id, user_id=user_id), "/mnt/acp-workspace", True),
        ]

-    def _get_skills_mount(self) -> tuple[str, str, bool] | None:
+    @staticmethod
+    def _get_skills_mount() -> tuple[str, str, bool] | None:
        """Get the skills directory mount configuration.

        Mount source uses DEER_FLOW_HOST_SKILLS_PATH when running inside Docker (DooD)
        so the host Docker daemon can resolve the path.
        """
        try:
-            skills_path = self._app_config.skills.get_skills_path()
-            container_path = self._app_config.skills.container_path
+            config = get_app_config()
+            skills_path = config.skills.get_skills_path()
+            container_path = config.skills.container_path

            if skills_path.exists():
                # When running inside Docker with DooD, use host-side skills path.
@@ -5,9 +5,9 @@ Web Search Tool - Search the web using DuckDuckGo (no API key required).
 import json
 import logging

-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config

 logger = logging.getLogger(__name__)

@@ -55,7 +55,6 @@ def _search_text(
@tool("web_search", parse_docstring=True)
 def web_search_tool(
    query: str,
-    runtime: ToolRuntime,
    max_results: int = 5,
 ) -> str:
    """Search the web for information. Use this tool to find current information, news, articles, and facts from the internet.
@@ -64,11 +63,11 @@ def web_search_tool(
        query: Search keywords describing what you want to find. Be specific for better results.
        max_results: Maximum number of results to return. Default is 5.
    """
-    tool_config = resolve_context(runtime).app_config.get_tool_config("web_search")
+    config = get_app_config().get_tool_config("web_search")

    # Override max_results from config if set
-    if tool_config is not None and "max_results" in tool_config.model_extra:
-        max_results = tool_config.model_extra.get("max_results", max_results)
+    if config is not None and "max_results" in config.model_extra:
+        max_results = config.model_extra.get("max_results", max_results)

    results = _search_text(
        query=query,
@@ -1,39 +1,37 @@
 import json

 from exa_py import Exa
-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config


-def _get_exa_client(app_config: AppConfig, tool_name: str = "web_search") -> Exa:
-    tool_config = app_config.get_tool_config(tool_name)
+def _get_exa_client(tool_name: str = "web_search") -> Exa:
+    config = get_app_config().get_tool_config(tool_name)
    api_key = None
-    if tool_config is not None and "api_key" in tool_config.model_extra:
-        api_key = tool_config.model_extra.get("api_key")
+    if config is not None and "api_key" in config.model_extra:
+        api_key = config.model_extra.get("api_key")
    return Exa(api_key=api_key)


@tool("web_search", parse_docstring=True)
-def web_search_tool(query: str, runtime: ToolRuntime) -> str:
+def web_search_tool(query: str) -> str:
    """Search the web.

    Args:
        query: The query to search for.
    """
    try:
-        app_config = resolve_context(runtime).app_config
-        tool_config = app_config.get_tool_config("web_search")
+        config = get_app_config().get_tool_config("web_search")
        max_results = 5
        search_type = "auto"
        contents_max_characters = 1000
-        if tool_config is not None:
-            max_results = tool_config.model_extra.get("max_results", max_results)
-            search_type = tool_config.model_extra.get("search_type", search_type)
-            contents_max_characters = tool_config.model_extra.get("contents_max_characters", contents_max_characters)
+        if config is not None:
+            max_results = config.model_extra.get("max_results", max_results)
+            search_type = config.model_extra.get("search_type", search_type)
+            contents_max_characters = config.model_extra.get("contents_max_characters", contents_max_characters)

-        client = _get_exa_client(app_config)
+        client = _get_exa_client()
        res = client.search(
            query,
            type=search_type,
@@ -56,7 +54,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:


@tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
+def web_fetch_tool(url: str) -> str:
    """Fetch the contents of a web page at a given URL.
    Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
    This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -67,7 +65,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
        url: The URL to fetch the contents of.
    """
    try:
-        client = _get_exa_client(resolve_context(runtime).app_config, "web_fetch")
+        client = _get_exa_client("web_fetch")
        res = client.get_contents([url], text={"max_characters": 4096})

        if res.results:
@@ -1,35 +1,33 @@
 import json

 from firecrawl import FirecrawlApp
-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config


-def _get_firecrawl_client(app_config: AppConfig, tool_name: str = "web_search") -> FirecrawlApp:
-    tool_config = app_config.get_tool_config(tool_name)
+def _get_firecrawl_client(tool_name: str = "web_search") -> FirecrawlApp:
+    config = get_app_config().get_tool_config(tool_name)
    api_key = None
-    if tool_config is not None and "api_key" in tool_config.model_extra:
-        api_key = tool_config.model_extra.get("api_key")
+    if config is not None and "api_key" in config.model_extra:
+        api_key = config.model_extra.get("api_key")
    return FirecrawlApp(api_key=api_key)  # type: ignore[arg-type]


@tool("web_search", parse_docstring=True)
-def web_search_tool(query: str, runtime: ToolRuntime) -> str:
+def web_search_tool(query: str) -> str:
    """Search the web.

    Args:
        query: The query to search for.
    """
    try:
-        app_config = resolve_context(runtime).app_config
-        tool_config = app_config.get_tool_config("web_search")
+        config = get_app_config().get_tool_config("web_search")
        max_results = 5
-        if tool_config is not None:
-            max_results = tool_config.model_extra.get("max_results", max_results)
+        if config is not None:
+            max_results = config.model_extra.get("max_results", max_results)

-        client = _get_firecrawl_client(app_config, "web_search")
+        client = _get_firecrawl_client("web_search")
        result = client.search(query, limit=max_results)

        # result.web contains list of SearchResultWeb objects
@@ -49,7 +47,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:


@tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
+def web_fetch_tool(url: str) -> str:
    """Fetch the contents of a web page at a given URL.
    Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
    This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -60,8 +58,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
        url: The URL to fetch the contents of.
    """
    try:
-        app_config = resolve_context(runtime).app_config
-        client = _get_firecrawl_client(app_config, "web_fetch")
+        client = _get_firecrawl_client("web_fetch")
        result = client.scrape(url, formats=["markdown"])

        markdown_content = result.markdown or ""
@@ -5,9 +5,9 @@ Image Search Tool - Search images using DuckDuckGo for reference in image genera
 import json
 import logging

-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config

 logger = logging.getLogger(__name__)

@@ -77,7 +77,6 @@ def _search_images(
@tool("image_search", parse_docstring=True)
 def image_search_tool(
    query: str,
-    runtime: ToolRuntime,
    max_results: int = 5,
    size: str | None = None,
    type_image: str | None = None,
@@ -100,11 +99,11 @@ def image_search_tool(
        type_image: Image type filter. Options: "photo", "clipart", "gif", "transparent", "line". Use "photo" for realistic references.
        layout: Layout filter. Options: "Square", "Tall", "Wide". Choose based on your generation needs.
    """
-    tool_config = resolve_context(runtime).app_config.get_tool_config("image_search")
+    config = get_app_config().get_tool_config("image_search")

    # Override max_results from config if set
-    if tool_config is not None and "max_results" in tool_config.model_extra:
-        max_results = tool_config.model_extra.get("max_results", max_results)
+    if config is not None and "max_results" in config.model_extra:
+        max_results = config.model_extra.get("max_results", max_results)

    results = _search_images(
        query=query,
@@ -1,7 +1,6 @@
-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config
 from deerflow.utils.readability import ReadabilityExtractor

 from .infoquest_client import InfoQuestClient
@@ -9,13 +8,13 @@ from .infoquest_client import InfoQuestClient
 readability_extractor = ReadabilityExtractor()


-def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
-    search_config = app_config.get_tool_config("web_search")
+def _get_infoquest_client() -> InfoQuestClient:
+    search_config = get_app_config().get_tool_config("web_search")
    search_time_range = -1
    if search_config is not None and "search_time_range" in search_config.model_extra:
        search_time_range = search_config.model_extra.get("search_time_range")

-    fetch_config = app_config.get_tool_config("web_fetch")
+    fetch_config = get_app_config().get_tool_config("web_fetch")
    fetch_time = -1
    if fetch_config is not None and "fetch_time" in fetch_config.model_extra:
        fetch_time = fetch_config.model_extra.get("fetch_time")
@@ -26,7 +25,7 @@ def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
    if fetch_config is not None and "navigation_timeout" in fetch_config.model_extra:
        navigation_timeout = fetch_config.model_extra.get("navigation_timeout")

-    image_search_config = app_config.get_tool_config("image_search")
+    image_search_config = get_app_config().get_tool_config("image_search")
    image_search_time_range = -1
    if image_search_config is not None and "image_search_time_range" in image_search_config.model_extra:
        image_search_time_range = image_search_config.model_extra.get("image_search_time_range")
@@ -45,18 +44,19 @@ def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:


@tool("web_search", parse_docstring=True)
-def web_search_tool(query: str, runtime: ToolRuntime) -> str:
+def web_search_tool(query: str) -> str:
    """Search the web.

    Args:
        query: The query to search for.
    """
-    client = _get_infoquest_client(resolve_context(runtime).app_config)
+
+    client = _get_infoquest_client()
    return client.web_search(query)


@tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
+def web_fetch_tool(url: str) -> str:
    """Fetch the contents of a web page at a given URL.
    Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
    This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -66,7 +66,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
    Args:
        url: The URL to fetch the contents of.
    """
-    client = _get_infoquest_client(resolve_context(runtime).app_config)
+    client = _get_infoquest_client()
    result = client.fetch(url)
    if result.startswith("Error: "):
        return result
@@ -75,7 +75,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:


@tool("image_search", parse_docstring=True)
-def image_search_tool(query: str, runtime: ToolRuntime) -> str:
+def image_search_tool(query: str) -> str:
    """Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.

