fix the lint errors

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

.github/workflows/e2e-tests.yml

@@ -0,0 +1,63 @@
+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

.github/workflows/frontend-unit-tests.yml

@@ -0,0 +1,43 @@
+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

.gitignore

@@ -40,6 +40,7 @@ coverage/
 skills/custom/*
 logs/
 log/
+debug.log
 
 # Local git hooks (keep only on this machine, do not push)
 .githooks/
@@ -55,5 +56,7 @@ web/
 backend/Dockerfile.langgraph
 config.yaml.bak
 .playwright-mcp
+/frontend/test-results/
+/frontend/playwright-report/
 .gstack/
 .worktrees

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+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]

CONTRIBUTING.md

@@ -166,7 +166,7 @@ Required tools:
 
 1. **Configure the application** (same as Docker setup above)
 
-2. **Install dependencies**:
+2. **Install dependencies** (this also sets up pre-commit hooks):
    ```bash
    make install
    ```
@@ -298,19 +298,24 @@ Nginx (port 2026) ← Unified entry point
 ```bash
 # Backend tests
 cd backend
-uv run pytest
+make test
 
-# Frontend checks
+# Frontend unit tests
 cd frontend
-pnpm check
+make test
+
+# Frontend E2E tests (requires Chromium; builds and auto-starts the Next.js production server)
+cd frontend
+make test-e2e
 ```
 
 ### PR Regression Checks
 
-Every pull request runs the backend regression workflow at [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml), including:
+Every pull request triggers the following CI workflows:
 
-- `tests/test_provisioner_kubeconfig.py`
+- **Backend unit tests** — [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml)
-- `tests/test_docker_sandbox_mode_detection.py`
+- **Frontend unit tests** — [.github/workflows/frontend-unit-tests.yml](.github/workflows/frontend-unit-tests.yml)
+- **Frontend E2E tests** — [.github/workflows/e2e-tests.yml](.github/workflows/e2e-tests.yml) (triggered only when `frontend/` files change)
 
 ## Code Style
 

Makefile

@@ -23,7 +23,7 @@ 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)"
+	@echo "  make install         - Install all dependencies (frontend + backend + pre-commit hooks)"
 	@echo "  make setup-sandbox   - Pre-pull sandbox container image (recommended)"
 	@echo "  make dev             - Start all services in development mode (with hot-reloading)"
 	@echo "  make dev-pro         - Start in dev + Gateway mode (experimental, no LangGraph server)"
@@ -73,6 +73,8 @@ 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 "=========================================="
@@ -99,7 +101,7 @@ setup-sandbox:
 	echo ""; \
 	if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
 		echo "Detected Apple Container on macOS, pulling image..."; \
-		container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
+		container image pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
 	fi; \
 	if command -v docker >/dev/null 2>&1; then \
 		echo "Pulling image using Docker..."; \

README.md

@@ -264,7 +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
+   make install  # Install backend + frontend dependencies + pre-commit hooks
    ```
 
 3. **(Optional) Pre-pull sandbox image**:
@@ -658,6 +658,8 @@ 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.

backend/CLAUDE.md

@@ -156,20 +156,26 @@ from deerflow.config import get_app_config
 
 ### Middleware Chain
 
-Middlewares execute in strict order in `packages/harness/deerflow/agents/lead_agent/agent.py`:
+Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`_build_middlewares`):
 
 1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local `.deer-flow/threads/{thread_id}` directory
 2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
 3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
-4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
+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. **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.
+5. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later middleware/tool stages run
-6. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
+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. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
+7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
-8. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
-9. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
-10. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-11. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
+11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
-12. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
+12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+15. **DeferredToolFilterMiddleware** - Hides deferred tool schemas from the bound model until tool search is enabled (optional)
+16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
+17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
+18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
 
 ### Configuration System
 

backend/app/channels/service.py

@@ -23,6 +23,16 @@ _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"
 
@@ -88,7 +98,16 @@ class ChannelService:
             if not isinstance(channel_config, dict):
                 continue
             if not channel_config.get("enabled", False):
-                logger.info("Channel %s is disabled, skipping", name)
+                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)
                 continue
 
             await self._start_channel(name, channel_config)

backend/app/channels/slack.py

@@ -16,13 +16,31 @@ 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. Empty = allow all.
+        - ``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.
     """
 
     def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -30,7 +48,7 @@ class SlackChannel(Channel):
         self._socket_client = None
         self._web_client = None
         self._loop: asyncio.AbstractEventLoop | None = None
-        self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}
+        self._allowed_users = _normalize_allowed_users(config.get("allowed_users", []))
 
     async def start(self) -> None:
         if self._running:

backend/app/gateway/app.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
@@ -32,6 +33,11 @@ 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
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
@@ -63,11 +69,19 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 
         yield
 
-        # Stop channel service on shutdown
+        # Stop channel service on shutdown (bounded to prevent worker hang)
         try:
             from app.channels.service import stop_channel_service
 
-            await 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,
+            )
         except Exception:
             logger.exception("Failed to stop channel service")
 

backend/app/gateway/routers/agents.py

@@ -8,6 +8,7 @@ import yaml
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
 
+from deerflow.config.agents_api_config import get_agents_api_config
 from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
 from deerflow.config.paths import get_paths
 
@@ -24,6 +25,7 @@ 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")
 
 
@@ -40,6 +42,7 @@ 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")
 
 
@@ -49,6 +52,7 @@ 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")
 
 
@@ -73,6 +77,15 @@ def _normalize_agent_name(name: str) -> str:
     return name.lower()
 
 
+def _require_agents_api_enabled() -> None:
+    """Reject access unless the custom-agent management API is explicitly enabled."""
+    if not get_agents_api_config().enabled:
+        raise HTTPException(
+            status_code=403,
+            detail=("Custom-agent management API is disabled. Set agents_api.enabled=true to expose agent and user-profile routes over HTTP."),
+        )
+
+
 def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
     """Convert AgentConfig to AgentResponse."""
     soul: str | None = None
@@ -84,6 +97,7 @@ 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,
     )
 
@@ -100,6 +114,8 @@ async def list_agents() -> AgentsListResponse:
     Returns:
         List of all custom agents with their metadata and soul content.
     """
+    _require_agents_api_enabled()
+
     try:
         agents = list_custom_agents()
         return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
@@ -125,6 +141,7 @@ async def check_agent_name(name: str) -> dict:
     Raises:
         HTTPException: 422 if the name is invalid.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     normalized = _normalize_agent_name(name)
     available = not get_paths().agent_dir(normalized).exists()
@@ -149,6 +166,7 @@ async def get_agent(name: str) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -181,6 +199,7 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     Raises:
         HTTPException: 409 if agent already exists, 422 if name is invalid.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(request.name)
     normalized_name = _normalize_agent_name(request.name)
 
@@ -200,6 +219,8 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
             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:
@@ -243,6 +264,7 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -255,21 +277,32 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
 
     try:
         # Update config if any config fields changed
-        config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])
+        # 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"})
 
         if config_changed:
             updated: dict = {
                 "name": agent_cfg.name,
-                "description": request.description if request.description is not None else agent_cfg.description,
+                "description": request.description if "description" in fields_set else agent_cfg.description,
             }
-            new_model = request.model if request.model is not None else agent_cfg.model
+            new_model = request.model if "model" in fields_set else agent_cfg.model
             if new_model is not None:
                 updated["model"] = new_model
 
-            new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
+            new_tool_groups = request.tool_groups if "tool_groups" in fields_set 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)
@@ -315,6 +348,8 @@ async def get_user_profile() -> UserProfileResponse:
     Returns:
         UserProfileResponse with content=None if USER.md does not exist yet.
     """
+    _require_agents_api_enabled()
+
     try:
         user_md_path = get_paths().user_md_file
         if not user_md_path.exists():
@@ -341,6 +376,8 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     Returns:
         UserProfileResponse with the saved content.
     """
+    _require_agents_api_enabled()
+
     try:
         paths = get_paths()
         paths.base_dir.mkdir(parents=True, exist_ok=True)
@@ -367,6 +404,7 @@ async def delete_agent(name: str) -> None:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 

backend/app/gateway/routers/models.py

@@ -17,10 +17,17 @@ 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(
@@ -36,7 +43,7 @@ async def list_models() -> ModelsListResponse:
     excluding sensitive fields like API keys and internal configuration.
 
     Returns:
-        A list of all configured models with their metadata.
+        A list of all configured models with their metadata and token usage display settings.
 
     Example Response:
         ```json
@@ -44,17 +51,24 @@ async def list_models() -> ModelsListResponse:
             "models": [
                 {
                     "name": "gpt-4",
+                    "model": "gpt-4",
                     "display_name": "GPT-4",
                     "description": "OpenAI GPT-4 model",
-                    "supports_thinking": false
+                    "supports_thinking": false,
+                    "supports_reasoning_effort": false
                 },
                 {
                     "name": "claude-3-opus",
+                    "model": "claude-3-opus",
                     "display_name": "Claude 3 Opus",
                     "description": "Anthropic Claude 3 Opus model",
-                    "supports_thinking": true
+                    "supports_thinking": true,
+                    "supports_reasoning_effort": false
                 }
-            ]
+            ],
+            "token_usage": {
+                "enabled": true
+            }
         }
         ```
     """
@@ -70,7 +84,10 @@ async def list_models() -> ModelsListResponse:
         )
         for model in config.models
     ]
-    return ModelsListResponse(models=models)
+    return ModelsListResponse(
+        models=models,
+        token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
+    )
 
 
 @router.get(

backend/app/gateway/routers/skills.py

@@ -1,3 +1,4 @@
+import errno
 import json
 import logging
 import shutil
@@ -201,18 +202,23 @@ async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
         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(
+        try:
-            skill_name,
+            append_history(
-            {
+                skill_name,
-                "action": "human_delete",
+                {
-                "author": "human",
+                    "action": "human_delete",
-                "thread_id": None,
+                    "author": "human",
-                "file_path": "SKILL.md",
+                    "thread_id": None,
-                "prev_content": prev_content,
+                    "file_path": "SKILL.md",
-                "new_content": None,
+                    "prev_content": prev_content,
-                "scanner": {"decision": "allow", "reason": "Deletion requested."},
+                    "new_content": None,
-            },
+                    "scanner": {"decision": "allow", "reason": "Deletion requested."},
-        )
+                },
+            )
+        except OSError as e:
+            if not isinstance(e, PermissionError) and e.errno not in {errno.EACCES, errno.EPERM, errno.EROFS}:
+                raise
+            logger.warning("Skipping delete history write for custom skill %s due to readonly/permission failure; continuing with skill directory removal: %s", skill_name, e)
         shutil.rmtree(skill_dir)
         await refresh_skills_system_prompt_cache_async()
         return {"success": True}

backend/app/gateway/routers/suggestions.py

@@ -121,7 +121,7 @@ async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> S
 
     try:
         model = create_chat_model(name=request.model_name, thinking_enabled=False)
-        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
+        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []
         cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]

backend/app/gateway/routers/uploads.py

@@ -7,8 +7,9 @@ import stat
 from fastapi import APIRouter, File, HTTPException, UploadFile
 from pydantic import BaseModel
 
+from deerflow.config.app_config import get_app_config
 from deerflow.config.paths import get_paths
-from deerflow.sandbox.sandbox_provider import get_sandbox_provider
+from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
 from deerflow.uploads.manager import (
     PathTraversalError,
     delete_file_safe,
@@ -53,6 +54,34 @@ 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(key: str, default: object) -> object:
+    """Read a value from the uploads config, supporting dict and attribute access."""
+    cfg = get_app_config()
+    uploads_cfg = getattr(cfg, "uploads", None)
+    if isinstance(uploads_cfg, dict):
+        return uploads_cfg.get(key, default)
+    return getattr(uploads_cfg, key, default)
+
+
+def _auto_convert_documents_enabled() -> bool:
+    """Return whether automatic host-side document conversion is enabled.
+
+    The secure default is disabled unless an operator explicitly opts in via
+    uploads.auto_convert_documents in config.yaml.
+    """
+    try:
+        raw = _get_uploads_config_value("auto_convert_documents", False)
+        if isinstance(raw, str):
+            return raw.strip().lower() in {"1", "true", "yes", "on"}
+        return bool(raw)
+    except Exception:
+        return False
+
+
 @router.post("", response_model=UploadResponse)
 async def upload_files(
     thread_id: str,
@@ -70,8 +99,12 @@ async def upload_files(
     uploaded_files = []
 
     sandbox_provider = get_sandbox_provider()
-    sandbox_id = sandbox_provider.acquire(thread_id)
+    sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
-    sandbox = sandbox_provider.get(sandbox_id)
+    sandbox = None
+    if sync_to_sandbox:
+        sandbox_id = sandbox_provider.acquire(thread_id)
+        sandbox = sandbox_provider.get(sandbox_id)
+    auto_convert_documents = _auto_convert_documents_enabled()
 
     for file in files:
         if not file.filename:
@@ -90,7 +123,7 @@ async def upload_files(
 
             virtual_path = upload_virtual_path(safe_filename)
 
-            if sandbox_id != "local":
+            if sync_to_sandbox and sandbox is not None:
                 _make_file_sandbox_writable(file_path)
                 sandbox.update_file(virtual_path, content)
 
@@ -105,12 +138,12 @@ 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 file_ext in CONVERTIBLE_EXTENSIONS:
+            if auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
                 md_path = await convert_file_to_markdown(file_path)
                 if md_path:
                     md_virtual_path = upload_virtual_path(md_path.name)
 
-                    if sandbox_id != "local":
+                    if sync_to_sandbox and sandbox is not None:
                         _make_file_sandbox_writable(md_path)
                         sandbox.update_file(md_virtual_path, md_path.read_bytes())
 

backend/app/gateway/services.py

@@ -12,6 +12,7 @@ import json
 import logging
 import re
 import time
+from collections.abc import Mapping
 from typing import Any
 
 from fastapi import HTTPException, Request
@@ -101,9 +102,10 @@ def resolve_agent_factory(assistant_id: str | None):
     """Resolve the agent factory callable from config.
 
     Custom agents are implemented as ``lead_agent`` + an ``agent_name``
-    injected into ``configurable`` — see :func:`build_run_config`.  All
+    injected into ``configurable`` or ``context`` — see
-    ``assistant_id`` values therefore map to the same factory; the routing
+    :func:`build_run_config`.  All ``assistant_id`` values therefore map to the
-    happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
+    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
 
@@ -120,10 +122,12 @@ 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
+    ``"lead_agent"`` / ``None``), the name is forwarded as ``agent_name`` in
-    ``configurable["agent_name"]``.  ``make_lead_agent`` reads this key to
+    whichever runtime options container is active: ``context`` for
-    load the matching ``agents/<name>/SOUL.md`` and per-agent config —
+    LangGraph >= 0.6.0 requests, otherwise ``configurable``.
-    without it the agent silently runs as the default lead agent.
+    ``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
@@ -142,7 +146,14 @@ def build_run_config(
                     thread_id,
                     list(request_config.get("configurable", {}).keys()),
                 )
-            config["context"] = request_config["context"]
+            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
         else:
             configurable = {"thread_id": thread_id}
             configurable.update(request_config.get("configurable", {}))
@@ -154,13 +165,19 @@ 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 configurable["agent_name"] in the request if already set.
+    # Honour an explicit agent_name in the active runtime options container.
-    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
+    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID:
-        if "agent_name" not in config["configurable"]:
+        normalized = assistant_id.strip().lower().replace("_", "-")
-            normalized = assistant_id.strip().lower().replace("_", "-")
+        if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
-            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.")
-                raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
+        if "configurable" in config:
-            config["configurable"]["agent_name"] = normalized
+            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
     if metadata:
         config.setdefault("metadata", {}).update(metadata)
     return config
@@ -298,6 +315,8 @@ 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:

backend/debug.py

@@ -19,24 +19,78 @@ import asyncio
 import logging
 
 from dotenv import load_dotenv
-from langchain_core.messages import HumanMessage
 
-from deerflow.agents import make_lead_agent
+try:
+    from prompt_toolkit import PromptSession
+    from prompt_toolkit.history import InMemoryHistory
+
+    _HAS_PROMPT_TOOLKIT = True
+except ImportError:
+    _HAS_PROMPT_TOOLKIT = False
 
 load_dotenv()
 
-logging.basicConfig(
+_LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    level=logging.INFO,
+_LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+
-    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)
 
 
 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}")
@@ -52,16 +106,27 @@ 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:
-            user_input = input("\nYou: ").strip()
+            if session:
+                user_input = (await session.prompt_async("\nYou: ")).strip()
+            else:
+                user_input = input("\nYou: ").strip()
             if not user_input:
                 continue
             if user_input.lower() in ("quit", "exit"):
@@ -70,15 +135,15 @@ async def main():
 
             # Invoke the agent
             state = {"messages": [HumanMessage(content=user_input)]}
-            result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
+            result = await agent.ainvoke(state, config=config)
 
             # Print the response
             if result.get("messages"):
                 last_message = result["messages"][-1]
                 print(f"\nAgent: {last_message.content}")
 
-        except KeyboardInterrupt:
+        except (KeyboardInterrupt, EOFError):
-            print("\nInterrupted. Goodbye!")
+            print("\nGoodbye!")
             break
         except Exception as e:
             print(f"\nError: {e}")

backend/docs/ARCHITECTURE.md

@@ -199,7 +199,7 @@ class ThreadState(AgentState):
 │   Built-in Tools    │  │  Configured Tools   │  │     MCP Tools       │
 │  (packages/harness/deerflow/tools/)       │  │  (config.yaml)      │  │  (extensions.json)  │
 ├─────────────────────┤  ├─────────────────────┤  ├─────────────────────┤
-│ - present_file      │  │ - web_search        │  │ - github            │
+│ - present_files     │  │ - web_search        │  │ - github            │
 │ - ask_clarification │  │ - web_fetch         │  │ - filesystem        │
 │ - view_image        │  │ - bash              │  │ - postgres          │
 │                     │  │ - read_file         │  │ - brave-search      │

backend/docs/FILE_UPLOAD.md

@@ -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，并自动将 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，并可选地将 Office 文档和 PDF 转换为 Markdown 格式。
 
 ## 功能特性
 
 - ✅ 支持多文件同时上传
-- ✅ 自动转换文档为 Markdown（PDF、PPT、Excel、Word）
+- ✅ 可选地转换文档为 Markdown（PDF、PPT、Excel、Word）
 - ✅ 文件存储在线程隔离的目录中
 - ✅ Agent 自动感知已上传的文件
 - ✅ 支持文件列表查询和删除
@@ -86,7 +86,7 @@ DELETE /api/threads/{thread_id}/uploads/{filename}
 
 ## 支持的文档格式
 
-以下格式会自动转换为 Markdown：
+以下格式在显式启用 `uploads.auto_convert_documents: true` 时会自动转换为 Markdown：
 - PDF (`.pdf`)
 - PowerPoint (`.ppt`, `.pptx`)
 - Excel (`.xls`, `.xlsx`)
@@ -94,6 +94,8 @@ 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
@@ -207,6 +209,7 @@ 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
 

backend/docs/GUARDRAILS.md

@@ -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_file` | Present file to user |
+| `present_files` | Present file to user |
 | `view_image` | Display image |
 | `ask_clarification` | Ask user a question |
 | `task` | Delegate to subagent |

backend/docs/MCP_SERVER.md

@@ -45,6 +45,41 @@ 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.

backend/docs/TODO.md

@@ -24,7 +24,7 @@
 - [ ] 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 sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
-  - Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
+  - [x] Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
   - Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
   - For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
 

backend/docs/summarization.md

@@ -41,6 +41,13 @@ 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
@@ -125,6 +132,26 @@ 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
@@ -147,6 +174,7 @@ 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
 

backend/packages/harness/deerflow/agents/checkpointer/provider.py

@@ -27,7 +27,7 @@ from langgraph.types import Checkpointer
 
 from deerflow.config.app_config import get_app_config
 from deerflow.config.checkpointer_config import CheckpointerConfig
-from deerflow.runtime.store._sqlite_utils import resolve_sqlite_conn_str
+from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
 
 logger = logging.getLogger(__name__)
 
@@ -67,6 +67,7 @@ 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)

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

@@ -1,28 +1,40 @@
 import logging
 
 from langchain.agents import create_agent
-from langchain.agents.middleware import AgentMiddleware, SummarizationMiddleware
+from langchain.agents.middleware import AgentMiddleware
 from langchain_core.runnables import RunnableConfig
 
 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
+from deerflow.config.agents_config import load_agent_config, validate_agent_name
 from deerflow.config.app_config import get_app_config
+from deerflow.config.memory_config import get_memory_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(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()
@@ -38,7 +50,7 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
     return default_model_name
 
 
-def _create_summarization_middleware() -> SummarizationMiddleware | None:
+def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
     """Create and configure the summarization middleware from config."""
     config = get_summarization_config()
 
@@ -77,7 +89,28 @@ def _create_summarization_middleware() -> SummarizationMiddleware | None:
     if config.summary_prompt is not None:
         kwargs["summary_prompt"] = config.summary_prompt
 
-    return SummarizationMiddleware(**kwargs)
+    hooks: list[BeforeSummarizationHook] = []
+    if get_memory_config().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 = get_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,
+    )
 
 
 def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
@@ -224,7 +257,8 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
         middlewares.append(summarization_middleware)
 
     # Add TodoList middleware if plan mode is enabled
-    is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
+    cfg = _get_runtime_config(config)
+    is_plan_mode = cfg.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)
@@ -253,9 +287,9 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
         middlewares.append(DeferredToolFilterMiddleware())
 
     # Add SubagentLimitMiddleware to truncate excess parallel task calls
-    subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
+    subagent_enabled = cfg.get("subagent_enabled", False)
     if subagent_enabled:
-        max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
+        max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
         middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
 
     # LoopDetectionMiddleware — detect and break repetitive tool call loops
@@ -275,7 +309,7 @@ def make_lead_agent(config: RunnableConfig):
     from deerflow.tools import get_available_tools
     from deerflow.tools.builtins import setup_agent
 
-    cfg = config.get("configurable", {})
+    cfg = _get_runtime_config(config)
 
     thinking_enabled = cfg.get("thinking_enabled", True)
     reasoning_effort = cfg.get("reasoning_effort", None)
@@ -284,7 +318,7 @@ def make_lead_agent(config: RunnableConfig):
     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 = cfg.get("agent_name")
+    agent_name = validate_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
@@ -325,6 +359,8 @@ def make_lead_agent(config: RunnableConfig):
             "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),
         }
     )
 

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

@@ -164,6 +164,36 @@ Skip simple one-off tasks.
 """
 
 
+def _build_available_subagents_description(available_names: list[str], bash_available: bool) -> 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)
+            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) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 
@@ -174,13 +204,12 @@ def _build_subagent_section(max_concurrent: int) -> str:
         Formatted subagent section string.
     """
     n = max_concurrent
-    bash_available = "bash" in get_available_subagent_names()
+    available_names = get_available_subagent_names()
-    available_subagents = (
+    bash_available = "bash" in available_names
-        "- **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
+    # Dynamically build subagent type descriptions from registry (aligned with Codex's
-        else "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n"
+    # agent_type_description pattern where all registered roles are listed in the tool spec).
-        "- **bash**: Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
+    available_subagents = _build_available_subagents_description(available_names, bash_available)
-    )
     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()'
@@ -420,7 +449,7 @@ 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_file` tool
+- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_files` tool
 {acp_section}
 </working_directory>
 
@@ -648,7 +677,7 @@ def _build_acp_section() -> 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_file`"
+        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_files`"
     )
 
 

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

@@ -0,0 +1,109 @@
+"""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

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

@@ -61,48 +61,88 @@ class MemoryUpdateQueue:
             return
 
         with self._lock:
-            existing_context = next(
+            self._enqueue_locked(
-                (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,
-                correction_detected=merged_correction_detected,
+                correction_detected=correction_detected,
-                reinforcement_detected=merged_reinforcement_detected,
+                reinforcement_detected=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,
+        correction_detected: bool = False,
+        reinforcement_detected: bool = False,
+    ) -> None:
+        """Add a conversation and start processing immediately in the background."""
+        config = get_memory_config()
+        if not config.enabled:
+            return
+
+        with self._lock:
+            self._enqueue_locked(
+                thread_id=thread_id,
+                messages=messages,
+                agent_name=agent_name,
+                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,
+        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,
+            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 = get_memory_config()
+        self._schedule_timer(config.debounce_seconds)
 
+        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(
-            config.debounce_seconds,
+            delay_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
@@ -110,8 +150,8 @@ class MemoryUpdateQueue:
 
         with self._lock:
             if self._processing:
-                # Already processing, reschedule
+                # Preserve immediate flush semantics even if another worker is active.
-                self._reset_timer()
+                self._schedule_timer(0)
                 return
 
             if not self._queue:
@@ -164,6 +204,13 @@ 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.
 

