update the code with review comments

Merge branch 'main' into fix-2804
fix(auth): persist auto-generated JWT secret to survive restarts (#2933 )
2026-05-16 17:07:53 +08:00 · 2026-05-16 09:27:40 +08:00 · 2026-05-16 09:24:40 +08:00 · 2026-05-16 08:48:19 +08:00 · 2026-05-15 22:30:05 +08:00 · 2026-05-15 22:27:10 +08:00
249 changed files with 18673 additions and 1994 deletions
@@ -1,3 +1,6 @@
+# Serper API Key (Google Search) - https://serper.dev
+SERPER_API_KEY=your-serper-api-key
+
 # TAVILY API Key
 TAVILY_API_KEY=your-tavily-api-key

@@ -6,8 +9,9 @@ JINA_API_KEY=your-jina-api-key

 # InfoQuest API Key
 INFOQUEST_API_KEY=your-infoquest-api-key
-# CORS Origins (comma-separated) - e.g., http://localhost:3000,http://localhost:3001
-# CORS_ORIGINS=http://localhost:3000
+# Browser CORS allowlist for split-origin or port-forwarded deployments (comma-separated exact origins).
+# Leave unset when using the unified nginx endpoint, e.g. http://localhost:2026.
+# GATEWAY_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000

 # Optional:
 # FIRECRAWL_API_KEY=your-firecrawl-api-key
@@ -45,3 +49,14 @@ INFOQUEST_API_KEY=your-infoquest-api-key

 # Set to "false" to disable Swagger UI, ReDoc, and OpenAPI schema in production
 # GATEWAY_ENABLE_DOCS=false
+
+# ── Frontend SSR → Gateway wiring ─────────────────────────────────────────────
+# The Next.js server uses these to reach the Gateway during SSR (auth checks,
+# /api/* rewrites). They default to localhost values that match `make dev` and
+# `make start`, so most local users do not need to set them.
+#
+# Override only when the Gateway is not on localhost:8001 (e.g. when the
+# frontend and gateway run on different hosts, in containers with a service
+# alias, or behind a different port). docker-compose already sets these.
+# DEER_FLOW_INTERNAL_GATEWAY_BASE_URL=http://localhost:8001
+# DEER_FLOW_TRUSTED_ORIGINS=http://localhost:3000,http://localhost:2026
@@ -0,0 +1,101 @@
+name: Publish Containers
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+
+  backend-container:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}-backend
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+      - name: Log in to the Container registry
+        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=ref,event=branch
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
+        with:
+          context: .
+          file: backend/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true
+
+  frontend-container:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}-frontend
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+      - name: Log in to the Container registry
+        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=ref,event=branch
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
+        with:
+          context: .
+          file: frontend/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true
@@ -46,12 +46,12 @@ Docker provides a consistent, isolated environment with all dependencies pre-con
   All services will start with hot-reload enabled:
   - Frontend changes are automatically reloaded
   - Backend changes trigger automatic restart
-   - LangGraph server supports hot-reload
+   - Gateway-hosted LangGraph-compatible runtime supports hot-reload

 4. **Access the application**:
   - Web Interface: http://localhost:2026
   - API Gateway: http://localhost:2026/api/*
-   - LangGraph: http://localhost:2026/api/langgraph/*
+   - LangGraph-compatible API: http://localhost:2026/api/langgraph/*

 #### Docker Commands

@@ -94,7 +94,7 @@ Use these as practical starting points for development and review environments:
 If `make docker-init`, `make docker-start`, or `make docker-stop` fails on Linux with an error like below, your current user likely does not have permission to access the Docker daemon socket:

 ```text
-unable to get image 'deer-flow-dev-langgraph': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
+unable to get image 'deer-flow-gateway': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
 ```

 Recommended fix: add your current user to the `docker` group so Docker commands work without `sudo`.
@@ -131,8 +131,7 @@ Host Machine
 Docker Compose (deer-flow-dev)
  ├→ nginx (port 2026) ← Reverse proxy
  ├→ web (port 3000) ← Frontend with hot-reload
-  ├→ api (port 8001) ← Gateway API with hot-reload
-   ├→ langgraph (port 2024) ← LangGraph server with hot-reload
+  ├→ gateway (port 8001) ← Gateway API + LangGraph-compatible runtime with hot-reload
  └→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
 ```

@@ -184,17 +183,13 @@ Required tools:

 If you need to start services individually:

-1. **Start backend services**:
+1. **Start backend service**:
   ```bash
-   # Terminal 1: Start LangGraph Server (port 2024)
+   # Terminal 1: Start Gateway API + embedded agent runtime (port 8001)
   cd backend
   make dev

-   # Terminal 2: Start Gateway API (port 8001)
-   cd backend
-   make gateway
-
-   # Terminal 3: Start Frontend (port 3000)
+   # Terminal 2: Start Frontend (port 3000)
   cd frontend
   pnpm dev
   ```
@@ -212,10 +207,10 @@ If you need to start services individually:

 The nginx configuration provides:
 - Unified entry point on port 2026
- Routes `/api/langgraph/*` to LangGraph Server (2024)
+- Rewrites `/api/langgraph/*` to Gateway's LangGraph-compatible API (8001)
 - Routes other `/api/*` endpoints to Gateway API (8001)
 - Routes non-API requests to Frontend (3000)
- Centralized CORS handling
+- Same-origin API routing; split-origin or port-forwarded browser clients should use the Gateway `GATEWAY_CORS_ORIGINS` allowlist
 - SSE/streaming support for real-time agent responses
 - Optimized timeouts for long-running operations

@@ -235,8 +230,8 @@ deer-flow/
 │       └── nginx.local.conf # Nginx config for local dev
 ├── backend/                 # Backend application
 │   ├── src/
-│   │   ├── gateway/        # Gateway API (port 8001)
-│   │   ├── agents/         # LangGraph agents (port 2024)
+│   │   ├── gateway/        # Gateway API and LangGraph-compatible runtime (port 8001)
+│   │   ├── agents/         # LangGraph agent runtime used by Gateway
 │   │   ├── mcp/            # Model Context Protocol integration
 │   │   ├── skills/         # Skills system
 │   │   └── sandbox/        # Sandbox execution
@@ -256,8 +251,7 @@ Browser
  ↓
 Nginx (port 2026) ← Unified entry point
  ├→ Frontend (port 3000) ← / (non-API requests)
-  ├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
-  └→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
+  └→ Gateway API (port 8001) ← /api/* and /api/langgraph/* (LangGraph-compatible agent interactions)
 ```

 ## Development Workflow
@@ -245,6 +245,8 @@ make down   # Stop and remove containers

 Access: http://localhost:2026

+The unified nginx endpoint is same-origin by default and does not emit browser CORS headers. If you run a split-origin or port-forwarded browser client, set `GATEWAY_CORS_ORIGINS` to comma-separated exact origins such as `http://localhost:3000`; the Gateway then applies the CORS allowlist and matching CSRF origin checks.
+
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.

 #### Option 2: Local Development
@@ -626,7 +628,7 @@ See [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerfl

 Complex tasks rarely fit in a single pass. DeerFlow decomposes them.

-The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
+The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output. When token usage tracking is enabled, completed sub-agent usage is attributed back to the dispatching step.

 This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.

@@ -228,7 +228,7 @@ make down   # Stop and remove containers
 ```

 > [!NOTE]
-> Le serveur d'agents LangGraph fonctionne actuellement via `langgraph dev` (le serveur CLI open source).
+> Le runtime d'agent s'exécute actuellement dans la Gateway. nginx réécrit `/api/langgraph/*` vers l'API compatible LangGraph servie par la Gateway.

 Accès : http://localhost:2026

@@ -296,8 +296,8 @@ DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les ca

 ```yaml
 channels:
-  # LangGraph Server URL (default: http://localhost:2024)
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
+  langgraph_url: http://localhost:8001/api
  # Gateway API URL (default: http://localhost:8001)
  gateway_url: http://localhost:8001

@@ -181,7 +181,7 @@ make down   # コンテナを停止して削除
 ```

 > [!NOTE]
-> LangGraphエージェントサーバーは現在`langgraph dev`（オープンソースCLIサーバー）経由で実行されます。
+> Agentランタイムは現在Gateway内で実行されます。`/api/langgraph/*`はnginxによってGatewayのLangGraph-compatible APIへ書き換えられます。

 アクセス: http://localhost:2026

@@ -249,8 +249,8 @@ DeerFlowはメッセージングアプリからのタスク受信をサポート

 ```yaml
 channels:
-  # LangGraphサーバーURL（デフォルト: http://localhost:2024）
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL（デフォルト: http://localhost:8001/api）
+  langgraph_url: http://localhost:8001/api
  # Gateway API URL（デフォルト: http://localhost:8001）
  gateway_url: http://localhost:8001

@@ -184,7 +184,7 @@ make down   # 停止并移除容器
 ```

 > [!NOTE]
-> 当前 LangGraph agent server 通过开源 CLI 服务 `langgraph dev` 运行。
+> 当前 Agent 运行时嵌入在 Gateway 中运行，`/api/langgraph/*` 会由 nginx 重写到 Gateway 的 LangGraph-compatible API。

 访问地址：http://localhost:2026

@@ -254,8 +254,8 @@ DeerFlow 支持从即时通讯应用接收任务。只要配置完成，对应

 ```yaml
 channels:
-  # LangGraph Server URL（默认：http://localhost:2024）
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL（默认：http://localhost:8001/api）
+  langgraph_url: http://localhost:8001/api
  # Gateway API URL（默认：http://localhost:8001）
  gateway_url: http://localhost:8001

@@ -165,7 +165,7 @@ Lead-agent middlewares are assembled in strict append order across `packages/har
 8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
 9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
 10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
+11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
 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)
@@ -207,6 +207,8 @@ Configuration priority:

 FastAPI application on port 8001 with health check at `GET /health`. Set `GATEWAY_ENABLE_DOCS=false` to disable `/docs`, `/redoc`, and `/openapi.json` in production (default: enabled).

+CORS is same-origin by default when requests enter through nginx on port 2026. Split-origin or port-forwarded browser clients must opt in with `GATEWAY_CORS_ORIGINS` (comma-separated exact origins); Gateway `CORSMiddleware` and `CSRFMiddleware` both read that variable so browser CORS and auth-origin checks stay aligned.
+
 **Routers**:

 | Router | Endpoints |
@@ -223,7 +225,7 @@ FastAPI application on port 8001 with health check at `GET /health`. Set `GATEWA
 | **Feedback** (`/api/threads/{id}/runs/{rid}/feedback`) | `PUT /` - upsert feedback; `DELETE /` - delete user feedback; `POST /` - create feedback; `GET /` - list feedback; `GET /stats` - aggregate stats; `DELETE /{fid}` - delete specific |
 | **Runs** (`/api/runs`) | `POST /stream` - stateless run + SSE; `POST /wait` - stateless run + block; `GET /{rid}/messages` - paginated messages by run_id `{data, has_more}` (cursor: `after_seq`/`before_seq`); `GET /{rid}/feedback` - list feedback by run_id |

-Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
+Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runtime, all other `/api/*` → Gateway REST APIs.

 ### Sandbox System (`packages/harness/deerflow/sandbox/`)

@@ -243,7 +245,7 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 - `bash` - Execute commands with path translation and error handling
 - `ls` - Directory listing (tree format, max 2 levels)
 - `read_file` - Read file contents with optional line range
- `write_file` - Write/append to files, creates directories
+- `write_file` - Write/append to files, creates directories; overwrites by default and exposes the `append` argument in the model-facing schema for end-of-file writes
 - `str_replace` - Substring replacement (single or all occurrences); same-path serialization is scoped to `(sandbox.id, path)` so isolated sandboxes do not contend on identical virtual paths inside one process

 ### Subagent System (`packages/harness/deerflow/subagents/`)
@@ -263,8 +265,10 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
   - `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
   - `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
   - `view_image` - Read image as base64 (added only if model supports vision)
+   - `setup_agent` - Bootstrap-only: persist a brand-new custom agent's `SOUL.md` and `config.yaml`. Bound only when `is_bootstrap=True`.
+   - `update_agent` - Custom-agent-only: persist self-updates to the current agent's `SOUL.md` / `config.yaml` from inside a normal chat (partial update + atomic write). Bound when `agent_name` is set and `is_bootstrap=False`.
 4. **Subagent tool** (if enabled):
-   - `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
+   - `task` - Delegate to subagent (description, prompt, subagent_type)

 **Community tools** (`packages/harness/deerflow/community/`):
 - `tavily/` - Web search (5 results default) and web fetch (4KB limit)
@@ -354,10 +358,11 @@ Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the
 **Per-User Isolation**:
 - Memory is stored per-user at `{base_dir}/users/{user_id}/memory.json`
 - Per-agent per-user memory at `{base_dir}/users/{user_id}/agents/{agent_name}/memory.json`
+- Custom agent definitions (`SOUL.md` + `config.yaml`) are also per-user at `{base_dir}/users/{user_id}/agents/{agent_name}/`. The legacy shared layout `{base_dir}/agents/{agent_name}/` remains read-only fallback for unmigrated installations
 - `user_id` is resolved via `get_effective_user_id()` from `deerflow.runtime.user_context`
 - In no-auth mode, `user_id` defaults to `"default"` (constant `DEFAULT_USER_ID`)
 - Absolute `storage_path` in config opts out of per-user isolation
- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json` and `threads/` into per-user layout; supports `--dry-run`
+- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json`, `threads/`, and `agents/` into per-user layout. Supports `--dry-run` (preview changes) and `--user-id USER_ID` (assign unowned legacy data to a user, defaults to `default`).

 **Data Structure** (stored in `{base_dir}/users/{user_id}/memory.json`):
 - **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
@@ -517,6 +522,7 @@ Multi-file upload with automatic document conversion:
 - Rejects directory inputs before copying so uploads stay all-or-nothing
 - Reuses one conversion worker per request when called from an active event loop
 - Files stored in thread-isolated directories
+- Duplicate filenames in a single upload request are auto-renamed with `_N` suffixes so later files do not truncate earlier files
 - Agent receives uploaded file list via `UploadsMiddleware`

 See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.
@@ -56,11 +56,8 @@ export OPENAI_API_KEY="your-api-key"
 ### Run the Development Server

 ```bash
-# Terminal 1: LangGraph server
+# Gateway API + embedded agent runtime
 make dev
-
-# Terminal 2: Gateway API
-make gateway
 ```

 ## Project Structure
@@ -50,6 +50,12 @@ COPY backend ./backend
 RUN --mount=type=cache,target=/root/.cache/uv \
    sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync ${UV_EXTRAS:+--extra $UV_EXTRAS}"

+# UTF-8 locale prevents UnicodeEncodeError on Chinese/emoji content in minimal
+# containers where locale configuration may be missing and the default encoding is not UTF-8.
+ENV LANG=C.UTF-8
+ENV LC_ALL=C.UTF-8
+ENV PYTHONIOENCODING=utf-8
+
 # ── Stage 2: Dev ──────────────────────────────────────────────────────────────
 # Retains compiler toolchain from builder so startup-time `uv sync` can build
 # source distributions in development containers.
@@ -66,6 +72,10 @@ CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app
 # Clean image without build-essential — reduces size (~200 MB) and attack surface.
 FROM python:3.12-slim-bookworm

+ENV LANG=C.UTF-8
+ENV LC_ALL=C.UTF-8
+ENV PYTHONIOENCODING=utf-8
+
 # Copy Node.js runtime from builder (provides npx for MCP servers)
 COPY --from=builder /usr/bin/node /usr/bin/node
 COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
@@ -11,31 +11,26 @@ DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent
                        │          Nginx (Port 2026)           │
                        │      Unified reverse proxy           │
                        └───────┬──────────────────┬───────────┘
+                                │
+            /api/langgraph/*    │    /api/* (other)
+            rewritten to /api/* │
+                                ▼
+               ┌────────────────────────────────────────┐
+               │        Gateway API (8001)              │
+               │        FastAPI REST + agent runtime    │
               │                                        │
-              /api/langgraph/*  │                  │  /api/* (other)
-                                ▼                  ▼
-               ┌────────────────────┐  ┌────────────────────────┐
-               │ LangGraph Server   │  │   Gateway API (8001)   │
-               │    (Port 2024)     │  │   FastAPI REST         │
-               │                    │  │                        │
-               │ ┌────────────────┐ │  │ Models, MCP, Skills,   │
-               │ │  Lead Agent    │ │  │ Memory, Uploads,       │
-               │ │  ┌──────────┐  │ │  │ Artifacts              │
-               │ │  │Middleware│  │ │  └────────────────────────┘
-               │ │  │  Chain   │  │ │
-               │ │  └──────────┘  │ │
-               │ │  ┌──────────┐  │ │
-               │ │  │  Tools   │  │ │
-               │ │  └──────────┘  │ │
-               │ │  ┌──────────┐  │ │
-               │ │  │Subagents │  │ │
-               │ │  └──────────┘  │ │
-               │ └────────────────┘ │
-               └────────────────────┘
+               │ Models, MCP, Skills, Memory, Uploads,  │
+               │ Artifacts, Threads, Runs, Streaming    │
+               │                                        │
+               │ ┌────────────────────────────────────┐ │
+               │ │ Lead Agent                         │ │
+               │ │ Middleware Chain, Tools, Subagents │ │
+               │ └────────────────────────────────────┘ │
+               └────────────────────────────────────────┘
 ```

 **Request Routing** (via Nginx):
- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
+- `/api/langgraph/*` → Gateway LangGraph-compatible API - agent interactions, threads, streaming
 - `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup
 - `/` (non-API) → Frontend - Next.js web interface

@@ -79,7 +74,7 @@ Per-thread isolated execution with virtual path translation:
 - **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory
 - **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths
 - **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
+- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`write_file` overwrites by default and exposes `append` for end-of-file writes; `bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)

 ### Subagent System

@@ -124,7 +119,7 @@ FastAPI application providing REST endpoints for frontend integration:
 | `POST /api/memory/reload` | Force memory reload |
 | `GET /api/memory/config` | Memory configuration |
 | `GET /api/memory/status` | Combined config + data |
-| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
+| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths, auto-renames duplicate filenames in one request) |
 | `GET /api/threads/{id}/uploads/list` | List uploaded files |
 | `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
 | `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
@@ -193,7 +188,7 @@ export OPENAI_API_KEY="your-api-key-here"
 **Full Application** (from project root):

 ```bash
-make dev  # Starts LangGraph + Gateway + Frontend + Nginx
+make dev  # Starts Gateway + Frontend + Nginx
 ```

 Access at: http://localhost:2026
@@ -201,14 +196,11 @@ Access at: http://localhost:2026
 **Backend Only** (from backend directory):

 ```bash
-# Terminal 1: LangGraph server
+# Gateway API + embedded agent runtime
 make dev
-
-# Terminal 2: Gateway API
-make gateway
 ```

-Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
+Direct access: Gateway at http://localhost:8001

 ---

@@ -244,12 +236,16 @@ backend/
 │   └── utils/                  # Utilities
 ├── docs/                       # Documentation
 ├── tests/                      # Test suite
-├── langgraph.json              # LangGraph server configuration
+├── langgraph.json              # LangGraph graph registry for tooling/Studio compatibility
 ├── pyproject.toml              # Python dependencies
 ├── Makefile                    # Development commands
 └── Dockerfile                  # Container build
 ```

+`langgraph.json` is not the default service entrypoint.  The scripts and Docker
+deployments run the Gateway embedded runtime; the file is kept for LangGraph
+tooling, Studio, or direct LangGraph Server compatibility.
+
 ---

 ## Configuration
@@ -362,8 +358,8 @@ If a provider is explicitly enabled but required credentials are missing, or the

 ```bash
 make install    # Install dependencies
-make dev        # Run LangGraph server (port 2024)
-make gateway    # Run Gateway API (port 8001)
+make dev        # Run Gateway API + embedded agent runtime (port 8001)
+make gateway    # Run Gateway API without reload (port 8001)
 make lint       # Run linter (ruff)
 make format     # Format code (ruff)
 ```
@@ -3,8 +3,10 @@
 from __future__ import annotations

 import asyncio
+import json
 import logging
 import threading
+from pathlib import Path
 from typing import Any

 from app.channels.base import Channel
@@ -21,6 +23,12 @@ class DiscordChannel(Channel):
    Configuration keys (in ``config.yaml`` under ``channels.discord``):
        - ``bot_token``: Discord Bot token.
        - ``allowed_guilds``: (optional) List of allowed Discord guild IDs. Empty = allow all.
+        - ``mention_only``: (optional) If true, only respond when the bot is mentioned.
+        - ``allowed_channels``: (optional) List of channel IDs where messages are always accepted
+          (even when mention_only is true). Use for channels where you want the bot to respond
+          without mentions. Empty = mention_only applies everywhere.
+        - ``thread_mode``: (optional) If true, group a channel conversation into a thread.
+          Default: same as ``mention_only``.
    """

    def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -32,6 +40,29 @@ class DiscordChannel(Channel):
                self._allowed_guilds.add(int(guild_id))
            except (TypeError, ValueError):
                continue
+        self._mention_only: bool = bool(config.get("mention_only", False))
+        self._thread_mode: bool = config.get("thread_mode", self._mention_only)
+        self._allowed_channels: set[str] = set()
+        for channel_id in config.get("allowed_channels", []):
+            self._allowed_channels.add(str(channel_id))
+
+        # Session tracking: channel_id -> Discord thread_id (in-memory, persisted to JSON).
+        # Uses a dedicated JSON file separate from ChannelStore, which maps IM
+        # conversations to DeerFlow thread IDs — a different concern.
+        self._active_threads: dict[str, str] = {}
+        # Reverse-lookup set for O(1) thread ID checks (avoids O(n) scan of _active_threads.values()).
+        self._active_thread_ids: set[str] = set()
+        # Lock protecting _active_threads and the JSON file from concurrent access.
+        # _run_client (Discord loop thread) and the main thread both read/write.
+        self._thread_store_lock = threading.Lock()
+        store = config.get("channel_store")
+        if store is not None:
+            self._thread_store_path = store._path.parent / "discord_threads.json"
+        else:
+            self._thread_store_path = Path.home() / ".deer-flow" / "channels" / "discord_threads.json"
+
+        # Typing indicator management
+        self._typing_tasks: dict[str, asyncio.Task] = {}

        self._client = None
        self._thread: threading.Thread | None = None
@@ -75,12 +106,56 @@ class DiscordChannel(Channel):

        self._thread = threading.Thread(target=self._run_client, daemon=True)
        self._thread.start()
+        self._load_active_threads()
        logger.info("Discord channel started")