    **When to use:**
@@ -89,5 +89,5 @@ def image_search_tool(query: str, runtime: ToolRuntime) -> str:
    Args:
        query: The query to search for images.
    """
-    client = _get_infoquest_client(resolve_context(runtime).app_config)
+    client = _get_infoquest_client()
    return client.image_search(query)
@@ -38,6 +38,6 @@ class JinaClient:

            return response.text
        except Exception as e:
-            error_message = f"Request to Jina API failed: {type(e).__name__}: {e}"
-            logger.warning(error_message)
+            error_message = f"Request to Jina API failed: {str(e)}"
+            logger.exception(error_message)
            return f"Error: {error_message}"
@@ -1,16 +1,14 @@
-import asyncio
-
-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool

 from deerflow.community.jina_ai.jina_client import JinaClient
-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config
 from deerflow.utils.readability import ReadabilityExtractor

 readability_extractor = ReadabilityExtractor()


@tool("web_fetch", parse_docstring=True)
-async def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
+async def web_fetch_tool(url: str) -> str:
    """Fetch the contents of a web page at a given URL.
    Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
    This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -22,11 +20,11 @@ async def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
    """
    jina_client = JinaClient()
    timeout = 10
-    tool_config = resolve_context(runtime).app_config.get_tool_config("web_fetch")
-    if tool_config is not None and "timeout" in tool_config.model_extra:
-        timeout = tool_config.model_extra.get("timeout")
+    config = get_app_config().get_tool_config("web_fetch")
+    if config is not None and "timeout" in config.model_extra:
+        timeout = config.model_extra.get("timeout")
    html_content = await jina_client.crawl(url, return_format="html", timeout=timeout)
    if isinstance(html_content, str) and html_content.startswith("Error:"):
        return html_content
-    article = await asyncio.to_thread(readability_extractor.extract_article, html_content)
+    article = readability_extractor.extract_article(html_content)
    return article.to_markdown()[:4096]
@@ -1,34 +1,32 @@
 import json

-from langchain.tools import ToolRuntime, tool
+from langchain.tools import tool
 from tavily import TavilyClient

-from deerflow.config.app_config import AppConfig
-from deerflow.config.deer_flow_context import resolve_context
+from deerflow.config import get_app_config


-def _get_tavily_client(app_config: AppConfig) -> TavilyClient:
-    tool_config = app_config.get_tool_config("web_search")
+def _get_tavily_client() -> TavilyClient:
+    config = get_app_config().get_tool_config("web_search")
    api_key = None
-    if tool_config is not None and "api_key" in tool_config.model_extra:
-        api_key = tool_config.model_extra.get("api_key")
+    if config is not None and "api_key" in config.model_extra:
+        api_key = config.model_extra.get("api_key")
    return TavilyClient(api_key=api_key)


@tool("web_search", parse_docstring=True)
-def web_search_tool(query: str, runtime: ToolRuntime) -> str:
+def web_search_tool(query: str) -> str:
    """Search the web.

    Args:
        query: The query to search for.
    """
-    app_config = resolve_context(runtime).app_config
-    tool_config = app_config.get_tool_config("web_search")
+    config = get_app_config().get_tool_config("web_search")
    max_results = 5
-    if tool_config is not None and "max_results" in tool_config.model_extra:
-        max_results = tool_config.model_extra.get("max_results")
+    if config is not None and "max_results" in config.model_extra:
+        max_results = config.model_extra.get("max_results")

-    client = _get_tavily_client(app_config)
+    client = _get_tavily_client()
    res = client.search(query, max_results=max_results)
    normalized_results = [
        {
@@ -43,7 +41,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:


@tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
+def web_fetch_tool(url: str) -> str:
    """Fetch the contents of a web page at a given URL.
    Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
    This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -53,8 +51,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
    Args:
        url: The URL to fetch the contents of.
    """
-    app_config = resolve_context(runtime).app_config
-    client = _get_tavily_client(app_config)
+    client = _get_tavily_client()
    res = client.extract([url])
    if "failed_results" in res and len(res["failed_results"]) > 0:
        return f"Error: {res['failed_results'][0]['error']}"
@@ -1,6 +1,6 @@
-from .app_config import AppConfig
-from .extensions_config import ExtensionsConfig
-from .memory_config import MemoryConfig
+from .app_config import get_app_config
+from .extensions_config import ExtensionsConfig, get_extensions_config
+from .memory_config import MemoryConfig, get_memory_config
 from .paths import Paths, get_paths
 from .skill_evolution_config import SkillEvolutionConfig
 from .skills_config import SkillsConfig
@@ -13,16 +13,18 @@ from .tracing_config import (
 )

 __all__ = [
-    "AppConfig",
-    "ExtensionsConfig",
-    "MemoryConfig",
-    "Paths",
+    "get_app_config",
    "SkillEvolutionConfig",
-    "SkillsConfig",
-    "get_enabled_tracing_providers",
-    "get_explicitly_enabled_tracing_providers",
+    "Paths",
    "get_paths",
+    "SkillsConfig",
+    "ExtensionsConfig",
+    "get_extensions_config",
+    "MemoryConfig",
+    "get_memory_config",
    "get_tracing_config",
+    "get_explicitly_enabled_tracing_providers",
+    "get_enabled_tracing_providers",
    "is_tracing_enabled",
    "validate_enabled_tracing_providers",
 ]
@@ -1,13 +1,16 @@
 """ACP (Agent Client Protocol) agent configuration loaded from config.yaml."""

-from pydantic import BaseModel, ConfigDict, Field
+import logging
+from collections.abc import Mapping
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)


 class ACPAgentConfig(BaseModel):
    """Configuration for a single ACP-compatible agent."""

-    model_config = ConfigDict(frozen=True)
-
    command: str = Field(description="Command to launch the ACP agent subprocess")
    args: list[str] = Field(default_factory=list, description="Additional command arguments")
    env: dict[str, str] = Field(default_factory=dict, description="Environment variables to inject into the agent subprocess. Values starting with $ are resolved from host environment variables.")
@@ -21,3 +24,28 @@ class ACPAgentConfig(BaseModel):
            "are denied — the agent must be configured to operate without requesting permissions."
        ),
    )
+
+
+_acp_agents: dict[str, ACPAgentConfig] = {}
+
+
+def get_acp_agents() -> dict[str, ACPAgentConfig]:
+    """Get the currently configured ACP agents.
+
+    Returns:
+        Mapping of agent name -> ACPAgentConfig.  Empty dict if no ACP agents are configured.
+    """
+    return _acp_agents
+
+
+def load_acp_config_from_dict(config_dict: Mapping[str, Mapping[str, object]] | None) -> None:
+    """Load ACP agent configuration from a dictionary (typically from config.yaml).
+
+    Args:
+        config_dict: Mapping of agent name -> config fields.
+    """
+    global _acp_agents
+    if config_dict is None:
+        config_dict = {}
+    _acp_agents = {name: ACPAgentConfig(**cfg) for name, cfg in config_dict.items()}
+    logger.info("ACP config loaded: %d agent(s): %s", len(_acp_agents), list(_acp_agents.keys()))
@@ -1,14 +0,0 @@
-"""Configuration for the custom agents management API."""
-
-from pydantic import BaseModel, ConfigDict, Field
-
-
-class AgentsApiConfig(BaseModel):
-    """Configuration for custom-agent and user-profile management routes."""
-
-    model_config = ConfigDict(frozen=True)
-
-    enabled: bool = Field(
-        default=False,
-        description=("Whether to expose the custom-agent management API over HTTP. When disabled, the gateway rejects read/write access to custom agent SOUL.md, config, and USER.md prompt-management routes."),
-    )
@@ -5,7 +5,7 @@ import re
 from typing import Any

 import yaml
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel

 from deerflow.config.paths import get_paths

@@ -15,22 +15,9 @@ SOUL_FILENAME = "SOUL.md"
 AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")


-def validate_agent_name(name: str | None) -> str | None:
-    """Validate a custom agent name before using it in filesystem paths."""
-    if name is None:
-        return None
-    if not isinstance(name, str):
-        raise ValueError("Invalid agent name. Expected a string or None.")
-    if not AGENT_NAME_PATTERN.fullmatch(name):
-        raise ValueError(f"Invalid agent name '{name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
-    return name
-
-
 class AgentConfig(BaseModel):
    """Configuration for a custom agent."""