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

@@ -4,6 +4,7 @@ import abc
 import json
 import logging
 import threading
+import uuid
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
@@ -66,6 +67,8 @@ class FileMemoryStorage(MemoryStorage):
         # Per-agent memory cache: keyed by agent_name (None = global)
         # Value: (memory_data, file_mtime)
         self._memory_cache: dict[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.
@@ -114,14 +117,17 @@ class FileMemoryStorage(MemoryStorage):
         except OSError:
             current_mtime = None
 
-        cached = self._memory_cache.get(agent_name)
+        with self._cache_lock:
+            cached = self._memory_cache.get(agent_name)
+            if cached is not None and cached[1] == current_mtime:
+                return cached[0]
 
-        if cached is None or cached[1] != current_mtime:
+        memory_data = self._load_memory_from_file(agent_name)
-            memory_data = self._load_memory_from_file(agent_name)
+
+        with self._cache_lock:
             self._memory_cache[agent_name] = (memory_data, current_mtime)
-            return memory_data
 
-        return cached[0]
+        return memory_data
 
     def reload(self, agent_name: str | None = None) -> dict[str, Any]:
         """Reload memory data from file, forcing cache invalidation."""
@@ -133,7 +139,8 @@ class FileMemoryStorage(MemoryStorage):
         except OSError:
             mtime = None
 
-        self._memory_cache[agent_name] = (memory_data, mtime)
+        with self._cache_lock:
+            self._memory_cache[agent_name] = (memory_data, mtime)
         return memory_data
 
     def save(self, memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
@@ -142,9 +149,12 @@ class FileMemoryStorage(MemoryStorage):
 
         try:
             file_path.parent.mkdir(parents=True, exist_ok=True)
-            memory_data["lastUpdated"] = utc_now_iso_z()
+            # 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()}
 
-            temp_path = file_path.with_suffix(".tmp")
+            temp_path = file_path.with_suffix(f".{uuid.uuid4().hex}.tmp")
             with open(temp_path, "w", encoding="utf-8") as f:
                 json.dump(memory_data, f, indent=2, ensure_ascii=False)
 
@@ -155,7 +165,8 @@ class FileMemoryStorage(MemoryStorage):
             except OSError:
                 mtime = None
 
-            self._memory_cache[agent_name] = (memory_data, mtime)
+            with self._cache_lock:
+                self._memory_cache[agent_name] = (memory_data, mtime)
             logger.info("Memory saved to %s", file_path)
             return True
         except OSError as e:

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

@@ -0,0 +1,31 @@
+"""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.memory_config import get_memory_config
+
+
+def memory_flush_hook(event: SummarizationEvent) -> None:
+    """Flush messages about to be summarized into the memory queue."""
+    if not get_memory_config().enabled or not event.thread_id:
+        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()
+    queue.add_nowait(
+        thread_id=event.thread_id,
+        messages=filtered_messages,
+        agent_name=event.agent_name,
+        correction_detected=correction_detected,
+        reinforcement_detected=reinforcement_detected,
+    )

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

@@ -1,10 +1,15 @@
 """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 (
@@ -21,6 +26,12 @@ 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."""
@@ -206,6 +217,39 @@ 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".
@@ -269,6 +313,117 @@ class MemoryUpdater:
         model_name = self._model_name or config.model_name
         return create_chat_model(name=model_name, thinking_enabled=False)
 
+    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,
+    ) -> tuple[dict[str, Any], str] | None:
+        """Load memory and build the update prompt for a conversation."""
+        config = get_memory_config()
+        if not config.enabled or not messages:
+            return None
+
+        current_memory = get_memory_data(agent_name)
+        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,
+    ) -> 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().save(updated_memory, agent_name)
+
+    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,
+    ) -> 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,
+            )
+            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,
+            )
+        except json.JSONDecodeError as e:
+            logger.warning("Failed to parse LLM response for memory update: %s", e)
+            return False
+        except Exception as e:
+            logger.exception("Memory update failed: %s", e)
+            return False
+
     def update_memory(
         self,
         messages: list[Any],
@@ -277,7 +432,7 @@ class MemoryUpdater:
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
     ) -> bool:
-        """Update memory based on conversation messages.
+        """Synchronously update memory via the async updater path.
 
         Args:
             messages: List of conversation messages.
@@ -289,78 +444,15 @@ class MemoryUpdater:
         Returns:
             True if update was successful, False otherwise.
         """
-        config = get_memory_config()
+        return _run_async_update_sync(
-        if not config.enabled:
+            self.aupdate_memory(
-            return False
+                messages=messages,
-
+                thread_id=thread_id,
-        if not messages:
+                agent_name=agent_name,
-            return False
+                correction_detected=correction_detected,
-
+                reinforcement_detected=reinforcement_detected,
-        try:
-            # Get current memory
-            current_memory = get_memory_data(agent_name)
-
-            # Format conversation for prompt
-            conversation_text = format_conversation_for_update(messages)
-
-            if not conversation_text.strip():
-                return False
-
-            # Build prompt
-            correction_hint = ""
-            if correction_detected:
-                correction_hint = (
-                    "IMPORTANT: Explicit correction signals were detected in this conversation. "
-                    "Pay special attention to what the agent got wrong, what the user corrected, "
-                    "and record the correct approach as a fact with category "
-                    '"correction" and confidence >= 0.95 when appropriate.'
-                )
-            if reinforcement_detected:
-                reinforcement_hint = (
-                    "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
-                    "The user explicitly confirmed the agent's approach was correct or helpful. "
-                    "Record the confirmed approach, style, or preference as a fact with category "
-                    '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
-                )
-                correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
-
-            prompt = MEMORY_UPDATE_PROMPT.format(
-                current_memory=json.dumps(current_memory, indent=2),
-                conversation=conversation_text,
-                correction_hint=correction_hint,
             )
-
+        )
-            # Call LLM
-            model = self._get_model()
-            response = model.invoke(prompt)
-            response_text = _extract_text(response.content).strip()
-
-            # Parse response
-            # Remove markdown code blocks if present
-            if response_text.startswith("```"):
-                lines = response_text.split("\n")
-                response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
-
-            update_data = json.loads(response_text)
-
-            # Apply updates
-            updated_memory = self._apply_updates(current_memory, update_data, thread_id)
-
-            # Strip file-upload mentions from all summaries before saving.
-            # Uploaded files are session-scoped and won't exist in future sessions,
-            # so recording upload events in long-term memory causes the agent to
-            # try (and fail) to locate those files in subsequent conversations.
-            updated_memory = _strip_upload_mentions_from_memory(updated_memory)
-
-            # Save
-            return get_memory_storage().save(updated_memory, agent_name)
-
-        except json.JSONDecodeError as e:
-            logger.warning("Failed to parse LLM response for memory update: %s", e)
-            return False
-        except Exception as e:
-            logger.exception("Memory update failed: %s", e)
-            return False
 
     def _apply_updates(
         self,

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

@@ -3,6 +3,7 @@
 import json
 import logging
 from collections.abc import Callable
+from hashlib import sha256
 from typing import override
 
 from langchain.agents import AgentState
@@ -36,6 +37,13 @@ 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.
 
@@ -131,6 +139,7 @@ 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",

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

@@ -13,6 +13,7 @@ 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
@@ -33,6 +34,44 @@ 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.
 
@@ -51,7 +90,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
         for msg in messages:
             if getattr(msg, "type", None) != "ai":
                 continue
-            for tc in getattr(msg, "tool_calls", None) or []:
+            for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
                 if tc_id and tc_id not in existing_tool_msg_ids:
                     needs_patch = True
@@ -70,7 +109,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
             patched.append(msg)
             if getattr(msg, "type", None) != "ai":
                 continue
-            for tc in getattr(msg, "tool_calls", None) or []:
+            for tc in self._message_tool_calls(msg):
                 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(

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

@@ -16,6 +16,9 @@ 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__)
 
@@ -35,7 +38,7 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
         if not registry:
             return request
 
-        deferred_names = {e.name for e in registry.entries}
+        deferred_names = registry.deferred_names
         active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
 
         if len(active_tools) < len(request.tools):
@@ -43,6 +46,28 @@ 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,
@@ -51,6 +76,17 @@ 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,
@@ -58,3 +94,14 @@ 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)

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

@@ -4,6 +4,7 @@ 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
@@ -19,6 +20,8 @@ from langchain.agents.middleware.types import (
 from langchain_core.messages import AIMessage
 from langgraph.errors import GraphBubbleUp
 
+from deerflow.config import get_app_config
+
 logger = logging.getLogger(__name__)
 
 _RETRIABLE_STATUS_CODES = {408, 409, 425, 429, 500, 502, 503, 504}
@@ -67,6 +70,80 @@ 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 = get_app_config()
+            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()
@@ -83,6 +160,8 @@ 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:
@@ -104,6 +183,9 @@ 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":
@@ -138,12 +220,20 @@ 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:
-                return handler(request)
+                response = handler(request)
+                self._record_success()
+                return response
             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)
@@ -166,6 +256,8 @@ 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
@@ -174,12 +266,20 @@ 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:
-                return await handler(request)
+                response = await handler(request)
+                self._record_success()
+                return response
             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)
@@ -202,6 +302,8 @@ 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))
 
 

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

@@ -17,6 +17,7 @@ import json
 import logging
 import threading
 from collections import OrderedDict, defaultdict
+from copy import deepcopy
 from typing import override
 
 from langchain.agents import AgentState
@@ -24,6 +25,8 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime
 
+from deerflow.utils.runtime import get_thread_id
+
 logger = logging.getLogger(__name__)
 
 # Defaults — can be overridden via constructor
@@ -182,10 +185,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
 
     def _get_thread_id(self, runtime: Runtime) -> str:
         """Extract thread_id from runtime context for per-thread tracking."""
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
+        return get_thread_id(runtime) or "default"
-        if thread_id:
-            return thread_id
-        return "default"
 
     def _evict_if_needed(self) -> None:
         """Evict least recently used threads if over the limit.
@@ -323,6 +323,26 @@ 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)
 
@@ -330,12 +350,8 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             # Strip tool_calls from the last AIMessage to force text output
             messages = state.get("messages", [])
             last_msg = messages[-1]
-            stripped_msg = last_msg.model_copy(
+            content = self._append_text(last_msg.content, warning or _HARD_STOP_MSG)
-                update={
+            stripped_msg = last_msg.model_copy(update=self._build_hard_stop_update(last_msg, content))
-                    "tool_calls": [],
-                    "content": self._append_text(last_msg.content, warning),
-                }
-            )
             return {"messages": [stripped_msg]}
 
         if warning:

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

@@ -1,50 +1,19 @@
 """Middleware for memory mechanism."""
 
 import logging
-import re
+from typing import override
-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.memory_config import get_memory_config
+from deerflow.utils.runtime import get_thread_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."""
@@ -52,125 +21,6 @@ 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.
 
@@ -207,13 +57,10 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         if not config.enabled:
             return None
 
-        # Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
+        # Resolve thread ID from the runtime or configured fallback sources
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
+        thread_id = get_thread_id(runtime)
-        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")
+            logger.debug("No thread_id could be resolved from runtime/config, skipping memory update")
             return None
 
         # Get messages from state
@@ -223,7 +70,7 @@ 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

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

@@ -14,6 +14,7 @@ from langgraph.prebuilt.tool_node import ToolCallRequest
 from langgraph.types import Command
 
 from deerflow.agents.thread_state import ThreadState
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
@@ -218,15 +219,7 @@ class SandboxAuditMiddleware(AgentMiddleware[ThreadState]):
     # ------------------------------------------------------------------
 
     def _get_thread_id(self, request: ToolCallRequest) -> str | None:
-        runtime = request.runtime  # ToolRuntime; may be None-like in tests
+        return get_thread_id(request.runtime)
-        if runtime is None:
-            return None
-        ctx = getattr(runtime, "context", None) or {}
-        thread_id = ctx.get("thread_id") if isinstance(ctx, dict) else None
-        if thread_id is None:
-            cfg = getattr(runtime, "config", None) or {}
-            thread_id = cfg.get("configurable", {}).get("thread_id")
-        return thread_id
 
     _AUDIT_COMMAND_LIMIT = 200
 

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

@@ -0,0 +1,337 @@
+"""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, runtime_checkable
+
+from langchain.agents import AgentState
+from langchain.agents.middleware import SummarizationMiddleware
+from langchain_core.messages import AIMessage, AnyMessage, RemoveMessage, ToolMessage
+from langgraph.config import get_config
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.runtime import Runtime
+
+from deerflow.utils.runtime import get_thread_id
+
+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_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,
+            ]
+        }
+
+    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=get_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)

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

@@ -3,11 +3,11 @@ 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.paths import Paths, get_paths
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
@@ -75,11 +75,7 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
 
     @override
     def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
-        context = runtime.context or {}
+        thread_id = get_thread_id(runtime)
-        thread_id = context.get("thread_id")
-        if thread_id is None:
-            config = get_config()
-            thread_id = config.get("configurable", {}).get("thread_id")
 
         if thread_id is None:
             raise ValueError("Thread ID is required in runtime context or config.configurable")

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

@@ -1,6 +1,7 @@
 """Middleware for automatic thread title generation."""
 
 import logging
+import re
 from typing import NotRequired, override
 
 from langchain.agents import AgentState
@@ -77,7 +78,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         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._normalize_content(assistant_msg_content)
+        assistant_msg = self._strip_think_tags(self._normalize_content(assistant_msg_content))
 
         prompt = config.prompt_template.format(
             max_words=config.max_words,
@@ -86,10 +87,15 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         )
         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) -> 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[: config.max_chars] if len(title) > config.max_chars else title
 
@@ -121,7 +127,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
                 model = create_chat_model(name=config.model_name, thinking_enabled=False)
             else:
                 model = create_chat_model(thinking_enabled=False)
-            response = await model.ainvoke(prompt)
+            response = await model.ainvoke(prompt, config={"run_name": "title_agent"})
             title = self._parse_title(response.content)
             if title:
                 return {"title": title}

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

@@ -1,9 +1,14 @@
-"""Middleware that extends TodoListMiddleware with context-loss detection.
+"""Middleware that extends TodoListMiddleware with context-loss detection and premature-exit prevention.
 
 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
@@ -12,6 +17,7 @@ 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
 
@@ -34,6 +40,11 @@ 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] = []
@@ -57,7 +68,7 @@ class TodoMiddleware(TodoListMiddleware):
     def before_model(
         self,
         state: PlanningState,
-        runtime: Runtime,  # noqa: ARG002
+        runtime: Runtime,
     ) -> 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]
@@ -98,3 +109,71 @@ 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)

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

@@ -11,6 +11,7 @@ from langgraph.runtime import Runtime
 
 from deerflow.config.paths import Paths, get_paths
 from deerflow.utils.file_conversion import extract_outline
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
@@ -213,14 +214,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
             return None
 
         # Resolve uploads directory for existence checks
-        thread_id = (runtime.context or {}).get("thread_id")
+        thread_id = get_thread_id(runtime)
-        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) if thread_id else None
 
         # Get newly uploaded files from the current message's additional_kwargs.files

backend/packages/harness/deerflow/client.py

@@ -722,6 +722,10 @@ 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": [
                 {
@@ -733,7 +737,8 @@ 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:

backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py

@@ -119,6 +119,16 @@ 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:

backend/packages/harness/deerflow/community/jina_ai/jina_client.py

@@ -38,6 +38,6 @@ class JinaClient:
 
             return response.text
         except Exception as e:
-            error_message = f"Request to Jina API failed: {str(e)}"
+            error_message = f"Request to Jina API failed: {type(e).__name__}: {e}"
-            logger.exception(error_message)
+            logger.warning(error_message)
             return f"Error: {error_message}"

backend/packages/harness/deerflow/community/jina_ai/tools.py

@@ -1,3 +1,5 @@
+import asyncio
+
 from langchain.tools import tool
 
 from deerflow.community.jina_ai.jina_client import JinaClient
@@ -26,5 +28,5 @@ async def web_fetch_tool(url: str) -> str:
     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 = readability_extractor.extract_article(html_content)
+    article = await asyncio.to_thread(readability_extractor.extract_article, html_content)
     return article.to_markdown()[:4096]

backend/packages/harness/deerflow/config/agents_api_config.py

@@ -0,0 +1,32 @@
+"""Configuration for the custom agents management API."""
+
+from pydantic import BaseModel, Field
+
+
+class AgentsApiConfig(BaseModel):
+    """Configuration for custom-agent and user-profile management routes."""
+
+    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."),
+    )
+
+
+_agents_api_config: AgentsApiConfig = AgentsApiConfig()
+
+
+def get_agents_api_config() -> AgentsApiConfig:
+    """Get the current agents API configuration."""
+    return _agents_api_config
+
+
+def set_agents_api_config(config: AgentsApiConfig) -> None:
+    """Set the agents API configuration."""
+    global _agents_api_config
+    _agents_api_config = config
+
+
+def load_agents_api_config_from_dict(config_dict: dict) -> None:
+    """Load agents API configuration from a dictionary."""
+    global _agents_api_config
+    _agents_api_config = AgentsApiConfig(**config_dict)

backend/packages/harness/deerflow/config/agents_config.py

@@ -15,6 +15,17 @@ 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."""
 
@@ -46,8 +57,7 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
     if name is None:
         return None
 
-    if not AGENT_NAME_PATTERN.match(name):
+    name = validate_agent_name(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"
 

backend/packages/harness/deerflow/config/app_config.py

@@ -9,6 +9,7 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, ConfigDict, Field
 
 from deerflow.config.acp_config import load_acp_config_from_dict
+from deerflow.config.agents_api_config import AgentsApiConfig, load_agents_api_config_from_dict
 from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
 from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
@@ -30,6 +31,13 @@ load_dotenv()
 logger = logging.getLogger(__name__)
 
 
+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]
@@ -53,8 +61,10 @@ 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)
     checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
     stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
@@ -117,6 +127,10 @@ class AppConfig(BaseModel):
         if "memory" in config_data:
             load_memory_config_from_dict(config_data["memory"])
 
+        # Always refresh agents API config so removed config sections reset
+        # singleton-backed state to its default/disabled values on reload.
+        load_agents_api_config_from_dict(config_data.get("agents_api") or {})
+
         # Load subagents config if present
         if "subagents" in config_data:
             load_subagents_config_from_dict(config_data["subagents"])
@@ -129,6 +143,10 @@ class AppConfig(BaseModel):
         if "guardrails" in config_data:
             load_guardrails_config_from_dict(config_data["guardrails"])
 
+        # Load circuit_breaker config if present
+        if "circuit_breaker" in config_data:
+            config_data["circuit_breaker"] = config_data["circuit_breaker"]
+
         # Load checkpointer config if present
         if "checkpointer" in config_data:
             load_checkpointer_config_from_dict(config_data["checkpointer"])

backend/packages/harness/deerflow/config/subagents_config.py

@@ -25,6 +25,47 @@ class SubagentOverrideConfig(BaseModel):
         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):
@@ -44,6 +85,10 @@ 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.
@@ -82,6 +127,20 @@ 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.
+
+        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
+
 
 _subagents_config: SubagentsAppConfig = SubagentsAppConfig()
 
@@ -105,15 +164,20 @@ def load_subagents_config_from_dict(config_dict: dict) -> None:
             parts.append(f"max_turns={override.max_turns}")
         if override.model is not None:
             parts.append(f"model={override.model}")
+        if override.skills is not None:
+            parts.append(f"skills={override.skills}")
         if parts:
             overrides_summary[name] = ", ".join(parts)
 
-    if overrides_summary:
+    custom_agents_names = list(_subagents_config.custom_agents.keys())
+
+    if overrides_summary or custom_agents_names:
         logger.info(
-            "Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s",
+            "Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s, custom_agents=%s",
             _subagents_config.timeout_seconds,
             _subagents_config.max_turns,
-            overrides_summary,
+            overrides_summary or "none",
+            custom_agents_names or "none",
         )
     else:
         logger.info(

backend/packages/harness/deerflow/config/summarization_config.py

@@ -51,6 +51,25 @@ 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

backend/packages/harness/deerflow/mcp/cache.py

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

backend/packages/harness/deerflow/mcp/tools.py

@@ -12,6 +12,7 @@ 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__)
 
@@ -95,6 +96,27 @@ 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

backend/packages/harness/deerflow/models/claude_provider.py

@@ -190,23 +190,33 @@ class ClaudeChatModel(ChatAnthropic):
             )
 
     def _apply_prompt_caching(self, payload: dict) -> None:
-        """Apply ephemeral cache_control to system and recent messages."""
+        """Apply ephemeral cache_control to system, recent messages, and last tool definition.
-        # Cache system messages
+
+        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
         system = payload.get("system")
         if system and isinstance(system, list):
             for block in system:
                 if isinstance(block, dict) and block.get("type") == "text":
-                    block["cache_control"] = {"type": "ephemeral"}
+                    candidates.append(block)
         elif system and isinstance(system, str):
-            payload["system"] = [
+            new_block: dict = {"type": "text", "text": system}
-                {
+            payload["system"] = [new_block]
-                    "type": "text",
+            candidates.append(new_block)
-                    "text": system,
-                    "cache_control": {"type": "ephemeral"},
-                }
-            ]
 
-        # Cache recent messages
+        # 2. Recent message blocks
         messages = payload.get("messages", [])
         cache_start = max(0, len(messages) - self.prompt_cache_size)
         for i in range(cache_start, len(messages)):
@@ -217,20 +227,21 @@ class ClaudeChatModel(ChatAnthropic):
             if isinstance(content, list):
                 for block in content:
                     if isinstance(block, dict):
-                        block["cache_control"] = {"type": "ephemeral"}
+                        candidates.append(block)
             elif isinstance(content, str) and content:
-                msg["content"] = [
+                new_block = {"type": "text", "text": content}
-                    {
+                msg["content"] = [new_block]
-                        "type": "text",
+                candidates.append(new_block)
-                        "text": content,
-                        "cache_control": {"type": "ephemeral"},
-                    }
-                ]
 
-        # Cache the last tool definition
+        # 3. Last tool definition
         tools = payload.get("tools", [])
         if tools and isinstance(tools[-1], dict):
-            tools[-1]["cache_control"] = {"type": "ephemeral"}
+            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"}
 
     def _apply_thinking_budget(self, payload: dict) -> None:
         """Auto-allocate thinking budget (80% of max_tokens)."""