+    def _load_active_threads(self) -> None:
+        """Restore Discord thread mappings from the dedicated JSON file on startup."""
+        with self._thread_store_lock:
+            try:
+                if not self._thread_store_path.exists():
+                    logger.debug("[Discord] no thread mappings file at %s", self._thread_store_path)
+                    return
+                data = json.loads(self._thread_store_path.read_text())
+                self._active_threads.clear()
+                self._active_thread_ids.clear()
+                for channel_id, thread_id in data.items():
+                    self._active_threads[channel_id] = thread_id
+                    self._active_thread_ids.add(thread_id)
+                if self._active_threads:
+                    logger.info("[Discord] restored %d thread mappings from %s", len(self._active_threads), self._thread_store_path)
+            except Exception:
+                logger.exception("[Discord] failed to load thread mappings")
+
+    def _save_thread(self, channel_id: str, thread_id: str) -> None:
+        """Persist a Discord thread mapping to the dedicated JSON file."""
+        with self._thread_store_lock:
+            try:
+                data: dict[str, str] = {}
+                if self._thread_store_path.exists():
+                    data = json.loads(self._thread_store_path.read_text())
+                old_id = data.get(channel_id)
+                data[channel_id] = thread_id
+                # Update reverse-lookup set
+                if old_id:
+                    self._active_thread_ids.discard(old_id)
+                self._active_thread_ids.add(thread_id)
+                self._thread_store_path.parent.mkdir(parents=True, exist_ok=True)
+                self._thread_store_path.write_text(json.dumps(data, indent=2))
+            except Exception:
+                logger.exception("[Discord] failed to save thread mapping for channel %s", channel_id)
+
    async def stop(self) -> None:
        self._running = False
        self.bus.unsubscribe_outbound(self._on_outbound)

+        # Cancel all active typing indicator tasks
+        for target_id, task in list(self._typing_tasks.items()):
+            if not task.done():
+                task.cancel()
+            logger.debug("[Discord] cancelled typing task for target %s", target_id)
+        self._typing_tasks.clear()
+
        if self._client and self._discord_loop and self._discord_loop.is_running():
            close_future = asyncio.run_coroutine_threadsafe(self._client.close(), self._discord_loop)
            try:
@@ -100,6 +175,10 @@ class DiscordChannel(Channel):
        logger.info("Discord channel stopped")

    async def send(self, msg: OutboundMessage) -> None:
+        # Stop typing indicator once we're sending the response
+        stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
+        await asyncio.wrap_future(stop_future)
+
        target = await self._resolve_target(msg)
        if target is None:
            logger.error("[Discord] target not found for chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
@@ -111,6 +190,9 @@ class DiscordChannel(Channel):
            await asyncio.wrap_future(send_future)

    async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
+        stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
+        await asyncio.wrap_future(stop_future)
+
        target = await self._resolve_target(msg)
        if target is None:
            logger.error("[Discord] target not found for file upload chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
@@ -130,6 +212,41 @@ class DiscordChannel(Channel):
            logger.exception("[Discord] failed to upload file: %s", attachment.filename)
            return False

+    async def _start_typing(self, channel, chat_id: str, thread_ts: str | None = None) -> None:
+        """Starts a loop to send periodic typing indicators."""
+        target_id = thread_ts or chat_id
+        if target_id in self._typing_tasks:
+            return  # Already typing for this target
+
+        async def _typing_loop():
+            try:
+                while True:
+                    try:
+                        await channel.trigger_typing()
+                    except Exception:
+                        pass
+                    await asyncio.sleep(10)
+            except asyncio.CancelledError:
+                pass
+
+        task = asyncio.create_task(_typing_loop())
+        self._typing_tasks[target_id] = task
+
+    async def _stop_typing(self, chat_id: str, thread_ts: str | None = None) -> None:
+        """Stops the typing loop for a specific target."""
+        target_id = thread_ts or chat_id
+        task = self._typing_tasks.pop(target_id, None)
+        if task and not task.done():
+            task.cancel()
+            logger.debug("[Discord] stopped typing indicator for target %s", target_id)
+
+    async def _add_reaction(self, message) -> None:
+        """Add a checkmark reaction to acknowledge the message was received."""
+        try:
+            await message.add_reaction("✅")
+        except Exception:
+            logger.debug("[Discord] failed to add reaction to message %s", message.id, exc_info=True)
+
    async def _on_message(self, message) -> None:
        if not self._running or not self._client:
            return
@@ -152,15 +269,143 @@ class DiscordChannel(Channel):
        if self._discord_module is None:
            return

-        if isinstance(message.channel, self._discord_module.Thread):
-            chat_id = str(message.channel.parent_id or message.channel.id)
-            thread_id = str(message.channel.id)
+        # Determine whether the bot is mentioned in this message
+        user = self._client.user if self._client else None
+        if user:
+            bot_mention = user.mention  # <@ID>
+            alt_mention = f"<@!{user.id}>"  # <@!ID> (ping variant)
+            standard_mention = f"<@{user.id}>"
        else:
-            thread = await self._create_thread(message)
-            if thread is None:
+            bot_mention = None
+            alt_mention = None
+            standard_mention = ""
+        has_mention = (bot_mention and bot_mention in message.content) or (alt_mention and alt_mention in message.content) or (standard_mention and standard_mention in message.content)
+
+        # Strip mention from text for processing
+        if has_mention:
+            text = text.replace(bot_mention or "", "").replace(alt_mention or "", "").replace(standard_mention or "", "").strip()
+            # Don't return early if text is empty — still process the mention (e.g., create thread)
+
+        # --- Determine thread/channel routing and typing target ---
+        thread_id = None
+        chat_id = None
+        typing_target = None  # The Discord object to type into
+
+        if isinstance(message.channel, self._discord_module.Thread):
+            # --- Message already inside a thread ---
+            thread_obj = message.channel
+            thread_id = str(thread_obj.id)
+            chat_id = str(thread_obj.parent_id or thread_obj.id)
+            typing_target = thread_obj
+
+            # If this is a known active thread, process normally
+            if thread_id in self._active_thread_ids:
+                msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
+                inbound = self._make_inbound(
+                    chat_id=chat_id,
+                    user_id=str(message.author.id),
+                    text=text,
+                    msg_type=msg_type,
+                    thread_ts=thread_id,
+                    metadata={
+                        "guild_id": str(guild.id) if guild else None,
+                        "channel_id": str(message.channel.id),
+                        "message_id": str(message.id),
+                    },
+                )
+                inbound.topic_id = thread_id
+                self._publish(inbound)
+                # Start typing indicator in the thread
+                if typing_target:
+                    asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
+                asyncio.create_task(self._add_reaction(message))
                return
-            chat_id = str(message.channel.id)
-            thread_id = str(thread.id)
+
+            # Thread not tracked (orphaned) — create new thread and handle below
+            logger.debug("[Discord] message in orphaned thread %s, will create new thread", thread_id)
+            thread_id = None
+            typing_target = None
+
+        # At this point we're guaranteed to be in a channel, not a thread
+        # (the Thread case is handled above). Apply mention_only for all
+        # non-thread messages — no special case needed.
+        channel_id = str(message.channel.id)
+
+        # Check if there's an active thread for this channel
+        if channel_id in self._active_threads:
+            # respect mention_only: if enabled, only process messages that mention the bot
+            # (unless the channel is in allowed_channels)
+            # Messages within a thread are always allowed through (continuation).
+            # At this code point we know the message is in a channel, not a thread
+            # (Thread case handled above), so always apply the check.
+            if self._mention_only and not has_mention and channel_id not in self._allowed_channels:
+                logger.debug("[Discord] skipping no-@ message in channel %s (not in thread)", channel_id)
+                return
+            # mention_only + fresh @ → create new thread instead of routing to existing one
+            if self._mention_only and has_mention:
+                thread_obj = await self._create_thread(message)
+                if thread_obj is not None:
+                    target_thread_id = str(thread_obj.id)
+                    self._active_threads[channel_id] = target_thread_id
+                    self._save_thread(channel_id, target_thread_id)
+                    thread_id = target_thread_id
+                    chat_id = channel_id
+                    typing_target = thread_obj
+                    logger.info("[Discord] created new thread %s in channel %s on mention (replacing existing thread)", target_thread_id, channel_id)
+                else:
+                    logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
+                    thread_id = channel_id
+                    chat_id = channel_id
+                    typing_target = message.channel
+            else:
+                # Existing session → route to the existing thread
+                target_thread_id = self._active_threads[channel_id]
+                logger.debug("[Discord] routing message in channel %s to existing thread %s", channel_id, target_thread_id)
+                thread_id = target_thread_id
+                chat_id = channel_id
+                typing_target = await self._get_channel_or_thread(target_thread_id)
+        elif self._mention_only and not has_mention and channel_id not in self._allowed_channels:
+            # Not mentioned and not in an allowed channel → skip
+            logger.debug("[Discord] skipping message without mention in channel %s", channel_id)
+            return
+        elif self._mention_only and has_mention:
+            # First mention in this channel → create thread
+            thread_obj = await self._create_thread(message)
+            if thread_obj is not None:
+                target_thread_id = str(thread_obj.id)
+                self._active_threads[channel_id] = target_thread_id
+                self._save_thread(channel_id, target_thread_id)
+                thread_id = target_thread_id
+                chat_id = channel_id
+                typing_target = thread_obj  # Type into the new thread
+                logger.info("[Discord] created thread %s in channel %s for user %s", target_thread_id, channel_id, message.author.display_name)
+            else:
+                # Fallback: thread creation failed (disabled/permissions), reply in channel
+                logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
+                thread_id = channel_id
+                chat_id = channel_id
+                typing_target = message.channel  # Type into the channel
+        elif self._thread_mode:
+            # thread_mode but mention_only is False → create thread anyway for conversation grouping
+            thread_obj = await self._create_thread(message)
+            if thread_obj is None:
+                # Thread creation failed (disabled/permissions), fall back to channel replies
+                logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
+                thread_id = channel_id
+                chat_id = channel_id
+                typing_target = message.channel  # Type into the channel
+            else:
+                target_thread_id = str(thread_obj.id)
+                self._active_threads[channel_id] = target_thread_id
+                self._save_thread(channel_id, target_thread_id)
+                thread_id = target_thread_id
+                chat_id = channel_id
+                typing_target = thread_obj  # Type into the new thread
+        else:
+            # No threading — reply directly in channel
+            thread_id = channel_id
+            chat_id = channel_id
+            typing_target = message.channel  # Type into the channel

        msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
        inbound = self._make_inbound(
@@ -177,6 +422,15 @@ class DiscordChannel(Channel):
        )
        inbound.topic_id = thread_id

+        # Start typing indicator in the correct target (thread or channel)
+        if typing_target:
+            asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
+
+        self._publish(inbound)
+        asyncio.create_task(self._add_reaction(message))
+
+    def _publish(self, inbound) -> None:
+        """Publish an inbound message to the main event loop."""
        if self._main_loop and self._main_loop.is_running():
            future = asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._main_loop)
            future.add_done_callback(lambda f: logger.exception("[Discord] publish_inbound failed", exc_info=f.exception()) if f.exception() else None)
@@ -198,14 +452,40 @@ class DiscordChannel(Channel):

    async def _create_thread(self, message):
        try:
+            if self._discord_module is None:
+                return None
+
+            # Only TextChannel (type 0) and NewsChannel (type 10) support threads
+            channel_type = message.channel.type
+            if channel_type not in (
+                self._discord_module.ChannelType.text,
+                self._discord_module.ChannelType.news,
+            ):
+                logger.info(
+                    "[Discord] channel type %s (%s) does not support threads",
+                    channel_type.value,
+                    channel_type.name,
+                )
+                return None
+
            thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
            return await message.create_thread(name=thread_name)
+        except self._discord_module.errors.HTTPException as exc:
+            if exc.code == 50024:
+                logger.info(
+                    "[Discord] cannot create thread in channel %s (error code 50024): %s",
+                    message.channel.id,
+                    channel_type.name if (channel_type := message.channel.type) else "unknown",
+                )
+            else:
+                logger.exception(
+                    "[Discord] failed to create thread for message=%s (HTTPException %s)",
+                    message.id,
+                    exc.code,
+                )
+            return None
        except Exception:
            logger.exception("[Discord] failed to create thread for message=%s (threads may be disabled or missing permissions)", message.id)
-            try:
-                await message.channel.send("Could not create a thread for your message. Please check that threads are enabled in this channel.")
-            except Exception:
-                pass
            return None

    async def _resolve_target(self, msg: OutboundMessage):
@@ -146,6 +146,13 @@ def _normalize_custom_agent_name(raw_value: str) -> str:
    return normalized


+def _strip_loop_warning_text(text: str) -> str:
+    """Remove middleware-authored loop warning lines from display text."""
+    if "[LOOP DETECTED]" not in text:
+        return text
+    return "\n".join(line for line in text.splitlines() if "[LOOP DETECTED]" not in line).strip()
+
+
 def _extract_response_text(result: dict | list) -> str:
    """Extract the last AI message text from a LangGraph runs.wait result.

@@ -155,7 +162,7 @@ def _extract_response_text(result: dict | list) -> str:
    Handles special cases:
    - Regular AI text responses
    - Clarification interrupts (``ask_clarification`` tool messages)
-    - AI messages with tool_calls but no text content
+    - Strips loop-detection warnings attached to tool-call AI messages
    """
    if isinstance(result, list):
        messages = result
@@ -185,7 +192,12 @@ def _extract_response_text(result: dict | list) -> str:
        # Regular AI message with text content
        if msg_type == "ai":
            content = msg.get("content", "")
+            has_tool_calls = bool(msg.get("tool_calls"))
            if isinstance(content, str) and content:
+                if has_tool_calls:
+                    content = _strip_loop_warning_text(content)
+                    if not content:
+                        continue
                return content
            # content can be a list of content blocks
            if isinstance(content, list):
@@ -196,6 +208,8 @@ def _extract_response_text(result: dict | list) -> str:
                    elif isinstance(block, str):
                        parts.append(block)
                text = "".join(parts)
+                if has_tool_calls:
+                    text = _strip_loop_warning_text(text)
                if text:
                    return text
    return ""
@@ -420,7 +434,13 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
    if not msg.files:
        return []

-    from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
+    from deerflow.uploads.manager import (
+        UnsafeUploadPathError,
+        claim_unique_filename,
+        ensure_uploads_dir,
+        normalize_filename,
+        write_upload_file_no_symlink,
+    )

    uploads_dir = ensure_uploads_dir(thread_id)
    seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
@@ -471,7 +491,10 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic

            dest = uploads_dir / safe_name
            try:
-                dest.write_bytes(data)
+                dest = write_upload_file_no_symlink(uploads_dir, safe_name, data)
+            except UnsafeUploadPathError:
+                logger.warning("[Manager] skipping inbound file with unsafe destination: %s", safe_name)
+                continue
            except Exception:
                logger.exception("[Manager] failed to write inbound file: %s", dest)
                continue
@@ -580,6 +603,17 @@ class ChannelManager:
            user_layer.get("config"),
        )

+        configurable = run_config.get("configurable")
+        if isinstance(configurable, Mapping):
+            configurable = dict(configurable)
+        else:
+            configurable = {}
+        run_config["configurable"] = configurable
+        # Pin channel-triggered runs to the root graph namespace so follow-up
+        # turns continue from the same conversation checkpoint.
+        configurable["checkpoint_ns"] = ""
+        configurable["thread_id"] = thread_id
+
        run_context = _merge_dicts(
            DEFAULT_RUN_CONTEXT,
            self._default_session.get("context"),
@@ -753,13 +787,22 @@ class ChannelManager:
            return

        logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
+        try:
            result = await client.runs.wait(
                thread_id,
                assistant_id,
                input={"messages": [{"role": "human", "content": msg.text}]},
                config=run_config,
                context=run_context,
+                multitask_strategy="reject",
            )
+        except Exception as exc:
+            if _is_thread_busy_error(exc):
+                logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
+                await self._send_error(msg, THREAD_BUSY_MESSAGE)
+                return
+            else:
+                raise

        response_text = _extract_response_text(result)
        artifacts = _extract_artifacts(result)
@@ -963,7 +1006,11 @@ class ChannelManager:

        try:
            async with httpx.AsyncClient() as http:
-                resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
+                resp = await http.get(
+                    f"{self._gateway_url}{path}",
+                    timeout=10,
+                    headers=create_internal_auth_headers(),
+                )
                resp.raise_for_status()
                data = resp.json()
        except Exception:
@@ -167,6 +167,8 @@ class ChannelService:
            return False

        try:
+            config = dict(config)
+            config["channel_store"] = self.store
            channel = channel_cls(bus=self.bus, config=config)
            self._channels[name] = channel
            await channel.start()
@@ -1,6 +1,5 @@
 import asyncio
 import logging
-import os
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager

@@ -9,7 +8,7 @@ from fastapi.middleware.cors import CORSMiddleware

 from app.gateway.auth_middleware import AuthMiddleware
 from app.gateway.config import get_gateway_config
-from app.gateway.csrf_middleware import CSRFMiddleware
+from app.gateway.csrf_middleware import CSRFMiddleware, get_configured_cors_origins
 from app.gateway.deps import langgraph_runtime
 from app.gateway.routers import (
    agents,
@@ -63,7 +62,7 @@ async def _ensure_admin_user(app: FastAPI) -> None:

    Subsequent boots (admin already exists):
      - Runs the one-time "no-auth → with-auth" orphan thread migration for
-        existing LangGraph thread metadata that has no owner_id.
+        existing LangGraph thread metadata that has no user_id.

    No SQL persistence migration is needed: the four user_id columns
    (threads_meta, runs, run_events, feedback) only come into existence
@@ -178,7 +177,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
    async with langgraph_runtime(app):
        logger.info("LangGraph runtime initialised")

-        # Ensure admin user exists (auto-create on first boot)
+        # Check admin bootstrap state and migrate orphan threads after admin exists.
        # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
        await _ensure_admin_user(app)

@@ -219,7 +218,9 @@ def create_app() -> FastAPI:
        Configured FastAPI application instance.
    """
    config = get_gateway_config()
-    docs_kwargs = {"docs_url": "/docs", "redoc_url": "/redoc", "openapi_url": "/openapi.json"} if config.enable_docs else {"docs_url": None, "redoc_url": None, "openapi_url": None}
+    docs_url = "/docs" if config.enable_docs else None
+    redoc_url = "/redoc" if config.enable_docs else None
+    openapi_url = "/openapi.json" if config.enable_docs else None

    app = FastAPI(
        title="DeerFlow API Gateway",
@@ -239,12 +240,14 @@ API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execu

 ### Architecture

-LangGraph requests are handled by nginx reverse proxy.
-This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
+LangGraph-compatible requests are routed through nginx to this gateway.
+This gateway provides runtime endpoints for agent runs plus custom endpoints for models, MCP configuration, skills, and artifacts.
        """,
        version="0.1.0",
        lifespan=lifespan,
-        **docs_kwargs,
+        docs_url=docs_url,
+        redoc_url=redoc_url,
+        openapi_url=openapi_url,
        openapi_tags=[
            {
                "name": "models",
@@ -307,17 +310,10 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
    # CSRF: Double Submit Cookie pattern for state-changing requests
    app.add_middleware(CSRFMiddleware)

-    # CORS: when GATEWAY_CORS_ORIGINS is set (dev without nginx), add CORS middleware.
-    # In production, nginx handles CORS and no middleware is needed.
-    cors_origins_env = os.environ.get("GATEWAY_CORS_ORIGINS", "")
-    if cors_origins_env:
-        cors_origins = [o.strip() for o in cors_origins_env.split(",") if o.strip()]
-        # Validate: wildcard origin with credentials is a security misconfiguration
-        for origin in cors_origins:
-            if origin == "*":
-                logger.error("GATEWAY_CORS_ORIGINS contains wildcard '*' with allow_credentials=True. This is a security misconfiguration — browsers will reject the response. Use explicit scheme://host:port origins instead.")
-                cors_origins = [o for o in cors_origins if o != "*"]
-                break
+    # CORS: the unified nginx endpoint is same-origin by default. Split-origin
+    # browser clients must opt in with this explicit Gateway allowlist so CORS
+    # and CSRF origin checks share the same source of truth.
+    cors_origins = sorted(get_configured_cors_origins())
    if cors_origins:
        app.add_middleware(
            CORSMiddleware,
@@ -374,7 +370,7 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
    app.include_router(runs.router)

    @app.get("/health", tags=["health"])
-    async def health_check() -> dict:
+    async def health_check() -> dict[str, str]:
        """Health check endpoint.

        Returns:
@@ -8,6 +8,8 @@ from pydantic import BaseModel, Field

 logger = logging.getLogger(__name__)

+_SECRET_FILE = ".jwt_secret"
+

 class AuthConfig(BaseModel):
    """JWT and auth-related configuration. Parsed once at startup.
@@ -30,6 +32,32 @@ class AuthConfig(BaseModel):
 _auth_config: AuthConfig | None = None


+def _load_or_create_secret() -> str:
+    """Load persisted JWT secret from ``{base_dir}/.jwt_secret``, or generate and persist a new one."""
+    from deerflow.config.paths import get_paths
+
+    paths = get_paths()
+    secret_file = paths.base_dir / _SECRET_FILE
+
+    try:
+        if secret_file.exists():
+            secret = secret_file.read_text(encoding="utf-8").strip()
+            if secret:
+                return secret
+    except OSError as exc:
+        raise RuntimeError(f"Failed to read JWT secret from {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can read its persisted auth secret.") from exc
+
+    secret = secrets.token_urlsafe(32)
+    try:
+        secret_file.parent.mkdir(parents=True, exist_ok=True)
+        fd = os.open(secret_file, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
+        with os.fdopen(fd, "w", encoding="utf-8") as fh:
+            fh.write(secret)
+    except OSError as exc:
+        raise RuntimeError(f"Failed to persist JWT secret to {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can store a stable auth secret.") from exc
+    return secret
+
+
 def get_auth_config() -> AuthConfig:
    """Get the global AuthConfig instance. Parses from env on first call."""
    global _auth_config
@@ -39,11 +67,11 @@ def get_auth_config() -> AuthConfig:
        load_dotenv()
        jwt_secret = os.environ.get("AUTH_JWT_SECRET")
        if not jwt_secret:
-            jwt_secret = secrets.token_urlsafe(32)
+            jwt_secret = _load_or_create_secret()
            os.environ["AUTH_JWT_SECRET"] = jwt_secret
            logger.warning(
-                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated ephemeral secret. "
-                "Sessions will be invalidated on restart. "
+                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated secret "
+                "persisted to .jwt_secret. Sessions will survive restarts. "
                "For production, add AUTH_JWT_SECRET to your .env file: "
                'python -c "import secrets; print(secrets.token_urlsafe(32))"'
            )
@@ -28,7 +28,7 @@ class User(BaseModel):
    oauth_id: str | None = Field(None, description="User ID from OAuth provider")

    # Auth lifecycle
-    needs_setup: bool = Field(default=False, description="True for auto-created admin until setup completes")
+    needs_setup: bool = Field(default=False, description="True when a reset account must complete setup")
    token_version: int = Field(default=0, description="Incremented on password change to invalidate old JWTs")


@@ -8,7 +8,6 @@ class GatewayConfig(BaseModel):

    host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
    port: int = Field(default=8001, description="Port to bind the gateway server")
-    cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
    enable_docs: bool = Field(default=True, description="Enable Swagger/ReDoc/OpenAPI endpoints")


@@ -19,11 +18,9 @@ def get_gateway_config() -> GatewayConfig:
    """Get gateway config, loading from environment if available."""
    global _gateway_config
    if _gateway_config is None:
-        cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
        _gateway_config = GatewayConfig(
            host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
            port=int(os.getenv("GATEWAY_PORT", "8001")),
-            cors_origins=cors_origins_str.split(","),
            enable_docs=os.getenv("GATEWAY_ENABLE_DOCS", "true").lower() == "true",
        )
    return _gateway_config
@@ -4,8 +4,10 @@ Per RFC-001:
 State-changing operations require CSRF protection.
 """