-    model_config = ConfigDict(frozen=True)
-
    name: str
    description: str = ""
    model: str | None = None
@@ -59,7 +46,8 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
    if name is None:
        return None

-    name = validate_agent_name(name)
+    if not AGENT_NAME_PATTERN.match(name):
+        raise ValueError(f"Invalid agent name '{name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
    agent_dir = get_paths().agent_dir(name)
    config_file = agent_dir / "config.yaml"

@@ -1,7 +1,6 @@
-from __future__ import annotations
-
 import logging
 import os
+from contextvars import ContextVar
 from pathlib import Path
 from typing import Any, Self

@@ -9,44 +8,30 @@ import yaml
 from dotenv import load_dotenv
 from pydantic import BaseModel, ConfigDict, Field

-from deerflow.config.acp_config import ACPAgentConfig
-from deerflow.config.agents_api_config import AgentsApiConfig
-from deerflow.config.checkpointer_config import CheckpointerConfig
+from deerflow.config.acp_config import load_acp_config_from_dict
+from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
 from deerflow.config.database_config import DatabaseConfig
 from deerflow.config.extensions_config import ExtensionsConfig
-from deerflow.config.guardrails_config import GuardrailsConfig
-from deerflow.config.memory_config import MemoryConfig
+from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
+from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.run_events_config import RunEventsConfig
 from deerflow.config.sandbox_config import SandboxConfig
 from deerflow.config.skill_evolution_config import SkillEvolutionConfig
 from deerflow.config.skills_config import SkillsConfig
-from deerflow.config.stream_bridge_config import StreamBridgeConfig
-from deerflow.config.subagents_config import SubagentsAppConfig
-from deerflow.config.summarization_config import SummarizationConfig
-from deerflow.config.title_config import TitleConfig
+from deerflow.config.stream_bridge_config import StreamBridgeConfig, load_stream_bridge_config_from_dict
+from deerflow.config.subagents_config import SubagentsAppConfig, load_subagents_config_from_dict
+from deerflow.config.summarization_config import SummarizationConfig, load_summarization_config_from_dict
+from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
 from deerflow.config.token_usage_config import TokenUsageConfig
 from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
-from deerflow.config.tool_search_config import ToolSearchConfig
+from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict

 load_dotenv()

 logger = logging.getLogger(__name__)


-CONFIG_FILE_DATABASE_DEFAULTS = {
-    "backend": "sqlite",
-    "sqlite_dir": ".deer-flow/data",
-}
-
-
-class CircuitBreakerConfig(BaseModel):
-    """Configuration for the LLM Circuit Breaker."""
-
-    failure_threshold: int = Field(default=5, description="Number of consecutive failures before tripping the circuit")
-    recovery_timeout_sec: int = Field(default=60, description="Time in seconds before attempting to recover the circuit")
-
-
 def _default_config_candidates() -> tuple[Path, ...]:
    """Return deterministic config.yaml locations without relying on cwd."""
    backend_dir = Path(__file__).resolve().parents[4]
@@ -70,16 +55,13 @@ class AppConfig(BaseModel):
    title: TitleConfig = Field(default_factory=TitleConfig, description="Automatic title generation configuration")
    summarization: SummarizationConfig = Field(default_factory=SummarizationConfig, description="Conversation summarization configuration")
    memory: MemoryConfig = Field(default_factory=MemoryConfig, description="Memory subsystem configuration")
-    agents_api: AgentsApiConfig = Field(default_factory=AgentsApiConfig, description="Custom-agent management API configuration")
    subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
    guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
-    circuit_breaker: CircuitBreakerConfig = Field(default_factory=CircuitBreakerConfig, description="LLM circuit breaker configuration")
+    model_config = ConfigDict(extra="allow", frozen=False)
    database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
    run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
-    model_config = ConfigDict(extra="allow", frozen=True)
    checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
    stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
-    acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP agent configurations keyed by agent name")

    @classmethod
    def resolve_config_path(cls, config_path: str | None = None) -> Path:
@@ -126,7 +108,41 @@ class AppConfig(BaseModel):
        cls._check_config_version(config_data, resolved_path)

        config_data = cls.resolve_env_variables(config_data)
-        cls._apply_database_defaults(config_data)
+
+        # Load title config if present
+        if "title" in config_data:
+            load_title_config_from_dict(config_data["title"])
+
+        # Load summarization config if present
+        if "summarization" in config_data:
+            load_summarization_config_from_dict(config_data["summarization"])
+
+        # Load memory config if present
+        if "memory" in config_data:
+            load_memory_config_from_dict(config_data["memory"])
+
+        # Load subagents config if present
+        if "subagents" in config_data:
+            load_subagents_config_from_dict(config_data["subagents"])
+
+        # Load tool_search config if present
+        if "tool_search" in config_data:
+            load_tool_search_config_from_dict(config_data["tool_search"])
+
+        # Load guardrails config if present
+        if "guardrails" in config_data:
+            load_guardrails_config_from_dict(config_data["guardrails"])
+
+        # Load checkpointer config if present
+        if "checkpointer" in config_data:
+            load_checkpointer_config_from_dict(config_data["checkpointer"])
+
+        # Load stream bridge config if present
+        if "stream_bridge" in config_data:
+            load_stream_bridge_config_from_dict(config_data["stream_bridge"])
+
+        # Always refresh ACP agent config so removed entries do not linger across reloads.
+        load_acp_config_from_dict(config_data.get("acp_agents", {}))

        # Load extensions config separately (it's in a different file)
        extensions_config = ExtensionsConfig.from_file()
@@ -135,18 +151,6 @@ class AppConfig(BaseModel):
        result = cls.model_validate(config_data)
        return result

-    @classmethod
-    def _apply_database_defaults(cls, config_data: dict[str, Any]) -> None:
-        """Apply config.yaml defaults for persistence when the section is absent."""
-        database_config = config_data.get("database")
-        if database_config is None:
-            database_config = {}
-            config_data["database"] = database_config
-        if not isinstance(database_config, dict):
-            return
-        for key, value in CONFIG_FILE_DATABASE_DEFAULTS.items():
-            database_config.setdefault(key, value)
-
    @classmethod
    def _check_config_version(cls, config_data: dict, config_path: Path) -> None:
        """Check if the user's config.yaml is outdated compared to config.example.yaml.
@@ -250,8 +254,130 @@ class AppConfig(BaseModel):
        """
        return next((group for group in self.tool_groups if group.name == name), None)

-    # AppConfig is a pure value object: construct with ``from_file()``, pass around.
-    # Composition roots that hold the resolved instance:
-    #   - Gateway:   ``app.state.config`` via ``Depends(get_config)``
-    #   - Client:    ``DeerFlowClient._app_config``
-    #   - Agent run: ``Runtime[DeerFlowContext].context.app_config``
+
+_app_config: AppConfig | None = None
+_app_config_path: Path | None = None
+_app_config_mtime: float | None = None
+_app_config_is_custom = False
+_current_app_config: ContextVar[AppConfig | None] = ContextVar("deerflow_current_app_config", default=None)
+_current_app_config_stack: ContextVar[tuple[AppConfig | None, ...]] = ContextVar("deerflow_current_app_config_stack", default=())
+
+
+def _get_config_mtime(config_path: Path) -> float | None:
+    """Get the modification time of a config file if it exists."""
+    try:
+        return config_path.stat().st_mtime
+    except OSError:
+        return None
+
+
+def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
+    """Load config from disk and refresh cache metadata."""
+    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
+
+    resolved_path = AppConfig.resolve_config_path(config_path)
+    _app_config = AppConfig.from_file(str(resolved_path))
+    _app_config_path = resolved_path
+    _app_config_mtime = _get_config_mtime(resolved_path)
+    _app_config_is_custom = False
+    return _app_config
+
+
+def get_app_config() -> AppConfig:
+    """Get the DeerFlow config instance.
+
+    Returns a cached singleton instance and automatically reloads it when the
+    underlying config file path or modification time changes. Use
+    `reload_app_config()` to force a reload, or `reset_app_config()` to clear
+    the cache.
+    """
+    global _app_config, _app_config_path, _app_config_mtime
+
+    runtime_override = _current_app_config.get()
+    if runtime_override is not None:
+        return runtime_override
+
+    if _app_config is not None and _app_config_is_custom:
+        return _app_config
+
+    resolved_path = AppConfig.resolve_config_path()
+    current_mtime = _get_config_mtime(resolved_path)
+
+    should_reload = _app_config is None or _app_config_path != resolved_path or _app_config_mtime != current_mtime
+    if should_reload:
+        if _app_config_path == resolved_path and _app_config_mtime is not None and current_mtime is not None and _app_config_mtime != current_mtime:
+            logger.info(
+                "Config file has been modified (mtime: %s -> %s), reloading AppConfig",
+                _app_config_mtime,
+                current_mtime,
+            )
+        _load_and_cache_app_config(str(resolved_path))
+    return _app_config
+
+
+def reload_app_config(config_path: str | None = None) -> AppConfig:
+    """Reload the config from file and update the cached instance.
+
+    This is useful when the config file has been modified and you want
+    to pick up the changes without restarting the application.
+
+    Args:
+        config_path: Optional path to config file. If not provided,
+                     uses the default resolution strategy.
+
+    Returns:
+        The newly loaded AppConfig instance.
+    """
+    return _load_and_cache_app_config(config_path)
+
+
+def reset_app_config() -> None:
+    """Reset the cached config instance.
+
+    This clears the singleton cache, causing the next call to
+    `get_app_config()` to reload from file. Useful for testing
+    or when switching between different configurations.
+    """
+    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
+    _app_config = None
+    _app_config_path = None
+    _app_config_mtime = None
+    _app_config_is_custom = False
+
+
+def set_app_config(config: AppConfig) -> None:
+    """Set a custom config instance.
+
+    This allows injecting a custom or mock config for testing purposes.
+
+    Args:
+        config: The AppConfig instance to use.
+    """
+    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
+    _app_config = config
+    _app_config_path = None
+    _app_config_mtime = None
+    _app_config_is_custom = True
+
+
+def peek_current_app_config() -> AppConfig | None:
+    """Return the runtime-scoped AppConfig override, if one is active."""
+    return _current_app_config.get()
+
+
+def push_current_app_config(config: AppConfig) -> None:
+    """Push a runtime-scoped AppConfig override for the current execution context."""
+    stack = _current_app_config_stack.get()
+    _current_app_config_stack.set(stack + (_current_app_config.get(),))
+    _current_app_config.set(config)
+
+
+def pop_current_app_config() -> None:
+    """Pop the latest runtime-scoped AppConfig override for the current execution context."""
+    stack = _current_app_config_stack.get()
+    if not stack:
+        _current_app_config.set(None)
+        return
+    previous = stack[-1]
+    _current_app_config_stack.set(stack[:-1])
+    _current_app_config.set(previous)
@@ -2,7 +2,7 @@

 from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field

 CheckpointerType = Literal["memory", "sqlite", "postgres"]

@@ -10,8 +10,6 @@ CheckpointerType = Literal["memory", "sqlite", "postgres"]
 class CheckpointerConfig(BaseModel):
    """Configuration for LangGraph state persistence checkpointer."""