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

@@ -30,6 +30,22 @@ 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, **kwargs) -> BaseChatModel:
     """Create a chat model instance from the config.
 
@@ -97,6 +113,8 @@ def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *
         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
 
@@ -113,6 +131,12 @@ def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *
         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)
+
     model_instance = model_class(**{**model_settings_from_config, **kwargs})
 
     callbacks = build_tracing_callbacks()

backend/packages/harness/deerflow/models/mindie_provider.py

@@ -0,0 +1,237 @@
+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)

backend/packages/harness/deerflow/sandbox/local/local_sandbox.py

@@ -288,10 +288,10 @@ class LocalSandbox(Sandbox):
                 timeout=600,
             )
         else:
+            args = [shell, "-c", resolved_command]
             result = subprocess.run(
-                resolved_command,
+                args,
-                executable=shell,
+                shell=False,
-                shell=True,
                 capture_output=True,
                 text=True,
                 timeout=600,

backend/packages/harness/deerflow/sandbox/local/local_sandbox_provider.py

@@ -11,6 +11,8 @@ _singleton: LocalSandbox | None = None
 
 
 class LocalSandboxProvider(SandboxProvider):
+    uses_thread_data_mounts = True
+
     def __init__(self):
         """Initialize the local sandbox provider with path mappings."""
         self._path_mappings = self._setup_path_mappings()

backend/packages/harness/deerflow/sandbox/middleware.py

@@ -7,6 +7,7 @@ from langgraph.runtime import Runtime
 
 from deerflow.agents.thread_state import SandboxState, ThreadDataState
 from deerflow.sandbox import get_sandbox_provider
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
@@ -56,7 +57,7 @@ class SandboxMiddleware(AgentMiddleware[SandboxMiddlewareState]):
 
         # Eager initialization (original behavior)
         if "sandbox" not in state or state["sandbox"] is None:
-            thread_id = (runtime.context or {}).get("thread_id")
+            thread_id = get_thread_id(runtime)
             if thread_id is None:
                 return super().before_agent(state, runtime)
             sandbox_id = self._acquire_sandbox(thread_id)

backend/packages/harness/deerflow/sandbox/sandbox_provider.py

@@ -8,6 +8,8 @@ from deerflow.sandbox.sandbox import Sandbox
 class SandboxProvider(ABC):
     """Abstract base class for sandbox providers"""
 
+    uses_thread_data_mounts: bool = False
+
     @abstractmethod
     def acquire(self, thread_id: str | None = None) -> str:
         """Acquire a sandbox environment and return its ID.

backend/packages/harness/deerflow/sandbox/tools.py

@@ -19,6 +19,7 @@ from deerflow.sandbox.sandbox import Sandbox
 from deerflow.sandbox.sandbox_provider import get_sandbox_provider
 from deerflow.sandbox.search import GrepMatch
 from deerflow.sandbox.security import LOCAL_HOST_BASH_DISABLED_MESSAGE, is_host_bash_allowed
+from deerflow.utils.runtime import get_thread_id
 
 _ABSOLUTE_PATH_PATTERN = re.compile(r"(?<![:\w])(?<!:/)/(?:[^\s\"'`;&|<>()]+)")
 _FILE_URL_PATTERN = re.compile(r"\bfile://\S+", re.IGNORECASE)
@@ -851,11 +852,9 @@ def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | Non
             # Sandbox was released, fall through to acquire new one
 
     # Lazy acquisition: get thread_id and acquire sandbox
-    thread_id = runtime.context.get("thread_id") if runtime.context else None
+    thread_id = get_thread_id(runtime)
     if thread_id is None:
-        thread_id = runtime.config.get("configurable", {}).get("thread_id") if runtime.config else None
+        raise SandboxRuntimeError("Thread ID not available in runtime context, runtime config, or LangGraph config")
-    if thread_id is None:
-        raise SandboxRuntimeError("Thread ID not available in runtime context")
 
     provider = get_sandbox_provider()
     sandbox_id = provider.acquire(thread_id)
@@ -1047,6 +1046,7 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
+        thread_data = None
         if is_local_sandbox(runtime):
             thread_data = get_thread_data(runtime)
             validate_local_tool_path(path, thread_data, read_only=True)
@@ -1061,6 +1061,8 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
         if not children:
             return "(empty)"
         output = "\n".join(children)
+        if thread_data is not None:
+            output = mask_local_paths_in_output(output, thread_data)
         try:
             from deerflow.config.app_config import get_app_config
 

backend/packages/harness/deerflow/skills/parser.py

@@ -2,21 +2,24 @@ import logging
 import re
 from pathlib import Path
 
+import yaml
+
 from .types import Skill
 
 logger = logging.getLogger(__name__)
 
 
 def parse_skill_file(skill_file: Path, category: str, relative_path: Path | None = None) -> Skill | None:
-    """
+    """Parse a SKILL.md file and extract metadata.
-    Parse a SKILL.md file and extract metadata.
 
     Args:
-        skill_file: Path to the SKILL.md file
+        skill_file: Path to the SKILL.md file.
-        category: Category of the skill ('public' or 'custom')
+        category: Category of the skill ('public' or 'custom').
+        relative_path: Relative path from the category root to the skill
+            directory.  Defaults to the skill directory name when omitted.
 
     Returns:
-        Skill object if parsing succeeds, None otherwise
+        Skill object if parsing succeeds, None otherwise.
     """
     if not skill_file.exists() or skill_file.name != "SKILL.md":
         return None
@@ -24,90 +27,42 @@ def parse_skill_file(skill_file: Path, category: str, relative_path: Path | None
     try:
         content = skill_file.read_text(encoding="utf-8")
 
-        # Extract YAML front matter
+        # Extract YAML front-matter block between leading ``---`` fences.
-        # Pattern: ---\nkey: value\n---
         front_matter_match = re.match(r"^---\s*\n(.*?)\n---\s*\n", content, re.DOTALL)
-
         if not front_matter_match:
             return None
 
-        front_matter = front_matter_match.group(1)
+        front_matter_text = front_matter_match.group(1)
 
-        # Parse YAML front matter with basic multiline string support
+        try:
-        metadata = {}
+            metadata = yaml.safe_load(front_matter_text)
-        lines = front_matter.split("\n")
+        except yaml.YAMLError as exc:
-        current_key = None
+            logger.error("Invalid YAML front-matter in %s: %s", skill_file, exc)
-        current_value = []
+            return None
-        is_multiline = False
-        multiline_style = None
-        indent_level = None
 
-        for line in lines:
+        if not isinstance(metadata, dict):
-            if is_multiline:
+            logger.error("Front-matter in %s is not a YAML mapping", skill_file)
-                if not line.strip():
+            return None
-                    current_value.append("")
-                    continue
 
-                current_indent = len(line) - len(line.lstrip())
+        # Extract required fields.  Both must be non-empty strings.
-
-                if indent_level is None:
-                    if current_indent > 0:
-                        indent_level = current_indent
-                        current_value.append(line[indent_level:])
-                        continue
-                elif current_indent >= indent_level:
-                    current_value.append(line[indent_level:])
-                    continue
-
-            # If we reach here, it's either a new key or the end of multiline
-            if current_key and is_multiline:
-                if multiline_style == "|":
-                    metadata[current_key] = "\n".join(current_value).rstrip()
-                else:
-                    text = "\n".join(current_value).rstrip()
-                    # Replace single newlines with spaces for folded blocks
-                    metadata[current_key] = re.sub(r"(?<!\n)\n(?!\n)", " ", text)
-
-                current_key = None
-                current_value = []
-                is_multiline = False
-                multiline_style = None
-                indent_level = None
-
-            if not line.strip():
-                continue
-
-            if ":" in line:
-                # Handle nested dicts simply by ignoring indentation for now,
-                # or just extracting top-level keys
-                key, value = line.split(":", 1)
-                key = key.strip()
-                value = value.strip()
-
-                if value in (">", "|"):
-                    current_key = key
-                    is_multiline = True
-                    multiline_style = value
-                    current_value = []
-                    indent_level = None
-                else:
-                    metadata[key] = value
-
-        if current_key and is_multiline:
-            if multiline_style == "|":
-                metadata[current_key] = "\n".join(current_value).rstrip()
-            else:
-                text = "\n".join(current_value).rstrip()
-                metadata[current_key] = re.sub(r"(?<!\n)\n(?!\n)", " ", text)
-
-        # Extract required fields
         name = metadata.get("name")
         description = metadata.get("description")
 
+        if not name or not isinstance(name, str):
+            return None
+        if not description or not isinstance(description, str):
+            return None
+
+        # Normalise: strip surrounding whitespace that YAML may preserve.
+        name = name.strip()
+        description = description.strip()
+
         if not name or not description:
             return None
 
         license_text = metadata.get("license")
+        if license_text is not None:
+            license_text = str(license_text).strip() or None
 
         return Skill(
             name=name,
@@ -117,9 +72,9 @@ def parse_skill_file(skill_file: Path, category: str, relative_path: Path | None
             skill_file=skill_file,
             relative_path=relative_path or Path(skill_file.parent.name),
             category=category,
-            enabled=True,  # Default to enabled, actual state comes from config file
+            enabled=True,  # Actual state comes from the extensions config file.
         )
 
-    except Exception as e:
+    except Exception:
-        logger.error("Error parsing skill file %s: %s", skill_file, e)
+        logger.exception("Unexpected error parsing skill file %s", skill_file)
         return None

backend/packages/harness/deerflow/skills/security_scanner.py

@@ -54,7 +54,8 @@ async def scan_skill_content(content: str, *, executable: bool = False, location
             [
                 {"role": "system", "content": rubric},
                 {"role": "user", "content": prompt},
-            ]
+            ],
+            config={"run_name": "security_agent"},
         )
         parsed = _extract_json_object(str(getattr(response, "content", "") or ""))
         if parsed and parsed.get("decision") in {"allow", "warn", "block"}:

backend/packages/harness/deerflow/subagents/config.py

@@ -13,6 +13,8 @@ class SubagentConfig:
         system_prompt: The system prompt that guides the subagent's behavior.
         tools: Optional list of tool names to allow. If None, inherits all tools.
         disallowed_tools: Optional list of tool names to deny.
+        skills: Optional list of skill names to load. If None, inherits all enabled skills.
+                If an empty list, no skills are loaded.
         model: Model to use - 'inherit' uses parent's model.
         max_turns: Maximum number of agent turns before stopping.
         timeout_seconds: Maximum execution time in seconds (default: 900 = 15 minutes).
@@ -23,6 +25,7 @@ class SubagentConfig:
     system_prompt: str
     tools: list[str] | None = None
     disallowed_tools: list[str] | None = field(default_factory=lambda: ["task"])
+    skills: list[str] | None = None
     model: str = "inherit"
     max_turns: int = 50
     timeout_seconds: int = 900

backend/packages/harness/deerflow/subagents/executor.py

@@ -13,7 +13,7 @@ from typing import Any
 
 from langchain.agents import create_agent
 from langchain.tools import BaseTool
-from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.runnables import RunnableConfig
 
 from deerflow.agents.thread_state import SandboxState, ThreadDataState, ThreadState
@@ -184,7 +184,63 @@ class SubagentExecutor:
             state_schema=ThreadState,
         )
 
-    def _build_initial_state(self, task: str) -> dict[str, Any]:
+    async def _load_skill_messages(self) -> list[SystemMessage]:
+        """Load skill content as conversation items based on config.skills.
+
+        Aligned with Codex's pattern: each subagent loads its own skills
+        per-session and injects them as conversation items (developer messages),
+        not as system prompt text. The config.skills whitelist controls which
+        skills are loaded:
+        - None: load all enabled skills
+        - []: no skills
+        - ["skill-a", "skill-b"]: only these skills
+
+        Returns:
+            List of SystemMessages containing skill content.
+        """
+        if self.config.skills is not None and len(self.config.skills) == 0:
+            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} skills=[] — skipping skill loading")
+            return []
+
+        try:
+            from deerflow.skills.loader import load_skills
+
+            # Use asyncio.to_thread to avoid blocking the event loop (LangGraph ASGI requirement)
+            all_skills = await asyncio.to_thread(load_skills, enabled_only=True)
+            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} loaded {len(all_skills)} enabled skills from disk")
+        except Exception:
+            logger.warning(f"[trace={self.trace_id}] Failed to load skills for subagent {self.config.name}", exc_info=True)
+            return []
+
+        if not all_skills:
+            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} no enabled skills found")
+            return []
+
+        # Filter by config.skills whitelist
+        if self.config.skills is not None:
+            allowed = set(self.config.skills)
+            skills = [s for s in all_skills if s.name in allowed]
+        else:
+            skills = all_skills
+
+        if not skills:
+            return []
+
+        # Read each skill's SKILL.md content and create conversation items
+        messages = []
+        for skill in skills:
+            try:
+                content = await asyncio.to_thread(skill.skill_file.read_text, encoding="utf-8")
+                content = content.strip()
+                if content:
+                    messages.append(SystemMessage(content=f'<skill name="{skill.name}">\n{content}\n</skill>'))
+                    logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} loaded skill: {skill.name}")
+            except Exception:
+                logger.debug(f"[trace={self.trace_id}] Failed to read skill {skill.name}", exc_info=True)
+
+        return messages
+
+    async def _build_initial_state(self, task: str) -> dict[str, Any]:
         """Build the initial state for agent execution.
 
         Args:
@@ -193,8 +249,17 @@ class SubagentExecutor:
         Returns:
             Initial state dictionary.
         """
+        # Load skills as conversation items (Codex pattern)
+        skill_messages = await self._load_skill_messages()
+
+        messages: list = []
+        # Skill content injected as developer/system messages before the task
+        messages.extend(skill_messages)
+        # Then the actual task
+        messages.append(HumanMessage(content=task))
+
         state: dict[str, Any] = {
-            "messages": [HumanMessage(content=task)],
+            "messages": messages,
         }
 
         # Pass through sandbox and thread data from parent
@@ -230,7 +295,7 @@ class SubagentExecutor:
 
         try:
             agent = self._create_agent()
-            state = self._build_initial_state(task)
+            state = await self._build_initial_state(task)
 
             # Build config with thread_id for sandbox access and recursion limit
             run_config: RunnableConfig = {

backend/packages/harness/deerflow/subagents/registry.py

@@ -10,53 +10,100 @@ from deerflow.subagents.config import SubagentConfig
 logger = logging.getLogger(__name__)
 
 
+def _build_custom_subagent_config(name: str) -> SubagentConfig | None:
+    """Build a SubagentConfig from config.yaml custom_agents section.
+
+    Args:
+        name: The name of the custom subagent.
+
+    Returns:
+        SubagentConfig if found in custom_agents, None otherwise.
+    """
+    from deerflow.config.subagents_config import get_subagents_app_config
+
+    app_config = get_subagents_app_config()
+    custom = app_config.custom_agents.get(name)
+    if custom is None:
+        return None
+
+    return SubagentConfig(
+        name=name,
+        description=custom.description,
+        system_prompt=custom.system_prompt,
+        tools=custom.tools,
+        disallowed_tools=custom.disallowed_tools,
+        skills=custom.skills,
+        model=custom.model,
+        max_turns=custom.max_turns,
+        timeout_seconds=custom.timeout_seconds,
+    )
+
+
 def get_subagent_config(name: str) -> SubagentConfig | None:
     """Get a subagent configuration by name, with config.yaml overrides applied.
 
+    Resolution order (mirrors Codex's config layering):
+    1. Built-in subagents (general-purpose, bash)
+    2. Custom subagents from config.yaml custom_agents section
+    3. Per-agent overrides from config.yaml agents section (timeout, max_turns, model, skills)
+
     Args:
         name: The name of the subagent.
 
     Returns:
         SubagentConfig if found (with any config.yaml overrides applied), None otherwise.
     """
+    # Step 1: Look up built-in, then fall back to custom_agents
     config = BUILTIN_SUBAGENTS.get(name)
+    if config is None:
+        config = _build_custom_subagent_config(name)
     if config is None:
         return None
 
-    # Apply runtime overrides (timeout, max_turns, model) from config.yaml
+    # Step 2: Apply per-agent overrides from config.yaml agents section.
+    # Only explicit per-agent overrides are applied here. Global defaults
+    # (timeout_seconds, max_turns at the top level) apply to built-in agents
+    # but must NOT override custom agents' own values — custom agents define
+    # their own defaults in the custom_agents section.
     # Lazy import to avoid circular deps.
     from deerflow.config.subagents_config import get_subagents_app_config
 
     app_config = get_subagents_app_config()
-    effective_timeout = app_config.get_timeout_for(name)
+    is_builtin = name in BUILTIN_SUBAGENTS
-    effective_max_turns = app_config.get_max_turns_for(name, config.max_turns)
+    agent_override = app_config.agents.get(name)
 
     overrides = {}
-    if effective_timeout != config.timeout_seconds:
+
-        logger.debug(
+    # Timeout: per-agent override > global default (builtins only) > config's own value
-            "Subagent '%s': timeout overridden by config.yaml (%ss -> %ss)",
+    if agent_override is not None and agent_override.timeout_seconds is not None:
-            name,
+        if agent_override.timeout_seconds != config.timeout_seconds:
-            config.timeout_seconds,
+            logger.debug("Subagent '%s': timeout overridden (%ss -> %ss)", name, config.timeout_seconds, agent_override.timeout_seconds)
-            effective_timeout,
+            overrides["timeout_seconds"] = agent_override.timeout_seconds
-        )
+    elif is_builtin and app_config.timeout_seconds != config.timeout_seconds:
-        overrides["timeout_seconds"] = effective_timeout
+        logger.debug("Subagent '%s': timeout from global default (%ss -> %ss)", name, config.timeout_seconds, app_config.timeout_seconds)
-    if effective_max_turns != config.max_turns:
+        overrides["timeout_seconds"] = app_config.timeout_seconds
-        logger.debug(
+
-            "Subagent '%s': max_turns overridden by config.yaml (%s -> %s)",
+    # Max turns: per-agent override > global default (builtins only) > config's own value
-            name,
+    if agent_override is not None and agent_override.max_turns is not None:
-            config.max_turns,
+        if agent_override.max_turns != config.max_turns:
-            effective_max_turns,
+            logger.debug("Subagent '%s': max_turns overridden (%s -> %s)", name, config.max_turns, agent_override.max_turns)
-        )
+            overrides["max_turns"] = agent_override.max_turns
-        overrides["max_turns"] = effective_max_turns
+    elif is_builtin and app_config.max_turns is not None and app_config.max_turns != config.max_turns:
+        logger.debug("Subagent '%s': max_turns from global default (%s -> %s)", name, config.max_turns, app_config.max_turns)
+        overrides["max_turns"] = app_config.max_turns
+
+    # Model: per-agent override only (no global default for model)
     effective_model = app_config.get_model_for(name)
     if effective_model is not None and effective_model != config.model:
-        logger.debug(
+        logger.debug("Subagent '%s': model overridden (%s -> %s)", name, config.model, effective_model)
-            "Subagent '%s': model overridden by config.yaml (%s -> %s)",
-            name,
-            config.model,
-            effective_model,
-        )
         overrides["model"] = effective_model
+
+    # Skills: per-agent override only (no global default for skills)
+    effective_skills = app_config.get_skills_for(name)
+    if effective_skills is not None and effective_skills != config.skills:
+        logger.debug("Subagent '%s': skills overridden (%s -> %s)", name, config.skills, effective_skills)
+        overrides["skills"] = effective_skills
+
     if overrides:
         config = replace(config, **overrides)
 
@@ -67,18 +114,33 @@ def list_subagents() -> list[SubagentConfig]:
     """List all available subagent configurations (with config.yaml overrides applied).
 
     Returns:
-        List of all registered SubagentConfig instances.
+        List of all registered SubagentConfig instances (built-in + custom).
     """
-    return [get_subagent_config(name) for name in BUILTIN_SUBAGENTS]
+    configs = []
+    for name in get_subagent_names():
+        config = get_subagent_config(name)
+        if config is not None:
+            configs.append(config)
+    return configs
 
 
 def get_subagent_names() -> list[str]:
-    """Get all available subagent names.
+    """Get all available subagent names (built-in + custom).
 
     Returns:
         List of subagent names.
     """
-    return list(BUILTIN_SUBAGENTS.keys())
+    names = list(BUILTIN_SUBAGENTS.keys())
+
+    # Merge custom_agents from config.yaml
+    from deerflow.config.subagents_config import get_subagents_app_config
+
+    app_config = get_subagents_app_config()
+    for custom_name in app_config.custom_agents:
+        if custom_name not in names:
+            names.append(custom_name)
+
+    return names
 
 
 def get_available_subagent_names() -> list[str]:
@@ -87,11 +149,11 @@ def get_available_subagent_names() -> list[str]:
     Returns:
         List of subagent names visible to the current sandbox configuration.
     """
-    names = list(BUILTIN_SUBAGENTS.keys())
+    names = get_subagent_names()
     try:
         host_bash_allowed = is_host_bash_allowed()
     except Exception:
-        logger.debug("Could not determine host bash availability; exposing all built-in subagents")
+        logger.debug("Could not determine host bash availability; exposing all subagents")
         return names
 
     if not host_bash_allowed:

backend/packages/harness/deerflow/tools/builtins/present_file_tool.py

@@ -8,6 +8,7 @@ from langgraph.typing import ContextT
 
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
+from deerflow.utils.runtime import get_thread_id
 
 OUTPUTS_VIRTUAL_PREFIX = f"{VIRTUAL_PATH_PREFIX}/outputs"
 
@@ -33,9 +34,9 @@ def _normalize_presented_filepath(
     if runtime.state is None:
         raise ValueError("Thread runtime state is not available")
 
-    thread_id = runtime.context.get("thread_id") if runtime.context else None
+    thread_id = get_thread_id(runtime)
     if not thread_id:
-        raise ValueError("Thread ID is not available in runtime context")
+        raise ValueError("Thread ID is not available in runtime context, runtime config, or LangGraph thread-local config")
 
     thread_data = runtime.state.get("thread_data") or {}
     outputs_path = thread_data.get("outputs_path")

backend/packages/harness/deerflow/tools/builtins/setup_agent_tool.py

@@ -6,6 +6,7 @@ from langchain_core.tools import tool
 from langgraph.prebuilt import ToolRuntime
 from langgraph.types import Command
 
+from deerflow.config.agents_config import validate_agent_name
 from deerflow.config.paths import get_paths
 
 logger = logging.getLogger(__name__)
@@ -16,19 +17,25 @@ def setup_agent(
     soul: str,
     description: str,
     runtime: ToolRuntime,
+    skills: list[str] | None = None,
 ) -> Command:
     """Setup the custom DeerFlow agent.
 