+import os
 import secrets
-from collections.abc import Callable
+from collections.abc import Awaitable, Callable
+from urllib.parse import urlsplit

 from fastapi import Request, Response
 from starlette.middleware.base import BaseHTTPMiddleware
@@ -19,7 +21,7 @@ CSRF_TOKEN_LENGTH = 64  # bytes

 def is_secure_request(request: Request) -> bool:
    """Detect whether the original client request was made over HTTPS."""
-    return request.headers.get("x-forwarded-proto", request.url.scheme) == "https"
+    return _request_scheme(request) == "https"


 def generate_csrf_token() -> str:
@@ -61,15 +63,129 @@ def is_auth_endpoint(request: Request) -> bool:
    return request.url.path.rstrip("/") in _AUTH_EXEMPT_PATHS


+def _host_with_optional_port(hostname: str, port: int | None, scheme: str) -> str:
+    """Return normalized host[:port], omitting default ports."""
+    host = hostname.lower()
+    if ":" in host and not host.startswith("["):
+        host = f"[{host}]"
+
+    if port is None or (scheme == "http" and port == 80) or (scheme == "https" and port == 443):
+        return host
+    return f"{host}:{port}"
+
+
+def _normalize_origin(origin: str) -> str | None:
+    """Return a normalized scheme://host[:port] origin, or None for invalid input."""
+    try:
+        parsed = urlsplit(origin.strip())
+        port = parsed.port
+    except ValueError:
+        return None
+
+    scheme = parsed.scheme.lower()
+    if scheme not in {"http", "https"} or not parsed.hostname:
+        return None
+
+    # Browser Origin is only scheme/host/port. Reject URL-shaped or credentialed values.
+    if parsed.username or parsed.password or parsed.path or parsed.query or parsed.fragment:
+        return None
+
+    return f"{scheme}://{_host_with_optional_port(parsed.hostname, port, scheme)}"
+
+
+def _configured_cors_origins() -> set[str]:
+    """Return explicit configured browser origins that may call auth routes."""
+    origins = set()
+    for raw_origin in os.environ.get("GATEWAY_CORS_ORIGINS", "").split(","):
+        origin = raw_origin.strip()
+        if not origin or origin == "*":
+            continue
+        normalized = _normalize_origin(origin)
+        if normalized:
+            origins.add(normalized)
+    return origins
+
+
+def get_configured_cors_origins() -> set[str]:
+    """Return normalized explicit browser origins from GATEWAY_CORS_ORIGINS."""
+    return _configured_cors_origins()
+
+
+def _first_header_value(value: str | None) -> str | None:
+    """Return the first value from a comma-separated proxy header."""
+    if not value:
+        return None
+    first = value.split(",", 1)[0].strip()
+    return first or None
+
+
+def _forwarded_param(request: Request, name: str) -> str | None:
+    """Extract a parameter from the first RFC 7239 Forwarded header entry."""
+    forwarded = _first_header_value(request.headers.get("forwarded"))
+    if not forwarded:
+        return None
+
+    for part in forwarded.split(";"):
+        key, sep, value = part.strip().partition("=")
+        if sep and key.lower() == name:
+            return value.strip().strip('"') or None
+    return None
+
+
+def _request_scheme(request: Request) -> str:
+    """Resolve the original request scheme from trusted proxy headers."""
+    scheme = _forwarded_param(request, "proto") or _first_header_value(request.headers.get("x-forwarded-proto")) or request.url.scheme
+    return scheme.lower()
+
+
+def _request_origin(request: Request) -> str | None:
+    """Build the origin for the URL the browser is targeting."""
+    scheme = _request_scheme(request)
+    host = _forwarded_param(request, "host") or _first_header_value(request.headers.get("x-forwarded-host")) or request.headers.get("host") or request.url.netloc
+
+    forwarded_port = _first_header_value(request.headers.get("x-forwarded-port"))
+    if forwarded_port and ":" not in host.rsplit("]", 1)[-1]:
+        host = f"{host}:{forwarded_port}"
+
+    return _normalize_origin(f"{scheme}://{host}")
+
+
+def is_allowed_auth_origin(request: Request) -> bool:
+    """Allow auth POSTs only from the same origin or explicit configured origins.
+
+    Login/register/initialize are exempt from the double-submit token because
+    first-time browser clients do not have a CSRF token yet. They still create
+    a session cookie, so browser requests with a hostile Origin header must be
+    rejected to prevent login CSRF / session fixation. Requests without Origin
+    are allowed for non-browser clients such as curl and mobile integrations.
+    """
+    origin = request.headers.get("origin")
+    if not origin:
+        return True
+
+    normalized_origin = _normalize_origin(origin)
+    if normalized_origin is None:
+        return False
+
+    request_origin = _request_origin(request)
+    return normalized_origin in _configured_cors_origins() or (request_origin is not None and normalized_origin == request_origin)
+
+
 class CSRFMiddleware(BaseHTTPMiddleware):
    """Middleware that implements CSRF protection using Double Submit Cookie pattern."""

    def __init__(self, app: ASGIApp) -> None:
        super().__init__(app)

-    async def dispatch(self, request: Request, call_next: Callable) -> Response:
+    async def dispatch(self, request: Request, call_next: Callable[[Request], Awaitable[Response]]) -> Response:
        _is_auth = is_auth_endpoint(request)

+        if should_check_csrf(request) and _is_auth and not is_allowed_auth_origin(request):
+            return JSONResponse(
+                status_code=403,
+                content={"detail": "Cross-site auth request denied."},
+            )
+
        if should_check_csrf(request) and not _is_auth:
            cookie_token = request.cookies.get(CSRF_COOKIE_NAME)
            header_token = request.headers.get(CSRF_HEADER_NAME)
@@ -1,8 +1,12 @@
-"""LangGraph Server auth handler — shares JWT logic with Gateway.
+"""LangGraph compatibility auth handler — shares JWT logic with Gateway.

-Loaded by LangGraph Server via langgraph.json ``auth.path``.
-Reuses the same ``decode_token`` / ``get_auth_config`` as Gateway,
-so both modes validate tokens with the same secret and rules.
+The default DeerFlow runtime is embedded in the FastAPI Gateway; scripts and
+Docker deployments do not load this module.  It is retained for LangGraph
+tooling, Studio, or direct LangGraph Server compatibility through
+``langgraph.json``'s ``auth.path``.
+
+When that compatibility path is used, this module reuses the same JWT and CSRF
+rules as Gateway so both modes validate sessions consistently.

 Two layers:
  1. @auth.authenticate — validates JWT cookie, extracts user_id,
@@ -11,6 +11,7 @@ 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
+from deerflow.runtime.user_context import get_effective_user_id

 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["agents"])
@@ -86,11 +87,11 @@ def _require_agents_api_enabled() -> None:
        )


-def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
+def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False, *, user_id: str | None = None) -> AgentResponse:
    """Convert AgentConfig to AgentResponse."""
    soul: str | None = None
    if include_soul:
-        soul = load_agent_soul(agent_cfg.name) or ""
+        soul = load_agent_soul(agent_cfg.name, user_id=user_id) or ""

    return AgentResponse(
        name=agent_cfg.name,
@@ -116,9 +117,10 @@ async def list_agents() -> AgentsListResponse:
    """
    _require_agents_api_enabled()

+    user_id = get_effective_user_id()
    try:
-        agents = list_custom_agents()
-        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
+        agents = list_custom_agents(user_id=user_id)
+        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True, user_id=user_id) for a in agents])
    except Exception as e:
        logger.error(f"Failed to list agents: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Failed to list agents: {str(e)}")
@@ -144,7 +146,12 @@ async def check_agent_name(name: str) -> dict:
    _require_agents_api_enabled()
    _validate_agent_name(name)
    normalized = _normalize_agent_name(name)
-    available = not get_paths().agent_dir(normalized).exists()
+    user_id = get_effective_user_id()
+    paths = get_paths()
+    # Treat the name as taken if either the per-user path or the legacy shared
+    # path holds an agent — picking a name that collides with an unmigrated
+    # legacy agent would shadow the legacy entry once migration runs.
+    available = not paths.user_agent_dir(user_id, normalized).exists() and not paths.agent_dir(normalized).exists()
    return {"available": available, "name": normalized}


@@ -169,10 +176,11 @@ async def get_agent(name: str) -> AgentResponse:
    _require_agents_api_enabled()
    _validate_agent_name(name)
    name = _normalize_agent_name(name)
+    user_id = get_effective_user_id()

    try:
-        agent_cfg = load_agent_config(name)
-        return _agent_config_to_response(agent_cfg, include_soul=True)
+        agent_cfg = load_agent_config(name, user_id=user_id)
+        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
    except Exception as e:
@@ -202,10 +210,13 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
    _require_agents_api_enabled()
    _validate_agent_name(request.name)
    normalized_name = _normalize_agent_name(request.name)
+    user_id = get_effective_user_id()
+    paths = get_paths()

-    agent_dir = get_paths().agent_dir(normalized_name)
+    agent_dir = paths.user_agent_dir(user_id, normalized_name)
+    legacy_dir = paths.agent_dir(normalized_name)

-    if agent_dir.exists():
+    if agent_dir.exists() or legacy_dir.exists():
        raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")

    try:
@@ -232,8 +243,8 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:

        logger.info(f"Created agent '{normalized_name}' at {agent_dir}")

-        agent_cfg = load_agent_config(normalized_name)
-        return _agent_config_to_response(agent_cfg, include_soul=True)
+        agent_cfg = load_agent_config(normalized_name, user_id=user_id)
+        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)

    except HTTPException:
        raise
@@ -267,13 +278,20 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
    _require_agents_api_enabled()
    _validate_agent_name(name)
    name = _normalize_agent_name(name)
+    user_id = get_effective_user_id()

    try:
-        agent_cfg = load_agent_config(name)
+        agent_cfg = load_agent_config(name, user_id=user_id)
    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")

-    agent_dir = get_paths().agent_dir(name)
+    paths = get_paths()
+    agent_dir = paths.user_agent_dir(user_id, name)
+    if not agent_dir.exists() and paths.agent_dir(name).exists():
+        raise HTTPException(
+            status_code=409,
+            detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating."),
+        )

    try:
        # Update config if any config fields changed
@@ -314,8 +332,8 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:

        logger.info(f"Updated agent '{name}'")

-        refreshed_cfg = load_agent_config(name)
-        return _agent_config_to_response(refreshed_cfg, include_soul=True)
+        refreshed_cfg = load_agent_config(name, user_id=user_id)
+        return _agent_config_to_response(refreshed_cfg, include_soul=True, user_id=user_id)

    except HTTPException:
        raise
@@ -402,15 +420,22 @@ async def delete_agent(name: str) -> None:
        name: The agent name.

    Raises:
-        HTTPException: 404 if agent not found.
+        HTTPException: 404 if no per-user copy exists; 409 if only a legacy
+            shared copy exists (suggesting the migration script).
    """
    _require_agents_api_enabled()
    _validate_agent_name(name)
    name = _normalize_agent_name(name)
-
-    agent_dir = get_paths().agent_dir(name)
+    user_id = get_effective_user_id()
+    paths = get_paths()
+    agent_dir = paths.user_agent_dir(user_id, name)

    if not agent_dir.exists():
+        if paths.agent_dir(name).exists():
+            raise HTTPException(
+                status_code=409,
+                detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
+            )
        raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")

    try:
@@ -20,6 +20,9 @@ ACTIVE_CONTENT_MIME_TYPES = {
    "image/svg+xml",
 }

+MAX_SKILL_ARCHIVE_MEMBER_BYTES = 16 * 1024 * 1024
+_SKILL_ARCHIVE_READ_CHUNK_SIZE = 64 * 1024
+

 def _build_content_disposition(disposition_type: str, filename: str) -> str:
    """Build an RFC 5987 encoded Content-Disposition header value."""
@@ -44,6 +47,22 @@ def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
        return False


+def _read_skill_archive_member(zip_ref: zipfile.ZipFile, info: zipfile.ZipInfo) -> bytes:
+    """Read a .skill archive member while enforcing an uncompressed size cap."""
+    if info.file_size > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
+        raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
+
+    chunks: list[bytes] = []
+    total_read = 0
+    with zip_ref.open(info, "r") as src:
+        while chunk := src.read(_SKILL_ARCHIVE_READ_CHUNK_SIZE):
+            total_read += len(chunk)
+            if total_read > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
+                raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
+            chunks.append(chunk)
+    return b"".join(chunks)
+
+
 def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
    """Extract a file from a .skill ZIP archive.

@@ -60,16 +79,16 @@ def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> byte
    try:
        with zipfile.ZipFile(zip_path, "r") as zip_ref:
            # List all files in the archive
-            namelist = zip_ref.namelist()
+            infos_by_name = {info.filename: info for info in zip_ref.infolist()}

            # Try direct path first
-            if internal_path in namelist:
-                return zip_ref.read(internal_path)
+            if internal_path in infos_by_name:
+                return _read_skill_archive_member(zip_ref, infos_by_name[internal_path])

            # Try with any top-level directory prefix (e.g., "skill-name/SKILL.md")
-            for name in namelist:
+            for name, info in infos_by_name.items():
                if name.endswith("/" + internal_path) or name == internal_path:
-                    return zip_ref.read(name)
+                    return _read_skill_archive_member(zip_ref, info)

            # Not found
            return None
@@ -305,7 +305,7 @@ async def login_local(
 async def register(request: Request, response: Response, body: RegisterRequest):
    """Register a new user account (always 'user' role).

-    Admin is auto-created on first boot. This endpoint creates regular users.
+    The first admin is created explicitly through /initialize. This endpoint creates regular users.
    Auto-login by setting the session cookie.
    """
    try:
@@ -68,6 +68,27 @@ class RunResponse(BaseModel):
    updated_at: str = ""


+class ThreadTokenUsageModelBreakdown(BaseModel):
+    tokens: int = 0
+    runs: int = 0
+
+
+class ThreadTokenUsageCallerBreakdown(BaseModel):
+    lead_agent: int = 0
+    subagent: int = 0
+    middleware: int = 0
+
+
+class ThreadTokenUsageResponse(BaseModel):
+    thread_id: str
+    total_tokens: int = 0
+    total_input_tokens: int = 0
+    total_output_tokens: int = 0
+    total_runs: int = 0
+    by_model: dict[str, ThreadTokenUsageModelBreakdown] = Field(default_factory=dict)
+    by_caller: ThreadTokenUsageCallerBreakdown = Field(default_factory=ThreadTokenUsageCallerBreakdown)
+
+
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
@@ -368,10 +389,10 @@ async def list_run_events(
    return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)


-@router.get("/{thread_id}/token-usage")
+@router.get("/{thread_id}/token-usage", response_model=ThreadTokenUsageResponse)
@require_permission("threads", "read", owner_check=True)
-async def thread_token_usage(thread_id: str, request: Request) -> dict:
+async def thread_token_usage(thread_id: str, request: Request) -> ThreadTokenUsageResponse:
    """Thread-level token usage aggregation."""
    run_store = get_run_store(request)
    agg = await run_store.aggregate_tokens_by_thread(thread_id)
-    return {"thread_id": thread_id, **agg}
+    return ThreadTokenUsageResponse(thread_id=thread_id, **agg)
@@ -13,11 +13,11 @@ matching the LangGraph Platform wire format expected by the
 from __future__ import annotations

 import logging
-import time
 import uuid
 from typing import Any

 from fastapi import APIRouter, HTTPException, Request
+from langgraph.checkpoint.base import empty_checkpoint
 from pydantic import BaseModel, Field, field_validator

 from app.gateway.authz import require_permission
@@ -26,6 +26,7 @@ from app.gateway.utils import sanitize_log_param
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime import serialize_channel_values
 from deerflow.runtime.user_context import get_effective_user_id
+from deerflow.utils.time import coerce_iso, now_iso

 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api/threads", tags=["threads"])
@@ -89,6 +90,28 @@ class ThreadSearchRequest(BaseModel):
    offset: int = Field(default=0, ge=0, description="Pagination offset")
    status: str | None = Field(default=None, description="Filter by thread status")

+    @field_validator("metadata")
+    @classmethod
+    def _validate_metadata_filters(cls, v: dict[str, Any]) -> dict[str, Any]:
+        """Reject filter entries the SQL backend cannot compile.
+
+        Enforces consistent behaviour across SQL and memory backends.
+        See ``deerflow.persistence.json_compat`` for the shared validators.
+        """
+        if not v:
+            return v
+        from deerflow.persistence.json_compat import validate_metadata_filter_key, validate_metadata_filter_value
+
+        bad_entries: list[str] = []
+        for key, value in v.items():
+            if not validate_metadata_filter_key(key):
+                bad_entries.append(f"{key!r} (unsafe key)")
+            elif not validate_metadata_filter_value(value):
+                bad_entries.append(f"{key!r} (unsupported value type {type(value).__name__})")
+        if bad_entries:
+            raise ValueError(f"Invalid metadata filter entries: {', '.join(bad_entries)}")
+        return v
+

 class ThreadStateResponse(BaseModel):
    """Response model for thread state."""
@@ -233,7 +256,7 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
    checkpointer = get_checkpointer(request)
    thread_store = get_thread_store(request)
    thread_id = body.thread_id or str(uuid.uuid4())
-    now = time.time()
+    now = now_iso()
    # ``body.metadata`` is already stripped of server-reserved keys by
    # ``ThreadCreateRequest._strip_reserved`` — see the model definition.

@@ -243,8 +266,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
        return ThreadResponse(
            thread_id=thread_id,
            status=existing_record.get("status", "idle"),
-            created_at=str(existing_record.get("created_at", "")),
-            updated_at=str(existing_record.get("updated_at", "")),
+            created_at=coerce_iso(existing_record.get("created_at", "")),
+            updated_at=coerce_iso(existing_record.get("updated_at", "")),
            metadata=existing_record.get("metadata", {}),
        )

@@ -262,8 +285,6 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
    # Write an empty checkpoint so state endpoints work immediately
    config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
    try:
-        from langgraph.checkpoint.base import empty_checkpoint
-
        ckpt_metadata = {
            "step": -1,
            "source": "input",
@@ -281,8 +302,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
    return ThreadResponse(
        thread_id=thread_id,
        status="idle",
-        created_at=str(now),
-        updated_at=str(now),
+        created_at=now,
+        updated_at=now,
        metadata=body.metadata,
    )

@@ -295,20 +316,27 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
    (SQL-backed for sqlite/postgres, Store-backed for memory mode).
    """
    from app.gateway.deps import get_thread_store
+    from deerflow.persistence.thread_meta import InvalidMetadataFilterError

    repo = get_thread_store(request)
+    try:
        rows = await repo.search(
            metadata=body.metadata or None,
            status=body.status,
            limit=body.limit,
            offset=body.offset,
        )
+    except InvalidMetadataFilterError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
    return [
        ThreadResponse(
            thread_id=r["thread_id"],
            status=r.get("status", "idle"),
-            created_at=r.get("created_at", ""),
-            updated_at=r.get("updated_at", ""),
+            # ``coerce_iso`` heals legacy unix-second values that
+            # ``MemoryThreadMetaStore`` historically wrote with ``time.time()``;
+            # SQL-backed rows already arrive as ISO strings and pass through.
+            created_at=coerce_iso(r.get("created_at", "")),
+            updated_at=coerce_iso(r.get("updated_at", "")),
            metadata=r.get("metadata", {}),
            values={"title": r["display_name"]} if r.get("display_name") else {},
            interrupts={},
@@ -340,8 +368,8 @@ async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Reques
    return ThreadResponse(
        thread_id=thread_id,
        status=record.get("status", "idle"),
-        created_at=str(record.get("created_at", "")),
-        updated_at=str(record.get("updated_at", "")),
+        created_at=coerce_iso(record.get("created_at", "")),
+        updated_at=coerce_iso(record.get("updated_at", "")),
        metadata=record.get("metadata", {}),
    )

@@ -381,8 +409,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
        record = {
            "thread_id": thread_id,
            "status": "idle",
-            "created_at": ckpt_meta.get("created_at", ""),
-            "updated_at": ckpt_meta.get("updated_at", ckpt_meta.get("created_at", "")),
+            "created_at": coerce_iso(ckpt_meta.get("created_at", "")),
+            "updated_at": coerce_iso(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
            "metadata": {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")},
        }

@@ -396,8 +424,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
    return ThreadResponse(
        thread_id=thread_id,
        status=status,
-        created_at=str(record.get("created_at", "")),
-        updated_at=str(record.get("updated_at", "")),
+        created_at=coerce_iso(record.get("created_at", "")),
+        updated_at=coerce_iso(record.get("updated_at", "")),
        metadata=record.get("metadata", {}),
        values=serialize_channel_values(channel_values),
    )
@@ -448,10 +476,10 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
        values=values,
        next=next_tasks,
        metadata=metadata,
-        checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
+        checkpoint={"id": checkpoint_id, "ts": coerce_iso(metadata.get("created_at", ""))},
        checkpoint_id=checkpoint_id,
        parent_checkpoint_id=parent_checkpoint_id,
-        created_at=str(metadata.get("created_at", "")),
+        created_at=coerce_iso(metadata.get("created_at", "")),
        tasks=tasks,
    )

@@ -501,7 +529,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
        channel_values.update(body.values)

    checkpoint["channel_values"] = channel_values
-    metadata["updated_at"] = time.time()
+    metadata["updated_at"] = now_iso()

    if body.as_node:
        metadata["source"] = "update"
@@ -542,7 +570,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
        next=[],
        metadata=metadata,
        checkpoint_id=new_checkpoint_id,
-        created_at=str(metadata.get("created_at", "")),
+        created_at=coerce_iso(metadata.get("created_at", "")),
    )


@@ -609,7 +637,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
                    parent_checkpoint_id=parent_id,
                    metadata=user_meta,
                    values=values,
-                    created_at=str(metadata.get("created_at", "")),
+                    created_at=coerce_iso(metadata.get("created_at", "")),
                    next=next_tasks,
                )
            )
@@ -5,7 +5,7 @@ import os
 import stat

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

 from app.gateway.authz import require_permission
 from app.gateway.deps import get_config
@@ -15,12 +15,15 @@ from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
 from deerflow.uploads.manager import (
    PathTraversalError,
+    UnsafeUploadPathError,
+    claim_unique_filename,
    delete_file_safe,
    enrich_file_listing,
    ensure_uploads_dir,
    get_uploads_dir,
    list_files_in_dir,
    normalize_filename,
+    open_upload_file_no_symlink,
    upload_artifact_url,
    upload_virtual_path,
 )
@@ -42,6 +45,7 @@ class UploadResponse(BaseModel):
    success: bool
    files: list[dict[str, str]]
    message: str
+    skipped_files: list[str] = Field(default_factory=list)


 class UploadLimits(BaseModel):
@@ -116,17 +120,18 @@ def _cleanup_uploaded_paths(paths: list[os.PathLike[str] | str]) -> None:
            logger.warning("Failed to clean up upload path after rejected request: %s", path, exc_info=True)