-    model_config = ConfigDict(frozen=True)
-
    type: CheckpointerType = Field(
        description="Checkpointer backend type. "
        "'memory' is in-process only (lost on restart). "
@@ -25,3 +23,24 @@ class CheckpointerConfig(BaseModel):
        "For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
        "For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
    )
+
+
+# Global configuration instance — None means no checkpointer is configured.
+_checkpointer_config: CheckpointerConfig | None = None
+
+
+def get_checkpointer_config() -> CheckpointerConfig | None:
+    """Get the current checkpointer configuration, or None if not configured."""
+    return _checkpointer_config
+
+
+def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
+    """Set the checkpointer configuration."""
+    global _checkpointer_config
+    _checkpointer_config = config
+
+
+def load_checkpointer_config_from_dict(config_dict: dict) -> None:
+    """Load checkpointer configuration from a dictionary."""
+    global _checkpointer_config
+    _checkpointer_config = CheckpointerConfig(**config_dict)
@@ -34,11 +34,10 @@ from __future__ import annotations
 import os
 from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class DatabaseConfig(BaseModel):
-    model_config = ConfigDict(frozen=True)
    backend: Literal["memory", "sqlite", "postgres"] = Field(
        default="memory",
        description=("Storage backend for both checkpointer and application data. 'memory' for development (no persistence across restarts), 'sqlite' for single-node deployment, 'postgres' for production multi-node deployment."),
@@ -1,55 +0,0 @@
-"""Per-invocation context for DeerFlow agent execution.
-
-Injected via LangGraph Runtime. Middleware and tools access this
-via Runtime[DeerFlowContext] parameters, through resolve_context().
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from deerflow.config.app_config import AppConfig
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class DeerFlowContext:
-    """Typed, immutable, per-invocation context injected via LangGraph Runtime.
-
-    Fields are all known at run start and never change during execution.
-    Mutable runtime state (e.g. sandbox_id) flows through ThreadState, not here.
-    """
-
-    app_config: AppConfig
-    thread_id: str
-    agent_name: str | None = None
-
-
-def resolve_context(runtime: Any) -> DeerFlowContext:
-    """Return the typed DeerFlowContext that the runtime carries.
-
-    Gateway mode (``DeerFlowClient``, ``run_agent``) always attaches a typed
-    ``DeerFlowContext`` via ``agent.astream(context=...)``; the LangGraph
-    Server path uses ``langgraph.json`` registration where the top-level
-    ``make_lead_agent`` loads ``AppConfig`` from disk itself, so we still
-    arrive here with a typed context.
-
-    Only the dict/None shapes that legacy tests used to exercise would fall
-    through this function; we now reject them loudly instead of papering
-    over the missing context with an ambient ``AppConfig`` lookup.
-    """
-    ctx = getattr(runtime, "context", None)
-    if isinstance(ctx, DeerFlowContext):
-        return ctx
-
-    raise RuntimeError(
-        "resolve_context: runtime.context is not a DeerFlowContext "
-        "(got type %s). Every entry point must attach one at invoke time — "
-        "Gateway/Client via agent.astream(context=DeerFlowContext(...)), "
-        "LangGraph Server via the make_lead_agent boundary that loads "
-        "AppConfig.from_file()." % type(ctx).__name__
-    )
@@ -11,8 +11,6 @@ from pydantic import BaseModel, ConfigDict, Field
 class McpOAuthConfig(BaseModel):
    """OAuth configuration for an MCP server (HTTP/SSE transports)."""

-    model_config = ConfigDict(extra="allow", frozen=True)
-
    enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
    token_url: str = Field(description="OAuth token endpoint URL")
    grant_type: Literal["client_credentials", "refresh_token"] = Field(
@@ -30,13 +28,12 @@ class McpOAuthConfig(BaseModel):
    default_token_type: str = Field(default="Bearer", description="Default token type when missing in token response")
    refresh_skew_seconds: int = Field(default=60, description="Refresh token this many seconds before expiry")
    extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
+    model_config = ConfigDict(extra="allow")


 class McpServerConfig(BaseModel):
    """Configuration for a single MCP server."""

-    model_config = ConfigDict(extra="allow", frozen=True)
-
    enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
    type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
    command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
@@ -46,13 +43,12 @@ class McpServerConfig(BaseModel):
    headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
    oauth: McpOAuthConfig | None = Field(default=None, description="OAuth configuration (for sse or http type)")
    description: str = Field(default="", description="Human-readable description of what this MCP server provides")
+    model_config = ConfigDict(extra="allow")


 class SkillStateConfig(BaseModel):
    """Configuration for a single skill's state."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(default=True, description="Whether this skill is enabled")


@@ -68,7 +64,7 @@ class ExtensionsConfig(BaseModel):
        default_factory=dict,
        description="Map of skill name to state configuration",
    )
-    model_config = ConfigDict(extra="allow", frozen=True, populate_by_name=True)
+    model_config = ConfigDict(extra="allow", populate_by_name=True)

    @classmethod
    def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
@@ -199,3 +195,62 @@ class ExtensionsConfig(BaseModel):
            # Default to enable for public & custom skill
            return skill_category in ("public", "custom")
        return skill_config.enabled
+
+
+_extensions_config: ExtensionsConfig | None = None
+
+
+def get_extensions_config() -> ExtensionsConfig:
+    """Get the extensions config instance.
+
+    Returns a cached singleton instance. Use `reload_extensions_config()` to reload
+    from file, or `reset_extensions_config()` to clear the cache.
+
+    Returns:
+        The cached ExtensionsConfig instance.
+    """
+    global _extensions_config
+    if _extensions_config is None:
+        _extensions_config = ExtensionsConfig.from_file()
+    return _extensions_config
+
+
+def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
+    """Reload the extensions config from file and update the cached instance.
+
+    This is useful when the config file has been modified and you want
+    to pick up the changes without restarting the application.
+
+    Args:
+        config_path: Optional path to extensions config file. If not provided,
+                     uses the default resolution strategy.
+
+    Returns:
+        The newly loaded ExtensionsConfig instance.
+    """
+    global _extensions_config
+    _extensions_config = ExtensionsConfig.from_file(config_path)
+    return _extensions_config
+
+
+def reset_extensions_config() -> None:
+    """Reset the cached extensions config instance.
+
+    This clears the singleton cache, causing the next call to
+    `get_extensions_config()` to reload from file. Useful for testing
+    or when switching between different configurations.
+    """
+    global _extensions_config
+    _extensions_config = None
+
+
+def set_extensions_config(config: ExtensionsConfig) -> None:
+    """Set a custom extensions config instance.
+
+    This allows injecting a custom or mock config for testing purposes.
+
+    Args:
+        config: The ExtensionsConfig instance to use.
+    """
+    global _extensions_config
+    _extensions_config = config
@@ -1,13 +1,11 @@
 """Configuration for pre-tool-call authorization."""

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class GuardrailProviderConfig(BaseModel):
    """Configuration for a guardrail provider."""

-    model_config = ConfigDict(frozen=True)
-
    use: str = Field(description="Class path (e.g. 'deerflow.guardrails.builtin:AllowlistProvider')")
    config: dict = Field(default_factory=dict, description="Provider-specific settings passed as kwargs")

@@ -20,9 +18,31 @@ class GuardrailsConfig(BaseModel):
    agent's passport reference, and returns an allow/deny decision.
    """

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(default=False, description="Enable guardrail middleware")
    fail_closed: bool = Field(default=True, description="Block tool calls if provider errors")
    passport: str | None = Field(default=None, description="OAP passport path or hosted agent ID")
    provider: GuardrailProviderConfig | None = Field(default=None, description="Guardrail provider configuration")
+
+
+_guardrails_config: GuardrailsConfig | None = None
+
+
+def get_guardrails_config() -> GuardrailsConfig:
+    """Get the guardrails config, returning defaults if not loaded."""
+    global _guardrails_config
+    if _guardrails_config is None:
+        _guardrails_config = GuardrailsConfig()
+    return _guardrails_config
+
+
+def load_guardrails_config_from_dict(data: dict) -> GuardrailsConfig:
+    """Load guardrails config from a dict (called during AppConfig loading)."""
+    global _guardrails_config
+    _guardrails_config = GuardrailsConfig.model_validate(data)
+    return _guardrails_config
+
+
+def reset_guardrails_config() -> None:
+    """Reset the cached config instance. Used in tests to prevent singleton leaks."""
+    global _guardrails_config
+    _guardrails_config = None
@@ -1,13 +1,11 @@
 """Configuration for memory mechanism."""