     Args:
         soul: Full SOUL.md content defining the agent's personality and behavior.
         description: One-line description of what the agent does.
+        skills: Optional list of skill names this agent should use. None means use all enabled skills, empty list means no skills.
     """
 
     agent_name: str | None = runtime.context.get("agent_name") if runtime.context else None
+    agent_dir = None
+    is_new_dir = False
 
     try:
+        agent_name = validate_agent_name(agent_name)
         paths = get_paths()
         agent_dir = paths.agent_dir(agent_name) if agent_name else paths.base_dir
+        is_new_dir = not agent_dir.exists()
         agent_dir.mkdir(parents=True, exist_ok=True)
 
         if agent_name:
@@ -36,6 +43,8 @@ def setup_agent(
             config_data: dict = {"name": agent_name}
             if description:
                 config_data["description"] = description
+            if skills is not None:
+                config_data["skills"] = skills
 
             config_file = agent_dir / "config.yaml"
             with open(config_file, "w", encoding="utf-8") as f:
@@ -55,8 +64,8 @@ def setup_agent(
     except Exception as e:
         import shutil
 
-        if agent_name and agent_dir.exists():
+        if agent_name and is_new_dir and agent_dir is not None and agent_dir.exists():
-            # Cleanup the custom agent directory only if it was created but an error occurred during setup
+            # Cleanup the custom agent directory only if it was newly created during this call
             shutil.rmtree(agent_dir)
         logger.error(f"[agent_creator] Failed to create agent '{agent_name}': {e}", exc_info=True)
         return Command(update={"messages": [ToolMessage(content=f"Error: {e}", tool_call_id=runtime.tool_call_id)]})

backend/packages/harness/deerflow/tools/builtins/task_tool.py

@@ -10,15 +10,26 @@ from langchain.tools import InjectedToolCallId, ToolRuntime, tool
 from langgraph.config import get_stream_writer
 from langgraph.typing import ContextT
 
-from deerflow.agents.lead_agent.prompt import get_skills_prompt_section
 from deerflow.agents.thread_state import ThreadState
 from deerflow.sandbox.security import LOCAL_BASH_SUBAGENT_DISABLED_MESSAGE, is_host_bash_allowed
 from deerflow.subagents import SubagentExecutor, get_available_subagent_names, get_subagent_config
 from deerflow.subagents.executor import SubagentStatus, cleanup_background_task, get_background_task_result, request_cancel_background_task
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
 
+def _merge_skill_allowlists(parent: list[str] | None, child: list[str] | None) -> list[str] | None:
+    """Return the effective subagent skill allowlist under the parent policy."""
+    if parent is None:
+        return child
+    if child is None:
+        return list(parent)
+
+    parent_set = set(parent)
+    return [skill for skill in child if skill in parent_set]
+
+
 @tool("task", parse_docstring=True)
 async def task_tool(
     runtime: ToolRuntime[ContextT, ThreadState],
@@ -35,7 +46,7 @@ async def task_tool(
     - Handle complex multi-step tasks autonomously
     - Execute commands or operations in isolated contexts
 
-    Available subagent types depend on the active sandbox configuration:
+    Built-in subagent types:
     - **general-purpose**: A capable agent for complex, multi-step tasks that require
       both exploration and action. Use when the task requires complex reasoning,
       multiple dependent steps, or would benefit from isolated context.
@@ -43,6 +54,11 @@ async def task_tool(
       available when host bash is explicitly allowed or when using an isolated shell
       sandbox such as `AioSandboxProvider`.
 
+    Additional custom subagent types may be defined in config.yaml under
+    `subagents.custom_agents`. Each custom type can have its own system prompt,
+    tools, skills, model, and timeout configuration. If an unknown subagent_type
+    is provided, the error message will list all available types.
+
     When to use this tool:
     - Complex tasks requiring multiple steps or tools
     - Tasks that produce verbose output
@@ -72,29 +88,25 @@ async def task_tool(
     # Build config overrides
     overrides: dict = {}
 
-    skills_section = get_skills_prompt_section()
+    # Skills are loaded by SubagentExecutor per-session (aligned with Codex's pattern:
-    if skills_section:
+    # each subagent loads its own skills based on config, injected as conversation items).
-        overrides["system_prompt"] = config.system_prompt + "\n\n" + skills_section
+    # No longer appended to system_prompt here.
 
     if max_turns is not None:
         overrides["max_turns"] = max_turns
 
-    if overrides:
-        config = replace(config, **overrides)
-
     # Extract parent context from runtime
     sandbox_state = None
     thread_data = None
     thread_id = None
     parent_model = None
     trace_id = None
+    metadata: dict = {}
 
     if runtime is not None:
         sandbox_state = runtime.state.get("sandbox")
         thread_data = runtime.state.get("thread_data")
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
+        thread_id = get_thread_id(runtime)
-        if thread_id is None:
-            thread_id = runtime.config.get("configurable", {}).get("thread_id")
 
         # Try to get parent model from configurable
         metadata = runtime.config.get("metadata", {})
@@ -103,12 +115,22 @@ async def task_tool(
         # Get or generate trace_id for distributed tracing
         trace_id = metadata.get("trace_id") or str(uuid.uuid4())[:8]
 
+    parent_available_skills = metadata.get("available_skills")
+    if parent_available_skills is not None:
+        overrides["skills"] = _merge_skill_allowlists(list(parent_available_skills), config.skills)
+
+    if overrides:
+        config = replace(config, **overrides)
+
     # Get available tools (excluding task tool to prevent nesting)
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
 
+    # Inherit parent agent's tool_groups so subagents respect the same restrictions
+    parent_tool_groups = metadata.get("tool_groups")
+
     # Subagents should not have subagent tools enabled (prevent recursive nesting)
-    tools = get_available_tools(model_name=parent_model, subagent_enabled=False)
+    tools = get_available_tools(model_name=parent_model, groups=parent_tool_groups, subagent_enabled=False)
 
     # Create executor
     executor = SubagentExecutor(

backend/packages/harness/deerflow/tools/builtins/tool_search.py

@@ -112,6 +112,15 @@ class DeferredToolRegistry:
     def entries(self) -> list[DeferredToolEntry]:
         return list(self._entries)
 
+    @property
+    def deferred_names(self) -> set[str]:
+        """Names of tools that are still hidden from model binding."""
+        return {entry.name for entry in self._entries}
+
+    def contains(self, name: str) -> bool:
+        """Return whether *name* is still deferred."""
+        return any(entry.name == name for entry in self._entries)
+
     def __len__(self) -> int:
         return len(self._entries)
 

backend/packages/harness/deerflow/tools/skill_manage_tool.py

@@ -28,6 +28,7 @@ from deerflow.skills.manager import (
     validate_skill_name,
 )
 from deerflow.skills.security_scanner import scan_skill_content
+from deerflow.utils.runtime import get_thread_id
 
 logger = logging.getLogger(__name__)
 
@@ -42,14 +43,6 @@ def _get_lock(name: str) -> asyncio.Lock:
     return lock
 
 
-def _get_thread_id(runtime: ToolRuntime[ContextT, ThreadState] | None) -> str | None:
-    if runtime is None:
-        return None
-    if runtime.context and runtime.context.get("thread_id"):
-        return runtime.context.get("thread_id")
-    return runtime.config.get("configurable", {}).get("thread_id")
-
-
 def _history_record(*, action: str, file_path: str, prev_content: str | None, new_content: str | None, thread_id: str | None, scanner: dict[str, Any]) -> dict[str, Any]:
     return {
         "action": action,
@@ -98,7 +91,7 @@ async def _skill_manage_impl(
     """
     name = validate_skill_name(name)
     lock = _get_lock(name)
-    thread_id = _get_thread_id(runtime)
+    thread_id = get_thread_id(runtime)
 
     async with lock:
         if action == "create":

backend/packages/harness/deerflow/tools/tools.py

@@ -59,7 +59,22 @@ def get_available_tools(
     if not is_host_bash_allowed(config):
         tool_configs = [tool for tool in tool_configs if not _is_host_bash_tool(tool)]
 
-    loaded_tools = [resolve_variable(tool.use, BaseTool) for tool in tool_configs]
+    loaded_tools_raw = [(cfg, resolve_variable(cfg.use, BaseTool)) for cfg in tool_configs]
+
+    # Warn when the config ``name`` field and the tool object's ``.name``
+    # attribute diverge — this mismatch is the root cause of issue #1803 where
+    # the LLM receives one name in its tool schema but the runtime router
+    # recognises a different name, producing "not a valid tool" errors.
+    for cfg, loaded in loaded_tools_raw:
+        if cfg.name != loaded.name:
+            logger.warning(
+                "Tool name mismatch: config name %r does not match tool .name %r (use: %s). The tool's own .name will be used for binding.",
+                cfg.name,
+                loaded.name,
+                cfg.use,
+            )
+
+    loaded_tools = [t for _, t in loaded_tools_raw]
 
     # Conditionally add tools based on config
     builtin_tools = BUILTIN_TOOLS.copy()
@@ -134,4 +149,20 @@ def get_available_tools(
         logger.warning(f"Failed to load ACP tool: {e}")
 
     logger.info(f"Total tools loaded: {len(loaded_tools)}, built-in tools: {len(builtin_tools)}, MCP tools: {len(mcp_tools)}, ACP tools: {len(acp_tools)}")
-    return loaded_tools + builtin_tools + mcp_tools + acp_tools
+
+    # Deduplicate by tool name — config-loaded tools take priority, followed by
+    # built-ins, MCP tools, and ACP tools.  Duplicate names cause the LLM to
+    # receive ambiguous or concatenated function schemas (issue #1803).
+    all_tools = loaded_tools + builtin_tools + mcp_tools + acp_tools
+    seen_names: set[str] = set()
+    unique_tools: list[BaseTool] = []
+    for t in all_tools:
+        if t.name not in seen_names:
+            unique_tools.append(t)
+            seen_names.add(t.name)
+        else:
+            logger.warning(
+                "Duplicate tool name %r detected and skipped — check your config.yaml and MCP server registrations (issue #1803).",
+                t.name,
+            )
+    return unique_tools

backend/packages/harness/deerflow/utils/file_conversion.py

@@ -19,6 +19,8 @@ import logging
 import re
 from pathlib import Path
 
+from deerflow.config.app_config import get_app_config
+
 logger = logging.getLogger(__name__)
 
 # File extensions that should be converted to markdown
@@ -286,6 +288,15 @@ def extract_outline(md_path: Path) -> list[dict]:
     return outline
 
 
+def _get_uploads_config_value(key: str, default: object) -> object:
+    """Read a value from the uploads config, supporting dict and attribute access."""
+    cfg = get_app_config()
+    uploads_cfg = getattr(cfg, "uploads", None)
+    if isinstance(uploads_cfg, dict):
+        return uploads_cfg.get(key, default)
+    return getattr(uploads_cfg, key, default)
+
+
 def _get_pdf_converter() -> str:
     """Read pdf_converter setting from app config, defaulting to 'auto'.
 
@@ -294,16 +305,11 @@ def _get_pdf_converter() -> str:
     fall through to unexpected behaviour.
     """
     try:
-        from deerflow.config.app_config import get_app_config
+        raw = str(_get_uploads_config_value("pdf_converter", "auto")).strip().lower()
-
+        if raw not in _ALLOWED_PDF_CONVERTERS:
-        cfg = get_app_config()
+            logger.warning("Invalid pdf_converter value %r; falling back to 'auto'", raw)
-        uploads_cfg = getattr(cfg, "uploads", None)
+            return "auto"
-        if uploads_cfg is not None:
+        return raw
-            raw = str(getattr(uploads_cfg, "pdf_converter", "auto")).strip().lower()
-            if raw not in _ALLOWED_PDF_CONVERTERS:
-                logger.warning("Invalid pdf_converter value %r; falling back to 'auto'", raw)
-                return "auto"
-            return raw
     except Exception:
         pass
     return "auto"

backend/packages/harness/deerflow/utils/runtime.py

@@ -0,0 +1,90 @@
+"""Runtime utilities for thread_id resolution and context access.
+
+Thread ID Resolution Strategy
+=============================
+
+DeerFlow resolves the current ``thread_id`` from a three-level cascade:
+
+1. **runtime.context["thread_id"]** -- Set by ``worker.py`` (gateway mode)
+   or by LangGraph Server (standard mode) when constructing the Runtime.
+2. **runtime.config["configurable"]["thread_id"]** -- Available on
+   ``ToolRuntime`` instances passed to tools via the ``@tool`` decorator.
+   Not available on ``Runtime`` instances received by middlewares.
+3. **get_config()["configurable"]["thread_id"]** -- LangGraph's thread-local
+   config, available when executing inside a graph's runnable context.
+
+About ``__pregel_runtime``
+===========================
+
+In gateway mode (``run_agent()`` in ``worker.py``), the agent graph does not
+run inside the LangGraph Server.  The server normally injects a ``Runtime``
+object automatically.  Since we run the graph ourselves, we must inject the
+Runtime manually via ``config["configurable"]["__pregel_runtime"]``.  This is
+the standard mechanism provided by LangGraph's Pregel engine for injecting
+runtime context into graph nodes.  It is not a private/internal hack -- it is
+the documented way to pass Runtime when running a graph outside the server.
+
+Duck Typing
+===========
+
+Both ``langgraph.runtime.Runtime`` (middlewares) and
+``langchain.tools.ToolRuntime`` (tools) expose a ``.context`` attribute (a
+dict or None).  ``ToolRuntime`` additionally exposes ``.config``.  The
+function below uses ``getattr`` with safe defaults so it works with either
+type, with ``SimpleNamespace`` in tests, or with ``None``.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def get_thread_id(runtime: Any | None) -> str | None:
+    """Resolve the current thread_id from a runtime object.
+
+    Follows a three-level fallback chain:
+
+    1. ``runtime.context.get("thread_id")`` -- if context is a non-empty dict.
+    2. ``runtime.config.get("configurable", {}).get("thread_id")`` -- if
+       the runtime has a config dict (ToolRuntime).
+    3. ``get_config().get("configurable", {}).get("thread_id")`` -- LangGraph's
+       thread-local config.  Wrapped in ``try/except RuntimeError`` because it
+       raises outside a runnable context (e.g., unit tests).
+
+    Args:
+        runtime: A Runtime, ToolRuntime, SimpleNamespace, or None.
+
+    Returns:
+        The thread_id string, or None if it cannot be resolved.
+    """
+    if runtime is None:
+        return None
+
+    # Level 1: runtime.context["thread_id"]
+    context = getattr(runtime, "context", None)
+    if context and isinstance(context, dict):
+        thread_id = context.get("thread_id")
+        if thread_id:
+            return thread_id
+
+    # Level 2: runtime.config["configurable"]["thread_id"]
+    config = getattr(runtime, "config", None)
+    if config and isinstance(config, dict):
+        thread_id = config.get("configurable", {}).get("thread_id")
+        if thread_id:
+            return thread_id
+
+    # Level 3: langgraph.config.get_config() -- only works inside runnable context
+    try:
+        from langgraph.config import get_config
+
+        config_data = get_config()
+        thread_id = config_data.get("configurable", {}).get("thread_id")
+        if thread_id:
+            return thread_id
+    except RuntimeError:
+        # Expected when not running inside a LangGraph runnable context (e.g., unit tests).
+        # In that case, thread_id cannot be resolved from thread-local config, so fall through.
+        pass
+
+    return None

backend/pyproject.toml

@@ -8,7 +8,7 @@ dependencies = [
     "deerflow-harness",
     "fastapi>=0.115.0",
     "httpx>=0.28.0",
-    "python-multipart>=0.0.20",
+    "python-multipart>=0.0.26",
     "sse-starlette>=2.1.0",
     "uvicorn[standard]>=0.34.0",
     "lark-oapi>=1.4.0",
@@ -20,7 +20,12 @@ dependencies = [
 ]
 
 [dependency-groups]
-dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
+dev = [
+    "prompt-toolkit>=3.0.0",
+    "pytest>=9.0.3",
+    "pytest-asyncio>=1.3.0",
+    "ruff>=0.14.11",
+]
 
 [tool.uv.workspace]
 members = ["packages/harness"]

backend/tests/test_app_config_reload.py

@@ -6,6 +6,7 @@ from pathlib import Path
 
 import yaml
 
+from deerflow.config.agents_api_config import get_agents_api_config
 from deerflow.config.app_config import get_app_config, reset_app_config
 
 
@@ -28,6 +29,30 @@ def _write_config(path: Path, *, model_name: str, supports_thinking: bool) -> No
     )
 
 
+def _write_config_with_agents_api(
+    path: Path,
+    *,
+    model_name: str,
+    supports_thinking: bool,
+    agents_api: dict | None = None,
+) -> None:
+    config = {
+        "sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"},
+        "models": [
+            {
+                "name": model_name,
+                "use": "langchain_openai:ChatOpenAI",
+                "model": "gpt-test",
+                "supports_thinking": supports_thinking,
+            }
+        ],
+    }
+    if agents_api is not None:
+        config["agents_api"] = agents_api
+
+    path.write_text(yaml.safe_dump(config), encoding="utf-8")
+
+
 def _write_extensions_config(path: Path) -> None:
     path.write_text(json.dumps({"mcpServers": {}, "skills": {}}), encoding="utf-8")
 
@@ -79,3 +104,38 @@ def test_get_app_config_reloads_when_config_path_changes(tmp_path, monkeypatch):
         assert second is not first
     finally:
         reset_app_config()
+
+
+def test_get_app_config_resets_agents_api_config_when_section_removed(tmp_path, monkeypatch):
+    config_path = tmp_path / "config.yaml"
+    extensions_path = tmp_path / "extensions_config.json"
+    _write_extensions_config(extensions_path)
+    _write_config_with_agents_api(
+        config_path,
+        model_name="first-model",
+        supports_thinking=False,
+        agents_api={"enabled": True},
+    )
+
+    monkeypatch.setenv("DEER_FLOW_CONFIG_PATH", str(config_path))
+    monkeypatch.setenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH", str(extensions_path))
+    reset_app_config()
+
+    try:
+        initial = get_app_config()
+        assert initial.models[0].name == "first-model"
+        assert get_agents_api_config().enabled is True
+
+        _write_config_with_agents_api(
+            config_path,
+            model_name="first-model",
+            supports_thinking=False,
+        )
+        next_mtime = config_path.stat().st_mtime + 5
+        os.utime(config_path, (next_mtime, next_mtime))
+
+        reloaded = get_app_config()
+        assert reloaded is not initial
+        assert get_agents_api_config().enabled is False
+    finally:
+        reset_app_config()

backend/tests/test_channels.py

@@ -2011,6 +2011,65 @@ class TestChannelService:
         assert service.manager._langgraph_url == "http://custom-langgraph:2024"
         assert service.manager._gateway_url == "http://custom-gateway:8001"
 
+    def test_disabled_channel_with_string_creds_emits_warning(self, caplog):
+        """Warning is emitted when a channel has string credentials but enabled=false."""
+        import logging
+
+        from app.channels.service import ChannelService
+
+        async def go():
+            service = ChannelService(
+                channels_config={
+                    "wecom": {"enabled": False, "bot_id": "corp123", "bot_secret": "secret"},
+                }
+            )
+            with caplog.at_level(logging.WARNING, logger="app.channels.service"):
+                await service.start()
+            await service.stop()
+
+        _run(go())
+        assert any("wecom" in r.message and r.levelno == logging.WARNING for r in caplog.records)
+
+    def test_disabled_channel_with_int_creds_emits_warning(self, caplog):
+        """Warning is emitted even when YAML-parsed integer credentials are present."""
+        import logging
+
+        from app.channels.service import ChannelService
+
+        async def go():
+            # Simulate YAML parsing a numeric token/ID as an int
+            service = ChannelService(
+                channels_config={
+                    "telegram": {"enabled": False, "bot_token": 123456789},
+                }
+            )
+            with caplog.at_level(logging.WARNING, logger="app.channels.service"):
+                await service.start()
+            await service.stop()
+
+        _run(go())
+        assert any("telegram" in r.message and r.levelno == logging.WARNING for r in caplog.records)
+
+    def test_disabled_channel_without_creds_emits_info(self, caplog):
+        """Only an info log (no warning) is emitted when a channel is disabled with no credentials."""
+        import logging
+
+        from app.channels.service import ChannelService
+
+        async def go():
+            service = ChannelService(
+                channels_config={
+                    "telegram": {"enabled": False},
+                }
+            )
+            with caplog.at_level(logging.DEBUG, logger="app.channels.service"):
+                await service.start()
+            await service.stop()
+
+        _run(go())
+        warning_records = [r for r in caplog.records if "telegram" in r.message and r.levelno == logging.WARNING]
+        assert not warning_records
+
 
 # ---------------------------------------------------------------------------
 # Slack send retry tests
@@ -2046,6 +2105,11 @@ class TestSlackSendRetry:
 
 
 class TestSlackAllowedUsers:
+    @staticmethod
+    def _submit_coro(coro, loop):
+        coro.close()
+        return MagicMock()
+
     def test_numeric_allowed_users_match_string_event_user_id(self):
         from app.channels.slack import SlackChannel
 
@@ -2067,13 +2131,9 @@ class TestSlackAllowedUsers:
             "ts": "1710000000.000100",
         }
 
-        def submit_coro(coro, loop):
-            coro.close()
-            return MagicMock()
-
         with patch(
             "app.channels.slack.asyncio.run_coroutine_threadsafe",
-            side_effect=submit_coro,
+            side_effect=self._submit_coro,
         ) as submit:
             channel._handle_message_event(event)
 
@@ -2085,6 +2145,74 @@ class TestSlackAllowedUsers:
         assert inbound.chat_id == "C123"
         assert inbound.text == "hello from slack"
 