-async def _write_upload_file_streaming(
+async def _write_upload_file_with_limits(
    file: UploadFile,
-    file_path: os.PathLike[str] | str,
    *,
+    uploads_dir: os.PathLike[str] | str,
    display_filename: str,
    max_single_file_size: int,
    max_total_size: int,
    total_size: int,
-) -> tuple[int, int]:
+) -> tuple[os.PathLike[str] | str, int, int]:
    file_size = 0
-    with open(file_path, "wb") as output:
+    file_path, fh = open_upload_file_no_symlink(uploads_dir, display_filename)
+    try:
        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
            file_size += len(chunk)
            total_size += len(chunk)
@@ -134,8 +139,17 @@ async def _write_upload_file_streaming(
                raise HTTPException(status_code=413, detail=f"File too large: {display_filename}")
            if total_size > max_total_size:
                raise HTTPException(status_code=413, detail="Total upload size too large")
-            output.write(chunk)
-    return file_size, total_size
+            fh.write(chunk)
+    except Exception:
+        fh.close()
+        try:
+            os.unlink(file_path)
+        except FileNotFoundError:
+            pass
+        raise
+    else:
+        fh.close()
+    return file_path, file_size, total_size


 def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
@@ -177,7 +191,12 @@ async def upload_files(
    uploaded_files = []
    written_paths = []
    sandbox_sync_targets = []
+    skipped_files = []
    total_size = 0
+    # Track filenames within this request so duplicate form parts do not
+    # silently truncate each other. Existing uploads keep the historical
+    # overwrite behavior for a single replacement upload.
+    seen_filenames: set[str] = set()

    sandbox_provider = get_sandbox_provider()
    sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
@@ -194,22 +213,22 @@ async def upload_files(
            continue

        try:
-            safe_filename = normalize_filename(file.filename)
+            original_filename = normalize_filename(file.filename)
+            safe_filename = claim_unique_filename(original_filename, seen_filenames)
        except ValueError:
            logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
            continue

        try:
-            file_path = uploads_dir / safe_filename
-            written_paths.append(file_path)
-            file_size, total_size = await _write_upload_file_streaming(
+            file_path, file_size, total_size = await _write_upload_file_with_limits(
                file,
-                file_path,
+                uploads_dir=uploads_dir,
                display_filename=safe_filename,
                max_single_file_size=limits.max_file_size,
                max_total_size=limits.max_total_size,
                total_size=total_size,
            )
+            written_paths.append(file_path)

            virtual_path = upload_virtual_path(safe_filename)

@@ -223,6 +242,8 @@ async def upload_files(
                "virtual_path": virtual_path,
                "artifact_url": upload_artifact_url(thread_id, safe_filename),
            }
+            if safe_filename != original_filename:
+                file_info["original_filename"] = original_filename

            logger.info(f"Saved file: {safe_filename} ({file_size} bytes) to {file_info['path']}")

@@ -246,6 +267,10 @@ async def upload_files(
        except HTTPException as e:
            _cleanup_uploaded_paths(written_paths)
            raise e
+        except UnsafeUploadPathError as e:
+            logger.warning("Skipping upload with unsafe destination %s: %s", file.filename, e)
+            skipped_files.append(safe_filename)
+            continue
        except Exception as e:
            logger.error(f"Failed to upload {file.filename}: {e}")
            _cleanup_uploaded_paths(written_paths)
@@ -256,10 +281,15 @@ async def upload_files(
            _make_file_sandbox_writable(file_path)
            sandbox.update_file(virtual_path, file_path.read_bytes())

+    message = f"Successfully uploaded {len(uploaded_files)} file(s)"
+    if skipped_files:
+        message += f"; skipped {len(skipped_files)} unsafe file(s)"
+
    return UploadResponse(
-        success=True,
+        success=not skipped_files,
        files=uploaded_files,
-        message=f"Successfully uploaded {len(uploaded_files)} file(s)",
+        message=message,
+        skipped_files=skipped_files,
    )


@@ -19,6 +19,7 @@ from langchain_core.messages import HumanMessage

 from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
 from app.gateway.utils import sanitize_log_param
+from deerflow.config.app_config import get_app_config
 from deerflow.runtime import (
    END_SENTINEL,
    HEARTBEAT_SENTINEL,
@@ -136,6 +137,24 @@ def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, An
                runtime_context.setdefault(key, context[key])


+def inject_authenticated_user_context(config: dict[str, Any], request: Request) -> None:
+    """Stamp the authenticated user into the run context for background tools.
+
+    Tool execution may happen after the request handler has returned, so tools
+    that persist user-scoped files should not rely only on ambient ContextVars.
+    The value comes from server-side auth state, never from client context.
+    """
+
+    user = getattr(request.state, "user", None)
+    user_id = getattr(user, "id", None)
+    if user_id is None:
+        return
+
+    runtime_context = config.setdefault("context", {})
+    if isinstance(runtime_context, dict):
+        runtime_context["user_id"] = str(user_id)
+
+
 def resolve_agent_factory(assistant_id: str | None):
    """Resolve the agent factory callable from config.

@@ -249,6 +268,23 @@ async def start_run(

    disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_

+    body_context = getattr(body, "context", None) or {}
+    model_name = body_context.get("model_name")
+
+    # Coerce non-string model_name values to str before truncation.
+    if model_name is not None and not isinstance(model_name, str):
+        model_name = str(model_name)
+
+    # Validate model against the allowlist when a model_name is provided.
+    if model_name:
+        app_config = get_app_config()
+        resolved = app_config.get_model_config(model_name)
+        if resolved is None:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Model {model_name!r} is not in the configured model allowlist",
+            )
+
    try:
        record = await run_mgr.create_or_reject(
            thread_id,
@@ -257,6 +293,7 @@ async def start_run(
            metadata=body.metadata or {},
            kwargs={"input": body.input, "config": body.config},
            multitask_strategy=body.multitask_strategy,
+            model_name=model_name,
        )
    except ConflictError as exc:
        raise HTTPException(status_code=409, detail=str(exc)) from exc
@@ -288,6 +325,7 @@ async def start_run(
    # that carries agent configuration (model_name, thinking_enabled, etc.).
    # Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
    merge_run_context_overrides(config, getattr(body, "context", None))
+    inject_authenticated_user_context(config, request)

    stream_modes = normalize_stream_modes(body.stream_mode)

@@ -79,7 +79,9 @@ async def main():
    from langgraph.runtime import Runtime

    from deerflow.agents import make_lead_agent
+    from deerflow.config.paths import get_paths
    from deerflow.mcp import initialize_mcp_tools
+    from deerflow.runtime.user_context import get_effective_user_id

    # Initialize MCP tools at startup
    try:
@@ -113,6 +115,8 @@ async def main():
        print("Tip: `uv sync --group dev` to enable arrow-key & history support")
    print("=" * 50)

+    seen_artifacts: set[str] = set()
+
    while True:
        try:
            if session:
@@ -134,6 +138,22 @@ async def main():
                last_message = result["messages"][-1]
                print(f"\nAgent: {last_message.content}")

+            # Show files presented to the user this turn (new artifacts only)
+            artifacts = result.get("artifacts") or []
+            new_artifacts = [p for p in artifacts if p not in seen_artifacts]
+            if new_artifacts:
+                thread_id = config["configurable"]["thread_id"]
+                user_id = get_effective_user_id()
+                paths = get_paths()
+                print("\n[Presented files]")
+                for virtual in new_artifacts:
+                    try:
+                        physical = paths.resolve_virtual_path(thread_id, virtual, user_id=user_id)
+                        print(f"  - {virtual}\n    → {physical}")
+                    except ValueError as exc:
+                        print(f"  - {virtual}    (failed to resolve physical path: {exc})")
+                seen_artifacts.update(new_artifacts)
+
        except (KeyboardInterrupt, EOFError):
            print("\nGoodbye!")
            break
@@ -6,16 +6,16 @@ This document provides a complete reference for the DeerFlow backend APIs.

 DeerFlow backend exposes two sets of APIs:

-1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
+1. **LangGraph-compatible API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
 2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)

 All APIs are accessed through the Nginx reverse proxy at port 2026.

-## LangGraph API
+## LangGraph-compatible API

 Base URL: `/api/langgraph`

-The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
+The public LangGraph-compatible API follows LangGraph SDK conventions. In the unified nginx deployment, Gateway owns `/api/langgraph/*` and translates those paths to its native `/api/*` run, thread, and streaming routers.

 ### Threads

@@ -104,17 +104,11 @@ Content-Type: application/json
 **Recursion Limit:**

 `config.recursion_limit` caps the number of graph steps LangGraph will execute
-in a single run. The `/api/langgraph/*` endpoints go straight to the LangGraph
-server and therefore inherit LangGraph's native default of **25**, which is
-too low for plan-mode or subagent-heavy runs — the agent typically errors out
-with `GraphRecursionError` after the first round of subagent results comes
-back, before the lead agent can synthesize the final answer.
-
-DeerFlow's own Gateway and IM-channel paths mitigate this by defaulting to
-`100` in `build_run_config` (see `backend/app/gateway/services.py`), but
-clients calling the LangGraph API directly must set `recursion_limit`
-explicitly in the request body. `100` matches the Gateway default and is a
-safe starting point; increase it if you run deeply nested subagent graphs.
+in a single run. The unified Gateway path defaults to `100` in
+`build_run_config` (see `backend/app/gateway/services.py`), which is a safer
+starting point for plan-mode or subagent-heavy runs. Clients can still set
+`recursion_limit` explicitly in the request body; increase it if you run deeply
+nested subagent graphs.

 **Configurable Options:**
 - `model_name` (string): Override the default model
@@ -541,14 +535,28 @@ All APIs return errors in a consistent format:

 ## Authentication

-Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
+DeerFlow enforces authentication for all non-public HTTP routes. Public routes are limited to health/docs metadata and these public auth endpoints:

-Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
+- `POST /api/v1/auth/initialize` creates the first admin account when no admin exists.
+- `POST /api/v1/auth/login/local` logs in with email/password and sets an HttpOnly `access_token` cookie.
+- `POST /api/v1/auth/register` creates a regular `user` account and sets the session cookie.
+- `POST /api/v1/auth/logout` clears the session cookie.
+- `GET /api/v1/auth/setup-status` reports whether the first admin still needs to be created.

-For production deployments, it is recommended to:
-1. Use Nginx for basic auth or OAuth integration
-2. Deploy behind a VPN or private network
-3. Implement custom authentication middleware
+The authenticated auth endpoints are:
+
+- `GET /api/v1/auth/me` returns the current user.
+- `POST /api/v1/auth/change-password` changes password, optionally changes email during setup, increments `token_version`, and reissues the cookie.
+
+Protected state-changing requests also require the CSRF double-submit token: send the `csrf_token` cookie value as the `X-CSRF-Token` header. Login/register/initialize/logout are bootstrap auth endpoints: they are exempt from the double-submit token but still reject hostile browser `Origin` headers.
+
+User isolation is enforced from the authenticated user context:
+
+- Thread metadata is scoped by `threads_meta.user_id`; search/read/write/delete APIs only expose the current user's threads.
+- Thread files live under `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/` and are exposed inside the sandbox as `/mnt/user-data/`.
+- Memory and custom agents are stored under `{base_dir}/users/{user_id}/...`.
+
+Note: MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers; that is separate from DeerFlow API authentication.

 ---

@@ -567,12 +575,13 @@ location /api/ {

 ---

-## WebSocket Support
+## Streaming Support

-The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
+Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE):

-```
-ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
+```http
+POST /api/langgraph/threads/{thread_id}/runs/stream
+Accept: text/event-stream
 ```

 ---
@@ -608,13 +617,21 @@ const response = await fetch('/api/models');
 const data = await response.json();
 console.log(data.models);

-// Using EventSource for streaming
-const eventSource = new EventSource(
-  `/api/langgraph/threads/${threadId}/runs/stream`
-);
-eventSource.onmessage = (event) => {
-  console.log(JSON.parse(event.data));
-};
+// Create a run and stream SSE events
+const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+    Accept: "text/event-stream",
+  },
+  body: JSON.stringify({
+    input: { messages: [{ role: "user", content: "Hello" }] },
+    stream_mode: ["values", "messages-tuple", "custom"],
+  }),
+});
+
+const reader = streamResponse.body?.getReader();
+// Decode and parse SSE frames from reader in your client code.
 ```

 ### cURL Examples
@@ -649,7 +666,7 @@ curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
  }'
 ```

-> The `/api/langgraph/*` endpoints bypass DeerFlow's Gateway and inherit
-> LangGraph's native `recursion_limit` default of 25, which is too low for
-> plan-mode or subagent runs. Set `config.recursion_limit` explicitly — see
-> the [Create Run](#create-run) section for details.
+> The unified Gateway path defaults `config.recursion_limit` to 100 for
+> plan-mode and subagent-heavy runs. Clients may still set
+> `config.recursion_limit` explicitly — see the [Create Run](#create-run)
+> section for details.
@@ -14,30 +14,28 @@ This document provides a comprehensive overview of the DeerFlow backend architec
 │                          Nginx (Port 2026)                               │
 │                    Unified Reverse Proxy Entry Point                      │
 │  ┌────────────────────────────────────────────────────────────────────┐  │
-│  │  /api/langgraph/*  →  LangGraph Server (2024)                      │  │
-│  │  /api/*            →  Gateway API (8001)                           │  │
+│  │  /api/langgraph/*  →  Gateway LangGraph-compatible runtime (8001)  │  │
+│  │  /api/*            →  Gateway REST APIs (8001)                     │  │
 │  │  /*                →  Frontend (3000)                               │  │
 │  └────────────────────────────────────────────────────────────────────┘  │
 └─────────────────────────────────┬────────────────────────────────────────┘
                                  │
-          ┌───────────────────────┼───────────────────────┐
-          │                       │                       │
-          ▼                       ▼                       ▼
-┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
-│   LangGraph Server  │ │    Gateway API      │ │     Frontend        │
-│     (Port 2024)     │ │    (Port 8001)      │ │    (Port 3000)      │
-│                     │ │                     │ │                     │
-│  - Agent Runtime    │ │  - Models API       │ │  - Next.js App      │
-│  - Thread Mgmt      │ │  - MCP Config       │ │  - React UI         │
-│  - SSE Streaming    │ │  - Skills Mgmt      │ │  - Chat Interface   │
-│  - Checkpointing    │ │  - File Uploads     │ │                     │
-│                     │ │  - Thread Cleanup   │ │                     │
-│                     │ │  - Artifacts        │ │                     │
-└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
-          │                       │
-          │     ┌─────────────────┘
+          ┌───────────────────────┴───────────────────────┐
          │                                               │
          ▼                                               ▼
+┌─────────────────────────────────────────────┐ ┌─────────────────────┐
+│              Gateway API                    │ │     Frontend        │
+│              (Port 8001)                    │ │    (Port 3000)      │
+│                                             │ │                     │
+│  - LangGraph-compatible runs/threads API    │ │  - Next.js App      │
+│  - Embedded Agent Runtime                   │ │  - React UI         │
+│  - SSE Streaming                            │ │  - Chat Interface   │
+│  - Checkpointing                            │ │                     │
+│  - Models, MCP, Skills, Uploads, Artifacts  │ │                     │
+│  - Thread Cleanup                           │ │                     │
+└─────────────────────────────────────────────┘ └─────────────────────┘
+          │
+          ▼
 ┌──────────────────────────────────────────────────────────────────────────┐
 │                         Shared Configuration                              │
 │  ┌─────────────────────────┐  ┌────────────────────────────────────────┐ │
@@ -52,9 +50,9 @@ This document provides a comprehensive overview of the DeerFlow backend architec

 ## Component Details

-### LangGraph Server
+### Gateway Embedded Agent Runtime

-The LangGraph server is the core agent runtime, built on LangGraph for robust multi-agent workflow orchestration.
+The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for robust multi-agent workflow orchestration. Nginx rewrites `/api/langgraph/*` to Gateway's native `/api/*` routes, so the public API remains compatible with LangGraph SDK clients without running a separate LangGraph server.

 **Entry Point**: `packages/harness/deerflow/agents/lead_agent/agent.py:make_lead_agent`

@@ -65,7 +63,8 @@ The LangGraph server is the core agent runtime, built on LangGraph for robust mu
 - Tool execution orchestration
 - SSE streaming for real-time responses

-**Configuration**: `langgraph.json`
+**Graph registry**: `langgraph.json` remains available for tooling, Studio, or direct LangGraph Server compatibility.
+It is not the default service entrypoint; scripts and Docker deployments run the Gateway embedded runtime.

 ```json
 {
@@ -78,12 +77,13 @@ The LangGraph server is the core agent runtime, built on LangGraph for robust mu

 ### Gateway API

-FastAPI application providing REST endpoints for non-agent operations.
+FastAPI application providing REST endpoints plus the public LangGraph-compatible `/api/langgraph/*` runtime routes.

 **Entry Point**: `app/gateway/app.py`

 **Routers**:
 - `models.py` - `/api/models` - Model listing and details
+- `thread_runs.py` / `runs.py` - `/api/threads/{id}/runs`, `/api/runs/*` - LangGraph-compatible runs and streaming
 - `mcp.py` - `/api/mcp` - MCP server configuration
 - `skills.py` - `/api/skills` - Skills management
 - `uploads.py` - `/api/threads/{id}/uploads` - File upload
@@ -91,7 +91,7 @@ FastAPI application providing REST endpoints for non-agent operations.
 - `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
 - `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation

-The web conversation delete flow is now split across both backend surfaces: LangGraph handles `DELETE /api/langgraph/threads/{thread_id}` for thread state, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
+The web conversation delete flow first deletes Gateway-managed thread state through the LangGraph-compatible route, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.

 ### Agent Architecture

@@ -353,10 +353,10 @@ SKILL.md Format:
   POST /api/langgraph/threads/{thread_id}/runs
   {"input": {"messages": [{"role": "user", "content": "Hello"}]}}

-2. Nginx → LangGraph Server (2024)
-   Proxied to LangGraph server
+2. Nginx → Gateway API (8001)
+   `/api/langgraph/*` is rewritten to Gateway's LangGraph-compatible `/api/*` routes