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class MemoryConfig(BaseModel):
    """Configuration for global memory mechanism."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(
        default=True,
        description="Whether to enable memory mechanism",
@@ -62,3 +60,24 @@ class MemoryConfig(BaseModel):
        le=8000,
        description="Maximum tokens to use for memory injection",
    )
+
+
+# Global configuration instance
+_memory_config: MemoryConfig = MemoryConfig()
+
+
+def get_memory_config() -> MemoryConfig:
+    """Get the current memory configuration."""
+    return _memory_config
+
+
+def set_memory_config(config: MemoryConfig) -> None:
+    """Set the memory configuration."""
+    global _memory_config
+    _memory_config = config
+
+
+def load_memory_config_from_dict(config_dict: dict) -> None:
+    """Load memory configuration from a dictionary."""
+    global _memory_config
+    _memory_config = MemoryConfig(**config_dict)
@@ -12,7 +12,7 @@ class ModelConfig(BaseModel):
        description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
    )
    model: str = Field(..., description="Model name")
-    model_config = ConfigDict(extra="allow", frozen=True)
+    model_config = ConfigDict(extra="allow")
    use_responses_api: bool | None = Field(
        default=None,
        description="Whether to route OpenAI ChatOpenAI calls through the /v1/responses API",
@@ -15,11 +15,10 @@ from __future__ import annotations

 from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class RunEventsConfig(BaseModel):
-    model_config = ConfigDict(frozen=True)
    backend: Literal["memory", "db", "jsonl"] = Field(
        default="memory",
        description="Storage backend for run events. 'memory' for development (no persistence), 'db' for production (SQL queries), 'jsonl' for lightweight single-node persistence.",
@@ -4,8 +4,6 @@ from pydantic import BaseModel, ConfigDict, Field
 class VolumeMountConfig(BaseModel):
    """Configuration for a volume mount."""

-    model_config = ConfigDict(frozen=True)
-
    host_path: str = Field(..., description="Path on the host machine")
    container_path: str = Field(..., description="Path inside the container")
    read_only: bool = Field(default=False, description="Whether the mount is read-only")
@@ -82,4 +80,4 @@ class SandboxConfig(BaseModel):
        description="Maximum characters to keep from ls tool output. Output exceeding this limit is head-truncated. Set to 0 to disable truncation.",
    )

-    model_config = ConfigDict(extra="allow", frozen=True)
+    model_config = ConfigDict(extra="allow")
@@ -1,11 +1,9 @@
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class SkillEvolutionConfig(BaseModel):
    """Configuration for agent-managed skill evolution."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(
        default=False,
        description="Whether the agent can create and modify skills under skills/custom.",
@@ -1,6 +1,6 @@
 from pathlib import Path

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 def _default_repo_root() -> Path:
@@ -11,8 +11,6 @@ def _default_repo_root() -> Path:
 class SkillsConfig(BaseModel):
    """Configuration for skills system"""

-    model_config = ConfigDict(frozen=True)
-
    path: str | None = Field(
        default=None,
        description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
@@ -2,7 +2,7 @@

 from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field

 StreamBridgeType = Literal["memory", "redis"]

@@ -10,8 +10,6 @@ StreamBridgeType = Literal["memory", "redis"]
 class StreamBridgeConfig(BaseModel):
    """Configuration for the stream bridge that connects agent workers to SSE endpoints."""

-    model_config = ConfigDict(frozen=True)
-
    type: StreamBridgeType = Field(
        default="memory",
        description="Stream bridge backend type. 'memory' uses in-process asyncio.Queue (single-process only). 'redis' uses Redis Streams (planned for Phase 2, not yet implemented).",
@@ -24,3 +22,25 @@ class StreamBridgeConfig(BaseModel):
        default=256,
        description="Maximum number of events buffered per run in the memory bridge.",
    )
+
+
+# Global configuration instance — None means no stream bridge is configured
+# (falls back to memory with defaults).
+_stream_bridge_config: StreamBridgeConfig | None = None
+
+
+def get_stream_bridge_config() -> StreamBridgeConfig | None:
+    """Get the current stream bridge configuration, or None if not configured."""
+    return _stream_bridge_config
+
+
+def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
+    """Set the stream bridge configuration."""
+    global _stream_bridge_config
+    _stream_bridge_config = config
+
+
+def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
+    """Load stream bridge configuration from a dictionary."""
+    global _stream_bridge_config
+    _stream_bridge_config = StreamBridgeConfig(**config_dict)
@@ -1,13 +1,15 @@
 """Configuration for the subagent system loaded from config.yaml."""

-from pydantic import BaseModel, ConfigDict, Field
+import logging
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)


 class SubagentOverrideConfig(BaseModel):
    """Per-agent configuration overrides."""

-    model_config = ConfigDict(frozen=True)
-
    timeout_seconds: int | None = Field(
        default=None,
        ge=1,
@@ -18,59 +20,11 @@ class SubagentOverrideConfig(BaseModel):
        ge=1,
        description="Maximum turns for this subagent (None = use global or builtin default)",
    )
-    model: str | None = Field(
-        default=None,
-        min_length=1,
-        description="Model name for this subagent (None = inherit from parent agent)",
-    )
-    skills: list[str] | None = Field(
-        default=None,
-        description="Skill names whitelist for this subagent (None = inherit all enabled skills, [] = no skills)",
-    )
-
-
-class CustomSubagentConfig(BaseModel):
-    """User-defined subagent type declared in config.yaml."""
-
-    description: str = Field(
-        description="When the lead agent should delegate to this subagent",
-    )
-    system_prompt: str = Field(
-        description="System prompt that guides the subagent's behavior",
-    )
-    tools: list[str] | None = Field(
-        default=None,
-        description="Tool names whitelist (None = inherit all tools from parent)",
-    )
-    disallowed_tools: list[str] | None = Field(
-        default_factory=lambda: ["task", "ask_clarification", "present_files"],
-        description="Tool names to deny",
-    )
-    skills: list[str] | None = Field(
-        default=None,
-        description="Skill names whitelist (None = inherit all enabled skills, [] = no skills)",
-    )
-    model: str = Field(
-        default="inherit",
-        description="Model to use - 'inherit' uses parent's model",
-    )
-    max_turns: int = Field(
-        default=50,
-        ge=1,
-        description="Maximum number of agent turns before stopping",
-    )
-    timeout_seconds: int = Field(
-        default=900,
-        ge=1,
-        description="Maximum execution time in seconds",
-    )


 class SubagentsAppConfig(BaseModel):
    """Configuration for the subagent system."""

-    model_config = ConfigDict(frozen=True)
-
    timeout_seconds: int = Field(
        default=900,
        ge=1,
@@ -85,10 +39,6 @@ class SubagentsAppConfig(BaseModel):
        default_factory=dict,
        description="Per-agent configuration overrides keyed by agent name",
    )
-    custom_agents: dict[str, CustomSubagentConfig] = Field(
-        default_factory=dict,
-        description="User-defined subagent types keyed by agent name",
-    )

    def get_timeout_for(self, agent_name: str) -> int:
        """Get the effective timeout for a specific agent.
@@ -104,20 +54,6 @@ class SubagentsAppConfig(BaseModel):
            return override.timeout_seconds
        return self.timeout_seconds

-    def get_model_for(self, agent_name: str) -> str | None:
-        """Get the model override for a specific agent.
-
-        Args:
-            agent_name: The name of the subagent.
-
-        Returns:
-            Model name if overridden, None otherwise (subagent will inherit parent model).
-        """
-        override = self.agents.get(agent_name)
-        if override is not None and override.model is not None:
-            return override.model
-        return None
-
    def get_max_turns_for(self, agent_name: str, builtin_default: int) -> int:
        """Get the effective max_turns for a specific agent."""
        override = self.agents.get(agent_name)
@@ -127,16 +63,40 @@ class SubagentsAppConfig(BaseModel):
            return self.max_turns
        return builtin_default

-    def get_skills_for(self, agent_name: str) -> list[str] | None:
-        """Get the skills override for a specific agent.

-        Args:
-            agent_name: The name of the subagent.
+_subagents_config: SubagentsAppConfig = SubagentsAppConfig()

-        Returns:
-            Skill names whitelist if overridden, None otherwise (subagent will inherit all enabled skills).
-        """
-        override = self.agents.get(agent_name)
-        if override is not None and override.skills is not None:
-            return override.skills
-        return None
+
+def get_subagents_app_config() -> SubagentsAppConfig:
+    """Get the current subagents configuration."""
+    return _subagents_config
+
+
+def load_subagents_config_from_dict(config_dict: dict) -> None:
+    """Load subagents configuration from a dictionary."""
+    global _subagents_config
+    _subagents_config = SubagentsAppConfig(**config_dict)
+
+    overrides_summary = {}
+    for name, override in _subagents_config.agents.items():
+        parts = []
+        if override.timeout_seconds is not None:
+            parts.append(f"timeout={override.timeout_seconds}s")
+        if override.max_turns is not None:
+            parts.append(f"max_turns={override.max_turns}")
+        if parts:
+            overrides_summary[name] = ", ".join(parts)
+
+    if overrides_summary:
+        logger.info(
+            "Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s",
+            _subagents_config.timeout_seconds,
+            _subagents_config.max_turns,
+            overrides_summary,
+        )
+    else:
+        logger.info(
+            "Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
+            _subagents_config.timeout_seconds,
+            _subagents_config.max_turns,
+        )
@@ -2,7 +2,7 @@

 from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field

 ContextSizeType = Literal["fraction", "tokens", "messages"]

@@ -10,8 +10,6 @@ ContextSizeType = Literal["fraction", "tokens", "messages"]
 class ContextSize(BaseModel):
    """Context size specification for trigger or keep parameters."""