+    def test_string_allowed_users_match_event_user_id(self):
+        from app.channels.slack import SlackChannel
+
+        bus = MessageBus()
+        bus.publish_inbound = AsyncMock()
+        channel = SlackChannel(
+            bus=bus,
+            config={"allowed_users": "U123456"},
+        )
+        channel._loop = MagicMock()
+        channel._loop.is_running.return_value = True
+        channel._add_reaction = MagicMock()
+        channel._send_running_reply = MagicMock()
+
+        event = {
+            "user": "U123456",
+            "text": "hello from slack",
+            "channel": "C123",
+            "ts": "1710000000.000100",
+        }
+
+        with patch(
+            "app.channels.slack.asyncio.run_coroutine_threadsafe",
+            side_effect=self._submit_coro,
+        ) as submit:
+            channel._handle_message_event(event)
+
+        channel._add_reaction.assert_called_once_with("C123", "1710000000.000100", "eyes")
+        channel._send_running_reply.assert_called_once_with("C123", "1710000000.000100")
+        submit.assert_called_once()
+        inbound = bus.publish_inbound.call_args.args[0]
+        assert inbound.user_id == "U123456"
+        assert inbound.chat_id == "C123"
+        assert inbound.text == "hello from slack"
+
+    def test_scalar_allowed_users_warns_and_matches_stringified_event_user_id(self, caplog):
+        from app.channels.slack import SlackChannel
+
+        bus = MessageBus()
+        bus.publish_inbound = AsyncMock()
+        with caplog.at_level("WARNING"):
+            channel = SlackChannel(
+                bus=bus,
+                config={"allowed_users": 123456},
+            )
+        channel._loop = MagicMock()
+        channel._loop.is_running.return_value = True
+        channel._add_reaction = MagicMock()
+        channel._send_running_reply = MagicMock()
+
+        event = {
+            "user": "123456",
+            "text": "hello from slack",
+            "channel": "C123",
+            "ts": "1710000000.000100",
+        }
+
+        with patch(
+            "app.channels.slack.asyncio.run_coroutine_threadsafe",
+            side_effect=self._submit_coro,
+        ) as submit:
+            channel._handle_message_event(event)
+
+        assert "Slack allowed_users should be a list" in caplog.text
+        submit.assert_called_once()
+        inbound = bus.publish_inbound.call_args.args[0]
+        assert inbound.user_id == "123456"
+
     def test_raises_after_all_retries_exhausted(self):
         from app.channels.slack import SlackChannel
 

backend/tests/test_check_script.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+CHECK_SCRIPT_PATH = REPO_ROOT / "scripts" / "check.py"
+
+
+spec = importlib.util.spec_from_file_location("deerflow_check_script", CHECK_SCRIPT_PATH)
+assert spec is not None
+assert spec.loader is not None
+check_script = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(check_script)
+
+
+def test_find_pnpm_command_prefers_resolved_executable(monkeypatch):
+    def fake_which(name: str) -> str | None:
+        if name == "pnpm":
+            return r"C:\Users\tester\AppData\Roaming\npm\pnpm.CMD"
+        if name == "pnpm.cmd":
+            return r"C:\Users\tester\AppData\Roaming\npm\pnpm.cmd"
+        return None
+
+    monkeypatch.setattr(check_script.shutil, "which", fake_which)
+
+    assert check_script.find_pnpm_command() == [r"C:\Users\tester\AppData\Roaming\npm\pnpm.CMD"]
+
+
+def test_find_pnpm_command_falls_back_to_corepack(monkeypatch):
+    def fake_which(name: str) -> str | None:
+        if name == "corepack":
+            return r"C:\Program Files\nodejs\corepack.exe"
+        return None
+
+    monkeypatch.setattr(check_script.shutil, "which", fake_which)
+
+    assert check_script.find_pnpm_command() == [
+        r"C:\Program Files\nodejs\corepack.exe",
+        "pnpm",
+    ]
+
+
+def test_find_pnpm_command_falls_back_to_corepack_cmd(monkeypatch):
+    def fake_which(name: str) -> str | None:
+        if name == "corepack":
+            return None
+        if name == "corepack.cmd":
+            return r"C:\Program Files\nodejs\corepack.cmd"
+        return None
+
+    monkeypatch.setattr(check_script.shutil, "which", fake_which)
+
+    assert check_script.find_pnpm_command() == [
+        r"C:\Program Files\nodejs\corepack.cmd",
+        "pnpm",
+    ]

backend/tests/test_checkpointer.py

@@ -150,6 +150,79 @@ class TestGetCheckpointer:
         mock_saver_cls.from_conn_string.assert_called_once()
         mock_saver_instance.setup.assert_called_once()
 
+    def test_sqlite_creates_parent_dir(self):
+        """Sync SQLite checkpointer should call ensure_sqlite_parent_dir before connecting.
+
+        This mirrors the async checkpointer's behaviour and prevents
+        'sqlite3.OperationalError: unable to open database file' when the
+        parent directory for the database file does not yet exist (e.g. when
+        using the harness package from an external virtualenv where the
+        .deer-flow directory has not been created).
+        """
+        load_checkpointer_config_from_dict({"type": "sqlite", "connection_string": "relative/test.db"})
+
+        mock_saver_instance = MagicMock()
+        mock_cm = MagicMock()
+        mock_cm.__enter__ = MagicMock(return_value=mock_saver_instance)
+        mock_cm.__exit__ = MagicMock(return_value=False)
+
+        mock_saver_cls = MagicMock()
+        mock_saver_cls.from_conn_string = MagicMock(return_value=mock_cm)
+
+        mock_module = MagicMock()
+        mock_module.SqliteSaver = mock_saver_cls
+
+        with (
+            patch.dict(sys.modules, {"langgraph.checkpoint.sqlite": mock_module}),
+            patch("deerflow.agents.checkpointer.provider.ensure_sqlite_parent_dir") as mock_ensure,
+            patch(
+                "deerflow.agents.checkpointer.provider.resolve_sqlite_conn_str",
+                return_value="/tmp/resolved/relative/test.db",
+            ),
+        ):
+            reset_checkpointer()
+            cp = get_checkpointer()
+
+        assert cp is mock_saver_instance
+        mock_ensure.assert_called_once_with("/tmp/resolved/relative/test.db")
+        mock_saver_cls.from_conn_string.assert_called_once_with("/tmp/resolved/relative/test.db")
+
+    def test_sqlite_ensure_parent_dir_before_connect(self):
+        """ensure_sqlite_parent_dir must be called before from_conn_string."""
+        load_checkpointer_config_from_dict({"type": "sqlite", "connection_string": "relative/test.db"})
+
+        call_order = []
+
+        mock_saver_instance = MagicMock()
+        mock_cm = MagicMock()
+        mock_cm.__enter__ = MagicMock(return_value=mock_saver_instance)
+        mock_cm.__exit__ = MagicMock(return_value=False)
+
+        mock_saver_cls = MagicMock()
+        mock_saver_cls.from_conn_string = MagicMock(side_effect=lambda *a, **kw: (call_order.append("connect"), mock_cm)[1])
+
+        mock_module = MagicMock()
+        mock_module.SqliteSaver = mock_saver_cls
+
+        def record_ensure(*a, **kw):
+            call_order.append("ensure")
+
+        with (
+            patch.dict(sys.modules, {"langgraph.checkpoint.sqlite": mock_module}),
+            patch(
+                "deerflow.agents.checkpointer.provider.ensure_sqlite_parent_dir",
+                side_effect=record_ensure,
+            ),
+            patch(
+                "deerflow.agents.checkpointer.provider.resolve_sqlite_conn_str",
+                return_value="/tmp/resolved/relative/test.db",
+            ),
+        ):
+            reset_checkpointer()
+            get_checkpointer()
+
+        assert call_order == ["ensure", "connect"]
+
     def test_postgres_creates_saver(self):
         """Postgres checkpointer is created when packages are available."""
         load_checkpointer_config_from_dict({"type": "postgres", "connection_string": "postgresql://localhost/db"})

backend/tests/test_clarification_middleware.py

@@ -1,8 +1,10 @@
 """Tests for ClarificationMiddleware, focusing on options type coercion."""
 
 import json
+from types import SimpleNamespace
 
 import pytest
+from langgraph.graph.message import add_messages
 
 from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
 
@@ -118,3 +120,60 @@ class TestFormatClarificationMessage:
         assert "2. 2" in result
         assert "3. True" in result
         assert "4. None" in result
+
+
+class TestClarificationCommandIdempotency:
+    """Clarification tool-call retries should not duplicate messages in state."""
+
+    def test_repeated_tool_call_uses_stable_message_id(self, middleware):
+        request = SimpleNamespace(
+            tool_call={
+                "name": "ask_clarification",
+                "id": "call-clarify-1",
+                "args": {
+                    "question": "Which environment should I use?",
+                    "clarification_type": "approach_choice",
+                    "options": ["dev", "prod"],
+                },
+            }
+        )
+
+        first = middleware.wrap_tool_call(request, lambda _req: pytest.fail("handler should not be called"))
+        second = middleware.wrap_tool_call(request, lambda _req: pytest.fail("handler should not be called"))
+
+        first_message = first.update["messages"][0]
+        second_message = second.update["messages"][0]
+
+        assert first_message.id == "clarification:call-clarify-1"
+        assert second_message.id == first_message.id
+        assert second_message.tool_call_id == first_message.tool_call_id
+
+        merged = add_messages(add_messages([], [first_message]), [second_message])
+
+        assert len(merged) == 1
+        assert merged[0].id == "clarification:call-clarify-1"
+        assert merged[0].content == first_message.content
+
+    def test_missing_tool_call_id_still_gets_stable_message_id(self, middleware):
+        request = SimpleNamespace(
+            tool_call={
+                "name": "ask_clarification",
+                "args": {
+                    "question": "Which environment should I use?",
+                    "clarification_type": "missing_info",
+                },
+            }
+        )
+
+        first = middleware.wrap_tool_call(request, lambda _req: pytest.fail("handler should not be called"))
+        second = middleware.wrap_tool_call(request, lambda _req: pytest.fail("handler should not be called"))
+
+        first_message = first.update["messages"][0]
+        second_message = second.update["messages"][0]
+
+        assert first_message.id.startswith("clarification:")
+        assert second_message.id == first_message.id
+
+        merged = add_messages(add_messages([], [first_message]), [second_message])
+
+        assert len(merged) == 1

backend/tests/test_claude_provider_prompt_caching.py

@@ -0,0 +1,249 @@
+"""Tests for ClaudeChatModel._apply_prompt_caching.
+
+Validates that the function never places more than 4 cache_control breakpoints
+(the hard limit enforced by the Anthropic API and AWS Bedrock) regardless of
+how many system blocks, message content blocks, or tool definitions are present.
+"""
+
+from unittest import mock
+
+import pytest
+
+from deerflow.models.claude_provider import ClaudeChatModel
+
+
+def _make_model(prompt_cache_size: int = 3) -> ClaudeChatModel:
+    """Return a minimal ClaudeChatModel instance without network calls."""
+    with mock.patch.object(ClaudeChatModel, "model_post_init"):
+        m = ClaudeChatModel(
+            model="claude-sonnet-4-6",
+            anthropic_api_key="sk-ant-fake",  # type: ignore[call-arg]
+            prompt_cache_size=prompt_cache_size,
+        )
+    m._is_oauth = False
+    m.enable_prompt_caching = True
+    return m
+
+
+def _count_cache_control(payload: dict) -> int:
+    """Count the total number of cache_control markers in a payload."""
+    count = 0
+
+    system = payload.get("system", [])
+    if isinstance(system, list):
+        for block in system:
+            if isinstance(block, dict) and "cache_control" in block:
+                count += 1
+
+    for msg in payload.get("messages", []):
+        if not isinstance(msg, dict):
+            continue
+        content = msg.get("content", [])
+        if isinstance(content, list):
+            for block in content:
+                if isinstance(block, dict) and "cache_control" in block:
+                    count += 1
+
+    for tool in payload.get("tools", []):
+        if isinstance(tool, dict) and "cache_control" in tool:
+            count += 1
+
+    return count
+
+
+@pytest.fixture()
+def model() -> ClaudeChatModel:
+    return _make_model()
+
+
+# ---------------------------------------------------------------------------
+# Basic correctness
+# ---------------------------------------------------------------------------
+
+
+def test_single_system_block_gets_cached(model):
+    payload: dict = {"system": [{"type": "text", "text": "sys"}]}
+    model._apply_prompt_caching(payload)
+    assert payload["system"][0].get("cache_control") == {"type": "ephemeral"}
+
+
+def test_string_system_converted_and_cached(model):
+    payload: dict = {"system": "you are helpful"}
+    model._apply_prompt_caching(payload)
+    assert isinstance(payload["system"], list)
+    assert payload["system"][0].get("cache_control") == {"type": "ephemeral"}
+
+
+def test_last_tool_gets_cached_when_budget_allows(model):
+    payload: dict = {
+        "tools": [{"name": "t1"}, {"name": "t2"}],
+    }
+    model._apply_prompt_caching(payload)
+    # With no system or messages the last tool should be cached.
+    assert payload["tools"][-1].get("cache_control") == {"type": "ephemeral"}
+    assert "cache_control" not in payload["tools"][0]
+
+
+def test_recent_messages_get_cached(model):
+    """The last prompt_cache_size messages' content blocks should be cached."""
+    payload: dict = {
+        "messages": [
+            {"role": "user", "content": [{"type": "text", "text": "hello"}]},
+        ],
+    }
+    model._apply_prompt_caching(payload)
+    assert payload["messages"][0]["content"][0].get("cache_control") == {"type": "ephemeral"}
+
+
+def test_string_message_content_converted_and_cached(model):
+    payload: dict = {
+        "messages": [
+            {"role": "user", "content": "simple string"},
+        ],
+    }
+    model._apply_prompt_caching(payload)
+    assert isinstance(payload["messages"][0]["content"], list)
+    assert payload["messages"][0]["content"][0].get("cache_control") == {"type": "ephemeral"}
+
+
+# ---------------------------------------------------------------------------
+# Budget enforcement (the core regression test for issue #2448)
+# ---------------------------------------------------------------------------
+
+
+def test_never_exceeds_4_breakpoints_with_large_system(model):
+    """Many system text blocks must not produce more than 4 breakpoints total."""
+    payload: dict = {
+        "system": [{"type": "text", "text": f"sys {i}"} for i in range(6)],
+        "tools": [{"name": "t1"}],
+    }
+    model._apply_prompt_caching(payload)
+    assert _count_cache_control(payload) <= 4
+
+
+def test_never_exceeds_4_breakpoints_multi_turn_with_multi_block_messages(model):
+    """Multi-turn conversation where each message has multiple content blocks."""
+    # 1 system block + 3 messages × 2 blocks + 1 tool = 8 candidates → must cap at 4
+    payload: dict = {
+        "system": [{"type": "text", "text": "system prompt"}],
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "user text"},
+                    {"type": "tool_result", "tool_use_id": "x", "content": "result"},
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "assistant text"},
+                    {"type": "tool_use", "id": "y", "name": "bash", "input": {}},
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "follow up"},
+                    {"type": "text", "text": "second block"},
+                ],
+            },
+        ],
+        "tools": [{"name": "bash"}],
+    }
+    model._apply_prompt_caching(payload)
+    total = _count_cache_control(payload)
+    assert total <= 4, f"Expected ≤ 4 breakpoints, got {total}"
+
+
+def test_never_exceeds_4_breakpoints_many_messages(model):
+    """Large number of messages with multiple blocks per message."""
+    messages = []
+    for i in range(10):
+        messages.append(
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": f"msg {i} block a"},
+                    {"type": "text", "text": f"msg {i} block b"},
+                ],
+            }
+        )
+    payload: dict = {
+        "system": [{"type": "text", "text": "sys 1"}, {"type": "text", "text": "sys 2"}],
+        "messages": messages,
+        "tools": [{"name": "tool_a"}, {"name": "tool_b"}],
+    }
+    model._apply_prompt_caching(payload)
+    total = _count_cache_control(payload)
+    assert total <= 4, f"Expected ≤ 4 breakpoints, got {total}"
+
+
+def test_exactly_4_breakpoints_when_4_or_more_candidates(model):
+    """When there are at least 4 candidates, exactly 4 breakpoints are placed."""
+    payload: dict = {
+        "system": [{"type": "text", "text": f"sys {i}"} for i in range(3)],
+        "messages": [
+            {"role": "user", "content": [{"type": "text", "text": "user"}]},
+            {"role": "assistant", "content": [{"type": "text", "text": "asst"}]},
+            {"role": "user", "content": [{"type": "text", "text": "follow"}]},
+        ],
+        "tools": [{"name": "bash"}],
+    }
+    model._apply_prompt_caching(payload)
+    total = _count_cache_control(payload)
+    assert total == 4
+
+
+def test_breakpoints_placed_on_last_candidates(model):
+    """Breakpoints should be on the *last* candidates, not the first."""
+    # 5 system blocks but budget = 4 → first system block should NOT be cached,
+    # last 4 (indices 1-4) should be.
+    payload: dict = {
+        "system": [{"type": "text", "text": f"sys {i}"} for i in range(5)],
+    }
+    model._apply_prompt_caching(payload)
+    # First block is NOT in the last-4 window
+    assert "cache_control" not in payload["system"][0]
+    # Last 4 blocks ARE cached
+    for i in range(1, 5):
+        assert payload["system"][i].get("cache_control") == {"type": "ephemeral"}, f"block {i} should be cached"
+
+
+# ---------------------------------------------------------------------------
+# Edge cases
+# ---------------------------------------------------------------------------
+
+
+def test_no_candidates_is_a_no_op(model):
+    payload: dict = {}
+    model._apply_prompt_caching(payload)
+    assert _count_cache_control(payload) == 0
+
+
+def test_non_text_system_blocks_not_added_as_candidates(model):
+    """Image blocks in system should not receive cache_control."""
+    payload: dict = {
+        "system": [
+            {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "abc"}},
+            {"type": "text", "text": "text block"},
+        ],
+    }
+    model._apply_prompt_caching(payload)
+    assert "cache_control" not in payload["system"][0]
+    assert payload["system"][1].get("cache_control") == {"type": "ephemeral"}
+
+
+def test_old_messages_outside_cache_window_not_cached(model):
+    """Messages older than prompt_cache_size should not be cached."""
+    m = _make_model(prompt_cache_size=1)
+    payload: dict = {
+        "messages": [
+            {"role": "user", "content": [{"type": "text", "text": "old message"}]},
+            {"role": "user", "content": [{"type": "text", "text": "recent message"}]},
+        ],
+    }
+    m._apply_prompt_caching(payload)
+    # Only the last message should be within the cache window
+    assert "cache_control" not in payload["messages"][0]["content"][0]
+    assert payload["messages"][1]["content"][0].get("cache_control") == {"type": "ephemeral"}

backend/tests/test_client.py

@@ -38,6 +38,7 @@ def mock_app_config():
 
     config = MagicMock()
     config.models = [model]
+    config.token_usage.enabled = False
     return config
 
 
@@ -107,6 +108,7 @@ class TestConfigQueries:
     def test_list_models(self, client):
         result = client.list_models()
         assert "models" in result
+        assert result["token_usage"] == {"enabled": False}
         assert len(result["models"]) == 1
         assert result["models"][0]["name"] == "test-model"
         # Verify Gateway-aligned fields are present
@@ -2196,7 +2198,9 @@ class TestGatewayConformance:
         model.display_name = "Test Model"
         model.description = "A test model"
         model.supports_thinking = False
+        model.supports_reasoning_effort = False
         mock_app_config.models = [model]
+        mock_app_config.token_usage.enabled = True
 
         with patch("deerflow.client.get_app_config", return_value=mock_app_config):
             client = DeerFlowClient()
@@ -2206,6 +2210,7 @@ class TestGatewayConformance:
         assert len(parsed.models) == 1
         assert parsed.models[0].name == "test-model"
         assert parsed.models[0].model == "gpt-test"
+        assert parsed.token_usage.enabled is True
 
     def test_get_model(self, mock_app_config):
         model = MagicMock()

backend/tests/test_custom_agent.py

@@ -9,6 +9,8 @@ import pytest
 import yaml
 from fastapi.testclient import TestClient
 
+from deerflow.config.agents_api_config import AgentsApiConfig, get_agents_api_config, set_agents_api_config
+
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
@@ -387,13 +389,38 @@ def _make_test_app(tmp_path: Path):
 @pytest.fixture()
 def agent_client(tmp_path):
     """TestClient with agents router, using tmp_path as base_dir."""
-    paths_instance = _make_paths(tmp_path)
+    import app.gateway.routers.agents as agents_router
 
-    with patch("deerflow.config.agents_config.get_paths", return_value=paths_instance), patch("app.gateway.routers.agents.get_paths", return_value=paths_instance):
+    paths_instance = _make_paths(tmp_path)
-        app = _make_test_app(tmp_path)
+    previous_config = AgentsApiConfig(**get_agents_api_config().model_dump())
-        with TestClient(app) as client:
+
-            client._tmp_path = tmp_path  # type: ignore[attr-defined]
+    with patch("deerflow.config.agents_config.get_paths", return_value=paths_instance), patch.object(agents_router, "get_paths", return_value=paths_instance):
-            yield client
+        set_agents_api_config(AgentsApiConfig(enabled=True))
+        try:
+            app = _make_test_app(tmp_path)
+            with TestClient(app) as client:
+                client._tmp_path = tmp_path  # type: ignore[attr-defined]
+                yield client
+        finally:
+            set_agents_api_config(previous_config)
+
+
+@pytest.fixture()
+def disabled_agent_client(tmp_path):
+    """TestClient with agents router while the management API is disabled."""
+    import app.gateway.routers.agents as agents_router
+
+    paths_instance = _make_paths(tmp_path)
+    previous_config = AgentsApiConfig(**get_agents_api_config().model_dump())
+
+    with patch("deerflow.config.agents_config.get_paths", return_value=paths_instance), patch.object(agents_router, "get_paths", return_value=paths_instance):
+        set_agents_api_config(AgentsApiConfig(enabled=False))
+        try:
+            app = _make_test_app(tmp_path)
+            with TestClient(app) as client:
+                yield client
+        finally:
+            set_agents_api_config(previous_config)
 
 
 class TestAgentsAPI:
@@ -559,3 +586,37 @@ class TestUserProfileAPI:
         response = agent_client.put("/api/user-profile", json={"content": ""})
         assert response.status_code == 200
         assert response.json()["content"] is None
+
+
+class TestAgentsApiDisabled:
+    def test_agents_list_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.get("/api/agents")
+        assert response.status_code == 403
+        assert "agents_api.enabled=true" in response.json()["detail"]
+
+    def test_agent_get_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.get("/api/agents/example-agent")
+        assert response.status_code == 403
+
+    def test_agent_name_check_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.get("/api/agents/check", params={"name": "example-agent"})
+        assert response.status_code == 403
+
+    def test_agent_create_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.post("/api/agents", json={"name": "example-agent", "soul": "blocked"})
+        assert response.status_code == 403
+
+    def test_agent_update_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.put("/api/agents/example-agent", json={"description": "blocked"})
+        assert response.status_code == 403
+
+    def test_agent_delete_returns_403(self, disabled_agent_client):
+        response = disabled_agent_client.delete("/api/agents/example-agent")
+        assert response.status_code == 403
+
+    def test_user_profile_routes_return_403(self, disabled_agent_client):
+        get_response = disabled_agent_client.get("/api/user-profile")
+        put_response = disabled_agent_client.put("/api/user-profile", json={"content": "blocked"})
+
+        assert get_response.status_code == 403
+        assert put_response.status_code == 403

backend/tests/test_dangling_tool_call_middleware.py

@@ -119,6 +119,31 @@ class TestBuildPatchedMessagesPatching:
         assert "interrupted" in tool_msg.content.lower()
         assert tool_msg.name == "bash"
 
+    def test_raw_provider_tool_calls_are_patched(self):
+        mw = DanglingToolCallMiddleware()
+        msgs = [
+            AIMessage(
+                content="",
+                tool_calls=[],
+                additional_kwargs={
+                    "tool_calls": [
+                        {
+                            "id": "call_1",
+                            "type": "function",
+                            "function": {"name": "bash", "arguments": '{"command":"ls"}'},
+                        }
+                    ]
+                },
+            )
+        ]
+        patched = mw._build_patched_messages(msgs)
+        assert patched is not None
+        assert len(patched) == 2
+        assert isinstance(patched[1], ToolMessage)
+        assert patched[1].tool_call_id == "call_1"
+        assert patched[1].name == "bash"
+        assert patched[1].status == "error"
+
 
 class TestWrapModelCall:
     def test_no_patch_passthrough(self):

backend/tests/test_file_conversion.py

@@ -12,6 +12,7 @@ from deerflow.utils.file_conversion import (
     _MIN_CHARS_PER_PAGE,
     MAX_OUTLINE_ENTRIES,
     _do_convert,
+    _get_pdf_converter,
     _pymupdf_output_too_sparse,
     convert_file_to_markdown,
     extract_outline,
@@ -214,9 +215,27 @@ class TestDoConvert:
         assert result == "MarkItDown fallback"
 