-3. LangGraph Server
+3. Gateway embedded runtime
   a. Load/create thread state
   b. Execute middleware chain:
      - ThreadDataMiddleware: Set up paths
@@ -412,7 +412,7 @@ SKILL.md Format:
 ### Thread Cleanup Flow

 ```
-1. Client deletes conversation via LangGraph
+1. Client deletes conversation via the LangGraph-compatible Gateway route
   DELETE /api/langgraph/threads/{thread_id}

 2. Web UI follows up with Gateway cleanup
@@ -0,0 +1,331 @@
+# 用户认证与隔离设计
+
+本文档描述 DeerFlow 当前内置认证模块的设计，而不是历史 RFC。它覆盖浏览器登录、API 认证、CSRF、用户隔离、首次初始化、密码重置、内部调用和升级迁移。
+
+## 设计目标
+
+认证模块的核心目标是把 DeerFlow 从“本地单用户工具”提升为“可多用户部署的 agent runtime”，并让用户身份贯穿 HTTP API、LangGraph-compatible runtime、文件系统、memory、自定义 agent 和反馈数据。
+
+设计约束：
+
+- 默认强制认证：除健康检查、文档和 auth bootstrap 端点外，HTTP 路由都必须有有效 session。
+- 服务端持有所有权：客户端 metadata 不能声明 `user_id` 或 `owner_id`。
+- 隔离默认开启：repository（仓储）、文件路径、memory、agent 配置默认按当前用户解析。
+- 旧数据可升级：无认证版本留下的 thread 可以在 admin 存在后迁移到 admin。
+- 密码不进日志：首次初始化由操作者设置密码；`reset_admin` 只写 0600 凭据文件。
+
+非目标：
+
+- 当前 OAuth 端点只是占位，尚未实现第三方登录。
+- 当前用户角色只有 `admin` 和 `user`，尚未实现细粒度 RBAC。
+- 当前登录限速是进程内字典，多 worker 下不是全局精确限速。
+
+## 核心模型
+
+```mermaid
+graph TB
+  classDef actor fill:#D8CFC4,stroke:#6E6259,color:#2F2A26;
+  classDef api fill:#C9D7D2,stroke:#5D706A,color:#21302C;
+  classDef state fill:#D7D3E8,stroke:#6B6680,color:#29263A;
+  classDef data fill:#E5D2C4,stroke:#806A5B,color:#30251E;
+
+  Browser["Browser — access_token cookie and csrf_token cookie"]:::actor
+  AuthMiddleware["AuthMiddleware — strict session gate"]:::api
+  CSRFMiddleware["CSRFMiddleware — double-submit token and Origin check"]:::api
+  AuthRoutes["Auth routes — initialize login register logout me change-password"]:::api
+  UserContext["Current user ContextVar — request-scoped identity"]:::state
+  Repositories["Repositories — AUTO resolves user_id from context"]:::state
+  Files["Filesystem — users/{user_id}/threads/{thread_id}/user-data"]:::data
+  Memory["Memory and agents — users/{user_id}/memory.json and agents"]:::data
+
+  Browser --> AuthMiddleware
+  Browser --> CSRFMiddleware
+  AuthMiddleware --> AuthRoutes
+  AuthMiddleware --> UserContext
+  UserContext --> Repositories
+  UserContext --> Files
+  UserContext --> Memory
+```
+
+### 用户表
+
+用户记录定义在 `app.gateway.auth.models.User`，持久化到 `users` 表。关键字段：
+
+| 字段 | 语义 |
+|---|---|
+| `id` | 用户主键，JWT `sub` 使用该值 |
+| `email` | 唯一登录名 |
+| `password_hash` | bcrypt hash，OAuth 用户可为空 |
+| `system_role` | `admin` 或 `user` |
+| `needs_setup` | reset 后要求用户完成邮箱 / 密码设置 |
+| `token_version` | 改密码或 reset 时递增，用于废弃旧 JWT |
+
+### 运行时身份
+
+认证成功后，`AuthMiddleware` 把用户同时写入：
+
+- `request.state.user`
+- `request.state.auth`
+- `deerflow.runtime.user_context` 的 `ContextVar`
+
+`ContextVar` 是这里的核心边界。上层 Gateway 负责写入身份，下层 persistence / file path 只读取结构化的当前用户，不反向依赖 `app.gateway.auth` 具体类型。
+
+可以把 repository 调用的用户参数理解成一个三态 ADT：
+
+```scala
+enum UserScope:
+  case AutoFromContext
+  case Explicit(userId: String)
+  case BypassForMigration
+```
+
+对应 Python 实现是 `AUTO | str | None`：
+
+- `AUTO`：从 `ContextVar` 解析当前用户；没有上下文则抛错。
+- `str`：显式指定用户，主要用于测试或管理脚本。
+- `None`：跳过用户过滤，只允许迁移脚本或 admin CLI 使用。
+
+## 登录与初始化流程
+
+### 首次初始化
+
+首次启动时，如果没有 admin，服务不会自动创建账号，只记录日志提示访问 `/setup`。
+
+流程：
+
+1. 用户访问 `/setup`。
+2. 前端调用 `GET /api/v1/auth/setup-status`。
+3. 如果返回 `{"needs_setup": true}`，前端展示创建 admin 表单。
+4. 表单提交 `POST /api/v1/auth/initialize`。
+5. 服务端确认当前没有 admin，创建 `system_role="admin"`、`needs_setup=false` 的用户。
+6. 服务端设置 `access_token` HttpOnly cookie，用户进入 workspace。
+
+`/api/v1/auth/initialize` 只在没有 admin 时可用。并发初始化由数据库唯一约束兜底，失败方返回 409。
+
+### 普通登录
+
+`POST /api/v1/auth/login/local` 使用 `OAuth2PasswordRequestForm`：
+
+- `username` 是邮箱。
+- `password` 是密码。
+- 成功后签发 JWT，放入 `access_token` HttpOnly cookie。
+- 响应体只返回 `expires_in` 和 `needs_setup`，不返回 token。
+
+登录失败会按客户端 IP 计数。IP 解析只在 TCP peer 属于 `AUTH_TRUSTED_PROXIES` 时信任 `X-Real-IP`，不使用 `X-Forwarded-For`。
+
+### 注册
+
+`POST /api/v1/auth/register` 创建普通 `user`，并自动登录。
+
+当前实现允许在没有 admin 时注册普通用户，但 `setup-status` 仍会返回 `needs_setup=true`，因为 admin 仍不存在。这是当前产品策略边界：如果后续要求“必须先初始化 admin 才能注册普通用户”，需要在 `/register` 增加 admin-exists gate。
+
+### 改密码与 reset setup
+
+`POST /api/v1/auth/change-password` 需要当前密码和新密码：
+
+- 校验当前密码。
+- 更新 bcrypt hash。
+- `token_version += 1`，使旧 JWT 立即失效。
+- 重新签发 cookie。
+- 如果 `needs_setup=true` 且传了 `new_email`，则更新邮箱并清除 `needs_setup`。
+
+`python -m app.gateway.auth.reset_admin` 会：
+
+- 找到 admin 或指定邮箱用户。
+- 生成随机密码。
+- 更新密码 hash。
+- `token_version += 1`。
+- 设置 `needs_setup=true`。
+- 写入 `.deer-flow/admin_initial_credentials.txt`，权限 `0600`。
+
+命令行只输出凭据文件路径，不输出明文密码。
+
+## HTTP 认证边界
+
+`AuthMiddleware` 是 fail-closed（默认拒绝）的全局认证门。
+
+公开路径：
+
+- `/health`
+- `/docs`
+- `/redoc`
+- `/openapi.json`
+- `/api/v1/auth/login/local`
+- `/api/v1/auth/register`
+- `/api/v1/auth/logout`
+- `/api/v1/auth/setup-status`
+- `/api/v1/auth/initialize`
+
+其余路径都要求有效 `access_token` cookie。存在 cookie 但 JWT 无效、过期、用户不存在或 `token_version` 不匹配时，直接返回 401，而不是让请求穿透到业务路由。
+
+路由级别的 owner check 由 `require_permission(..., owner_check=True)` 完成：
+
+- 读类请求允许旧的未追踪 legacy thread 兼容读取。
+- 写 / 删除类请求使用 `require_existing=True`，要求 thread row 存在且属于当前用户，避免删除后缺 row 导致其他用户误通过。
+
+## CSRF 设计
+
+DeerFlow 使用 Double Submit Cookie：
+
+- 服务端设置 `csrf_token` cookie。
+- 前端 state-changing 请求发送同值 `X-CSRF-Token` header。
+- 服务端用 `secrets.compare_digest` 比较 cookie/header。
+
+需要 CSRF 的方法：
+
+- `POST`
+- `PUT`
+- `DELETE`
+- `PATCH`
+
+auth bootstrap 端点（login/register/initialize/logout）不要求 double-submit token，因为首次调用时浏览器还没有 token；但这些端点会校验 browser `Origin`，拒绝 hostile Origin，避免 login CSRF / session fixation。
+
+## 用户隔离
+
+### Thread metadata
+
+Thread metadata 存在 `threads_meta`，关键隔离字段是 `user_id`。
+
+创建 thread 时：
+
+- 客户端传入的 `metadata.user_id` 和 `metadata.owner_id` 会被剥离。
+- `ThreadMetaRepository.create(..., user_id=AUTO)` 从 `ContextVar` 解析真实用户。
+- `/api/threads/search` 默认只返回当前用户的 thread。
+
+读取 / 修改 / 删除时：
+
+- `get()` 默认按当前用户过滤。
+- `check_access()` 用于路由 owner check。
+- 对其他用户的 thread 返回 404，避免泄露资源存在性。
+
+### 文件系统
+
+当前线程文件布局：
+
+```text
+{base_dir}/users/{user_id}/threads/{thread_id}/user-data/
+├── workspace/
+├── uploads/
+└── outputs/
+```
+
+agent 在 sandbox 内看到统一虚拟路径：
+
+```text
+/mnt/user-data/workspace
+/mnt/user-data/uploads
+/mnt/user-data/outputs
+```
+
+`ThreadDataMiddleware` 使用 `get_effective_user_id()` 解析当前用户并生成线程路径。没有认证上下文时会落到 `default` 用户桶，主要用于内部调用、嵌入式 client 或无 HTTP 的本地执行路径。
+
+### Memory
+
+默认 memory 存储：
+
+```text
+{base_dir}/users/{user_id}/memory.json
+{base_dir}/users/{user_id}/agents/{agent_name}/memory.json
+```
+
+有用户上下文时，空或相对 `memory.storage_path` 都使用上述 per-user 默认路径；只有绝对 `memory.storage_path` 会视为显式 opt-out（退出） per-user isolation，所有用户共享该路径。无用户上下文的 legacy 路径仍会把相对 `storage_path` 解析到 `Paths.base_dir` 下。
+
+### 自定义 agent
+
+用户自定义 agent 写入：
+
+```text
+{base_dir}/users/{user_id}/agents/{agent_name}/
+├── config.yaml
+├── SOUL.md
+└── memory.json
+```
+
+旧布局 `{base_dir}/agents/{agent_name}/` 只作为只读兼容回退。更新或删除旧共享 agent 会要求先运行迁移脚本。
+
+## 内部调用与 IM 渠道
+
+IM channel worker 不是浏览器用户，不持有浏览器 cookie。它们通过 Gateway 内部认证：
+
+- 请求带 `X-DeerFlow-Internal-Token`。
+- 同时带匹配的 CSRF cookie/header。
+- 服务端识别为内部用户，`id="default"`、`system_role="internal"`。
+
+这意味着 channel 产生的数据默认进入 `default` 用户桶。这个选择适合“平台级 bot 身份”，但不是“每个 IM 用户单独隔离”。如果后续要做到外部 IM 用户隔离，需要把外部 platform user 映射到 DeerFlow user，并让 channel manager 设置对应的 scoped identity。
+
+## LangGraph-compatible 认证
+
+Gateway 内嵌 runtime 路径由 `AuthMiddleware` 和 `CSRFMiddleware` 保护。
+
+仓库仍保留 `app.gateway.langgraph_auth`，用于 LangGraph Server 直连模式：
+
+- `@auth.authenticate` 校验 JWT cookie、CSRF、用户存在性和 `token_version`。
+- `@auth.on` 在写入 metadata 时注入 `user_id`，并在读路径返回 `{"user_id": current_user}` 过滤条件。
+
+这保证 Gateway 路由和 LangGraph-compatible 直连模式使用同一 JWT 语义。
+
+## 升级与迁移
+
+从无认证版本升级时，可能存在没有 `user_id` 的历史 thread。
+
+当前策略：
+
+1. 首次启动如果没有 admin，只提示访问 `/setup`，不迁移。
+2. 操作者创建 admin。
+3. 后续启动时，`_ensure_admin_user()` 找到 admin，并把 LangGraph store 中缺少 `metadata.user_id` 的 thread 迁移到 admin。
+
+文件系统旧布局迁移由脚本处理：
+
+```bash
+cd backend
+PYTHONPATH=. python scripts/migrate_user_isolation.py --dry-run
+PYTHONPATH=. python scripts/migrate_user_isolation.py --user-id <target-user-id>
+```
+
+迁移脚本覆盖 legacy `memory.json`、`threads/` 和 `agents/` 到 per-user layout。
+
+## 安全不变量
+
+必须长期保持的不变量：
+
+- JWT 只在 HttpOnly cookie 中传输，不出现在响应 JSON。
+- 任何非 public HTTP 路由都不能只靠“cookie 存在”放行，必须严格验证 JWT。
+- `token_version` 不匹配必须拒绝，保证改密码 / reset 后旧 session 失效。
+- 客户端 metadata 中的 `user_id` / `owner_id` 必须剥离。
+- repository 默认 `AUTO` 必须从当前用户上下文解析，不能静默退化成全局查询。
+- 只有迁移脚本和 admin CLI 可以显式传 `user_id=None` 绕过隔离。
+- 本地文件路径必须通过 `Paths` 和 sandbox path validation 解析，不能拼接未校验的用户输入。
+- 捕获认证、迁移、后台任务异常必须记录日志；不能空 catch。
+
+## 已知边界
+
+| 边界 | 当前行为 | 后续方向 |
+|---|---|---|
+| 无 admin 时注册普通用户 | 允许注册普通 `user` | 如产品要求先初始化 admin，给 `/register` 加 gate |
+| 登录限速 | 进程内 dict，单 worker 精确，多 worker 近似 | Redis / DB-backed rate limiter |
+| OAuth | 端点占位，未实现 | 接入 provider 并统一 `token_version` / role 语义 |
+| IM 用户隔离 | channel 使用 `default` 内部用户 | 建立外部用户到 DeerFlow user 的映射 |
+| 绝对 memory path | 显式共享 memory | UI / docs 明确提示 opt-out 风险 |
+
+## 相关文件
+
+| 文件 | 职责 |
+|---|---|
+| `app/gateway/auth_middleware.py` | 全局认证门、JWT 严格验证、写入 user context |
+| `app/gateway/csrf_middleware.py` | CSRF double-submit 和 auth Origin 校验 |
+| `app/gateway/routers/auth.py` | initialize/login/register/logout/me/change-password |
+| `app/gateway/auth/jwt.py` | JWT 创建与解析 |
+| `app/gateway/auth/reset_admin.py` | 密码 reset CLI |
+| `app/gateway/auth/credential_file.py` | 0600 凭据文件写入 |
+| `app/gateway/authz.py` | 路由权限与 owner check |
+| `deerflow/runtime/user_context.py` | 当前用户 ContextVar 与 `AUTO` sentinel |
+| `deerflow/persistence/thread_meta/` | thread metadata owner filter |
+| `deerflow/config/paths.py` | per-user filesystem layout |
+| `deerflow/agents/middlewares/thread_data_middleware.py` | run 时解析用户线程目录 |
+| `deerflow/agents/memory/storage.py` | per-user memory storage |
+| `deerflow/config/agents_config.py` | per-user custom agents |
+| `app/channels/manager.py` | IM channel 内部认证调用 |
+| `scripts/migrate_user_isolation.py` | legacy 数据迁移到 per-user layout |
+| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库，包含 users / threads_meta / runs / feedback 等表 |
+| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
+| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件（0600，读完应删除） |
@@ -24,11 +24,11 @@ All other test plan sections were executed against either:

 | Case | Title | What it covers | Why not run |
 |---|---|---|---|
-| TC-DOCKER-01 | `users.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
+| TC-DOCKER-01 | `deerflow.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
 | TC-DOCKER-02 | Session persistence across container restart | `AUTH_JWT_SECRET` env var keeps cookies valid after `docker compose down && up` | needs `docker compose down/up` |
 | TC-DOCKER-03 | Per-worker rate limiter divergence | Confirms in-process `_login_attempts` dict doesn't share state across `gunicorn` workers (4 by default in the compose file); known limitation, documented | needs multi-worker container |
-| TC-DOCKER-04 | IM channels skip AuthMiddleware | Verify Feishu/Slack/Telegram dispatchers run in-container against `http://langgraph:2024` without going through nginx | needs `docker logs` |
-| TC-DOCKER-05 | Admin credentials surfacing | **Updated post-simplify** — was "log scrape", now "0600 credential file in `DEER_FLOW_HOME`". The file-based behavior is already validated by TC-1.1 + TC-UPG-13 on sg_dev (non-Docker), so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
+| TC-DOCKER-04 | IM channels use internal Gateway auth | Verify Feishu/Slack/Telegram dispatchers attach the process-local internal auth header plus CSRF cookie/header when calling Gateway-compatible LangGraph APIs | needs `docker logs` |
+| TC-DOCKER-05 | Reset credentials surfacing | `reset_admin` writes a 0600 credential file in `DEER_FLOW_HOME` instead of logging plaintext. The file-based behavior is validated by non-Docker reset tests, so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
 | TC-DOCKER-06 | Gateway-mode Docker deploy | `./scripts/deploy.sh --gateway` produces a 3-container topology (no `langgraph` container); same auth flow as standard mode | needs `docker compose --profile gateway` |

 ## Coverage already provided by non-Docker tests
@@ -41,8 +41,8 @@ the test cases that ran on sg_dev or local:
 | TC-DOCKER-01 (volume persistence) | TC-REENT-01 on sg_dev (admin row survives gateway restart) — same SQLite file, just no container layer between |
 | TC-DOCKER-02 (session persistence) | TC-API-02/03/06 (cookie roundtrip), plus TC-REENT-04 (multi-cookie) — JWT verification is process-state-free, container restart is equivalent to `pkill uvicorn && uv run uvicorn` |
 | TC-DOCKER-03 (per-worker rate limit) | TC-GW-04 + TC-REENT-09 (single-worker rate limit + 5min expiry). The cross-worker divergence is an architectural property of the in-memory dict; no auth code path differs |
-| TC-DOCKER-04 (IM channels skip auth) | Code-level only: `app/channels/manager.py` uses `langgraph_sdk` directly with no cookie handling. The langgraph_auth handler is bypassed by going through SDK, not HTTP |
-| TC-DOCKER-05 (credential surfacing) | TC-1.1 on sg_dev (file at `~/deer-flow/backend/.deer-flow/admin_initial_credentials.txt`, mode 0600, password 22 chars) — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
+| TC-DOCKER-04 (IM channels use internal auth) | Code-level: `app/channels/manager.py` creates the `langgraph_sdk` client with `create_internal_auth_headers()` plus CSRF cookie/header, so channel workers do not rely on browser cookies |
+| TC-DOCKER-05 (credential surfacing) | `reset_admin` writes `.deer-flow/admin_initial_credentials.txt` with mode 0600 and logs only the path — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
 | TC-DOCKER-06 (gateway-mode container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (gateway-mode auth flow on sg_dev) — same Gateway code, container is just a packaging change |

 ## Reproduction steps when Docker becomes available
@@ -72,6 +72,6 @@ Then run TC-DOCKER-01..06 from the test plan as written.
  about *container packaging* details (bind mounts, multi-worker, log
  collection), not about whether the auth code paths work.
 - **TC-DOCKER-05 was updated in place** in `AUTH_TEST_PLAN.md` to reflect
-  the post-simplify reality (credentials file → 0600 file, no log leak).
+  the current reset flow (`reset_admin` → 0600 credentials file, no log leak).
  The old "grep 'Password:' in docker logs" expectation would have failed
  silently and given a false sense of coverage.
@@ -19,7 +19,7 @@

 ```bash
 # 清除已有数据
-rm -f backend/.deer-flow/users.db
+rm -f backend/.deer-flow/data/deerflow.db

 # 选择模式启动
 make dev          # 标准模式
@@ -28,10 +28,11 @@ make dev-pro      # Gateway 模式
 ```

 **验证点：**
- [ ] 控制台输出 admin 邮箱和随机密码
- [ ] 密码格式为 `secrets.token_urlsafe(16)` 的 22 字符字符串
- [ ] 邮箱为 `admin@deerflow.dev`
- [ ] 提示 `Change it after login: Settings -> Account`
+- [ ] 控制台不输出 admin 邮箱或明文密码
+- [ ] 控制台提示 `First boot detected — no admin account exists.`
+- [ ] 控制台提示访问 `/setup` 完成 admin 创建
+- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": true}`
+- [ ] 前端访问 `/login` 会跳转 `/setup`

 ### 1.2 非首次启动

@@ -42,7 +43,8 @@ make dev

 **验证点：**
 - [ ] 控制台不输出密码
- [ ] 如果 admin 仍 `needs_setup=True`，控制台有 warning 提示
+- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": false}`
+- [ ] 已登录用户如果 `needs_setup=True`，访问 workspace 会被引导到 `/setup` 完成改邮箱 / 改密码流程

 ### 1.3 环境变量配置

@@ -76,19 +78,22 @@ make dev
 curl -s $BASE/api/v1/auth/setup-status | jq .
 ```

-**预期：** 返回 `{"needs_setup": false}`（admin 在启动时已自动创建，`count_users() > 0`）。仅在启动完成前的极短窗口内可能返回 `true`。
+**预期：**
+- 干净数据库且尚未初始化 admin：返回 `{"needs_setup": true}`
+- 已存在 admin：返回 `{"needs_setup": false}`

-#### TC-API-02: Admin 首次登录
+#### TC-API-02: 首次初始化 Admin

 ```bash
-curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<控制台密码>" \
+curl -s -X POST $BASE/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
  -c cookies.txt | jq .
 ```

 **预期：**
- 状态码 200
- Body: `{"expires_in": 604800, "needs_setup": true}`
+- 状态码 201
+- Body: `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`
 - `cookies.txt` 包含 `access_token`（HttpOnly）和 `csrf_token`（非 HttpOnly）

 #### TC-API-03: 获取当前用户
@@ -97,9 +102,9 @@ curl -s -X POST $BASE/api/v1/auth/login/local \
 curl -s $BASE/api/v1/auth/me -b cookies.txt | jq .
 ```

-**预期：** `{"id": "...", "email": "admin@deerflow.dev", "system_role": "admin", "needs_setup": true}`
+**预期：** `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`

-#### TC-API-04: Setup 流程（改邮箱 + 改密码）
+#### TC-API-04: 改密码流程

 ```bash
 CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
@@ -107,13 +112,36 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
  -b cookies.txt \
  -H "Content-Type: application/json" \
  -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<控制台密码>","new_password":"NewPass123!","new_email":"admin@example.com"}' | jq .
+  -d '{"current_password":"AdminPass1!","new_password":"NewPass123!"}' | jq .
 ```

 **预期：**
 - 状态码 200
 - `{"message": "Password changed successfully"}`
- 再调 `/auth/me` 邮箱变为 `admin@example.com`，`needs_setup` 变为 `false`
+- 再调 `/auth/me` 仍为 `admin@example.com`，`needs_setup` 仍为 `false`
+
+#### TC-API-04a: reset_admin 后的 Setup 流程（改邮箱 + 改密码）
+
+```bash
+cd backend
+python -m app.gateway.auth.reset_admin --email admin@example.com
+# 从 .deer-flow/admin_initial_credentials.txt 读取 reset 后密码
+
+curl -s -X POST $BASE/api/v1/auth/login/local \
+  -d "username=admin@example.com&password=<凭据文件密码>" \
+  -c cookies.txt | jq .
+
+CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
+curl -s -X POST $BASE/api/v1/auth/change-password \
+  -b cookies.txt \
+  -H "Content-Type: application/json" \
+  -H "X-CSRF-Token: $CSRF" \
+  -d '{"current_password":"<凭据文件密码>","new_password":"AdminPass2!","new_email":"admin2@example.com"}' | jq .
+```
+
+**预期：**
+- 登录返回 `{"expires_in": 604800, "needs_setup": true}`
+- `change-password` 后 `/auth/me` 邮箱变为 `admin2@example.com`，`needs_setup` 变为 `false`

 #### TC-API-05: 普通用户注册

@@ -493,7 +521,7 @@ curl -s -X POST $BASE/api/v1/auth/register \

 ```bash
 # 检查数据库
-sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMIT 3;"
+sqlite3 backend/.deer-flow/data/deerflow.db "SELECT email, password_hash FROM users LIMIT 3;"
 ```

 **预期：** `password_hash` 以 `$2b$` 开头（bcrypt 格式）
@@ -506,24 +534,25 @@ sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMI

 ### 4.1 首次登录流程

-#### TC-UI-01: 访问首页跳转登录
+#### TC-UI-01: 无 admin 时访问 workspace 跳转 setup

 1. 打开 `http://localhost:2026/workspace`
-2. **预期：** 自动跳转到 `/login`
+2. **预期：** 自动跳转到 `/setup`

-#### TC-UI-02: Login 页面
+#### TC-UI-02: Setup 页面创建 admin

-1. 输入 admin 邮箱和控制台密码
-2. 点击 Login
-3. **预期：** 跳转到 `/setup`（因为 `needs_setup=true`）
-
-#### TC-UI-03: Setup 页面
-
-1. 输入新邮箱、控制台密码（current）、新密码、确认密码
-2. 点击 Complete Setup
+1. 输入 admin 邮箱、密码、确认密码
+2. 点击 Create Admin Account
 3. **预期：** 跳转到 `/workspace`
 4. 刷新页面不跳回 `/setup`

+#### TC-UI-03: 已初始化后 Login 页面
+
+1. 退出登录后访问 `/login`
+2. 输入 admin 邮箱和密码
+3. 点击 Login
+4. **预期：** 跳转到 `/workspace`
+
 #### TC-UI-04: Setup 密码不匹配

 1. 新密码和确认密码不一致
@@ -602,7 +631,7 @@ sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMI
 #### TC-UI-15: reset_admin 后重新登录

 1. 执行 `cd backend && python -m app.gateway.auth.reset_admin`
-2. 使用新密码登录
+2. 从 `.deer-flow/admin_initial_credentials.txt` 读取新密码并登录
 3. **预期：** 跳转到 `/setup` 页面（`needs_setup` 被重置为 true）
 4. 旧 session 已失效

@@ -645,18 +674,28 @@ make install
 make dev
 ```

-#### TC-UPG-01: 首次启动创建 admin
+#### TC-UPG-01: 首次启动等待 admin 初始化

 **预期：**
- [ ] 控制台输出 admin 邮箱（`admin@deerflow.dev`）和随机密码
+- [ ] 控制台不输出 admin 邮箱或随机密码
+- [ ] 访问 `/setup` 可创建第一个 admin
 - [ ] 无报错，正常启动

 #### TC-UPG-02: 旧 Thread 迁移到 admin

 ```bash
+# 创建第一个 admin
+curl -s -X POST http://localhost:2026/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
+  -c cookies.txt
+
+# 重启一次：启动迁移只在已有 admin 的启动路径执行
+make stop && make dev
+
 # 登录 admin
 curl -s -X POST http://localhost:2026/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<控制台密码>" \
+  -d "username=admin@example.com&password=AdminPass1!" \
  -c cookies.txt

 # 查看 thread 列表
@@ -670,8 +709,8 @@ curl -s -X POST http://localhost:2026/api/threads/search \

 **预期：**
 - [ ] 返回的 thread 数量 ≥ 旧版创建的数量
- [ ] 控制台日志有 `Migrated N orphaned thread(s) to admin`
- [ ] 每个 thread 的 `metadata.owner_id` 都已被设为 admin 的 ID
+- [ ] 控制台日志有 `Migrated N orphan LangGraph thread(s) to admin`
+- [ ] 旧 thread 只对 admin 可见

 #### TC-UPG-03: 旧 Thread 内容完整

@@ -683,7 +722,7 @@ curl -s http://localhost:2026/api/threads/<old-thread-id> \

 **预期：**
 - [ ] `metadata.title` 保留原值（如 `old-thread-1`）
- [ ] `metadata.owner_id` 已填充
+- [ ] 响应不回显服务端保留的 `user_id` / `owner_id`

 #### TC-UPG-04: 新用户看不到旧 Thread

@@ -706,18 +745,19 @@ curl -s -X POST http://localhost:2026/api/threads/search \

 ### 5.3 数据库 Schema 兼容

-#### TC-UPG-05: 无 users.db 时自动创建
+#### TC-UPG-05: 无 deerflow.db 时创建 schema 但不创建默认用户