-    model_config = ConfigDict(frozen=True)
-
    type: ContextSizeType = Field(description="Type of context size specification")
    value: int | float = Field(description="Value for the context size specification")

@@ -23,8 +21,6 @@ class ContextSize(BaseModel):
 class SummarizationConfig(BaseModel):
    """Configuration for automatic conversation summarization."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(
        default=False,
        description="Whether to enable automatic conversation summarization",
@@ -55,22 +51,24 @@ class SummarizationConfig(BaseModel):
        default=None,
        description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
    )
-    preserve_recent_skill_count: int = Field(
-        default=5,
-        ge=0,
-        description="Number of most-recently-loaded skill files to exclude from summarization. Set to 0 to disable skill preservation.",
-    )
-    preserve_recent_skill_tokens: int = Field(
-        default=25000,
-        ge=0,
-        description="Total token budget reserved for recently-loaded skill files that must be preserved across summarization.",
-    )
-    preserve_recent_skill_tokens_per_skill: int = Field(
-        default=5000,
-        ge=0,
-        description="Per-skill token cap when preserving skill files across summarization. Skill reads above this size are not rescued.",
-    )
-    skill_file_read_tool_names: list[str] = Field(
-        default_factory=lambda: ["read_file", "read", "view", "cat"],
-        description="Tool names treated as skill file reads when preserving recently-loaded skills across summarization.",
-    )
+
+
+# Global configuration instance
+_summarization_config: SummarizationConfig = SummarizationConfig()
+
+
+def get_summarization_config() -> SummarizationConfig:
+    """Get the current summarization configuration."""
+    return _summarization_config
+
+
+def set_summarization_config(config: SummarizationConfig) -> None:
+    """Set the summarization configuration."""
+    global _summarization_config
+    _summarization_config = config
+
+
+def load_summarization_config_from_dict(config_dict: dict) -> None:
+    """Load summarization configuration from a dictionary."""
+    global _summarization_config
+    _summarization_config = SummarizationConfig(**config_dict)
@@ -1,13 +1,11 @@
 """Configuration for automatic thread title generation."""

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class TitleConfig(BaseModel):
    """Configuration for automatic thread title generation."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(
        default=True,
        description="Whether to enable automatic title generation",
@@ -32,3 +30,24 @@ class TitleConfig(BaseModel):
        default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
        description="Prompt template for title generation",
    )
+
+
+# Global configuration instance
+_title_config: TitleConfig = TitleConfig()
+
+
+def get_title_config() -> TitleConfig:
+    """Get the current title configuration."""
+    return _title_config
+
+
+def set_title_config(config: TitleConfig) -> None:
+    """Set the title configuration."""
+    global _title_config
+    _title_config = config
+
+
+def load_title_config_from_dict(config_dict: dict) -> None:
+    """Load title configuration from a dictionary."""
+    global _title_config
+    _title_config = TitleConfig(**config_dict)
@@ -1,9 +1,7 @@
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class TokenUsageConfig(BaseModel):
    """Configuration for token usage tracking."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(default=False, description="Enable token usage tracking middleware")
@@ -5,7 +5,7 @@ class ToolGroupConfig(BaseModel):
    """Config section for a tool group"""

    name: str = Field(..., description="Unique name for the tool group")
-    model_config = ConfigDict(extra="allow", frozen=True)
+    model_config = ConfigDict(extra="allow")


 class ToolConfig(BaseModel):
@@ -17,4 +17,4 @@ class ToolConfig(BaseModel):
        ...,
        description="Variable name of the tool provider(e.g. deerflow.sandbox.tools:bash_tool)",
    )
-    model_config = ConfigDict(extra="allow", frozen=True)
+    model_config = ConfigDict(extra="allow")
@@ -1,6 +1,6 @@
 """Configuration for deferred tool loading via tool_search."""

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field


 class ToolSearchConfig(BaseModel):
@@ -11,9 +11,25 @@ class ToolSearchConfig(BaseModel):
    via the tool_search tool at runtime.
    """

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(
        default=False,
        description="Defer tools and enable tool_search",
    )
+
+
+_tool_search_config: ToolSearchConfig | None = None
+
+
+def get_tool_search_config() -> ToolSearchConfig:
+    """Get the tool search config, loading from AppConfig if needed."""
+    global _tool_search_config
+    if _tool_search_config is None:
+        _tool_search_config = ToolSearchConfig()
+    return _tool_search_config
+
+
+def load_tool_search_config_from_dict(data: dict) -> ToolSearchConfig:
+    """Load tool search config from a dict (called during AppConfig loading)."""
+    global _tool_search_config
+    _tool_search_config = ToolSearchConfig.model_validate(data)
+    return _tool_search_config
@@ -1,7 +1,7 @@
 import os
 import threading

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field

 _config_lock = threading.Lock()

@@ -9,8 +9,6 @@ _config_lock = threading.Lock()
 class LangSmithTracingConfig(BaseModel):
    """Configuration for LangSmith tracing."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(...)
    api_key: str | None = Field(...)
    project: str = Field(...)
@@ -28,8 +26,6 @@ class LangSmithTracingConfig(BaseModel):
 class LangfuseTracingConfig(BaseModel):
    """Configuration for Langfuse tracing."""

-    model_config = ConfigDict(frozen=True)
-
    enabled: bool = Field(...)
    public_key: str | None = Field(...)
    secret_key: str | None = Field(...)
@@ -54,8 +50,6 @@ class LangfuseTracingConfig(BaseModel):
 class TracingConfig(BaseModel):
    """Tracing configuration for supported providers."""

-    model_config = ConfigDict(frozen=True)
-
    langsmith: LangSmithTracingConfig = Field(...)
    langfuse: LangfuseTracingConfig = Field(...)

@@ -118,13 +118,9 @@ def get_cached_mcp_tools() -> list[BaseTool]:
                loop.run_until_complete(initialize_mcp_tools())
        except RuntimeError:
            # No event loop exists, create one
-            try:
-                asyncio.run(initialize_mcp_tools())
-            except Exception:
-                logger.exception("Failed to lazy-initialize MCP tools")
-                return []
-        except Exception:
-            logger.exception("Failed to lazy-initialize MCP tools")
+            asyncio.run(initialize_mcp_tools())
+        except Exception as e:
+            logger.error(f"Failed to lazy-initialize MCP tools: {e}")
            return []

    return _mcp_tools_cache or []
@@ -12,7 +12,6 @@ from langchain_core.tools import BaseTool
 from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.mcp.client import build_servers_config
 from deerflow.mcp.oauth import build_oauth_tool_interceptor, get_initial_oauth_headers
-from deerflow.reflection import resolve_variable

 logger = logging.getLogger(__name__)

@@ -96,27 +95,6 @@ async def get_mcp_tools() -> list[BaseTool]:
        if oauth_interceptor is not None:
            tool_interceptors.append(oauth_interceptor)

-        # Load custom interceptors declared in extensions_config.json
-        # Format: "mcpInterceptors": ["pkg.module:builder_func", ...]
-        raw_interceptor_paths = (extensions_config.model_extra or {}).get("mcpInterceptors")
-        if isinstance(raw_interceptor_paths, str):
-            raw_interceptor_paths = [raw_interceptor_paths]
-        elif not isinstance(raw_interceptor_paths, list):
-            if raw_interceptor_paths is not None:
-                logger.warning(f"mcpInterceptors must be a list of strings, got {type(raw_interceptor_paths).__name__}; skipping")
-            raw_interceptor_paths = []
-        for interceptor_path in raw_interceptor_paths:
-            try:
-                builder = resolve_variable(interceptor_path)
-                interceptor = builder()
-                if callable(interceptor):
-                    tool_interceptors.append(interceptor)
-                    logger.info(f"Loaded MCP interceptor: {interceptor_path}")
-                elif interceptor is not None:
-                    logger.warning(f"Builder {interceptor_path} returned non-callable {type(interceptor).__name__}; skipping")
-            except Exception as e:
-                logger.warning(f"Failed to load MCP interceptor {interceptor_path}: {e}", exc_info=True)
-
        client = MultiServerMCPClient(servers_config, tool_interceptors=tool_interceptors, tool_name_prefix=True)

        # Get all tools from all servers
@@ -190,33 +190,23 @@ class ClaudeChatModel(ChatAnthropic):
            )

    def _apply_prompt_caching(self, payload: dict) -> None:
-        """Apply ephemeral cache_control to system, recent messages, and last tool definition.
-
-        Uses a budget of MAX_CACHE_BREAKPOINTS (4) breakpoints — the hard limit
-        enforced by both the Anthropic API and AWS Bedrock.  Breakpoints are
-        placed on the *last* eligible blocks because later breakpoints cover a
-        larger prefix and yield better cache hit rates.
-        """
-        MAX_CACHE_BREAKPOINTS = 4
-
-        # Collect candidate blocks in document order:
-        #   1. system text blocks
-        #   2. content blocks of the last prompt_cache_size messages
-        #   3. the last tool definition
-        candidates: list[dict] = []
-
-        # 1. System blocks
+        """Apply ephemeral cache_control to system and recent messages."""
+        # Cache system messages
        system = payload.get("system")
        if system and isinstance(system, list):
            for block in system:
                if isinstance(block, dict) and block.get("type") == "text":
-                    candidates.append(block)
+                    block["cache_control"] = {"type": "ephemeral"}
        elif system and isinstance(system, str):
-            new_block: dict = {"type": "text", "text": system}
-            payload["system"] = [new_block]
-            candidates.append(new_block)
+            payload["system"] = [
+                {
+                    "type": "text",
+                    "text": system,
+                    "cache_control": {"type": "ephemeral"},
+                }
+            ]