 
-# ---------------------------------------------------------------------------
+class TestGetPdfConverter:
-# convert_file_to_markdown — async + file writing
+    def test_reads_dict_backed_uploads_config(self):
-# ---------------------------------------------------------------------------
+        cfg = MagicMock()
+        cfg.uploads = {"pdf_converter": "markitdown"}
+
+        with patch("deerflow.utils.file_conversion.get_app_config", return_value=cfg):
+            assert _get_pdf_converter() == "markitdown"
+
+    def test_reads_attribute_backed_uploads_config(self):
+        cfg = MagicMock()
+        cfg.uploads = MagicMock(pdf_converter="pymupdf4llm")
+
+        with patch("deerflow.utils.file_conversion.get_app_config", return_value=cfg):
+            assert _get_pdf_converter() == "pymupdf4llm"
+
+    def test_invalid_value_falls_back_to_auto(self):
+        cfg = MagicMock()
+        cfg.uploads = {"pdf_converter": "not-a-real-converter"}
+
+        with patch("deerflow.utils.file_conversion.get_app_config", return_value=cfg):
+            assert _get_pdf_converter() == "auto"
 
 
 class TestConvertFileToMarkdown:

backend/tests/test_gateway_lifespan_shutdown.py

@@ -0,0 +1,68 @@
+"""Regression tests for Gateway lifespan shutdown.
+
+These tests guard the invariant that lifespan shutdown is *bounded*: a
+misbehaving channel whose ``stop()`` blocks forever must not keep the
+uvicorn worker alive. A hung worker is the precondition for the
+signal-reentrancy deadlock described in
+``app.gateway.app._SHUTDOWN_HOOK_TIMEOUT_SECONDS``.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from contextlib import asynccontextmanager
+from unittest.mock import MagicMock, patch
+
+from fastapi import FastAPI
+
+
+@asynccontextmanager
+async def _noop_langgraph_runtime(_app):
+    yield
+
+
+async def _run_lifespan_with_hanging_stop() -> float:
+    """Drive the lifespan context with stop_channel_service hanging forever.
+
+    Returns the elapsed wall-clock seconds.
+    """
+    from app.gateway.app import _SHUTDOWN_HOOK_TIMEOUT_SECONDS, lifespan
+
+    async def hang_forever() -> None:
+        await asyncio.sleep(3600)
+
+    app = FastAPI()
+
+    fake_service = MagicMock()
+    fake_service.get_status = MagicMock(return_value={})
+
+    async def fake_start():
+        return fake_service
+
+    with (
+        patch("app.gateway.app.get_app_config"),
+        patch("app.gateway.app.get_gateway_config", return_value=MagicMock(host="x", port=0)),
+        patch("app.gateway.app.langgraph_runtime", _noop_langgraph_runtime),
+        patch("app.channels.service.start_channel_service", side_effect=fake_start),
+        patch("app.channels.service.stop_channel_service", side_effect=hang_forever),
+    ):
+        loop = asyncio.get_event_loop()
+        start = loop.time()
+        async with lifespan(app):
+            pass
+        elapsed = loop.time() - start
+
+    assert _SHUTDOWN_HOOK_TIMEOUT_SECONDS < 30.0, "Timeout constant must stay modest"
+    return elapsed
+
+
+def test_shutdown_is_bounded_when_channel_stop_hangs():
+    """Lifespan exit must complete near the configured timeout, not hang."""
+    from app.gateway.app import _SHUTDOWN_HOOK_TIMEOUT_SECONDS
+
+    elapsed = asyncio.run(_run_lifespan_with_hanging_stop())
+
+    # Generous upper bound: timeout + 2s slack for scheduling overhead.
+    assert elapsed < _SHUTDOWN_HOOK_TIMEOUT_SECONDS + 2.0, f"Lifespan shutdown took {elapsed:.2f}s; expected <= {_SHUTDOWN_HOOK_TIMEOUT_SECONDS + 2.0:.1f}s"
+    # Lower bound: the wait_for should actually have waited.
+    assert elapsed >= _SHUTDOWN_HOOK_TIMEOUT_SECONDS - 0.5, f"Lifespan exited too quickly ({elapsed:.2f}s); wait_for may not have been invoked."

backend/tests/test_gateway_services.py

@@ -145,6 +145,21 @@ def test_build_run_config_explicit_agent_name_not_overwritten():
     assert config["configurable"]["agent_name"] == "explicit-agent"
 
 
+def test_build_run_config_context_custom_agent_injects_agent_name():
+    """Custom assistant_id must be forwarded as context['agent_name'] in context mode."""
+    from app.gateway.services import build_run_config
+
+    config = build_run_config(
+        "thread-1",
+        {"context": {"model_name": "deepseek-v3"}},
+        None,
+        assistant_id="finalis",
+    )
+
+    assert config["context"]["agent_name"] == "finalis"
+    assert "configurable" not in config
+
+
 def test_resolve_agent_factory_returns_make_lead_agent():
     """resolve_agent_factory always returns make_lead_agent regardless of assistant_id."""
     from app.gateway.services import resolve_agent_factory
@@ -298,6 +313,36 @@ def test_build_run_config_with_context():
     assert config["recursion_limit"] == 100
 
 
+def test_build_run_config_null_context_becomes_empty_context():
+    """When caller sends context=null, treat it as an empty context object."""
+    from app.gateway.services import build_run_config
+
+    config = build_run_config("thread-1", {"context": None}, None)
+
+    assert config["context"] == {}
+    assert "configurable" not in config
+
+
+def test_build_run_config_rejects_non_mapping_context():
+    """When caller sends a non-object context, raise a clear error instead of a TypeError."""
+    import pytest
+
+    from app.gateway.services import build_run_config
+
+    with pytest.raises(ValueError, match="context"):
+        build_run_config("thread-1", {"context": "bad-context"}, None)
+
+
+def test_build_run_config_null_context_custom_agent_injects_agent_name():
+    """Custom assistant_id can still be injected when context=null starts context mode."""
+    from app.gateway.services import build_run_config
+
+    config = build_run_config("thread-1", {"context": None}, None, assistant_id="finalis")
+
+    assert config["context"] == {"agent_name": "finalis"}
+    assert "configurable" not in config
+
+
 def test_build_run_config_context_plus_configurable_warns(caplog):
     """When caller sends both 'context' and 'configurable', prefer 'context' and log a warning."""
     import logging

backend/tests/test_jina_client.py

@@ -80,6 +80,28 @@ async def test_crawl_network_error(jina_client, monkeypatch):
     assert "failed" in result.lower()
 
 
+@pytest.mark.anyio
+async def test_crawl_transient_failure_logs_without_traceback(jina_client, monkeypatch, caplog):
+    """Transient network failures must log at WARNING without a traceback and include the exception type."""
+
+    async def mock_post(self, url, **kwargs):
+        raise httpx.ConnectTimeout("timed out")
+
+    monkeypatch.setattr(httpx.AsyncClient, "post", mock_post)
+
+    with caplog.at_level(logging.DEBUG, logger="deerflow.community.jina_ai.jina_client"):
+        result = await jina_client.crawl("https://example.com")
+
+    jina_records = [r for r in caplog.records if r.name == "deerflow.community.jina_ai.jina_client"]
+    assert len(jina_records) == 1, f"expected exactly one log record, got {len(jina_records)}"
+    record = jina_records[0]
+    assert record.levelno == logging.WARNING, f"expected WARNING, got {record.levelname}"
+    assert record.exc_info is None, "transient failures must not attach a traceback"
+    assert "ConnectTimeout" in record.getMessage()
+    assert result.startswith("Error:")
+    assert "ConnectTimeout" in result
+
+
 @pytest.mark.anyio
 async def test_crawl_passes_headers(jina_client, monkeypatch):
     """Test that correct headers are sent."""
@@ -175,3 +197,30 @@ async def test_web_fetch_tool_returns_markdown_on_success(monkeypatch):
     result = await web_fetch_tool.ainvoke("https://example.com")
     assert "Hello world" in result
     assert not result.startswith("Error:")
+
+
+@pytest.mark.anyio
+async def test_web_fetch_tool_offloads_extraction_to_thread(monkeypatch):
+    """Test that readability extraction is offloaded via asyncio.to_thread to avoid blocking the event loop."""
+    import asyncio
+
+    async def mock_crawl(self, url, **kwargs):
+        return "<html><body><p>threaded</p></body></html>"
+
+    mock_config = MagicMock()
+    mock_config.get_tool_config.return_value = None
+    monkeypatch.setattr("deerflow.community.jina_ai.tools.get_app_config", lambda: mock_config)
+    monkeypatch.setattr(JinaClient, "crawl", mock_crawl)
+
+    to_thread_called = False
+    original_to_thread = asyncio.to_thread
+
+    async def tracking_to_thread(func, *args, **kwargs):
+        nonlocal to_thread_called
+        to_thread_called = True
+        return await original_to_thread(func, *args, **kwargs)
+
+    monkeypatch.setattr("deerflow.community.jina_ai.tools.asyncio.to_thread", tracking_to_thread)
+    result = await web_fetch_tool.ainvoke("https://example.com")
+    assert to_thread_called, "extract_article must be called via asyncio.to_thread to avoid blocking the event loop"
+    assert "threaded" in result

backend/tests/test_lead_agent_model_resolution.py

@@ -8,6 +8,7 @@ import pytest
 
 from deerflow.agents.lead_agent import agent as lead_agent_module
 from deerflow.config.app_config import AppConfig
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.sandbox_config import SandboxConfig
 from deerflow.config.summarization_config import SummarizationConfig
@@ -112,6 +113,74 @@ def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkey
     assert result["model"] is not None
 
 
+def test_make_lead_agent_reads_runtime_options_from_context(monkeypatch):
+    app_config = _make_app_config(
+        [
+            _make_model("default-model", supports_thinking=False),
+            _make_model("context-model", supports_thinking=True),
+        ]
+    )
+
+    import deerflow.tools as tools_module
+
+    get_available_tools = MagicMock(return_value=[])
+    monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
+    monkeypatch.setattr(tools_module, "get_available_tools", get_available_tools)
+    monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda config, model_name, agent_name=None: [])
+
+    captured: dict[str, object] = {}
+
+    def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None):
+        captured["name"] = name
+        captured["thinking_enabled"] = thinking_enabled
+        captured["reasoning_effort"] = reasoning_effort
+        return object()
+
+    monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
+    monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
+
+    result = lead_agent_module.make_lead_agent(
+        {
+            "context": {
+                "model_name": "context-model",
+                "thinking_enabled": False,
+                "reasoning_effort": "high",
+                "is_plan_mode": True,
+                "subagent_enabled": True,
+                "max_concurrent_subagents": 7,
+            }
+        }
+    )
+
+    assert captured == {
+        "name": "context-model",
+        "thinking_enabled": False,
+        "reasoning_effort": "high",
+    }
+    get_available_tools.assert_called_once_with(model_name="context-model", groups=None, subagent_enabled=True)
+    assert result["model"] is not None
+
+
+def test_make_lead_agent_rejects_invalid_bootstrap_agent_name(monkeypatch):
+    app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
+
+    monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
+
+    with pytest.raises(ValueError, match="Invalid agent name"):
+        lead_agent_module.make_lead_agent(
+            {
+                "configurable": {
+                    "model_name": "safe-model",
+                    "thinking_enabled": False,
+                    "is_plan_mode": False,
+                    "subagent_enabled": False,
+                    "is_bootstrap": True,
+                    "agent_name": "../../../tmp/evil",
+                }
+            }
+        )
+
+
 def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
     app_config = _make_app_config(
         [
@@ -145,6 +214,7 @@ def test_create_summarization_middleware_uses_configured_model_alias(monkeypatch
         "get_summarization_config",
         lambda: SummarizationConfig(enabled=True, model_name="model-masswork"),
     )
+    monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=False))
 
     captured: dict[str, object] = {}
     fake_model = object()
@@ -156,10 +226,56 @@ def test_create_summarization_middleware_uses_configured_model_alias(monkeypatch
         return fake_model
 
     monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
-    monkeypatch.setattr(lead_agent_module, "SummarizationMiddleware", lambda **kwargs: kwargs)
+    monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
 
     middleware = lead_agent_module._create_summarization_middleware()
 
     assert captured["name"] == "model-masswork"
     assert captured["thinking_enabled"] is False
     assert middleware["model"] is fake_model
+
+
+def test_create_summarization_middleware_registers_memory_flush_hook_when_memory_enabled(monkeypatch):
+    monkeypatch.setattr(
+        lead_agent_module,
+        "get_summarization_config",
+        lambda: SummarizationConfig(enabled=True),
+    )
+    monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=True))
+    monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
+
+    captured: dict[str, object] = {}
+
+    def _fake_middleware(**kwargs):
+        captured.update(kwargs)
+        return kwargs
+
+    monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", _fake_middleware)
+
+    lead_agent_module._create_summarization_middleware()
+
+    assert captured["before_summarization"] == [lead_agent_module.memory_flush_hook]
+
+
+def test_create_summarization_middleware_passes_skill_read_tool_names(monkeypatch):
+    app_config = _make_app_config([_make_model("default-model", supports_thinking=False)])
+    monkeypatch.setattr(
+        lead_agent_module,
+        "get_summarization_config",
+        lambda: SummarizationConfig(enabled=True, skill_file_read_tool_names=["read_file", "cat"]),
+    )
+    monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=False))
+    monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
+    monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
+
+    captured: dict[str, object] = {}
+
+    def _fake_middleware(**kwargs):
+        captured.update(kwargs)
+        return kwargs
+
+    monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", _fake_middleware)
+
+    lead_agent_module._create_summarization_middleware()
+
+    assert captured["skill_file_read_tool_names"] == ["read_file", "cat"]

backend/tests/test_llm_error_handling_middleware.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import asyncio
 from types import SimpleNamespace
+from typing import Any
 
 import pytest
 from langchain_core.messages import AIMessage
@@ -134,3 +135,303 @@ def test_async_model_call_propagates_graph_bubble_up() -> None:
 
     with pytest.raises(GraphBubbleUp):
         asyncio.run(middleware.awrap_model_call(SimpleNamespace(), handler))
+
+
+def test_circuit_half_open_graph_bubble_up_resets_probe() -> None:
+    """Verify that GraphBubbleUp in half_open state resets probe_in_flight."""
+    middleware = _build_middleware()
+
+    # Step 1: Manually set state to half_open and check_circuit() to set probe_in_flight=True
+    middleware._circuit_state = "half_open"
+    middleware._circuit_probe_in_flight = False
+    # Call _check_circuit() once to simulate the probe being allowed through
+    assert middleware._check_circuit() is False
+    assert middleware._circuit_probe_in_flight is True
+
+    # Step 2: Now trigger handler that raises GraphBubbleUp
+    def handler(_request) -> AIMessage:
+        raise GraphBubbleUp()
+
+    # Mock _check_circuit() to return False (since we already did the probe check)
+    import unittest.mock
+
+    with unittest.mock.patch.object(middleware, "_check_circuit", return_value=False):
+        with pytest.raises(GraphBubbleUp):
+            middleware.wrap_model_call(SimpleNamespace(), handler)
+
+    # Verify probe_in_flight was reset, state should remain half_open
+    assert middleware._circuit_probe_in_flight is False
+    assert middleware._circuit_state == "half_open"
+
+
+@pytest.mark.anyio
+async def test_async_circuit_half_open_graph_bubble_up_resets_probe() -> None:
+    """Verify that GraphBubbleUp in half_open state resets probe_in_flight (async version)."""
+    middleware = _build_middleware()
+
+    # Step 1: Manually set state to half_open and check_circuit() to set probe_in_flight=True
+    middleware._circuit_state = "half_open"
+    middleware._circuit_probe_in_flight = False
+    # Call _check_circuit() once to simulate the probe being allowed through
+    assert middleware._check_circuit() is False
+    assert middleware._circuit_probe_in_flight is True
+
+    # Step 2: Now trigger handler that raises GraphBubbleUp
+    async def handler(_request) -> AIMessage:
+        raise GraphBubbleUp()
+
+    # Mock _check_circuit() to return False (since we already did the probe check)
+    import unittest.mock
+
+    with unittest.mock.patch.object(middleware, "_check_circuit", return_value=False):
+        with pytest.raises(GraphBubbleUp):
+            await middleware.awrap_model_call(SimpleNamespace(), handler)
+
+    # Verify probe_in_flight was reset, state should remain half_open
+    assert middleware._circuit_probe_in_flight is False
+    assert middleware._circuit_state == "half_open"
+
+
+# ---------- Circuit Breaker Tests ----------
+
+
+def transient_failing_handler(request: Any) -> Any:
+    raise FakeError("Server Error", status_code=502)  # Used for transient error
+
+
+def quota_failing_handler(request: Any) -> Any:
+    raise FakeError("Quota exceeded", body={"error": {"code": "insufficient_quota"}})  # Used for quota error
+
+
+def success_handler(request: Any) -> Any:
+    return AIMessage(content="Success")
+
+
+def mock_classify_retriable(exc: BaseException) -> tuple[bool, str]:
+    return True, "transient"
+
+
+def mock_classify_non_retriable(exc: BaseException) -> tuple[bool, str]:
+    return False, "quota"
+
+
+def test_circuit_breaker_trips_and_recovers(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Verify that circuit breaker trips, fast fails, correctly transitions to Half-Open, and recovers or re-opens."""
+
+    # Mock time.sleep to avoid slow tests during retry loops (Speed up from ~4s to 0.1s)
+    waits: list[float] = []
+    monkeypatch.setattr("time.sleep", lambda d: waits.append(d))
+
+    # Mock time.time to decouple from private implementation details and enable time travel
+    current_time = 1000.0
+    monkeypatch.setattr("time.time", lambda: current_time)
+
+    middleware = LLMErrorHandlingMiddleware()
+    middleware.circuit_failure_threshold = 3
+    middleware.circuit_recovery_timeout_sec = 10
+    monkeypatch.setattr(middleware, "_classify_error", mock_classify_retriable)
+
+    request: Any = {"messages": []}
+
+    # --- 0. Test initial state & Success ---
+    # Success handler does not increase count. If it's already 0, it stays 0.
+    middleware.wrap_model_call(request, success_handler)
+    assert middleware._circuit_failure_count == 0
+    assert middleware._check_circuit() is False
+
+    # --- 1. Trip the circuit ---
+    # Fails 3 overall calls. Threshold (3) is reached.
+    middleware.wrap_model_call(request, transient_failing_handler)
+    assert middleware._circuit_failure_count == 1
+    middleware.wrap_model_call(request, transient_failing_handler)
+    assert middleware._circuit_failure_count == 2
+    middleware.wrap_model_call(request, transient_failing_handler)
+    assert middleware._circuit_failure_count == 3
+    assert middleware._check_circuit() is True  # Circuit is OPEN
+
+    # --- 2. Fast Fail ---
+    # 2nd call: fast fail immediately without calling handler.
+    # Count should not increase during OPEN state.
+    result = middleware.wrap_model_call(request, success_handler)
+    assert result.content == middleware._build_circuit_breaker_message()
+    assert middleware._circuit_failure_count == 3
+
+    # --- 3. Half-Open -> Fail -> Re-Open ---
+    # Time travel 11 seconds (timeout is 10s). Current time becomes 1011.0
+    current_time += 11.0
+
+    # Verify that the timeout was set EXACTLY relative to current_time + timeout_sec
+    assert middleware._circuit_open_until == current_time - 11.0 + middleware.circuit_recovery_timeout_sec
+
+    # Fails again! The request will go through the 3-attempt retry loop again.
+    middleware.wrap_model_call(request, transient_failing_handler)
+    assert middleware._circuit_failure_count == middleware.circuit_failure_threshold
+    assert middleware._circuit_state == "open"  # Re-OPENed
+
+    # --- 4. Half-Open -> Success -> Reset ---
+    # Time travel another 11 seconds
+    current_time += 11.0
+
+    # Succeeds this time! Should completely reset.
+    result = middleware.wrap_model_call(request, success_handler)
+    assert result.content == "Success"
+    assert middleware._circuit_failure_count == 0  # Fully RESET!
+    assert middleware._check_circuit() is False
+
+
+def test_circuit_breaker_does_not_trip_on_non_retriable_errors(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Verify that circuit breaker ignores business errors like Quota or Auth."""
+    waits: list[float] = []
+    monkeypatch.setattr("time.sleep", lambda d: waits.append(d))
+
+    middleware = LLMErrorHandlingMiddleware()
+    middleware.circuit_failure_threshold = 3
+    monkeypatch.setattr(middleware, "_classify_error", mock_classify_non_retriable)
+
+    request: Any = {"messages": []}
+
+    for _ in range(3):
+        middleware.wrap_model_call(request, quota_failing_handler)
+
+    assert middleware._circuit_failure_count == 0
+    assert middleware._check_circuit() is False
+
+
+# ---------- ReadError / RemoteProtocolError retriable classification ----------
+
+
+class _ReadError(Exception):
+    """Local stand-in for httpx.ReadError — same class name, no httpx dependency."""
+
+
+class _RemoteProtocolError(Exception):
+    """Local stand-in for httpx.RemoteProtocolError — same class name, no httpx dependency."""
+
+
+_ReadError.__name__ = "ReadError"
+_RemoteProtocolError.__name__ = "RemoteProtocolError"
+
+
+def test_classify_error_read_error_is_retriable() -> None:
+    middleware = _build_middleware()
+    exc = _ReadError("Connection dropped mid-stream")
+    exc.__class__.__name__ = "ReadError"
+    retriable, reason = middleware._classify_error(exc)
+    assert retriable is True
+    assert reason == "transient"
+
+
+def test_classify_error_remote_protocol_error_is_retriable() -> None:
+    middleware = _build_middleware()
+    exc = _RemoteProtocolError("Server closed connection unexpectedly")
+    exc.__class__.__name__ = "RemoteProtocolError"
+    retriable, reason = middleware._classify_error(exc)
+    assert retriable is True
+    assert reason == "transient"
+
+
+def test_sync_read_error_triggers_retry_loop(monkeypatch: pytest.MonkeyPatch) -> None:
+    middleware = _build_middleware(retry_max_attempts=3, retry_base_delay_ms=10, retry_cap_delay_ms=10)
+    attempts = 0
+    waits: list[float] = []
+    monkeypatch.setattr("time.sleep", lambda d: waits.append(d))
+
+    def handler(_request) -> AIMessage:
+        nonlocal attempts
+        attempts += 1
+        raise _ReadError("Connection dropped mid-stream")
+
+    result = middleware.wrap_model_call(SimpleNamespace(), handler)
+
+    assert isinstance(result, AIMessage)
+    assert "temporarily unavailable" in result.content
+    assert attempts == 3  # exhausted all retries
+    assert len(waits) == 2  # slept between attempts 1→2 and 2→3
+
+
+@pytest.mark.anyio
+async def test_async_read_error_triggers_retry_loop(monkeypatch: pytest.MonkeyPatch) -> None:
+    middleware = _build_middleware(retry_max_attempts=3, retry_base_delay_ms=10, retry_cap_delay_ms=10)
+    attempts = 0
+    waits: list[float] = []
+
+    async def fake_sleep(d: float) -> None:
+        waits.append(d)
+
+    monkeypatch.setattr(asyncio, "sleep", fake_sleep)
+
+    async def handler(_request) -> AIMessage:
+        nonlocal attempts
+        attempts += 1
+        raise _ReadError("Connection dropped mid-stream")
+
+    result = await middleware.awrap_model_call(SimpleNamespace(), handler)
+
+    assert isinstance(result, AIMessage)
+    assert "temporarily unavailable" in result.content
+    assert attempts == 3  # exhausted all retries
+    assert len(waits) == 2  # slept between attempts 1→2 and 2→3
+
+
+@pytest.mark.anyio
+async def test_async_circuit_breaker_trips_and_recovers(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Verify async version of circuit breaker correctly handles state transitions."""
+    waits: list[float] = []
+
+    async def fake_sleep(d: float) -> None:
+        waits.append(d)
+
+    monkeypatch.setattr(asyncio, "sleep", fake_sleep)
+
+    current_time = 1000.0
+    monkeypatch.setattr("time.time", lambda: current_time)
+
+    middleware = LLMErrorHandlingMiddleware()
+    middleware.circuit_failure_threshold = 3
+    middleware.circuit_recovery_timeout_sec = 10
+    monkeypatch.setattr(middleware, "_classify_error", mock_classify_retriable)
+
+    async def async_failing_handler(request: Any) -> Any:
+        raise FakeError("Server Error", status_code=502)
+
+    request: Any = {"messages": []}
+
+    # --- 1. Trip the circuit ---
+    # Fails 3 overall calls. Threshold (3) is reached.
+    await middleware.awrap_model_call(request, async_failing_handler)
+    assert middleware._circuit_failure_count == 1
+    await middleware.awrap_model_call(request, async_failing_handler)
+    assert middleware._circuit_failure_count == 2
+    await middleware.awrap_model_call(request, async_failing_handler)
+    assert middleware._circuit_failure_count == 3
+    assert middleware._check_circuit() is True
+
+    # --- 2. Fast Fail ---
+    # 2nd call: fast fail immediately without calling handler
+    async def async_success_handler(request: Any) -> Any:
+        return AIMessage(content="Success")
+
+    result = await middleware.awrap_model_call(request, async_success_handler)
+    assert result.content == middleware._build_circuit_breaker_message()
+    assert middleware._circuit_failure_count == 3  # Unchanged
+
+    # --- 3. Half-Open -> Fail -> Re-Open ---
+    # Time travel 11 seconds
+    current_time += 11.0
+
+    # Verify timeout formula
+    assert middleware._circuit_open_until == current_time - 11.0 + middleware.circuit_recovery_timeout_sec
+
+    # Fails again! The request goes through the 3-attempt retry loop.
+    await middleware.awrap_model_call(request, async_failing_handler)
+    assert middleware._circuit_failure_count == middleware.circuit_failure_threshold
+    assert middleware._circuit_state == "open"  # Re-OPENed
+
+    # --- 4. Half-Open -> Success -> Reset ---
+    # Time travel another 11 seconds
+    current_time += 11.0
+
+    result = await middleware.awrap_model_call(request, async_success_handler)
+    assert result.content == "Success"
+    assert middleware._circuit_failure_count == 0  # RESET
+    assert middleware._check_circuit() is False