 ```bash
-ls -la backend/.deer-flow/users.db
+ls -la backend/.deer-flow/data/deerflow.db
+sqlite3 backend/.deer-flow/data/deerflow.db "SELECT COUNT(*) FROM users;"
 ```

-**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列
+**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列；未调用 `/initialize` 前用户数为 0

-#### TC-UPG-06: users.db WAL 模式
+#### TC-UPG-06: deerflow.db WAL 模式

 ```bash
-sqlite3 backend/.deer-flow/users.db "PRAGMA journal_mode;"
+sqlite3 backend/.deer-flow/data/deerflow.db "PRAGMA journal_mode;"
 ```

 **预期：** 返回 `wal`
@@ -768,9 +808,9 @@ make dev
 ```

 **预期：**
- [ ] 服务正常启动（忽略 `users.db`，无 auth 相关代码不报错）
+- [ ] 服务正常启动（忽略 `deerflow.db`，无 auth 相关代码不报错）
 - [ ] 旧对话数据仍然可访问
- [ ] `users.db` 文件残留但不影响运行
+- [ ] `deerflow.db` 文件残留但不影响运行

 #### TC-UPG-12: 再次升级到 auth 分支

@@ -781,51 +821,47 @@ make dev
 ```

 **预期：**
- [ ] 识别已有 `users.db`，不重新创建 admin
- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `users.db`）
+- [ ] 识别已有 `deerflow.db`，不重新创建 admin
+- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `deerflow.db`）

-### 5.7 休眠 Admin（初始密码未使用/未更改）
+### 5.7 Admin 初始化与 reset_admin

-> 首次启动生成 admin + 随机密码，但运维未登录、未改密码。
-> 密码只在首次启动的控制台闪过一次，后续启动不再显示。
+> 首次启动不生成默认 admin，也不在日志输出密码。忘记密码时走 `reset_admin`，新密码写入 0600 凭据文件。

-#### TC-UPG-13: 重启后自动重置密码并打印
+#### TC-UPG-13: 未初始化 admin 时重启不创建默认账号

 ```bash
-# 首次启动，记录密码
-rm -f backend/.deer-flow/users.db
+rm -f backend/.deer-flow/data/deerflow.db
 make dev
-# 控制台输出密码 P0，不登录
 make stop

-# 隔了几天，再次启动
 make dev
-# 控制台输出新密码 P1
+curl -s $BASE/api/v1/auth/setup-status | jq .
 ```

 **预期：**
- [ ] 控制台输出 `Admin account setup incomplete — password reset`
- [ ] 输出新密码 P1（P0 已失效）
- [ ] 用 P1 可以登录，P0 不可以
- [ ] 登录后 `needs_setup=true`，跳转 `/setup`
- [ ] `token_version` 递增（旧 session 如有也失效）
+- [ ] 控制台不输出密码
+- [ ] `setup-status` 仍为 `{"needs_setup": true}`
+- [ ] 访问 `/setup` 仍可创建第一个 admin

-#### TC-UPG-14: 密码丢失 — 无需 CLI，重启即可
+#### TC-UPG-14: 密码丢失 — reset_admin 写入凭据文件

 ```bash
-# 忘记了控制台密码 → 直接重启服务
-make stop && make dev
-# 控制台自动输出新密码
+python -m app.gateway.auth.reset_admin --email admin@example.com
+ls -la backend/.deer-flow/admin_initial_credentials.txt
+cat backend/.deer-flow/admin_initial_credentials.txt
 ```

 **预期：**
- [ ] 无需 `reset_admin`，重启服务即可拿到新密码
- [ ] `reset_admin` CLI 仍然可用作手动备选方案
+- [ ] 命令行只输出凭据文件路径，不输出明文密码
+- [ ] 凭据文件权限为 `0600`
+- [ ] 凭据文件包含 email + password 行
+- [ ] 该用户下次登录返回 `needs_setup=true`

-#### TC-UPG-15: 休眠 admin 期间普通用户注册
+#### TC-UPG-15: 未初始化 admin 期间普通用户注册策略边界

 ```bash
-# admin 存在但从未登录，普通用户先注册
+# admin 尚不存在，普通用户尝试注册
 curl -s -X POST $BASE/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{"email":"earlybird@example.com","password":"EarlyPass1!"}' \
@@ -833,11 +869,11 @@ curl -s -X POST $BASE/api/v1/auth/register \
 ```

 **预期：**
- [ ] 注册成功（201），角色为 `user`
- [ ] 无法提权为 admin
- [ ] 普通用户的数据与 admin 隔离
+- [ ] 当前代码允许注册普通用户并自动登录（201，角色为 `user`）
+- [ ] 但 `setup-status` 仍为 `{"needs_setup": true}`，因为 admin 仍不存在
+- [ ] 这是一个产品策略边界：若要求“必须先有 admin”，需要在 `/register` 增加 admin-exists gate

-#### TC-UPG-16: 休眠 admin 不影响后续操作
+#### TC-UPG-16: 普通用户数据与后续 admin 隔离

 ```bash
 # 普通用户正常创建 thread、发消息
@@ -849,14 +885,13 @@ curl -s -X POST $BASE/api/threads \
  -d '{"metadata":{}}' | jq .thread_id
 ```

-**预期：** 正常创建，不受休眠 admin 影响
+**预期：** 普通用户正常创建 thread；后续 admin 创建后，搜索不到该普通用户 thread

-#### TC-UPG-17: 休眠 admin 最终完成 Setup
+#### TC-UPG-17: reset_admin 后完成 Setup

 ```bash
-# 运维终于登录
 curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<P0或P1>" \
+  -d "username=admin@example.com&password=<凭据文件密码>" \
  -c admin.txt | jq .needs_setup
 # 预期: true

@@ -866,7 +901,7 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
  -b admin.txt \
  -H "Content-Type: application/json" \
  -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
+  -d '{"current_password":"<凭据文件密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
  -c admin.txt

 # 验证
@@ -876,7 +911,7 @@ curl -s $BASE/api/v1/auth/me -b admin.txt | jq '{email, needs_setup}'
 **预期：**
 - [ ] `email` 变为 `admin@real.com`
 - [ ] `needs_setup` 变为 `false`
- [ ] 后续重启控制台不再有 warning
+- [ ] 后续登录使用新密码

 #### TC-UPG-18: 长期未用后 JWT 密钥轮换

@@ -890,8 +925,8 @@ make stop && make dev

 **预期：**
 - [ ] 服务正常启动
- [ ] 旧密码仍可登录（密码存在 DB，与 JWT 密钥无关）
- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）— 但因为从未登录过也没有旧 token
+- [ ] 账号密码仍可登录（密码存在 DB，与 JWT 密钥无关）
+- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）

 ---

@@ -910,7 +945,7 @@ for i in 1 2 3; do
 done

 # 检查 admin 数量
-sqlite3 backend/.deer-flow/users.db \
+sqlite3 backend/.deer-flow/data/deerflow.db \
  "SELECT COUNT(*) FROM users WHERE system_role='admin';"
 ```

@@ -1055,7 +1090,7 @@ curl -s -X POST $BASE/api/v1/auth/register \
 wait

 # 检查用户数
-sqlite3 backend/.deer-flow/users.db \
+sqlite3 backend/.deer-flow/data/deerflow.db \
  "SELECT COUNT(*) FROM users WHERE email='race@example.com';"
 ```

@@ -1165,13 +1200,16 @@ curl -s -w "%{http_code}" -X DELETE "$BASE/api/threads/$TID" \
 ```bash
 cd backend
 python -m app.gateway.auth.reset_admin
-# 记录密码 P1
+cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p1.txt
+P1=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p1.txt)

 python -m app.gateway.auth.reset_admin
-# 记录密码 P2
+cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p2.txt
+P2=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p2.txt)
 ```

 **预期：**
+- [ ] `.deer-flow/admin_initial_credentials.txt` 每次都会被重写，文件权限为 `0600`
 - [ ] P1 ≠ P2（每次生成新随机密码）
 - [ ] P1 不可用，只有 P2 有效
 - [ ] `token_version` 递增了 2
@@ -1324,7 +1362,8 @@ done
 ```bash
 GW=http://localhost:8001

-for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local /api/v1/auth/register; do
+for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local \
+            /api/v1/auth/register /api/v1/auth/initialize /api/v1/auth/logout; do
  echo "$path: $(curl -s -w '%{http_code}' -o /dev/null $GW$path)"
 done
 # 预期: 200 或 405/422（方法不对但不是 401）
@@ -1399,9 +1438,9 @@ done
 >
 > 前置条件：
 > - `.env` 中设置 `AUTH_JWT_SECRET`（否则每次容器重启 session 全部失效）
-> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `users.db`）
+> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `deerflow.db`）

-#### TC-DOCKER-01: users.db 通过 volume 持久化
+#### TC-DOCKER-01: deerflow.db 通过 volume 持久化

 ```bash
 # 启动容器
@@ -1416,13 +1455,13 @@ curl -s -X POST $BASE/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{"email":"docker-test@example.com","password":"DockerTest1!"}' -w "\nHTTP %{http_code}"

-# 检查宿主机上的 users.db
-ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db
-sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db \
+# 检查宿主机上的 deerflow.db
+ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db
+sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db \
  "SELECT email FROM users WHERE email='docker-test@example.com';"
 ```

-**预期：** users.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。
+**预期：** deerflow.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。

 #### TC-DOCKER-02: 重启容器后 session 保持

@@ -1466,22 +1505,24 @@ done

 **已知限制：** In-process rate limiter 不跨 worker 共享。生产环境如需精确限速，需要 Redis 等外部存储。

-#### TC-DOCKER-04: IM 渠道不经过 auth
+#### TC-DOCKER-04: IM 渠道使用内部认证

 ```bash
-# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 通信
-# 不走 nginx，不经过 AuthMiddleware
+# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 调 Gateway
+# 请求携带 process-local internal auth header，并带匹配的 CSRF cookie/header

 # 验证方式：检查 gateway 日志中 channel manager 的请求不包含 auth 错误
 docker logs deer-flow-gateway 2>&1 | grep -E "ChannelManager|channel" | head -10
 ```

-**预期：** 无 auth 相关错误。渠道通过 `langgraph-sdk` 直连 LangGraph Server（`http://langgraph:2024`），不走 auth 层。
+**预期：** 无 auth 相关错误。渠道不依赖浏览器 cookie；服务端通过内部认证头把请求归入 `default` 用户桶。

-#### TC-DOCKER-05: admin 密码写入 0600 凭证文件（不再走日志）
+#### TC-DOCKER-05: reset_admin 密码写入 0600 凭证文件（不再走日志）

 ```bash
-# 凭证文件写在挂载到宿主机的 DEER_FLOW_HOME 下
+# 首次启动不会自动生成 admin 密码。先重置已有 admin，凭据文件写在挂载到宿主机的 DEER_FLOW_HOME 下。
+docker exec deer-flow-gateway python -m app.gateway.auth.reset_admin --email docker-test@example.com
+
 ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/admin_initial_credentials.txt
 # 预期文件权限: -rw------- (0600)

@@ -1512,14 +1553,15 @@ sleep 15
 docker ps --filter name=deer-flow-langgraph --format '{{.Names}}' | wc -l
 # 预期: 0

-# auth 流程正常
+# auth 流程正常：未登录受保护接口返回 401
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models
 # 预期: 401

-curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<日志密码>" \
+curl -s -X POST $BASE/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
  -c cookies.txt -w "\nHTTP %{http_code}"
-# 预期: 200
+# 预期: 201
 ```

 ### 7.4 补充边界用例
@@ -1587,13 +1629,15 @@ curl -s -D - -X POST $BASE/api/v1/auth/login/local \
 #### TC-EDGE-05: HTTP 无 max_age / HTTPS 有 max_age

 ```bash
+GW=http://localhost:8001
+
 # HTTP
-curl -s -D - -X POST $BASE/api/v1/auth/login/local \
+curl -s -D - -X POST $GW/api/v1/auth/login/local \
  -d "username=admin@example.com&password=正确密码" 2>/dev/null \
  | grep "access_token=" | grep -oi "max-age=[0-9]*" || echo "NO max-age (HTTP session cookie)"

-# HTTPS
-curl -s -D - -X POST $BASE/api/v1/auth/login/local \
+# HTTPS：直连 Gateway 才能用 X-Forwarded-Proto 模拟 HTTPS；nginx 会覆盖该 header
+curl -s -D - -X POST $GW/api/v1/auth/login/local \
  -H "X-Forwarded-Proto: https" \
  -d "username=admin@example.com&password=正确密码" 2>/dev/null \
  | grep "access_token=" | grep -oi "max-age=[0-9]*"
@@ -1712,10 +1756,10 @@ curl -s -X POST $BASE/api/threads \
  -b cookies.txt \
  -H "Content-Type: application/json" \
  -H "X-CSRF-Token: $CSRF" \
-  -d '{"metadata":{"owner_id":"victim-user-id"}}' | jq .metadata.owner_id
+  -d '{"metadata":{"owner_id":"victim-user-id","user_id":"victim-user-id"}}' | jq .metadata
 ```

-**预期：** 返回的 `metadata.owner_id` 应为当前登录用户的 ID，不是请求中注入的 `victim-user-id`。服务端应覆盖客户端提供的 `user_id`。
+**预期：** 返回的 `metadata` 不包含 `owner_id` 或 `user_id`。真实所有权写入 `threads_meta.user_id`，不从客户端 metadata 接收，也不通过 metadata 回显。

 #### 7.5.6 HTTP Method 探测

@@ -1796,6 +1840,6 @@ cd backend && PYTHONPATH=. uv run pytest \
 # 核心接口冒烟
 curl -s $BASE/health                              # 200
 curl -s $BASE/api/models                          # 401 (无 cookie)
-curl -s -X POST $BASE/api/v1/auth/setup-status    # 200
+curl -s $BASE/api/v1/auth/setup-status            # 200
 curl -s $BASE/api/v1/auth/me -b cookies.txt       # 200 (有 cookie)
 ```
@@ -2,13 +2,16 @@

 DeerFlow 内置了认证模块。本文档面向从无认证版本升级的用户。

+完整设计见 [AUTH_DESIGN.md](AUTH_DESIGN.md)。
+
 ## 核心概念

 认证模块采用**始终强制**策略：

- 首次启动时自动创建 admin 账号，随机密码打印到控制台日志
+- 首次启动时不会自动创建账号；首次访问 `/setup` 时由操作者创建第一个 admin 账号
 - 认证从一开始就是强制的，无竞争窗口
- 历史对话（升级前创建的 thread）自动迁移到 admin 名下
+- 已有 admin 后，服务启动时会把历史对话（升级前创建且缺少 `user_id` 的 thread）迁移到 admin 名下
+- 新数据按用户隔离：thread、workspace/uploads/outputs、memory、自定义 agent 都归属当前用户

 ## 升级步骤

@@ -25,39 +28,41 @@ cd backend && make install
 make dev
 ```

-控制台会输出：
+如果没有 admin 账号，控制台只会提示：

 ```
 ============================================================
-  Admin account created on first boot
-  Email:    admin@deerflow.dev
-  Password: aB3xK9mN_pQ7rT2w
-  Change it after login: Settings → Account
+  First boot detected — no admin account exists.
+  Visit /setup to complete admin account creation.
 ============================================================
 ```

-如果未登录就重启了服务，不用担心——只要 setup 未完成，每次启动都会重置密码并重新打印到控制台。
+首次启动不会在日志里打印随机密码，也不会写入默认 admin。这样避免启动日志泄露凭据，也避免在操作者创建账号前出现可被猜测的默认身份。

-### 3. 登录
+### 3. 创建 admin

-访问 `http://localhost:2026/login`，使用控制台输出的邮箱和密码登录。
+访问 `http://localhost:2026/setup`，填写邮箱和密码创建第一个 admin 账号。创建成功后会自动登录并进入 workspace。

-### 4. 修改密码
+如果这是从无认证版本升级，创建 admin 后重启一次服务，让启动迁移把缺少 `user_id` 的历史 thread 归属到 admin。

-登录后进入 Settings → Account → Change Password。
+### 4. 登录
+
+后续访问 `http://localhost:2026/login`，使用已创建的邮箱和密码登录。

 ### 5. 添加用户（可选）

-其他用户通过 `/login` 页面注册，自动获得 **user** 角色。每个用户只能看到自己的对话。
+其他用户通过 `/login` 页面注册，自动获得 **user** 角色。每个用户只能看到自己的对话、上传文件、输出文件、memory 和自定义 agent。

 ## 安全机制

 | 机制 | 说明 |
 |------|------|
 | JWT HttpOnly Cookie | Token 不暴露给 JavaScript，防止 XSS 窃取 |
-| CSRF Double Submit Cookie | 所有 POST/PUT/DELETE 请求需携带 `X-CSRF-Token` |
+| CSRF Double Submit Cookie | 受保护的 POST/PUT/PATCH/DELETE 请求需携带 `X-CSRF-Token`；登录/注册/初始化/登出走 auth 端点 Origin 校验 |
 | bcrypt 密码哈希 | 密码不以明文存储 |
-| 多租户隔离 | 用户只能访问自己的 thread |
+| Thread owner filter | `threads_meta.user_id` 由服务端认证上下文写入，搜索、读取、更新、删除默认按当前用户过滤 |
+| 文件系统隔离 | 线程数据写入 `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/`，sandbox 内统一映射为 `/mnt/user-data/` |
+| Memory / agent 隔离 | 用户 memory 和自定义 agent 写入 `{base_dir}/users/{user_id}/...`；旧共享 agent 只作为只读兼容回退 |
 | HTTPS 自适应 | 检测 `x-forwarded-proto`，自动设置 `Secure` cookie 标志 |

 ## 常见操作
@@ -74,23 +79,27 @@ python -m app.gateway.auth.reset_admin
 python -m app.gateway.auth.reset_admin --email user@example.com
 ```

-会输出新的随机密码。
+会把新的随机密码写入 `.deer-flow/admin_initial_credentials.txt`，文件权限为 `0600`。命令行只输出文件路径，不输出明文密码。

 ### 完全重置

-删除用户数据库，重启后自动创建新 admin：
+删除统一 SQLite 数据库，重启后重新访问 `/setup` 创建新 admin：

 ```bash
-rm -f backend/.deer-flow/users.db
-# 重启服务，控制台输出新密码
+rm -f backend/.deer-flow/data/deerflow.db
+# 重启服务后访问 http://localhost:2026/setup
 ```

 ## 数据存储

 | 文件 | 内容 |
 |------|------|
-| `.deer-flow/users.db` | SQLite 用户数据库（密码哈希、角色） |
-| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥（未设置时自动生成临时密钥，重启后 session 失效） |
+| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库（users、threads_meta、runs、feedback 等应用数据） |
+| `.deer-flow/users/{user_id}/threads/{thread_id}/user-data/` | 用户线程的 workspace、uploads、outputs |
+| `.deer-flow/users/{user_id}/memory.json` | 用户级 memory |
+| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
+| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件（0600，读完应删除） |
+| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥（未设置时自动生成并持久化到 `.deer-flow/.jwt_secret`，重启后 session 保持） |

 ### 生产环境建议

@@ -111,19 +120,21 @@ python -c "import secrets; print(secrets.token_urlsafe(32))"
 | `/api/v1/auth/me` | GET | 获取当前用户信息 |
 | `/api/v1/auth/change-password` | POST | 修改密码 |
 | `/api/v1/auth/setup-status` | GET | 检查 admin 是否存在 |
+| `/api/v1/auth/initialize` | POST | 首次初始化第一个 admin（仅无 admin 时可调用） |

 ## 兼容性

- **标准模式**（`make dev`）：完全兼容，admin 自动创建
+- **标准模式**（`make dev`）：完全兼容；无 admin 时访问 `/setup` 初始化
 - **Gateway 模式**（`make dev-pro`）：完全兼容
- **Docker 部署**：完全兼容，`.deer-flow/users.db` 需持久化卷挂载
- **IM 渠道**（Feishu/Slack/Telegram）：通过 LangGraph SDK 通信，不经过认证层
+- **Docker 部署**：完全兼容，`.deer-flow/data/deerflow.db` 需持久化卷挂载
+- **IM 渠道**（Feishu/Slack/Telegram）：通过 Gateway 内部认证通信，使用 `default` 用户桶
 - **DeerFlowClient**（嵌入式）：不经过 HTTP，不受认证影响

 ## 故障排查

 | 症状 | 原因 | 解决 |
 |------|------|------|
-| 启动后没看到密码 | admin 已存在（非首次启动） | 用 `reset_admin` 重置，或删 `users.db` |
+| 启动后没看到密码 | 当前实现不在启动日志输出密码 | 首次安装访问 `/setup`；忘记密码用 `reset_admin` |
+| `/login` 自动跳到 `/setup` | 系统还没有 admin | 在 `/setup` 创建第一个 admin |
 | 登录后 POST 返回 403 | CSRF token 缺失 | 确认前端已更新 |
-| 重启后需要重新登录 | `AUTH_JWT_SECRET` 未持久化 | 在 `.env` 中设置固定密钥 |
+| 重启后需要重新登录 | `.jwt_secret` 文件被删除且 `.env` 未设置 `AUTH_JWT_SECRET` | 在 `.env` 中设置固定密钥 |
@@ -8,6 +8,7 @@ This directory contains detailed documentation for the DeerFlow backend.
 |----------|-------------|
 | [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
 | [API.md](API.md) | Complete API reference |
+| [AUTH_DESIGN.md](AUTH_DESIGN.md) | User authentication, CSRF, and per-user isolation design |
 | [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
 | [SETUP.md](SETUP.md) | Quick setup guide |

@@ -42,6 +43,7 @@ docs/
 ├── README.md                  # This file
 ├── ARCHITECTURE.md            # System architecture
 ├── API.md                     # API reference
+├── AUTH_DESIGN.md             # User authentication and isolation design
 ├── CONFIGURATION.md           # Configuration guide
 ├── SETUP.md                   # Setup instructions
 ├── FILE_UPLOAD.md             # File upload feature
@@ -173,7 +173,7 @@ def _assemble_from_features(
      9.   MemoryMiddleware (memory feature)
      10.  ViewImageMiddleware (vision feature)
      11.  SubagentLimitMiddleware (subagent feature)
-      12.  LoopDetectionMiddleware (always)
+      12.  LoopDetectionMiddleware (loop_detection feature)
      13.  ClarificationMiddleware (always last)

    Two-phase ordering:
@@ -272,10 +272,15 @@ def _assemble_from_features(

        extra_tools.append(task_tool)

-    # --- [12] LoopDetection (always) ---
+    # --- [12] LoopDetection ---
+    if feat.loop_detection is not False:
+        if isinstance(feat.loop_detection, AgentMiddleware):
+            chain.append(feat.loop_detection)
+        else:
            from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
+            from deerflow.config.loop_detection_config import LoopDetectionConfig

-    chain.append(LoopDetectionMiddleware())
+            chain.append(LoopDetectionMiddleware.from_config(LoopDetectionConfig()))

    # --- [13] Clarification (always last among built-ins) ---
    chain.append(ClarificationMiddleware())
@@ -31,6 +31,7 @@ class RuntimeFeatures:
    vision: bool | AgentMiddleware = False
    auto_title: bool | AgentMiddleware = False
    guardrail: Literal[False] | AgentMiddleware = False
+    loop_detection: bool | AgentMiddleware = True


 # ---------------------------------------------------------------------------
@@ -20,6 +20,8 @@ from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import load_agent_config, validate_agent_name
 from deerflow.config.app_config import AppConfig, get_app_config
 from deerflow.models import create_chat_model
+from deerflow.skills.tool_policy import filter_tools_by_skill_allowed_tools
+from deerflow.skills.types import Skill

 logger = logging.getLogger(__name__)