-        # 2. Recent message blocks
+        # Cache recent messages
        messages = payload.get("messages", [])
        cache_start = max(0, len(messages) - self.prompt_cache_size)
        for i in range(cache_start, len(messages)):
@@ -227,21 +217,20 @@ class ClaudeChatModel(ChatAnthropic):
            if isinstance(content, list):
                for block in content:
                    if isinstance(block, dict):
-                        candidates.append(block)
+                        block["cache_control"] = {"type": "ephemeral"}
            elif isinstance(content, str) and content:
-                new_block = {"type": "text", "text": content}
-                msg["content"] = [new_block]
-                candidates.append(new_block)
+                msg["content"] = [
+                    {
+                        "type": "text",
+                        "text": content,
+                        "cache_control": {"type": "ephemeral"},
+                    }
+                ]

-        # 3. Last tool definition
+        # Cache the last tool definition
        tools = payload.get("tools", [])
        if tools and isinstance(tools[-1], dict):
-            candidates.append(tools[-1])
-
-        # Apply cache_control only to the last MAX_CACHE_BREAKPOINTS candidates
-        # to stay within the API limit.
-        for block in candidates[-MAX_CACHE_BREAKPOINTS:]:
-            block["cache_control"] = {"type": "ephemeral"}
+            tools[-1]["cache_control"] = {"type": "ephemeral"}

    def _apply_thinking_budget(self, payload: dict) -> None:
        """Auto-allocate thinking budget (80% of max_tokens)."""
@@ -2,7 +2,7 @@ import logging

 from langchain.chat_models import BaseChatModel

-from deerflow.config.app_config import AppConfig
+from deerflow.config import get_app_config
 from deerflow.reflection import resolve_class
 from deerflow.tracing import build_tracing_callbacks

@@ -30,39 +30,16 @@ def _vllm_disable_chat_template_kwargs(chat_template_kwargs: dict) -> dict:
    return disable_kwargs


-def _enable_stream_usage_by_default(model_use_path: str, model_settings_from_config: dict) -> None:
-    """Enable stream usage for OpenAI-compatible models unless explicitly configured.
-
-    LangChain only auto-enables ``stream_usage`` for OpenAI models when no custom
-    base URL or client is configured. DeerFlow frequently uses OpenAI-compatible
-    gateways, so token usage tracking would otherwise stay empty and the
-    TokenUsageMiddleware would have nothing to log.
-    """
-    if model_use_path != "langchain_openai:ChatOpenAI":
-        return
-    if "stream_usage" in model_settings_from_config:
-        return
-    if "base_url" in model_settings_from_config or "openai_api_base" in model_settings_from_config:
-        model_settings_from_config["stream_usage"] = True
-
-
-def create_chat_model(
-    name: str | None = None,
-    thinking_enabled: bool = False,
-    *,
-    app_config: "AppConfig",
-    **kwargs,
-) -> BaseChatModel:
+def create_chat_model(name: str | None = None, thinking_enabled: bool = False, **kwargs) -> BaseChatModel:
    """Create a chat model instance from the config.

    Args:
        name: The name of the model to create. If None, the first model in the config will be used.
-        app_config: Application config — required.

    Returns:
        A chat model instance.
    """
-    config = app_config
+    config = get_app_config()
    if name is None:
        name = config.models[0].name
    model_config = config.get_model_config(name)
@@ -120,8 +97,6 @@ def create_chat_model(
        kwargs.pop("reasoning_effort", None)
        model_settings_from_config.pop("reasoning_effort", None)

-    _enable_stream_usage_by_default(model_config.use, model_settings_from_config)
-
    # For Codex Responses API models: map thinking mode to reasoning_effort
    from deerflow.models.openai_codex_provider import CodexChatModel

@@ -138,12 +113,6 @@ def create_chat_model(
        elif "reasoning_effort" not in model_settings_from_config:
            model_settings_from_config["reasoning_effort"] = "medium"

-    # For MindIE models: enforce conservative retry defaults.
-    # Timeout normalization is handled inside MindIEChatModel itself.
-    if getattr(model_class, "__name__", "") == "MindIEChatModel":
-        # Enforce max_retries constraint to prevent cascading timeouts.
-        model_settings_from_config["max_retries"] = model_settings_from_config.get("max_retries", 1)
-
    # Ensure stream_usage is enabled so that token usage metadata is available
    # in streaming responses.  LangChain's BaseChatOpenAI only defaults
    # stream_usage=True when no custom base_url/api_base is set, so models
@@ -1,237 +0,0 @@
-import ast
-import json
-import re
-import uuid
-from collections.abc import Iterator
-
-import httpx
-from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
-from langchain_core.outputs import ChatGenerationChunk, ChatResult
-from langchain_openai import ChatOpenAI
-
-
-def _fix_messages(messages: list) -> list:
-    """Sanitize incoming messages for MindIE compatibility.
-
-    MindIE's chat template may fail to parse LangChain's native tool_calls
-    or ToolMessage roles, resulting in 0-token generation errors. This function
-    flattens multi-modal list contents into strings and converts tool-related
-    messages into raw text with XML tags expected by the underlying model.
-    """
-    fixed = []
-    for msg in messages:
-        # Flatten content if it's a list of blocks
-        if isinstance(msg.content, list):
-            parts = []
-            for block in msg.content:
-                if isinstance(block, str):
-                    parts.append(block)
-                elif isinstance(block, dict) and block.get("type") == "text":
-                    parts.append(block.get("text", ""))
-            text = "".join(parts)
-        else:
-            text = msg.content or ""
-
-        # Convert AIMessage with tool_calls to raw XML text format
-        if isinstance(msg, AIMessage) and getattr(msg, "tool_calls", []):
-            xml_parts = []
-            for tool in msg.tool_calls:
-                args_xml = " ".join(f"<parameter={k}>{json.dumps(v, ensure_ascii=False)}</parameter>" for k, v in tool.get("args", {}).items())
-                xml_parts.append(f"<tool_call> <function={tool['name']}> {args_xml} </function> </tool_call>")
-            full_text = f"{text}\n" + "\n".join(xml_parts) if text else "\n".join(xml_parts)
-            fixed.append(AIMessage(content=full_text.strip() or " "))
-            continue
-
-        # Wrap tool execution results in XML tags and convert to HumanMessage
-        if isinstance(msg, ToolMessage):
-            tool_result_text = f"<tool_response>\n{text}\n</tool_response>"
-            fixed.append(HumanMessage(content=tool_result_text))
-            continue
-
-        # Fallback to prevent completely empty message content
-        if not text.strip():
-            text = " "
-
-        fixed.append(msg.model_copy(update={"content": text}))
-
-    return fixed
-
-
-def _parse_xml_tool_call_to_dict(content: str) -> tuple[str, list[dict]]:
-    """Parse XML-style tool calls from model output into LangChain dicts.
-
-    Args:
-        content: The raw text output from the model.
-
-    Returns:
-        A tuple containing the cleaned text (with XML blocks removed) and
-        a list of tool call dictionaries formatted for LangChain.
-    """
-    if not isinstance(content, str) or "<tool_call>" not in content:
-        return content, []
-
-    tool_calls = []
-    clean_parts: list[str] = []
-    cursor = 0
-    for start, end, inner_content in _iter_tool_call_blocks(content):
-        clean_parts.append(content[cursor:start])
-        cursor = end
-
-        func_match = re.search(r"<function=([^>]+)>", inner_content)
-        if not func_match:
-            continue
-        function_name = func_match.group(1).strip()
-
-        args = {}
-        param_pattern = re.compile(r"<parameter=([^>]+)>(.*?)</parameter>", re.DOTALL)
-        for param_match in param_pattern.finditer(inner_content):
-            key = param_match.group(1).strip()
-            raw_value = param_match.group(2).strip()
-
-            # Attempt to deserialize string values into native Python types
-            # to satisfy downstream Pydantic validation.
-            parsed_value = raw_value
-            if raw_value.startswith(("[", "{")) or raw_value in ("true", "false", "null") or raw_value.isdigit():
-                try:
-                    parsed_value = json.loads(raw_value)
-                except json.JSONDecodeError:
-                    try:
-                        parsed_value = ast.literal_eval(raw_value)
-                    except (ValueError, SyntaxError):
-                        pass
-
-            args[key] = parsed_value
-
-        tool_calls.append({"name": function_name, "args": args, "id": f"call_{uuid.uuid4().hex[:10]}"})
-    clean_parts.append(content[cursor:])
-
-    return "".join(clean_parts).strip(), tool_calls
-
-
-def _iter_tool_call_blocks(content: str) -> Iterator[tuple[int, int, str]]:
-    """Iterate `<tool_call>...</tool_call>` blocks and tolerate nesting."""
-    token_pattern = re.compile(r"</?tool_call>")
-    depth = 0
-    block_start = -1
-
-    for match in token_pattern.finditer(content):
-        token = match.group(0)
-        if token == "<tool_call>":
-            if depth == 0:
-                block_start = match.start()
-            depth += 1
-            continue
-
-        if depth == 0:
-            continue
-
-        depth -= 1
-        if depth == 0 and block_start != -1:
-            block_end = match.end()
-            inner_start = block_start + len("<tool_call>")
-            inner_end = match.start()
-            yield block_start, block_end, content[inner_start:inner_end]
-            block_start = -1
-
-
-def _decode_escaped_newlines_outside_fences(content: str) -> str:
-    """Decode literal `\\n` outside fenced code blocks."""
-    if "\\n" not in content:
-        return content
-
-    parts = re.split(r"(```[\s\S]*?```)", content)
-    for idx, part in enumerate(parts):
-        if part.startswith("```"):
-            continue
-        parts[idx] = part.replace("\\n", "\n")
-    return "".join(parts)
-
-
-class MindIEChatModel(ChatOpenAI):
-    """Chat model adapter for MindIE engine.
-
-    Addresses compatibility issues including:
-    - Flattening multimodal list contents to strings.
-    - Intercepting and parsing hardcoded XML tool calls into LangChain standard.
-    - Handling stream=True dropping choices when tools are present by falling back
-      to non-streaming generation and yielding simulated chunks.
-    - Fixing over-escaped newline characters from gateway responses.
-    """
-
-    def __init__(self, **kwargs):
-        """Normalize timeout kwargs without creating long-lived clients."""
-        connect_timeout = kwargs.pop("connect_timeout", 30.0)
-        read_timeout = kwargs.pop("read_timeout", 900.0)
-        write_timeout = kwargs.pop("write_timeout", 60.0)
-        pool_timeout = kwargs.pop("pool_timeout", 30.0)
-
-        kwargs.setdefault(
-            "timeout",
-            httpx.Timeout(
-                connect=connect_timeout,
-                read=read_timeout,
-                write=write_timeout,
-                pool=pool_timeout,
-            ),
-        )
-        super().__init__(**kwargs)
-
-    def _patch_result_with_tools(self, result: ChatResult) -> ChatResult:
-        """Apply post-generation fixes to the model result."""
-        for gen in result.generations:
-            msg = gen.message
-
-            if isinstance(msg.content, str):
-                # Keep escaped newlines inside fenced code blocks untouched.
-                msg.content = _decode_escaped_newlines_outside_fences(msg.content)
-
-                if "<tool_call>" in msg.content:
-                    clean_content, extracted_tools = _parse_xml_tool_call_to_dict(msg.content)
-
-                    if extracted_tools:
-                        msg.content = clean_content
-                        if getattr(msg, "tool_calls", None) is None:
-                            msg.tool_calls = []
-                        msg.tool_calls.extend(extracted_tools)
-        return result
-
-    def _generate(self, messages, stop=None, run_manager=None, **kwargs):
-        result = super()._generate(_fix_messages(messages), stop=stop, run_manager=run_manager, **kwargs)
-        return self._patch_result_with_tools(result)
-
-    async def _agenerate(self, messages, stop=None, run_manager=None, **kwargs):
-        result = await super()._agenerate(_fix_messages(messages), stop=stop, run_manager=run_manager, **kwargs)
-        return self._patch_result_with_tools(result)
-
-    async def _astream(self, messages, stop=None, run_manager=None, **kwargs):
-        # Route standard queries to native streaming for lower TTFB
-        if not kwargs.get("tools"):
-            async for chunk in super()._astream(_fix_messages(messages), stop=stop, run_manager=run_manager, **kwargs):
-                if isinstance(chunk.message.content, str):
-                    chunk.message.content = _decode_escaped_newlines_outside_fences(chunk.message.content)
-                yield chunk
-            return
-
-        # Fallback for tool-enabled requests:
-        # MindIE currently drops choices when stream=True and tools are present.
-        # We await the full generation and yield chunks to simulate streaming.
-        result = await self._agenerate(messages, stop=stop, run_manager=run_manager, **kwargs)
-
-        for gen in result.generations:
-            msg = gen.message
-            content = msg.content
-            standard_tool_calls = getattr(msg, "tool_calls", [])
-
-            # Yield text in chunks to allow downstream UI/Markdown parsers to render smoothly
-            if isinstance(content, str) and content:
-                chunk_size = 15
-                for i in range(0, len(content), chunk_size):
-                    chunk_text = content[i : i + chunk_size]
-                    chunk_msg = AIMessageChunk(content=chunk_text, id=msg.id, response_metadata=msg.response_metadata if i == 0 else {})
-                    yield ChatGenerationChunk(message=chunk_msg, generation_info=gen.generation_info if i == 0 else None)
-
-                if standard_tool_calls:
-                    yield ChatGenerationChunk(message=AIMessageChunk(content="", id=msg.id, tool_calls=standard_tool_calls, invalid_tool_calls=getattr(msg, "invalid_tool_calls", [])))
-            else:
-                chunk_msg = AIMessageChunk(content=content, id=msg.id, tool_calls=standard_tool_calls, invalid_tool_calls=getattr(msg, "invalid_tool_calls", []))
-                yield ChatGenerationChunk(message=chunk_msg, generation_info=gen.generation_info)
@@ -24,7 +24,7 @@ from collections.abc import AsyncIterator