backend/tests/test_local_sandbox_provider_mounts.py

@@ -255,7 +255,9 @@ class TestMultipleMounts:
 
         sandbox.execute_command("cat /mnt/data/test.txt")
         # Verify the command received the resolved local path
-        assert str(data_dir) in captured.get("command", "")
+        command = captured.get("command", [])
+        assert isinstance(command, list) and len(command) >= 3
+        assert str(data_dir) in command[2]
 
     def test_reverse_resolve_path_does_not_match_partial_prefix(self, tmp_path):
         foo_dir = tmp_path / "foo"

backend/tests/test_loop_detection_middleware.py

@@ -413,6 +413,45 @@ class TestHardStopWithListContent:
         assert msg.content.startswith("thinking...")
         assert _HARD_STOP_MSG in msg.content
 
+    def test_hard_stop_clears_raw_tool_call_metadata(self):
+        """Forced-stop messages must not retain provider-level raw tool-call payloads."""
+        mw = LoopDetectionMiddleware(warn_threshold=2, hard_limit=4)
+        runtime = _make_runtime()
+        call = [_bash_call("ls")]
+
+        def _make_provider_state():
+            return {
+                "messages": [
+                    AIMessage(
+                        content="thinking...",
+                        tool_calls=call,
+                        additional_kwargs={
+                            "tool_calls": [
+                                {
+                                    "id": "call_ls",
+                                    "type": "function",
+                                    "function": {"name": "bash", "arguments": '{"command":"ls"}'},
+                                    "thought_signature": "sig-1",
+                                }
+                            ],
+                            "function_call": {"name": "bash", "arguments": '{"command":"ls"}'},
+                        },
+                        response_metadata={"finish_reason": "tool_calls"},
+                    )
+                ]
+            }
+
+        for _ in range(3):
+            mw._apply(_make_provider_state(), runtime)
+
+        result = mw._apply(_make_provider_state(), runtime)
+        assert result is not None
+        msg = result["messages"][0]
+        assert msg.tool_calls == []
+        assert "tool_calls" not in msg.additional_kwargs
+        assert "function_call" not in msg.additional_kwargs
+        assert msg.response_metadata["finish_reason"] == "stop"
+
 
 class TestToolFrequencyDetection:
     """Tests for per-tool-type frequency detection (Layer 2).

backend/tests/test_mcp_custom_interceptors.py

@@ -0,0 +1,274 @@
+"""Tests for custom MCP tool interceptors loaded via extensions_config.json."""
+
+import asyncio
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from deerflow.mcp.tools import get_mcp_tools
+
+
+def _make_patches(*, interceptor_paths=None):
+    """Set up mocks for get_mcp_tools() with optional custom interceptors.
+
+    Returns a dict of patch context managers.
+    """
+    mock_client = MagicMock()
+    mock_client.get_tools = AsyncMock(return_value=[])
+
+    extra = {}
+    if interceptor_paths is not None:
+        extra["mcpInterceptors"] = interceptor_paths
+
+    return {
+        "client_cls": patch(
+            "langchain_mcp_adapters.client.MultiServerMCPClient",
+            return_value=mock_client,
+        ),
+        "from_file": patch(
+            "deerflow.config.extensions_config.ExtensionsConfig.from_file",
+            return_value=MagicMock(
+                model_extra=extra,
+                get_enabled_mcp_servers=MagicMock(return_value={}),
+            ),
+        ),
+        "build_servers": patch(
+            "deerflow.mcp.tools.build_servers_config",
+            return_value={"test-server": {}},
+        ),
+        "oauth_headers": patch(
+            "deerflow.mcp.tools.get_initial_oauth_headers",
+            new_callable=AsyncMock,
+            return_value={},
+        ),
+        "oauth_interceptor": patch(
+            "deerflow.mcp.tools.build_oauth_tool_interceptor",
+            return_value=None,
+        ),
+    }
+
+
+def _get_interceptors(mock_cls):
+    """Extract the tool_interceptors list passed to MultiServerMCPClient."""
+    kw = mock_cls.call_args
+    return kw.kwargs.get("tool_interceptors") or kw[1].get("tool_interceptors", [])
+
+
+def test_custom_interceptor_loaded_and_appended():
+    """A valid interceptor builder path is resolved, called, and appended to tool_interceptors."""
+
+    async def fake_interceptor(request, handler):
+        return await handler(request)
+
+    def fake_builder():
+        return fake_interceptor
+
+    p = _make_patches(interceptor_paths=["my_package.auth:build_interceptor"])
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", return_value=fake_builder),
+    ):
+        asyncio.run(get_mcp_tools())
+
+        interceptors = _get_interceptors(mock_cls)
+        assert len(interceptors) == 1
+        assert interceptors[0] is fake_interceptor
+
+
+def test_multiple_custom_interceptors():
+    """Multiple interceptor paths are all loaded in order."""
+
+    async def interceptor_a(request, handler):
+        return await handler(request)
+
+    async def interceptor_b(request, handler):
+        return await handler(request)
+
+    builders = {
+        "pkg.a:build_a": lambda: interceptor_a,
+        "pkg.b:build_b": lambda: interceptor_b,
+    }
+
+    p = _make_patches(interceptor_paths=["pkg.a:build_a", "pkg.b:build_b"])
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", side_effect=lambda path: builders[path]),
+    ):
+        asyncio.run(get_mcp_tools())
+
+        interceptors = _get_interceptors(mock_cls)
+        assert len(interceptors) == 2
+        assert interceptors[0] is interceptor_a
+        assert interceptors[1] is interceptor_b
+
+
+def test_custom_interceptor_builder_returning_none_is_skipped():
+    """If a builder returns None, it is not appended to the interceptor list."""
+    p = _make_patches(interceptor_paths=["pkg.noop:build_noop"])
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", return_value=lambda: None),
+    ):
+        asyncio.run(get_mcp_tools())
+
+        assert len(_get_interceptors(mock_cls)) == 0
+
+
+def test_custom_interceptor_resolve_error_logs_warning_and_continues():
+    """A broken interceptor path logs a warning and does not block tool loading."""
+    p = _make_patches(interceptor_paths=["broken.path:does_not_exist"])
+
+    with (
+        p["client_cls"],
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", side_effect=ImportError("no such module")),
+        patch("deerflow.mcp.tools.logger.warning") as mock_warn,
+    ):
+        tools = asyncio.run(get_mcp_tools())
+
+        assert tools == []
+        mock_warn.assert_called_once()
+        assert "broken.path:does_not_exist" in mock_warn.call_args[0][0]
+
+
+def test_custom_interceptor_builder_exception_logs_warning_and_continues():
+    """If the builder function itself raises, the error is caught and logged."""
+
+    def exploding_builder():
+        raise RuntimeError("builder exploded")
+
+    p = _make_patches(interceptor_paths=["pkg.bad:exploding_builder"])
+
+    with (
+        p["client_cls"],
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", return_value=exploding_builder),
+        patch("deerflow.mcp.tools.logger.warning") as mock_warn,
+    ):
+        tools = asyncio.run(get_mcp_tools())
+
+        assert tools == []
+        mock_warn.assert_called_once()
+        assert "pkg.bad:exploding_builder" in mock_warn.call_args[0][0]
+
+
+def test_no_mcp_interceptors_field_is_safe():
+    """When mcpInterceptors is absent from config, no interceptors are added."""
+    p = _make_patches(interceptor_paths=None)
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+    ):
+        asyncio.run(get_mcp_tools())
+
+        assert len(_get_interceptors(mock_cls)) == 0
+
+
+def test_custom_interceptor_coexists_with_oauth_interceptor():
+    """Custom interceptors are appended after the OAuth interceptor."""
+
+    async def oauth_fn(request, handler):
+        return await handler(request)
+
+    async def custom_fn(request, handler):
+        return await handler(request)
+
+    p = _make_patches(interceptor_paths=["pkg.custom:build_custom"])
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        patch("deerflow.mcp.tools.build_oauth_tool_interceptor", return_value=oauth_fn),
+        patch("deerflow.mcp.tools.resolve_variable", return_value=lambda: custom_fn),
+    ):
+        asyncio.run(get_mcp_tools())
+
+        interceptors = _get_interceptors(mock_cls)
+        assert len(interceptors) == 2
+        assert interceptors[0] is oauth_fn
+        assert interceptors[1] is custom_fn
+
+
+def test_mcp_interceptors_single_string_is_normalized():
+    """A single string value for mcpInterceptors is normalized to a list."""
+
+    async def fake_interceptor(request, handler):
+        return await handler(request)
+
+    p = _make_patches(interceptor_paths="pkg.single:build_it")
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", return_value=lambda: fake_interceptor),
+    ):
+        asyncio.run(get_mcp_tools())
+
+        assert len(_get_interceptors(mock_cls)) == 1
+
+
+def test_mcp_interceptors_invalid_type_logs_warning():
+    """A non-list, non-string value for mcpInterceptors logs a warning and is skipped."""
+    p = _make_patches(interceptor_paths=42)
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.logger.warning") as mock_warn,
+    ):
+        asyncio.run(get_mcp_tools())
+
+        assert len(_get_interceptors(mock_cls)) == 0
+        mock_warn.assert_called_once()
+        assert "must be a list" in mock_warn.call_args[0][0]
+
+
+def test_custom_interceptor_non_callable_return_logs_warning():
+    """If a builder returns a non-callable value, it is skipped with a warning."""
+    p = _make_patches(interceptor_paths=["pkg.bad:returns_string"])
+
+    with (
+        p["client_cls"] as mock_cls,
+        p["from_file"],
+        p["build_servers"],
+        p["oauth_headers"],
+        p["oauth_interceptor"],
+        patch("deerflow.mcp.tools.resolve_variable", return_value=lambda: "not_a_callable"),
+        patch("deerflow.mcp.tools.logger.warning") as mock_warn,
+    ):
+        asyncio.run(get_mcp_tools())
+
+        assert len(_get_interceptors(mock_cls)) == 0
+        mock_warn.assert_called_once()
+        assert "non-callable" in mock_warn.call_args[0][0]

backend/tests/test_memory_queue.py

@@ -1,3 +1,5 @@
+import threading
+import time
 from unittest.mock import MagicMock, patch
 
 from deerflow.agents.memory.queue import ConversationContext, MemoryUpdateQueue
@@ -89,3 +91,74 @@ def test_process_queue_forwards_reinforcement_flag_to_updater() -> None:
         correction_detected=False,
         reinforcement_detected=True,
     )
+
+
+def test_flush_nowait_cancels_existing_timer_and_starts_immediate_timer() -> None:
+    queue = MemoryUpdateQueue()
+    existing_timer = MagicMock()
+    queue._timer = existing_timer
+    created_timer = MagicMock()
+
+    with patch("deerflow.agents.memory.queue.threading.Timer", return_value=created_timer) as timer_cls:
+        queue.flush_nowait()
+
+    existing_timer.cancel.assert_called_once_with()
+    timer_cls.assert_called_once_with(0, queue._process_queue)
+    assert created_timer.daemon is True
+    created_timer.start.assert_called_once_with()
+    assert queue._timer is created_timer
+
+
+def test_add_nowait_cancels_existing_timer_and_starts_immediate_timer() -> None:
+    queue = MemoryUpdateQueue()
+    existing_timer = MagicMock()
+    queue._timer = existing_timer
+    created_timer = MagicMock()
+
+    with (
+        patch("deerflow.agents.memory.queue.get_memory_config", return_value=_memory_config(enabled=True)),
+        patch("deerflow.agents.memory.queue.threading.Timer", return_value=created_timer) as timer_cls,
+    ):
+        queue.add_nowait(thread_id="thread-1", messages=["conversation"], agent_name="lead-agent")
+
+    existing_timer.cancel.assert_called_once_with()
+    timer_cls.assert_called_once_with(0, queue._process_queue)
+    assert queue.pending_count == 1
+    assert queue._queue[0].agent_name == "lead-agent"
+    assert created_timer.daemon is True
+    created_timer.start.assert_called_once_with()
+
+
+def test_process_queue_reschedules_immediately_when_already_processing() -> None:
+    queue = MemoryUpdateQueue()
+    queue._processing = True
+    created_timer = MagicMock()
+
+    with patch("deerflow.agents.memory.queue.threading.Timer", return_value=created_timer) as timer_cls:
+        queue._process_queue()
+
+    timer_cls.assert_called_once_with(0, queue._process_queue)
+    assert created_timer.daemon is True
+    created_timer.start.assert_called_once_with()
+
+
+def test_flush_nowait_is_non_blocking() -> None:
+    queue = MemoryUpdateQueue()
+    started = threading.Event()
+    finished = threading.Event()
+
+    def _slow_process_queue() -> None:
+        started.set()
+        time.sleep(0.2)
+        finished.set()
+
+    queue._process_queue = _slow_process_queue
+
+    start = time.perf_counter()
+    queue.flush_nowait()
+    elapsed = time.perf_counter() - start
+
+    assert started.wait(0.1) is True
+    assert elapsed < 0.1
+    assert finished.is_set() is False
+    assert finished.wait(1.0) is True

backend/tests/test_memory_storage.py

@@ -110,6 +110,93 @@ class TestFileMemoryStorage:
                 assert result is True
                 assert memory_file.exists()
 
+    def test_save_does_not_mutate_caller_dict(self, tmp_path):
+        """save() must not mutate the caller's dict (lastUpdated side-effect)."""
+        memory_file = tmp_path / "memory.json"
+
+        def mock_get_paths():
+            mock_paths = MagicMock()
+            mock_paths.memory_file = memory_file
+            return mock_paths
+
+        with patch("deerflow.agents.memory.storage.get_paths", side_effect=mock_get_paths):
+            with patch("deerflow.agents.memory.storage.get_memory_config", return_value=MemoryConfig(storage_path="")):
+                storage = FileMemoryStorage()
+                original = {"version": "1.0", "facts": []}
+                before_keys = set(original.keys())
+                storage.save(original)
+                assert set(original.keys()) == before_keys, "save() must not add keys to caller's dict"
+                assert "lastUpdated" not in original
+
+    def test_cache_not_corrupted_when_save_fails(self, tmp_path):
+        """Cache must remain clean when save() raises OSError.
+
+        If save() fails, the cache must NOT be updated with the new data.
+        Together with the deepcopy in updater._finalize_update(), this prevents
+        stale mutations from leaking into the cache when persistence fails.
+        """
+        memory_file = tmp_path / "memory.json"
+        memory_file.parent.mkdir(parents=True, exist_ok=True)
+        original_data = {"version": "1.0", "facts": [{"content": "original"}]}
+        import json as _json
+
+        memory_file.write_text(_json.dumps(original_data))
+
+        def mock_get_paths():
+            mock_paths = MagicMock()
+            mock_paths.memory_file = memory_file
+            return mock_paths
+
+        with patch("deerflow.agents.memory.storage.get_paths", side_effect=mock_get_paths):
+            with patch("deerflow.agents.memory.storage.get_memory_config", return_value=MemoryConfig(storage_path="")):
+                storage = FileMemoryStorage()
+                # Warm the cache
+                cached = storage.load()
+                assert cached["facts"][0]["content"] == "original"
+
+                # Simulate save failure: mkdir succeeds but open() raises
+                modified = {"version": "1.0", "facts": [{"content": "mutated"}]}
+                with patch("builtins.open", side_effect=OSError("disk full")):
+                    result = storage.save(modified)
+                assert result is False
+
+                # Cache must still reflect the original data, not the failed write
+                after = storage.load()
+                assert after["facts"][0]["content"] == "original"
+
+    def test_cache_thread_safety(self, tmp_path):
+        """Concurrent load/reload calls must not race on _memory_cache."""
+        memory_file = tmp_path / "memory.json"
+        memory_file.parent.mkdir(parents=True, exist_ok=True)
+        import json as _json
+
+        memory_file.write_text(_json.dumps({"version": "1.0", "facts": []}))
+
+        def mock_get_paths():
+            mock_paths = MagicMock()
+            mock_paths.memory_file = memory_file
+            return mock_paths
+
+        errors: list[Exception] = []
+
+        def load_many(storage: FileMemoryStorage) -> None:
+            try:
+                for _ in range(50):
+                    storage.load()
+            except Exception as exc:
+                errors.append(exc)
+
+        with patch("deerflow.agents.memory.storage.get_paths", side_effect=mock_get_paths):
+            with patch("deerflow.agents.memory.storage.get_memory_config", return_value=MemoryConfig(storage_path="")):
+                storage = FileMemoryStorage()
+                threads = [threading.Thread(target=load_many, args=(storage,)) for _ in range(8)]
+                for t in threads:
+                    t.start()
+                for t in threads:
+                    t.join()
+
+        assert not errors, f"Thread-safety errors: {errors}"
+
     def test_reload_forces_cache_invalidation(self, tmp_path):
         """Should force reload from file and invalidate cache."""
         memory_file = tmp_path / "memory.json"

backend/tests/test_memory_updater.py

@@ -1,9 +1,13 @@
-from unittest.mock import MagicMock, patch
+import asyncio
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
 
 from deerflow.agents.memory.prompt import format_conversation_for_update
 from deerflow.agents.memory.updater import (
     MemoryUpdater,
     _extract_text,
+    _run_async_update_sync,
     clear_memory_data,
     create_memory_fact,
     delete_memory_fact,
@@ -523,15 +527,16 @@ class TestUpdateMemoryStructuredResponse:
         model = MagicMock()
         response = MagicMock()
         response.content = content
-        model.invoke.return_value = response
+        model.ainvoke = AsyncMock(return_value=response)
         return model
 
     def test_string_response_parses(self):
         updater = MemoryUpdater()
         valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
 
         with (
-            patch.object(updater, "_get_model", return_value=self._make_mock_model(valid_json)),
+            patch.object(updater, "_get_model", return_value=model),
             patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
             patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
             patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
@@ -546,6 +551,7 @@ class TestUpdateMemoryStructuredResponse:
             result = updater.update_memory([msg, ai_msg])
 
         assert result is True
+        model.ainvoke.assert_awaited_once()
 
     def test_list_content_response_parses(self):
         """LLM response as list-of-blocks should be extracted, not repr'd."""
@@ -570,6 +576,30 @@ class TestUpdateMemoryStructuredResponse:
 
         assert result is True
 
+    def test_async_update_memory_uses_ainvoke(self):
+        updater = MemoryUpdater()
+        valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
+
+        with (
+            patch.object(updater, "_get_model", return_value=model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "Hello"
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Hi there"
+            ai_msg.tool_calls = []
+            result = asyncio.run(updater.aupdate_memory([msg, ai_msg]))
+
+        assert result is True
+        model.ainvoke.assert_awaited_once()
+        assert model.ainvoke.await_args.kwargs["config"] == {"run_name": "memory_agent"}
+
     def test_correction_hint_injected_when_detected(self):
         updater = MemoryUpdater()
         valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
@@ -592,7 +622,7 @@ class TestUpdateMemoryStructuredResponse:
             result = updater.update_memory([msg, ai_msg], correction_detected=True)
 
         assert result is True
-        prompt = model.invoke.call_args[0][0]
+        prompt = model.ainvoke.await_args.args[0]
         assert "Explicit correction signals were detected" in prompt
 
     def test_correction_hint_empty_when_not_detected(self):
@@ -617,9 +647,89 @@ class TestUpdateMemoryStructuredResponse:
             result = updater.update_memory([msg, ai_msg], correction_detected=False)
 
         assert result is True
-        prompt = model.invoke.call_args[0][0]
+        prompt = model.ainvoke.await_args.args[0]
         assert "Explicit correction signals were detected" not in prompt
 