@@ -256,6 +258,12 @@ def _build_middlewares(
    resolved_app_config = app_config or get_app_config()
    middlewares = build_lead_runtime_middlewares(app_config=resolved_app_config, lazy_init=True)

+    # Always inject current date (and optionally memory) as <system-reminder> into the
+    # first HumanMessage to keep the system prompt fully static for prefix-cache reuse.
+    from deerflow.agents.middlewares.dynamic_context_middleware import DynamicContextMiddleware
+
+    middlewares.append(DynamicContextMiddleware(agent_name=agent_name, app_config=resolved_app_config))
+
    # Add summarization middleware if enabled
    summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
    if summarization_middleware is not None:
@@ -297,7 +305,9 @@ def _build_middlewares(
        middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))

    # LoopDetectionMiddleware — detect and break repetitive tool call loops
-    middlewares.append(LoopDetectionMiddleware())
+    loop_detection_config = resolved_app_config.loop_detection
+    if loop_detection_config.enabled:
+        middlewares.append(LoopDetectionMiddleware.from_config(loop_detection_config))

    # Inject custom middlewares before ClarificationMiddleware
    if custom_middlewares:
@@ -308,6 +318,28 @@ def _build_middlewares(
    return middlewares


+def _available_skill_names(agent_config, is_bootstrap: bool) -> set[str] | None:
+    if is_bootstrap:
+        return {"bootstrap"}
+    if agent_config and agent_config.skills is not None:
+        return set(agent_config.skills)
+    return None
+
+
+def _load_enabled_skills_for_tool_policy(available_skills: set[str] | None, *, app_config: AppConfig) -> list[Skill]:
+    try:
+        from deerflow.agents.lead_agent.prompt import get_enabled_skills_for_config
+
+        skills = get_enabled_skills_for_config(app_config)
+    except Exception:
+        logger.exception("Failed to load skills for allowed-tools policy")
+        raise
+
+    if available_skills is None:
+        return skills
+    return [skill for skill in skills if skill.name in available_skills]
+
+
 def make_lead_agent(config: RunnableConfig):
    """LangGraph graph factory; keep the signature compatible with LangGraph Server."""
    runtime_config = _get_runtime_config(config)
@@ -318,7 +350,7 @@ def make_lead_agent(config: RunnableConfig):
 def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
    # Lazy import to avoid circular dependency
    from deerflow.tools import get_available_tools
-    from deerflow.tools.builtins import setup_agent
+    from deerflow.tools.builtins import setup_agent, update_agent

    cfg = _get_runtime_config(config)
    resolved_app_config = app_config
@@ -333,6 +365,7 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
    agent_name = validate_agent_name(cfg.get("agent_name"))

    agent_config = load_agent_config(agent_name) if not is_bootstrap else None
+    available_skills = _available_skill_names(agent_config, is_bootstrap)
    # Custom agent model from agent config (if any), or None to let _resolve_model_name pick the default
    agent_model_name = agent_config.model if agent_config and agent_config.model else None

@@ -371,15 +404,18 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
            "is_plan_mode": is_plan_mode,
            "subagent_enabled": subagent_enabled,
            "tool_groups": agent_config.tool_groups if agent_config else None,
-            "available_skills": ["bootstrap"] if is_bootstrap else (agent_config.skills if agent_config and agent_config.skills is not None else None),
+            "available_skills": sorted(available_skills) if available_skills is not None else None,
        }
    )

+    skills_for_tool_policy = _load_enabled_skills_for_tool_policy(available_skills, app_config=resolved_app_config)
+
    if is_bootstrap:
        # Special bootstrap agent with minimal prompt for initial custom agent creation flow
+        tools = get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent]
        return create_agent(
            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config),
-            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent],
+            tools=filter_tools_by_skill_allowed_tools(tools, skills_for_tool_policy),
            middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
            system_prompt=apply_prompt_template(
                subagent_enabled=subagent_enabled,
@@ -390,15 +426,14 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
            state_schema=ThreadState,
        )

+    # Custom agents can update their own SOUL.md / config via update_agent.
+    # The default agent (no agent_name) does not see this tool.
+    extra_tools = [update_agent] if agent_name else []
    # Default lead agent (unchanged behavior)
+    tools = get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=resolved_app_config)
    return create_agent(
        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config),
-        tools=get_available_tools(
-            model_name=model_name,
-            groups=agent_config.tool_groups if agent_config else None,
-            subagent_enabled=subagent_enabled,
-            app_config=resolved_app_config,
-        ),
+        tools=filter_tools_by_skill_allowed_tools(tools + extra_tools, skills_for_tool_policy),
        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config),
        system_prompt=apply_prompt_template(
            subagent_enabled=subagent_enabled,
@@ -3,7 +3,6 @@ from __future__ import annotations
 import asyncio
 import logging
 import threading
-from datetime import datetime
 from functools import lru_cache
 from typing import TYPE_CHECKING

@@ -20,6 +19,7 @@ logger = logging.getLogger(__name__)
 _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
 _enabled_skills_lock = threading.Lock()
 _enabled_skills_cache: list[Skill] | None = None
+_enabled_skills_by_config_cache: dict[int, tuple[object, list[Skill]]] = {}
 _enabled_skills_refresh_active = False
 _enabled_skills_refresh_version = 0
 _enabled_skills_refresh_event = threading.Event()
@@ -84,6 +84,7 @@ def _invalidate_enabled_skills_cache() -> threading.Event:
    _get_cached_skills_prompt_section.cache_clear()
    with _enabled_skills_lock:
        _enabled_skills_cache = None
+        _enabled_skills_by_config_cache.clear()
        _enabled_skills_refresh_version += 1
        _enabled_skills_refresh_event.clear()
        if _enabled_skills_refresh_active:
@@ -107,6 +108,15 @@ def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_W


 def _get_enabled_skills():
+    return get_cached_enabled_skills()
+
+
+def get_cached_enabled_skills() -> list[Skill]:
+    """Return the cached enabled-skills list, kicking off a background refresh on miss.
+
+    Safe to call from request paths: never blocks on disk I/O. Returns an empty
+    list on cache miss; the next call will see the warmed result.
+    """
    with _enabled_skills_lock:
        cached = _enabled_skills_cache

@@ -117,17 +127,29 @@ def _get_enabled_skills():
    return []


-def _get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
+def get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
    """Return enabled skills using the caller's config source.

-    When a concrete ``app_config`` is supplied, bypass the global enabled-skills
-    cache so the skill list and skill paths are resolved from the same config
-    object. This keeps request-scoped config injection consistent even while the
-    release branch still supports global fallback paths.
+    When a concrete ``app_config`` is supplied, cache the loaded skills by that
+    config object's identity so request-scoped config injection still resolves
+    skill paths from the matching config without rescanning storage on every
+    agent factory call.
    """
    if app_config is None:
        return _get_enabled_skills()
-    return list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
+
+    cache_key = id(app_config)
+    with _enabled_skills_lock:
+        cached = _enabled_skills_by_config_cache.get(cache_key)
+        if cached is not None:
+            cached_config, cached_skills = cached
+            if cached_config is app_config:
+                return list(cached_skills)
+
+    skills = list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
+    with _enabled_skills_lock:
+        _enabled_skills_by_config_cache[cache_key] = (app_config, skills)
+    return list(skills)


 def _skill_mutability_label(category: SkillCategory | str) -> str:
@@ -344,8 +366,7 @@ You are {agent_name}, an open-source super agent.
 </role>

 {soul}
-{memory_context}
-
+{self_update_section}
 <thinking_style>
 - Think concisely and strategically about the user's request BEFORE taking action
 - Break down the task: What is clear? What is ambiguous? What is missing?
@@ -604,7 +625,7 @@ You have access to skills that provide optimized workflows for specific tasks. E

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

    if app_config is None:
        try:
@@ -643,6 +664,26 @@ def get_agent_soul(agent_name: str | None) -> str:
    return ""


+def _build_self_update_section(agent_name: str | None) -> str:
+    """Prompt block that teaches the custom agent to persist self-updates via update_agent."""
+    if not agent_name:
+        return ""
+    return f"""<self_update>
+You are running as the custom agent **{agent_name}** with a persisted SOUL.md and config.yaml.
+
+When the user asks you to update your own description, personality, behaviour, skill set, tool groups, or default model,
+you MUST persist the change with the `update_agent` tool. Do NOT use `bash`, `write_file`, or any sandbox tool to edit
+SOUL.md or config.yaml — those write into a temporary sandbox/tool workspace and the changes will be lost on the next turn.
+
+Rules:
+- Always pass the FULL replacement text for `soul` (no patch semantics). Start from your current SOUL above and apply the user's edits.
+- Only pass the fields that should change. Omit the others to preserve them.
+- Pass `skills=[]` to disable all skills, or omit `skills` to keep the existing whitelist.
+- After `update_agent` returns successfully, tell the user the change is persisted and will take effect on the next turn.
+</self_update>
+"""
+
+
 def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
    """Generate <available-deferred-tools> block for the system prompt.

@@ -732,9 +773,6 @@ def apply_prompt_template(
    available_skills: set[str] | None = None,
    app_config: AppConfig | None = None,
 ) -> str:
-    # Get memory context
-    memory_context = _get_memory_context(agent_name, app_config=app_config)
-
    # Include subagent section only if enabled (from runtime parameter)
    n = max_concurrent_subagents
    subagent_section = _build_subagent_section(n, app_config=app_config) if subagent_enabled else ""
@@ -768,17 +806,18 @@ def apply_prompt_template(
    custom_mounts_section = _build_custom_mounts_section(app_config=app_config)
    acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)

-    # Format the prompt with dynamic skills and memory
-    prompt = SYSTEM_PROMPT_TEMPLATE.format(
+    # Build and return the fully static system prompt.
+    # Memory and current date are injected per-turn via DynamicContextMiddleware
+    # as a <system-reminder> in the first HumanMessage, keeping this prompt
+    # identical across users and sessions for maximum prefix-cache reuse.
+    return SYSTEM_PROMPT_TEMPLATE.format(
        agent_name=agent_name or "DeerFlow 2.0",
        soul=get_agent_soul(agent_name),
+        self_update_section=_build_self_update_section(agent_name),
        skills_section=skills_section,
        deferred_tools_section=deferred_tools_section,
-        memory_context=memory_context,
        subagent_section=subagent_section,
        subagent_reminder=subagent_reminder,
        subagent_thinking=subagent_thinking,
        acp_section=acp_and_mounts_section,
    )
-
-    return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"
@@ -40,6 +40,15 @@ class MemoryUpdateQueue:
        self._timer: threading.Timer | None = None
        self._processing = False

+    @staticmethod
+    def _queue_key(
+        thread_id: str,
+        user_id: str | None,
+        agent_name: str | None,
+    ) -> tuple[str, str | None, str | None]:
+        """Return the debounce identity for a memory update target."""
+        return (thread_id, user_id, agent_name)
+
    def add(
        self,
        thread_id: str,
@@ -115,8 +124,9 @@ class MemoryUpdateQueue:
        correction_detected: bool,
        reinforcement_detected: bool,
    ) -> None:
+        queue_key = self._queue_key(thread_id, user_id, agent_name)
        existing_context = next(
-            (context for context in self._queue if context.thread_id == thread_id),
+            (context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) == queue_key),
            None,
        )
        merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
@@ -130,7 +140,7 @@ class MemoryUpdateQueue:
            reinforcement_detected=merged_reinforcement_detected,
        )

-        self._queue = [c for c in self._queue if c.thread_id != thread_id]
+        self._queue = [context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) != queue_key]
        self._queue.append(context)

    def _reset_timer(self) -> None:
@@ -6,6 +6,7 @@ from deerflow.agents.memory.message_processing import detect_correction, detect_
 from deerflow.agents.memory.queue import get_memory_queue
 from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
 from deerflow.config.memory_config import get_memory_config
+from deerflow.runtime.user_context import resolve_runtime_user_id


 def memory_flush_hook(event: SummarizationEvent) -> None:
@@ -21,11 +22,13 @@ def memory_flush_hook(event: SummarizationEvent) -> None:

    correction_detected = detect_correction(filtered_messages)
    reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
+    user_id = resolve_runtime_user_id(event.runtime)
    queue = get_memory_queue()
    queue.add_nowait(
        thread_id=event.thread_id,
        messages=filtered_messages,
        agent_name=event.agent_name,
+        user_id=user_id,
        correction_detected=correction_detected,
        reinforcement_detected=reinforcement_detected,
    )
@@ -36,13 +36,22 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):

    @staticmethod
    def _message_tool_calls(msg) -> list[dict]:
-        """Return normalized tool calls from structured fields or raw provider payloads."""
+        """Return normalized tool calls from structured fields or raw provider payloads.
+
+        LangChain stores malformed provider function calls in ``invalid_tool_calls``.
+        They do not execute, but provider adapters may still serialize enough of
+        the call id/name back into the next request that strict OpenAI-compatible
+        validators expect a matching ToolMessage. Treat them as dangling calls so
+        the next model request stays well-formed and the model sees a recoverable
+        tool error instead of another provider 400.
+        """
+        normalized: list[dict] = []
+
        tool_calls = getattr(msg, "tool_calls", None) or []
-        if tool_calls:
-            return list(tool_calls)
+        normalized.extend(list(tool_calls))

        raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
-        normalized: list[dict] = []
+        if not tool_calls:
            for raw_tc in raw_tool_calls:
                if not isinstance(raw_tc, dict):
                    continue
@@ -70,59 +79,86 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
                    }
                )

+        for invalid_tc in getattr(msg, "invalid_tool_calls", None) or []:
+            if not isinstance(invalid_tc, dict):
+                continue
+            normalized.append(
+                {
+                    "id": invalid_tc.get("id"),
+                    "name": invalid_tc.get("name") or "unknown",
+                    "args": {},
+                    "invalid": True,
+                    "error": invalid_tc.get("error"),
+                }
+            )
+
        return normalized

-    def _build_patched_messages(self, messages: list) -> list | None:
-        """Return a new message list with patches inserted at the correct positions.
+    @staticmethod
+    def _synthetic_tool_message_content(tool_call: dict) -> str:
+        if tool_call.get("invalid"):
+            error = tool_call.get("error")
+            if isinstance(error, str) and error:
+                return f"[Tool call could not be executed because its arguments were invalid: {error}]"
+            return "[Tool call could not be executed because its arguments were invalid.]"
+        return "[Tool call was interrupted and did not return a result.]"

-        For each AIMessage with dangling tool_calls (no corresponding ToolMessage),
-        a synthetic ToolMessage is inserted immediately after that AIMessage.
-        Returns None if no patches are needed.
+    def _build_patched_messages(self, messages: list) -> list | None:
+        """Return messages with tool results grouped after their tool-call AIMessage.
+
+        This normalizes model-bound causal order before provider serialization while
+        preserving already-valid transcripts unchanged.
        """
-        # Collect IDs of all existing ToolMessages
-        existing_tool_msg_ids: set[str] = set()
+        tool_messages_by_id: dict[str, ToolMessage] = {}
        for msg in messages:
            if isinstance(msg, ToolMessage):
-                existing_tool_msg_ids.add(msg.tool_call_id)
+                tool_messages_by_id.setdefault(msg.tool_call_id, msg)

-        # Check if any patching is needed
-        needs_patch = False
+        tool_call_ids: set[str] = set()
        for msg in messages:
            if getattr(msg, "type", None) != "ai":
                continue
            for tc in self._message_tool_calls(msg):
                tc_id = tc.get("id")
-                if tc_id and tc_id not in existing_tool_msg_ids:
-                    needs_patch = True
-                    break
-            if needs_patch:
-                break
+                if tc_id:
+                    tool_call_ids.add(tc_id)

-        if not needs_patch:
-            return None
-
-        # Build new list with patches inserted right after each dangling AIMessage
        patched: list = []
-        patched_ids: set[str] = set()
+        consumed_tool_msg_ids: set[str] = set()
        patch_count = 0
        for msg in messages:
+            if isinstance(msg, ToolMessage) and msg.tool_call_id in tool_call_ids:
+                continue
+
            patched.append(msg)
            if getattr(msg, "type", None) != "ai":
                continue
+
            for tc in self._message_tool_calls(msg):
                tc_id = tc.get("id")
-                if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
+                if not tc_id or tc_id in consumed_tool_msg_ids:
+                    continue
+
+                existing_tool_msg = tool_messages_by_id.get(tc_id)
+                if existing_tool_msg is not None:
+                    patched.append(existing_tool_msg)
+                    consumed_tool_msg_ids.add(tc_id)
+                else:
                    patched.append(
                        ToolMessage(
-                            content="[Tool call was interrupted and did not return a result.]",
+                            content=self._synthetic_tool_message_content(tc),
                            tool_call_id=tc_id,
                            name=tc.get("name", "unknown"),
                            status="error",
                        )
                    )
-                    patched_ids.add(tc_id)
+                    consumed_tool_msg_ids.add(tc_id)
                    patch_count += 1

+        if patched == messages:
+            return None
+
+        if patch_count:
            logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
        return patched

@@ -0,0 +1,204 @@
+"""Middleware to inject dynamic context (memory, current date) as a system-reminder.
+
+The system prompt is kept fully static for maximum prefix-cache reuse across users
+and sessions.  The current date is always injected.  Per-user memory is also injected
+when ``memory.injection_enabled`` is True in the app config.  Both are delivered once
+per conversation as a dedicated <system-reminder> HumanMessage inserted before the
+first user message (frozen-snapshot pattern).
+
+When a conversation spans midnight the middleware detects the date change and injects
+a lightweight date-update reminder as a separate HumanMessage before the current turn.
+This correction is persisted so subsequent turns on the new day see a consistent history
+and do not re-inject.
+
+Reminder format:
+
+    <system-reminder>
+    <memory>...</memory>
+
+    <current_date>2026-05-08, Friday</current_date>
+    </system-reminder>
+
+Date-update format:
+
+    <system-reminder>
+    <current_date>2026-05-09, Saturday</current_date>
+    </system-reminder>
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+import uuid
+from datetime import datetime
+from typing import TYPE_CHECKING, override
+
+from langchain.agents.middleware import AgentMiddleware
+from langchain_core.messages import HumanMessage
+from langgraph.runtime import Runtime
+
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
+logger = logging.getLogger(__name__)
+
+_DATE_RE = re.compile(r"<current_date>([^<]+)</current_date>")
+_DYNAMIC_CONTEXT_REMINDER_KEY = "dynamic_context_reminder"
+_SUMMARY_MESSAGE_NAME = "summary"
+
+
+def _extract_date(content: str) -> str | None:
+    """Return the first <current_date> value found in *content*, or None."""
+    m = _DATE_RE.search(content)
+    return m.group(1) if m else None
+
+
+def is_dynamic_context_reminder(message: object) -> bool:
+    """Return whether *message* is a hidden dynamic-context reminder."""
+    return isinstance(message, HumanMessage) and bool(message.additional_kwargs.get(_DYNAMIC_CONTEXT_REMINDER_KEY))
+
+
+def _last_injected_date(messages: list) -> str | None:
+    """Scan messages in reverse and return the most recently injected date.
+
+    Detection uses the ``dynamic_context_reminder`` additional_kwargs flag rather
+    than content substring matching, so user messages containing ``<system-reminder>``
+    are not mistakenly treated as injected reminders.
+    """
+    for msg in reversed(messages):
+        if is_dynamic_context_reminder(msg):
+            content_str = msg.content if isinstance(msg.content, str) else str(msg.content)
+            return _extract_date(content_str)
+    return None
+
+
+def _is_user_injection_target(message: object) -> bool:
+    """Return whether *message* can receive a dynamic-context reminder."""
+    return isinstance(message, HumanMessage) and not is_dynamic_context_reminder(message) and message.name != _SUMMARY_MESSAGE_NAME
+
+
+class DynamicContextMiddleware(AgentMiddleware):
+    """Inject memory and current date into HumanMessages as a <system-reminder>.
+
+    First turn
+    ----------
+    Prepends a full system-reminder (memory + date) to the first HumanMessage and
+    persists it (same message ID).  The first message is then frozen for the whole
+    session — its content never changes again, so the prefix cache can hit on every
+    subsequent turn.
+
+    Midnight crossing
+    -----------------
+    If the conversation spans midnight, the current date differs from the date that
+    was injected earlier.  In that case a lightweight date-update reminder is prepended
+    to the **current** (last) HumanMessage and persisted.  Subsequent turns on the new
+    day see the corrected date in history and skip re-injection.
+    """
+
+    def __init__(self, agent_name: str | None = None, *, app_config: AppConfig | None = None):
+        super().__init__()
+        self._agent_name = agent_name
+        self._app_config = app_config
+
+    def _build_full_reminder(self) -> str:
+        from deerflow.agents.lead_agent.prompt import _get_memory_context
+
+        # Memory injection is gated by injection_enabled; date is always included.
+        injection_enabled = self._app_config.memory.injection_enabled if self._app_config else True
+        memory_context = _get_memory_context(self._agent_name, app_config=self._app_config) if injection_enabled else ""
+        current_date = datetime.now().strftime("%Y-%m-%d, %A")
+
+        lines: list[str] = ["<system-reminder>"]
+        if memory_context:
+            lines.append(memory_context.strip())
+            lines.append("")  # blank line separating memory from date
+        lines.append(f"<current_date>{current_date}</current_date>")
+        lines.append("</system-reminder>")
+
+        return "\n".join(lines)
+
+    def _build_date_update_reminder(self) -> str:
+        current_date = datetime.now().strftime("%Y-%m-%d, %A")
+        return "\n".join(
+            [
+                "<system-reminder>",
+                f"<current_date>{current_date}</current_date>",
+                "</system-reminder>",
+            ]
+        )
+
+    @staticmethod
+    def _make_reminder_and_user_messages(original: HumanMessage, reminder_content: str) -> tuple[HumanMessage, HumanMessage]:
+        """Return (reminder_msg, user_msg) using the ID-swap technique.
+
+        reminder_msg takes the original message's ID so that add_messages replaces it
+        in-place (preserving position).  user_msg carries the original content with a
+        derived ``{id}__user`` ID and is appended immediately after by add_messages.
+
+        If the original message has no ID a stable UUID is generated so the derived
+        ``{id}__user`` ID never collapses to the ambiguous ``None__user`` string.
+        """
+        stable_id = original.id or str(uuid.uuid4())
+        reminder_msg = HumanMessage(
+            content=reminder_content,
+            id=stable_id,
+            additional_kwargs={"hide_from_ui": True, _DYNAMIC_CONTEXT_REMINDER_KEY: True},
+        )
+        user_msg = HumanMessage(
+            content=original.content,
+            id=f"{stable_id}__user",
+            name=original.name,
+            additional_kwargs=original.additional_kwargs,
+        )
+        return reminder_msg, user_msg
+
+    def _inject(self, state) -> dict | None:
+        messages = list(state.get("messages", []))
+        if not messages:
+            return None
+
+        current_date = datetime.now().strftime("%Y-%m-%d, %A")
+        last_date = _last_injected_date(messages)
+        logger.debug(
+            "DynamicContextMiddleware._inject: msg_count=%d last_date=%r current_date=%r",
+            len(messages),
+            last_date,
+            current_date,
+        )
+
+        if last_date is None:
+            # ── First turn: inject full reminder as a separate HumanMessage ─────
+            first_idx = next((i for i, m in enumerate(messages) if _is_user_injection_target(m)), None)
+            if first_idx is None:
+                return None
+            full_reminder = self._build_full_reminder()
+            logger.info(
+                "DynamicContextMiddleware: injecting full reminder (len=%d, has_memory=%s) into first HumanMessage id=%r",
+                len(full_reminder),
+                "<memory>" in full_reminder,
+                messages[first_idx].id,
+            )
+            reminder_msg, user_msg = self._make_reminder_and_user_messages(messages[first_idx], full_reminder)
+            return {"messages": [reminder_msg, user_msg]}
+
+        if last_date == current_date:
+            # ── Same day: nothing to do ──────────────────────────────────────────
+            return None
+
+        # ── Midnight crossed: inject date-update reminder as a separate HumanMessage ──
+        last_human_idx = next((i for i in reversed(range(len(messages))) if _is_user_injection_target(messages[i])), None)
+        if last_human_idx is None:
+            return None
+
+        reminder_msg, user_msg = self._make_reminder_and_user_messages(messages[last_human_idx], self._build_date_update_reminder())
+        logger.info("DynamicContextMiddleware: midnight crossing detected — injected date update before current turn")
+        return {"messages": [reminder_msg, user_msg]}
+
+    @override
+    def before_agent(self, state, runtime: Runtime) -> dict | None:
+        return self._inject(state)
+
+    @override
+    async def abefore_agent(self, state, runtime: Runtime) -> dict | None:
+        return self._inject(state)
@@ -12,19 +12,23 @@ Detection strategy:
     response so the agent is forced to produce a final text answer.
 """

+from __future__ import annotations
+
 import hashlib
 import json
 import logging
 import threading
 from collections import OrderedDict, defaultdict
 from copy import deepcopy
-from typing import override
+from typing import TYPE_CHECKING, override

 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
-from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime

+if TYPE_CHECKING:
+    from deerflow.config.loop_detection_config import LoopDetectionConfig
+
 logger = logging.getLogger(__name__)

 # Defaults — can be overridden via constructor
@@ -140,6 +144,9 @@ _TOOL_FREQ_HARD_STOP_MSG = "[FORCED STOP] Tool {tool_name} called {count} times
 class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
    """Detects and breaks repetitive tool call loops.