 from langgraph.types import Checkpointer

-from deerflow.config.app_config import AppConfig
+from deerflow.config.app_config import get_app_config
 from deerflow.runtime.checkpointer.provider import (
    POSTGRES_CONN_REQUIRED,
    POSTGRES_INSTALL,
@@ -123,11 +123,11 @@ async def _async_checkpointer_from_database(db_config) -> AsyncIterator[Checkpoi


@contextlib.asynccontextmanager
-async def make_checkpointer(app_config: AppConfig) -> AsyncIterator[Checkpointer]:
+async def make_checkpointer() -> AsyncIterator[Checkpointer]:
    """Async context manager that yields a checkpointer for the caller's lifetime.
    Resources are opened on enter and closed on exit -- no global state::

-        async with make_checkpointer(app_config) as checkpointer:
+        async with make_checkpointer() as checkpointer:
            app.state.checkpointer = checkpointer

    Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
@@ -138,14 +138,16 @@ async def make_checkpointer(app_config: AppConfig) -> AsyncIterator[Checkpointer
    3. Default InMemorySaver
    """

+    config = get_app_config()
+
    # Legacy: standalone checkpointer config takes precedence
-    if app_config.checkpointer is not None:
-        async with _async_checkpointer(app_config.checkpointer) as saver:
+    if config.checkpointer is not None:
+        async with _async_checkpointer(config.checkpointer) as saver:
            yield saver
            return

    # Unified database config
-    db_config = getattr(app_config, "database", None)
+    db_config = getattr(config, "database", None)
    if db_config is not None and db_config.backend != "memory":
        async with _async_checkpointer_from_database(db_config) as saver:
            yield saver
@@ -25,9 +25,9 @@ from collections.abc import Iterator

 from langgraph.types import Checkpointer

-from deerflow.config.app_config import AppConfig
+from deerflow.config.app_config import get_app_config
 from deerflow.config.checkpointer_config import CheckpointerConfig
-from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
+from deerflow.runtime.store._sqlite_utils import resolve_sqlite_conn_str

 logger = logging.getLogger(__name__)

@@ -67,7 +67,6 @@ def _sync_checkpointer_cm(config: CheckpointerConfig) -> Iterator[Checkpointer]:
            raise ImportError(SQLITE_INSTALL) from exc

        conn_str = resolve_sqlite_conn_str(config.connection_string or "store.db")
-        ensure_sqlite_parent_dir(conn_str)
        with SqliteSaver.from_conn_string(conn_str) as saver:
            saver.setup()
            logger.info("Checkpointer: using SqliteSaver (%s)", conn_str)
@@ -100,13 +99,10 @@ _checkpointer: Checkpointer | None = None
 _checkpointer_ctx = None  # open context manager keeping the connection alive


-def get_checkpointer(app_config: AppConfig) -> Checkpointer:
+def get_checkpointer() -> Checkpointer:
    """Return the global sync checkpointer singleton, creating it on first call.

-    Returns an ``InMemorySaver`` only when ``checkpointer`` is explicitly
-    absent from config.yaml. Any other failure (missing config, invalid
-    backend, connection error) propagates — silent degradation to in-memory
-    would drop persistent-run state on process restart.
+    Returns an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.

    Raises:
        ImportError: If the required package for the configured backend is not installed.
@@ -117,7 +113,25 @@ def get_checkpointer(app_config: AppConfig) -> Checkpointer:
    if _checkpointer is not None:
        return _checkpointer

-    config = app_config.checkpointer
+    # Ensure app config is loaded before checking checkpointer config
+    # This prevents returning InMemorySaver when config.yaml actually has a checkpointer section
+    # but hasn't been loaded yet
+    from deerflow.config.app_config import _app_config
+    from deerflow.config.checkpointer_config import get_checkpointer_config
+
+    config = get_checkpointer_config()
+
+    if config is None and _app_config is None:
+        # Only load app config lazily when neither the app config nor an explicit
+        # checkpointer config has been initialized yet. This keeps tests that
+        # intentionally set the global checkpointer config isolated from any
+        # ambient config.yaml on disk.
+        try:
+            get_app_config()
+        except FileNotFoundError:
+            # In test environments without config.yaml, this is expected.
+            pass
+        config = get_checkpointer_config()
    if config is None:
        from langgraph.checkpoint.memory import InMemorySaver

@@ -153,23 +167,25 @@ def reset_checkpointer() -> None:


@contextlib.contextmanager
-def checkpointer_context(app_config: AppConfig) -> Iterator[Checkpointer]:
+def checkpointer_context() -> Iterator[Checkpointer]:
    """Sync context manager that yields a checkpointer and cleans up on exit.

    Unlike :func:`get_checkpointer`, this does **not** cache the instance —
    each ``with`` block creates and destroys its own connection.  Use it in
    CLI scripts or tests where you want deterministic cleanup::

-        with checkpointer_context(app_config) as cp:
+        with checkpointer_context() as cp:
            graph.invoke(input, config={"configurable": {"thread_id": "1"}})

    Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
    """
-    if app_config.checkpointer is None:
+
+    config = get_app_config()
+    if config.checkpointer is None:
        from langgraph.checkpoint.memory import InMemorySaver

        yield InMemorySaver()
        return

-    with _sync_checkpointer_cm(app_config.checkpointer) as saver:
+    with _sync_checkpointer_cm(config.checkpointer) as saver:
        yield saver
--- a/Show More
+++ b/Show More