+    def test_sync_update_memory_wrapper_works_in_running_loop(self):
+        updater = MemoryUpdater()
+        valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
+
+        with (
+            patch.object(updater, "_get_model", return_value=model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "Hello from loop"
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Hi"
+            ai_msg.tool_calls = []
+
+            async def run_in_loop():
+                return updater.update_memory([msg, ai_msg])
+
+            result = asyncio.run(run_in_loop())
+
+        assert result is True
+        model.ainvoke.assert_awaited_once()
+
+    def test_sync_update_memory_returns_false_when_bridge_submit_fails(self):
+        updater = MemoryUpdater()
+
+        with (
+            patch(
+                "deerflow.agents.memory.updater._SYNC_MEMORY_UPDATER_EXECUTOR.submit",
+                side_effect=RuntimeError("executor down"),
+            ),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "Hello from loop"
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Hi"
+            ai_msg.tool_calls = []
+
+            async def run_in_loop():
+                return updater.update_memory([msg, ai_msg])
+
+            result = asyncio.run(run_in_loop())
+
+        assert result is False
+
+
+class TestRunAsyncUpdateSync:
+    def test_closes_unawaited_awaitable_when_bridge_fails_before_handoff(self):
+        class CloseableAwaitable:
+            def __init__(self):
+                self.closed = False
+
+            def __await__(self):
+                pytest.fail("awaitable should not have been awaited")
+                yield
+
+            def close(self):
+                self.closed = True
+
+        awaitable = CloseableAwaitable()
+
+        with patch(
+            "deerflow.agents.memory.updater._SYNC_MEMORY_UPDATER_EXECUTOR.submit",
+            side_effect=RuntimeError("executor down"),
+        ):
+
+            async def run_in_loop():
+                return _run_async_update_sync(awaitable)
+
+            result = asyncio.run(run_in_loop())
+
+        assert result is False
+        assert awaitable.closed is True
+
 
 class TestFactDeduplicationCaseInsensitive:
     """Tests that fact deduplication is case-insensitive."""
@@ -694,7 +804,7 @@ class TestReinforcementHint:
         model = MagicMock()
         response = MagicMock()
         response.content = f"```json\n{json_response}\n```"
-        model.invoke.return_value = response
+        model.ainvoke = AsyncMock(return_value=response)
         return model
 
     def test_reinforcement_hint_injected_when_detected(self):
@@ -719,7 +829,7 @@ class TestReinforcementHint:
             result = updater.update_memory([msg, ai_msg], reinforcement_detected=True)
 
         assert result is True
-        prompt = model.invoke.call_args[0][0]
+        prompt = model.ainvoke.await_args.args[0]
         assert "Positive reinforcement signals were detected" in prompt
 
     def test_reinforcement_hint_absent_when_not_detected(self):
@@ -744,7 +854,7 @@ class TestReinforcementHint:
             result = updater.update_memory([msg, ai_msg], reinforcement_detected=False)
 
         assert result is True
-        prompt = model.invoke.call_args[0][0]
+        prompt = model.ainvoke.await_args.args[0]
         assert "Positive reinforcement signals were detected" not in prompt
 
     def test_both_hints_present_when_both_detected(self):
@@ -769,6 +879,56 @@ class TestReinforcementHint:
             result = updater.update_memory([msg, ai_msg], correction_detected=True, reinforcement_detected=True)
 
         assert result is True
-        prompt = model.invoke.call_args[0][0]
+        prompt = model.ainvoke.await_args.args[0]
         assert "Explicit correction signals were detected" in prompt
         assert "Positive reinforcement signals were detected" in prompt
+
+
+class TestFinalizeCacheIsolation:
+    """_finalize_update must not mutate the cached memory object."""
+
+    def test_deepcopy_prevents_cache_corruption_on_save_failure(self):
+        """If save() fails, the in-memory snapshot used by _finalize_update
+        must remain independent of any object the storage layer may still hold in
+        its cache.  The deepcopy in _finalize_update achieves this — the object
+        passed to _apply_updates is always a fresh copy, never the cache reference.
+        """
+        updater = MemoryUpdater()
+        original_memory = _make_memory(facts=[{"id": "fact_orig", "content": "original", "category": "context", "confidence": 0.9, "createdAt": "2024-01-01T00:00:00Z", "source": "t1"}])
+
+        import json as _json
+
+        new_fact_json = _json.dumps(
+            {
+                "user": {},
+                "history": {},
+                "newFacts": [{"content": "new fact", "category": "context", "confidence": 0.9}],
+                "factsToRemove": [],
+            }
+        )
+        mock_response = MagicMock()
+        mock_response.content = new_fact_json
+        mock_model = AsyncMock()
+        mock_model.ainvoke = AsyncMock(return_value=mock_response)
+
+        saved_objects: list[dict] = []
+        save_mock = MagicMock(side_effect=lambda m, a=None: saved_objects.append(m) or False)  # always fails
+
+        with (
+            patch.object(updater, "_get_model", return_value=mock_model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True, fact_confidence_threshold=0.7)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=original_memory),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=save_mock)),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "hello"
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "world"
+            ai_msg.tool_calls = []
+            updater.update_memory([msg, ai_msg], thread_id="t1")
+
+        # original_memory must not have been mutated — deepcopy isolates the mutation
+        assert len(original_memory["facts"]) == 1, "original_memory must not be mutated by _apply_updates"
+        assert original_memory["facts"][0]["content"] == "original"

backend/tests/test_memory_upload_filtering.py

@@ -3,14 +3,14 @@
 Covers two functions introduced to prevent ephemeral file-upload context from
 persisting in long-term memory:
 
-  - _filter_messages_for_memory  (memory_middleware)
+  - filter_messages_for_memory  (message_processing)
   - _strip_upload_mentions_from_memory  (updater)
 """
 
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 
+from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 from deerflow.agents.memory.updater import _strip_upload_mentions_from_memory
-from deerflow.agents.middlewares.memory_middleware import _filter_messages_for_memory, detect_correction, detect_reinforcement
 
 # ---------------------------------------------------------------------------
 # Helpers
@@ -31,7 +31,7 @@ def _ai(text: str, tool_calls=None) -> AIMessage:
 
 
 # ===========================================================================
-# _filter_messages_for_memory
+# filter_messages_for_memory
 # ===========================================================================
 
 
@@ -45,7 +45,7 @@ class TestFilterMessagesForMemory:
             _human(_UPLOAD_BLOCK),
             _ai("I have read the file. It says: Hello."),
         ]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         assert result == []
 
     def test_upload_with_real_question_preserves_question(self):
@@ -56,7 +56,7 @@ class TestFilterMessagesForMemory:
             _human(combined),
             _ai("The file contains: Hello DeerFlow."),
         ]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
 
         assert len(result) == 2
         human_result = result[0]
@@ -71,7 +71,7 @@ class TestFilterMessagesForMemory:
             _human("What is the capital of France?"),
             _ai("The capital of France is Paris."),
         ]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         assert len(result) == 2
         assert result[0].content == "What is the capital of France?"
         assert result[1].content == "The capital of France is Paris."
@@ -84,7 +84,7 @@ class TestFilterMessagesForMemory:
             ToolMessage(content="Search results", tool_call_id="1"),
             _ai("Here are the results."),
         ]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         human_msgs = [m for m in result if m.type == "human"]
         ai_msgs = [m for m in result if m.type == "ai"]
         assert len(human_msgs) == 1
@@ -101,7 +101,7 @@ class TestFilterMessagesForMemory:
             _human("What is 2 + 2?"),
             _ai("4"),
         ]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         human_contents = [m.content for m in result if m.type == "human"]
         ai_contents = [m.content for m in result if m.type == "ai"]
 
@@ -121,14 +121,14 @@ class TestFilterMessagesForMemory:
             ]
         )
         msgs = [msg, _ai("Done.")]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         assert result == []
 
     def test_file_path_not_in_filtered_content(self):
         """After filtering, no upload file path should appear in any message."""
         combined = _UPLOAD_BLOCK + "\n\nSummarise the file please."
         msgs = [_human(combined), _ai("It says hello.")]
-        result = _filter_messages_for_memory(msgs)
+        result = filter_messages_for_memory(msgs)
         all_content = " ".join(m.content for m in result if isinstance(m.content, str))
         assert "/mnt/user-data/uploads/" not in all_content
         assert "<uploaded_files>" not in all_content

backend/tests/test_mindie_provider.py

@@ -0,0 +1,397 @@
+"""
+Unit tests for MindIEChatModel adapter.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
+from langchain_core.outputs import ChatGeneration, ChatResult
+
+# ── Import the module under test ──────────────────────────────────────────────
+from deerflow.models.mindie_provider import (
+    MindIEChatModel,
+    _fix_messages,
+    _parse_xml_tool_call_to_dict,
+)
+
+# ═════════════════════════════════════════════════════════════════════════════
+# Helpers
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+def _make_chat_result(content: str, tool_calls=None) -> ChatResult:
+    msg = AIMessage(content=content)
+    if tool_calls:
+        msg.tool_calls = tool_calls
+    gen = ChatGeneration(message=msg)
+    return ChatResult(generations=[gen])
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 1.  _fix_messages
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestFixMessages:
+    # ── list content → str ────────────────────────────────────────────────────
+
+    def test_list_content_extracted_to_str(self):
+        msg = HumanMessage(
+            content=[
+                {"type": "text", "text": "Hello"},
+                {"type": "text", "text": " world"},
+            ]
+        )
+        result = _fix_messages([msg])
+        assert result[0].content == "Hello world"
+
+    def test_list_content_ignores_non_text_blocks(self):
+        msg = HumanMessage(
+            content=[
+                {"type": "image_url", "image_url": "http://x.com/img.png"},
+                {"type": "text", "text": "caption"},
+            ]
+        )
+        result = _fix_messages([msg])
+        assert result[0].content == "caption"
+
+    def test_empty_list_content_becomes_space(self):
+        msg = HumanMessage(content=[])
+        result = _fix_messages([msg])
+        assert result[0].content == " "
+
+    # ── plain str content ─────────────────────────────────────────────────────
+
+    def test_plain_string_content_preserved(self):
+        msg = HumanMessage(content="hi there")
+        result = _fix_messages([msg])
+        assert result[0].content == "hi there"
+
+    def test_empty_string_content_becomes_space(self):
+        msg = HumanMessage(content="")
+        result = _fix_messages([msg])
+        assert result[0].content == " "
+
+    # ── AIMessage with tool_calls → XML ───────────────────────────────────────
+
+    def test_ai_message_with_tool_calls_serialised_to_xml(self):
+        msg = AIMessage(
+            content="Sure",
+            tool_calls=[
+                {
+                    "name": "get_weather",
+                    "args": {"city": "London"},
+                    "id": "call_abc",
+                }
+            ],
+        )
+        result = _fix_messages([msg])
+        out = result[0]
+        assert isinstance(out, AIMessage)
+        assert "<tool_call>" in out.content
+        assert "<function=get_weather>" in out.content
+        assert '<parameter=city>"London"</parameter>' in out.content
+        assert not getattr(out, "tool_calls", [])
+
+    def test_ai_message_text_preserved_before_xml(self):
+        msg = AIMessage(
+            content="Here you go",
+            tool_calls=[{"name": "search", "args": {"q": "pytest"}, "id": "x"}],
+        )
+        result = _fix_messages([msg])
+        assert result[0].content.startswith("Here you go")
+
+    def test_ai_message_multiple_tool_calls(self):
+        msg = AIMessage(
+            content="",
+            tool_calls=[
+                {"name": "tool_a", "args": {"x": 1}, "id": "id1"},
+                {"name": "tool_b", "args": {"y": 2}, "id": "id2"},
+            ],
+        )
+        result = _fix_messages([msg])
+        content = result[0].content
+        assert content.count("<tool_call>") == 2
+        assert "<function=tool_a>" in content
+        assert "<function=tool_b>" in content
+
+    # ── ToolMessage → HumanMessage ────────────────────────────────────────────
+
+    def test_tool_message_becomes_human_message(self):
+        msg = ToolMessage(content="42 degrees", tool_call_id="call_abc")
+        result = _fix_messages([msg])
+        out = result[0]
+        assert isinstance(out, HumanMessage)
+        assert "<tool_response>" in out.content
+        assert "42 degrees" in out.content
+
+    def test_tool_message_with_list_content(self):
+        msg = ToolMessage(
+            content=[{"type": "text", "text": "result"}],
+            tool_call_id="call_xyz",
+        )
+        result = _fix_messages([msg])
+        assert isinstance(result[0], HumanMessage)
+        assert "result" in result[0].content
+
+    # ── Mixed message list ────────────────────────────────────────────────────
+
+    def test_mixed_message_types_ordering_preserved(self):
+        msgs = [
+            HumanMessage(content="q"),
+            AIMessage(content="a"),
+            ToolMessage(content="tool out", tool_call_id="c1"),
+            HumanMessage(content="follow up"),
+        ]
+        result = _fix_messages(msgs)
+        assert len(result) == 4
+        assert isinstance(result[2], HumanMessage)
+        assert result[3].content == "follow up"
+
+    # ── SystemMessage pass-through ────────────────────────────────────────────
+
+    def test_system_message_passed_through_unchanged(self):
+        msg = SystemMessage(content="You are helpful.")
+        result = _fix_messages([msg])
+        assert result[0].content == "You are helpful."
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 2.  _parse_xml_tool_call_to_dict
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestParseXmlToolCalls:
+    def test_no_tool_call_returns_original(self):
+        content = "Just a normal reply."
+        clean, calls = _parse_xml_tool_call_to_dict(content)
+        assert clean == content
+        assert calls == []
+
+    def test_single_tool_call_parsed(self):
+        content = "<tool_call> <function=search> <parameter=query>pytest</parameter> </function> </tool_call>"
+        clean, calls = _parse_xml_tool_call_to_dict(content)
+        assert clean == ""
+        assert len(calls) == 1
+        assert calls[0]["name"] == "search"
+        assert calls[0]["args"]["query"] == "pytest"
+        assert calls[0]["id"].startswith("call_")
+
+    def test_multiple_tool_calls_parsed(self):
+        content = "<tool_call><function=a><parameter=x>1</parameter></function></tool_call><tool_call><function=b><parameter=y>2</parameter></function></tool_call>"
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert len(calls) == 2
+        assert calls[0]["name"] == "a"
+        assert calls[1]["name"] == "b"
+
+    def test_text_before_tool_call_preserved(self):
+        content = "Here is the answer.\n<tool_call><function=f><parameter=k>v</parameter></function></tool_call>"
+        clean, calls = _parse_xml_tool_call_to_dict(content)
+        assert clean == "Here is the answer."
+        assert len(calls) == 1
+
+    def test_integer_param_deserialised(self):
+        content = "<tool_call><function=f><parameter=n>42</parameter></function></tool_call>"
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert calls[0]["args"]["n"] == 42
+
+    def test_list_param_deserialised(self):
+        content = '<tool_call><function=f><parameter=lst>["a","b"]</parameter></function></tool_call>'
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert calls[0]["args"]["lst"] == ["a", "b"]
+
+    def test_dict_param_deserialised(self):
+        content = '<tool_call><function=f><parameter=d>{"k": 1}</parameter></function></tool_call>'
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert calls[0]["args"]["d"] == {"k": 1}
+
+    def test_bool_param_deserialised(self):
+        content = "<tool_call><function=f><parameter=flag>true</parameter></function></tool_call>"
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert calls[0]["args"]["flag"] is True
+
+    def test_malformed_param_stays_string(self):
+        content = "<tool_call><function=f><parameter=bad>{broken json</parameter></function></tool_call>"
+        _, calls = _parse_xml_tool_call_to_dict(content)
+        assert calls[0]["args"]["bad"] == "{broken json"
+
+    def test_non_string_input_returned_as_is(self):
+        result = _parse_xml_tool_call_to_dict(None)
+        assert result == (None, [])
+
+    def test_unique_ids_generated(self):
+        block = "<tool_call><function=f><parameter=k>v</parameter></function></tool_call>"
+        _, c1 = _parse_xml_tool_call_to_dict(block)
+        _, c2 = _parse_xml_tool_call_to_dict(block)
+        assert c1[0]["id"] != c2[0]["id"]
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 3.  MindIEChatModel._patch_result_with_tools
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestPatchResult:
+    def _model(self):
+        with patch.object(MindIEChatModel, "__init__", return_value=None):
+            m = MindIEChatModel.__new__(MindIEChatModel)
+        return m
+
+    def test_escaped_newlines_fixed(self):
+        model = self._model()
+        result = _make_chat_result("line1\\nline2")
+        patched = model._patch_result_with_tools(result)
+        assert patched.generations[0].message.content == "line1\nline2"
+
+    def test_xml_tool_calls_extracted(self):
+        model = self._model()
+        content = "<tool_call><function=calc><parameter=expr>1+1</parameter></function></tool_call>"
+        result = _make_chat_result(content)
+        patched = model._patch_result_with_tools(result)
+        msg = patched.generations[0].message
+        assert msg.content == ""
+        assert len(msg.tool_calls) == 1
+        assert msg.tool_calls[0]["name"] == "calc"
+
+    def test_patch_result_appends_to_existing_tool_calls(self):
+        model = self._model()
+        existing = [{"name": "existing", "args": {}, "id": "e1"}]
+        content = "<tool_call><function=new_tool><parameter=k>v</parameter></function></tool_call>"
+        result = _make_chat_result(content, tool_calls=existing)
+        patched = model._patch_result_with_tools(result)
+        msg = patched.generations[0].message
+        assert len(msg.tool_calls) == 2
+        names = [tc["name"] for tc in msg.tool_calls]
+        assert "existing" in names
+        assert "new_tool" in names
+
+    def test_no_tool_call_content_unchanged(self):
+        model = self._model()
+        result = _make_chat_result("plain reply")
+        patched = model._patch_result_with_tools(result)
+        assert patched.generations[0].message.content == "plain reply"
+
+    def test_non_string_content_skipped(self):
+        model = self._model()
+        msg = AIMessage(content=[{"type": "text", "text": "hi"}])
+        gen = ChatGeneration(message=msg)
+        result = ChatResult(generations=[gen])
+        patched = model._patch_result_with_tools(result)
+        assert patched is not None
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 4.  MindIEChatModel._generate  (sync)
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestGenerate:
+    def test_generate_calls_fix_messages_and_patch(self):
+        with patch("deerflow.models.mindie_provider.ChatOpenAI._generate") as mock_super_gen, patch.object(MindIEChatModel, "__init__", return_value=None):
+            mock_super_gen.return_value = _make_chat_result("hello")
+            model = MindIEChatModel.__new__(MindIEChatModel)
+
+            msgs = [HumanMessage(content="ping")]
+            result = model._generate(msgs)
+
+            assert mock_super_gen.called
+            called_msgs = mock_super_gen.call_args[0][0]
+            assert all(isinstance(m.content, str) for m in called_msgs)
+            assert result.generations[0].message.content == "hello"
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 5.  MindIEChatModel._agenerate  (async)
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestAGenerate:
+    @pytest.mark.asyncio
+    async def test_agenerate_patches_result(self):
+        with patch("deerflow.models.mindie_provider.ChatOpenAI._agenerate", new_callable=AsyncMock) as mock_ag, patch.object(MindIEChatModel, "__init__", return_value=None):
+            mock_ag.return_value = _make_chat_result("world\\nfoo")
+            model = MindIEChatModel.__new__(MindIEChatModel)
+
+            result = await model._agenerate([HumanMessage(content="hi")])
+            assert result.generations[0].message.content == "world\nfoo"
+
+
+# ═════════════════════════════════════════════════════════════════════════════
+# 6.  MindIEChatModel._astream  (async generator)
+# ═════════════════════════════════════════════════════════════════════════════
+
+
+class TestAStream:
+    async def _collect(self, gen):
+        chunks = []
+        async for chunk in gen:
+            chunks.append(chunk)
+        return chunks
+
+    @pytest.mark.asyncio
+    async def test_no_tools_uses_real_stream(self):
+        from langchain_core.messages import AIMessageChunk
+        from langchain_core.outputs import ChatGenerationChunk
+
+        async def fake_stream(*args, **kwargs):
+            for char in ["hel", "lo"]:
+                yield ChatGenerationChunk(message=AIMessageChunk(content=char))
+
+        with patch("deerflow.models.mindie_provider.ChatOpenAI._astream", side_effect=fake_stream), patch.object(MindIEChatModel, "__init__", return_value=None):
+            model = MindIEChatModel.__new__(MindIEChatModel)
+            chunks = await self._collect(model._astream([HumanMessage(content="hi")]))
+
+        assert "".join(c.message.content for c in chunks) == "hello"
+
+    @pytest.mark.asyncio
+    async def test_no_tools_fixes_escaped_newlines_in_stream(self):
+        from langchain_core.messages import AIMessageChunk
+        from langchain_core.outputs import ChatGenerationChunk
+
+        async def fake_stream(*args, **kwargs):
+            yield ChatGenerationChunk(message=AIMessageChunk(content="a\\nb"))
+
+        with patch("deerflow.models.mindie_provider.ChatOpenAI._astream", side_effect=fake_stream), patch.object(MindIEChatModel, "__init__", return_value=None):
+            model = MindIEChatModel.__new__(MindIEChatModel)
+            chunks = await self._collect(model._astream([HumanMessage(content="x")]))
+
+        assert chunks[0].message.content == "a\nb"
+
+    @pytest.mark.asyncio
+    async def test_with_tools_fake_streams_text_in_chunks(self):
+        with patch.object(MindIEChatModel, "_agenerate", new_callable=AsyncMock) as mock_ag, patch.object(MindIEChatModel, "__init__", return_value=None):
+            long_text = "A" * 50
+            mock_ag.return_value = _make_chat_result(long_text)
+            model = MindIEChatModel.__new__(MindIEChatModel)
+
+            chunks = await self._collect(model._astream([HumanMessage(content="q")], tools=[{"type": "function", "function": {"name": "dummy"}}]))
+
+        full = "".join(c.message.content for c in chunks)
+        assert full == long_text
+        assert len(chunks) > 1
+
+    @pytest.mark.asyncio
+    async def test_with_tools_emits_tool_call_chunk(self):
+
+        tool_calls = [{"name": "fn", "args": {}, "id": "c1"}]
+        with patch.object(MindIEChatModel, "_agenerate", new_callable=AsyncMock) as mock_ag, patch.object(MindIEChatModel, "__init__", return_value=None):
+            mock_ag.return_value = _make_chat_result("ok", tool_calls=tool_calls)
+            model = MindIEChatModel.__new__(MindIEChatModel)
+
+            chunks = await self._collect(model._astream([HumanMessage(content="q")], tools=[{"type": "function", "function": {"name": "fn"}}]))
+
+        tool_chunks = [c for c in chunks if getattr(c.message, "tool_calls", [])]
+        assert tool_chunks, "No chunk carried tool_calls"
+        assert tool_chunks[-1].message.tool_calls[0]["name"] == "fn"
+
+    @pytest.mark.asyncio
+    async def test_with_tools_empty_text_still_emits_tool_chunk(self):
+        tool_calls = [{"name": "x", "args": {}, "id": "c2"}]
+        with patch.object(MindIEChatModel, "_agenerate", new_callable=AsyncMock) as mock_ag, patch.object(MindIEChatModel, "__init__", return_value=None):
+            mock_ag.return_value = _make_chat_result("", tool_calls=tool_calls)
+            model = MindIEChatModel.__new__(MindIEChatModel)
+
+            chunks = await self._collect(model._astream([HumanMessage(content="q")], tools=[{"type": "function", "function": {"name": "x"}}]))
+
+        assert any(getattr(c.message, "tool_calls", []) for c in chunks)