+    Threshold parameters are validated upstream by :class:`LoopDetectionConfig`;
+    construct via :meth:`from_config` to ensure values pass Pydantic validation.
+
    Args:
        warn_threshold: Number of identical tool call sets before injecting
            a warning message. Default: 3.
@@ -155,6 +162,14 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            Default: 30.
        tool_freq_hard_limit: Number of calls to the same tool type before
            forcing a stop. Default: 50.
+        tool_freq_overrides: Per-tool overrides for frequency thresholds,
+            keyed by tool name. Each value is a ``(warn, hard_limit)`` tuple
+            that replaces ``tool_freq_warn`` / ``tool_freq_hard_limit`` for
+            that specific tool. Tools not listed here fall back to the global
+            thresholds. Useful for raising limits on intentionally
+            high-frequency tools (e.g. ``bash`` in batch pipelines) without
+            weakening protection on all other tools. Default: ``None``
+            (no overrides).
    """

    def __init__(
@@ -165,6 +180,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
        max_tracked_threads: int = _DEFAULT_MAX_TRACKED_THREADS,
        tool_freq_warn: int = _DEFAULT_TOOL_FREQ_WARN,
        tool_freq_hard_limit: int = _DEFAULT_TOOL_FREQ_HARD_LIMIT,
+        tool_freq_overrides: dict[str, tuple[int, int]] | None = None,
    ):
        super().__init__()
        self.warn_threshold = warn_threshold
@@ -173,14 +189,26 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
        self.max_tracked_threads = max_tracked_threads
        self.tool_freq_warn = tool_freq_warn
        self.tool_freq_hard_limit = tool_freq_hard_limit
+        self._tool_freq_overrides: dict[str, tuple[int, int]] = tool_freq_overrides or {}
        self._lock = threading.Lock()
-        # Per-thread tracking using OrderedDict for LRU eviction
        self._history: OrderedDict[str, list[str]] = OrderedDict()
        self._warned: dict[str, set[str]] = defaultdict(set)
-        # Per-thread, per-tool-type cumulative call counts
        self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
        self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)

+    @classmethod
+    def from_config(cls, config: LoopDetectionConfig) -> LoopDetectionMiddleware:
+        """Construct from a Pydantic-validated config, trusting its validation."""
+        return cls(
+            warn_threshold=config.warn_threshold,
+            hard_limit=config.hard_limit,
+            window_size=config.window_size,
+            max_tracked_threads=config.max_tracked_threads,
+            tool_freq_warn=config.tool_freq_warn,
+            tool_freq_hard_limit=config.tool_freq_hard_limit,
+            tool_freq_overrides={name: (o.warn, o.hard_limit) for name, o in config.tool_freq_overrides.items()},
+        )
+
    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
@@ -280,7 +308,12 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
                freq[name] += 1
                tc_count = freq[name]

-                if tc_count >= self.tool_freq_hard_limit:
+                if name in self._tool_freq_overrides:
+                    eff_warn, eff_hard = self._tool_freq_overrides[name]
+                else:
+                    eff_warn, eff_hard = self.tool_freq_warn, self.tool_freq_hard_limit
+
+                if tc_count >= eff_hard:
                    logger.error(
                        "Tool frequency hard limit reached — forcing stop",
                        extra={
@@ -291,7 +324,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
                    )
                    return _TOOL_FREQ_HARD_STOP_MSG.format(tool_name=name, count=tc_count), True

-                if tc_count >= self.tool_freq_warn:
+                if tc_count >= eff_warn:
                    warned = self._tool_freq_warned[thread_id]
                    if name not in warned:
                        warned.add(name)
@@ -356,13 +389,30 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
            return {"messages": [stripped_msg]}

        if warning:
-            # Inject as HumanMessage instead of SystemMessage to avoid
-            # Anthropic's "multiple non-consecutive system messages" error.
-            # Anthropic models require system messages only at the start of
-            # the conversation; injecting one mid-conversation crashes
-            # langchain_anthropic's _format_messages(). HumanMessage works
-            # with all providers. See #1299.
-            return {"messages": [HumanMessage(content=warning, name="loop_warning")]}
+            # WORKAROUND for v2.0-m1 — see #2724.
+            #
+            # Append the warning to the AIMessage content instead of
+            # injecting a separate HumanMessage. Inserting any non-tool
+            # message between an AIMessage(tool_calls=...) and its
+            # ToolMessage responses breaks OpenAI/Moonshot strict pairing
+            # validation ("tool_call_ids did not have response messages")
+            # because the tools node has not run yet at after_model time.
+            # tool_calls are preserved so the tools node still executes.
+            #
+            # This is a temporary mitigation: mutating an existing
+            # AIMessage to carry framework-authored text leaks loop-warning
+            # text into downstream consumers (MemoryMiddleware fact
+            # extraction, TitleMiddleware, telemetry, model replay) as if
+            # the model said it. The proper fix is to defer warning
+            # injection from after_model to wrap_model_call so every prior
+            # ToolMessage is already in the request — see RFC #2517 (which
+            # lists "loop intervention does not leave invalid
+            # tool-call/tool-message state" as acceptance criteria) and
+            # the prototype on `fix/loop-detection-tool-call-pairing`.
+            messages = state.get("messages", [])
+            last_msg = messages[-1]
+            patched_msg = last_msg.model_copy(update={"content": self._append_text(last_msg.content, warning)})
+            return {"messages": [patched_msg]}

        return None

@@ -7,6 +7,7 @@ from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langgraph.runtime import Runtime

+from deerflow.agents.middlewares.tool_call_metadata import clone_ai_message_with_tool_calls
 from deerflow.subagents.executor import MAX_CONCURRENT_SUBAGENTS

 logger = logging.getLogger(__name__)
@@ -63,7 +64,7 @@ class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
        logger.warning(f"Truncated {dropped_count} excess task tool call(s) from model response (limit: {self.max_concurrent})")

        # Replace the AIMessage with truncated tool_calls (same id triggers replacement)
-        updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
+        updated_msg = clone_ai_message_with_tool_calls(last_msg, truncated_tool_calls)
        return {"messages": [updated_msg]}

    @override
@@ -10,10 +10,14 @@ from typing import Any, Protocol, override, runtime_checkable
 from langchain.agents import AgentState
 from langchain.agents.middleware import SummarizationMiddleware
 from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage
+from langchain_core.messages.utils import get_buffer_string
 from langgraph.config import get_config
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
 from langgraph.runtime import Runtime

+from deerflow.agents.middlewares.dynamic_context_middleware import is_dynamic_context_reminder
+from deerflow.agents.middlewares.tool_call_metadata import clone_ai_message_with_tool_calls
+
 logger = logging.getLogger(__name__)


@@ -78,10 +82,7 @@ def _clone_ai_message(
    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)
+    return clone_ai_message_with_tool_calls(message, tool_calls, content=content)


@dataclass
@@ -136,6 +137,7 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
            return None

        messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
+        messages_to_summarize, preserved_messages = self._preserve_dynamic_context_reminders(messages_to_summarize, preserved_messages)
        self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
        summary = self._create_summary(messages_to_summarize)
        new_messages = self._build_new_messages(summary)
@@ -161,6 +163,7 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
            return None

        messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
+        messages_to_summarize, preserved_messages = self._preserve_dynamic_context_reminders(messages_to_summarize, preserved_messages)
        self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
        summary = await self._acreate_summary(messages_to_summarize)
        new_messages = self._build_new_messages(summary)
@@ -173,12 +176,102 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
            ]
        }

+    @override
+    def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary without emitting streaming events to the client.
+
+        Suppresses callbacks to prevent the internal summarization LLM call from
+        producing visible AI message chunks in the frontend's ``messages-tuple``
+        stream (issue #2804).
+        """
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed:
+            return "Previous conversation was too long to summarize."
+
+        formatted = get_buffer_string(trimmed)
+
+        try:
+            response = self.model.with_config(callbacks=[]).invoke(
+                self.summary_prompt.format(messages=formatted).rstrip(),
+                config={
+                    "metadata": {"lc_source": "summarization"},
+                    "callbacks": [],
+                },
+            )
+            return self._extract_summary_text(response)
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    @override
+    async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary without emitting streaming events to the client.
+
+        Suppresses callbacks to prevent the internal summarization LLM call from
+        producing visible AI message chunks in the frontend's ``messages-tuple``
+        stream (issue #2804).
+        """
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed:
+            return "Previous conversation was too long to summarize."
+
+        formatted = get_buffer_string(trimmed)
+
+        try:
+            response = await self.model.with_config(callbacks=[]).ainvoke(
+                self.summary_prompt.format(messages=formatted).rstrip(),
+                config={
+                    "metadata": {"lc_source": "summarization"},
+                    "callbacks": [],
+                },
+            )
+            return self._extract_summary_text(response)
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    def _extract_summary_text(self, response: Any) -> str:
+        # Prefer .text which normalizes list content blocks (e.g. [{"type": "text", "text": "..."}]).
+        # Fall back to .content for non-LangChain responses.
+        summary_text = getattr(response, "text", None)
+        if summary_text is None:
+            summary_text = getattr(response, "content", "")
+        return summary_text.strip() if isinstance(summary_text, str) else str(summary_text).strip()
+
    @override
    def _build_new_messages(self, summary: str) -> list[HumanMessage]:
        """Override the base implementation to let the human message with the special name 'summary'.
        And this message will be ignored to display in the frontend, but still can be used as context for the model.
        """
-        return [HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}", name="summary")]
+        return [
+            HumanMessage(
+                content=f"Here is a summary of the conversation to date:\n\n{summary}",
+                name="summary",
+                additional_kwargs={"hide_from_ui": True},
+            )
+        ]
+
+    def _preserve_dynamic_context_reminders(
+        self,
+        messages_to_summarize: list[AnyMessage],
+        preserved_messages: list[AnyMessage],
+    ) -> tuple[list[AnyMessage], list[AnyMessage]]:
+        """Keep hidden dynamic-context reminders out of summary compression.
+
+        These reminders carry the current date and optional memory. If summarization
+        removes them, DynamicContextMiddleware can mistake the summary HumanMessage
+        for the first user message and inject the reminder in the wrong place.
+        """
+        reminders = [msg for msg in messages_to_summarize if is_dynamic_context_reminder(msg)]
+        if not reminders:
+            return messages_to_summarize, preserved_messages
+
+        remaining = [msg for msg in messages_to_summarize if not is_dynamic_context_reminder(msg)]
+        return remaining, reminders + preserved_messages

    def _partition_with_skill_rescue(
        self,
@@ -9,6 +9,7 @@ from langchain.agents.middleware import AgentMiddleware
 from langgraph.config import get_config
 from langgraph.runtime import Runtime

+from deerflow.agents.middlewares.dynamic_context_middleware import is_dynamic_context_reminder
 from deerflow.config.title_config import get_title_config
 from deerflow.models import create_chat_model

@@ -61,6 +62,10 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):

        return ""

+    @staticmethod
+    def _is_user_message_for_title(message: object) -> bool:
+        return getattr(message, "type", None) == "human" and not is_dynamic_context_reminder(message)
+
    def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
        """Check if we should generate a title for this thread."""
        config = self._get_title_config()
@@ -77,7 +82,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
            return False

        # Count user and assistant messages
-        user_messages = [m for m in messages if m.type == "human"]
+        user_messages = [m for m in messages if self._is_user_message_for_title(m)]
        assistant_messages = [m for m in messages if m.type == "ai"]

        # Generate title after first complete exchange
@@ -91,7 +96,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
        config = self._get_title_config()
        messages = state.get("messages", [])

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

        user_msg = self._normalize_content(user_msg_content)
@@ -7,17 +7,21 @@ 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.
+(no tool calls) but todos are not yet complete, the middleware queues a reminder
+for the next model request and jumps back to the model node to force continued
+engagement. The completion reminder is injected via ``wrap_model_call`` instead
+of being persisted into graph state as a normal user-visible message.
 """

 from __future__ import annotations

+import threading
+from collections.abc import Awaitable, Callable
 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.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse, hook_config
 from langchain_core.messages import AIMessage, HumanMessage
 from langgraph.runtime import Runtime

@@ -55,6 +59,51 @@ def _format_todos(todos: list[Todo]) -> str:
    return "\n".join(lines)


+def _format_completion_reminder(todos: list[Todo]) -> str:
+    """Format a completion reminder for incomplete todo items."""
+    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)
+    return (
+        "<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>"
+    )
+
+
+_TOOL_CALL_FINISH_REASONS = {"tool_calls", "function_call"}
+
+
+def _has_tool_call_intent_or_error(message: AIMessage) -> bool:
+    """Return True when an AIMessage is not a clean final answer.
+
+    Todo completion reminders should only fire when the model has produced a
+    plain final response. Provider/tool parsing details have moved across
+    LangChain versions and integrations, so keep all tool-intent/error signals
+    behind this helper instead of checking one concrete field at the call site.
+    """
+    if message.tool_calls:
+        return True
+
+    if getattr(message, "invalid_tool_calls", None):
+        return True
+
+    # Backward/provider compatibility: some integrations preserve raw or legacy
+    # tool-call intent in additional_kwargs even when structured tool_calls is
+    # empty. If this helper changes, update the matching sentinel test
+    # `TestToolCallIntentOrError.test_langchain_ai_message_tool_fields_are_explicitly_handled`;
+    # if that test fails after a LangChain upgrade, review this helper so new
+    # tool-call/error fields are not silently treated as clean final answers.
+    additional_kwargs = getattr(message, "additional_kwargs", {}) or {}
+    if additional_kwargs.get("tool_calls") or additional_kwargs.get("function_call"):
+        return True
+
+    response_metadata = getattr(message, "response_metadata", {}) or {}
+    return response_metadata.get("finish_reason") in _TOOL_CALL_FINISH_REASONS
+
+
 class TodoMiddleware(TodoListMiddleware):
    """Extends TodoListMiddleware with `write_todos` context-loss detection.

@@ -89,6 +138,7 @@ class TodoMiddleware(TodoListMiddleware):
        formatted = _format_todos(todos)
        reminder = HumanMessage(
            name="todo_reminder",
+            additional_kwargs={"hide_from_ui": True},
            content=(
                "<system_reminder>\n"
                "Your todo list from earlier is no longer visible in the current context window, "
@@ -113,6 +163,100 @@ class TodoMiddleware(TodoListMiddleware):
    # 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
+    # Hard cap for per-run reminder bookkeeping in long-lived middleware instances.
+    _MAX_COMPLETION_REMINDER_KEYS = 4096
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self._lock = threading.Lock()
+        self._pending_completion_reminders: dict[tuple[str, str], list[str]] = {}
+        self._completion_reminder_counts: dict[tuple[str, str], int] = {}
+        self._completion_reminder_touch_order: dict[tuple[str, str], int] = {}
+        self._completion_reminder_next_order = 0
+
+    @staticmethod
+    def _get_thread_id(runtime: Runtime) -> str:
+        context = getattr(runtime, "context", None)
+        thread_id = context.get("thread_id") if context else None
+        return str(thread_id) if thread_id else "default"
+
+    @staticmethod
+    def _get_run_id(runtime: Runtime) -> str:
+        context = getattr(runtime, "context", None)
+        run_id = context.get("run_id") if context else None
+        return str(run_id) if run_id else "default"
+
+    def _pending_key(self, runtime: Runtime) -> tuple[str, str]:
+        return self._get_thread_id(runtime), self._get_run_id(runtime)
+
+    def _touch_completion_reminder_key_locked(self, key: tuple[str, str]) -> None:
+        self._completion_reminder_next_order += 1
+        self._completion_reminder_touch_order[key] = self._completion_reminder_next_order
+
+    def _completion_reminder_keys_locked(self) -> set[tuple[str, str]]:
+        keys = set(self._pending_completion_reminders)
+        keys.update(self._completion_reminder_counts)
+        keys.update(self._completion_reminder_touch_order)
+        return keys
+
+    def _drop_completion_reminder_key_locked(self, key: tuple[str, str]) -> None:
+        self._pending_completion_reminders.pop(key, None)
+        self._completion_reminder_counts.pop(key, None)
+        self._completion_reminder_touch_order.pop(key, None)
+
+    def _prune_completion_reminder_state_locked(self, protected_key: tuple[str, str]) -> None:
+        keys = self._completion_reminder_keys_locked()
+        overflow = len(keys) - self._MAX_COMPLETION_REMINDER_KEYS
+        if overflow <= 0:
+            return
+
+        candidates = [key for key in keys if key != protected_key]
+        candidates.sort(key=lambda key: self._completion_reminder_touch_order.get(key, 0))
+        for key in candidates[:overflow]:
+            self._drop_completion_reminder_key_locked(key)
+
+    def _queue_completion_reminder(self, runtime: Runtime, reminder: str) -> None:
+        key = self._pending_key(runtime)
+        with self._lock:
+            self._pending_completion_reminders.setdefault(key, []).append(reminder)
+            self._completion_reminder_counts[key] = self._completion_reminder_counts.get(key, 0) + 1
+            self._touch_completion_reminder_key_locked(key)
+            self._prune_completion_reminder_state_locked(protected_key=key)
+
+    def _completion_reminder_count_for_runtime(self, runtime: Runtime) -> int:
+        key = self._pending_key(runtime)
+        with self._lock:
+            return self._completion_reminder_counts.get(key, 0)
+
+    def _drain_completion_reminders(self, runtime: Runtime) -> list[str]:
+        key = self._pending_key(runtime)
+        with self._lock:
+            reminders = self._pending_completion_reminders.pop(key, [])
+            if reminders or key in self._completion_reminder_counts:
+                self._touch_completion_reminder_key_locked(key)
+            return reminders
+
+    def _clear_other_run_completion_reminders(self, runtime: Runtime) -> None:
+        thread_id, current_run_id = self._pending_key(runtime)
+        with self._lock:
+            for key in self._completion_reminder_keys_locked():
+                if key[0] == thread_id and key[1] != current_run_id:
+                    self._drop_completion_reminder_key_locked(key)
+
+    def _clear_current_run_completion_reminders(self, runtime: Runtime) -> None:
+        key = self._pending_key(runtime)
+        with self._lock:
+            self._drop_completion_reminder_key_locked(key)
+
+    @override
+    def before_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_other_run_completion_reminders(runtime)
+        return None
+
+    @override
+    async def abefore_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_other_run_completion_reminders(runtime)
+        return None

    @hook_config(can_jump_to=["model"])
    @override
@@ -137,10 +281,12 @@ class TodoMiddleware(TodoListMiddleware):
        if base_result is not None:
            return base_result

-        # 2. Only intervene when the agent wants to exit (no tool calls).
+        # 2. Only intervene when the agent wants to exit cleanly. Tool-call
+        # intent or tool-call parse errors should be handled by the tool path
+        # instead of being masked by todo reminders.
        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:
+        if not last_ai or _has_tool_call_intent_or_error(last_ai):
            return None

        # 3. Allow exit when all todos are completed or there are no todos.
@@ -149,24 +295,14 @@ class TodoMiddleware(TodoListMiddleware):
            return None

        # 4. Enforce a reminder cap to prevent infinite re-engagement loops.
-        if _completion_reminder_count(messages) >= self._MAX_COMPLETION_REMINDERS:
+        if self._completion_reminder_count_for_runtime(runtime) >= 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]}
+        # 5. Queue a reminder for the next model request and jump back. We must
+        # not persist this control prompt as a normal HumanMessage, otherwise it
+        # can leak into user-visible message streams and saved transcripts.
+        self._queue_completion_reminder(runtime, _format_completion_reminder(todos))
+        return {"jump_to": "model"}

    @override
    @hook_config(can_jump_to=["model"])
@@ -177,3 +313,47 @@ class TodoMiddleware(TodoListMiddleware):
    ) -> dict[str, Any] | None:
        """Async version of after_model."""
        return self.after_model(state, runtime)
+
+    @staticmethod
+    def _format_pending_completion_reminders(reminders: list[str]) -> str:
+        return "\n\n".join(dict.fromkeys(reminders))
+
+    def _augment_request(self, request: ModelRequest) -> ModelRequest:
+        reminders = self._drain_completion_reminders(request.runtime)
+        if not reminders:
+            return request
+        new_messages = [
+            *request.messages,
+            HumanMessage(
+                content=self._format_pending_completion_reminders(reminders),
+                name="todo_completion_reminder",
+                additional_kwargs={"hide_from_ui": True},
+            ),
+        ]
+        return request.override(messages=new_messages)
+
+    @override
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelCallResult:
+        return handler(self._augment_request(request))
+
+    @override
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelCallResult:
+        return await handler(self._augment_request(request))
+
+    @override
+    def after_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_current_run_completion_reminders(runtime)
+        return None
+
+    @override
+    async def aafter_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_current_run_completion_reminders(runtime)
+        return None
@@ -1,37 +1,358 @@
-"""Middleware for logging LLM token usage."""
+"""Middleware for logging token usage and annotating step attribution."""
+
+from __future__ import annotations

 import logging
-from typing import override
+from collections import defaultdict
+from typing import Any, override

 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware.todo import Todo
+from langchain_core.messages import AIMessage, ToolMessage
 from langgraph.runtime import Runtime

 logger = logging.getLogger(__name__)

+TOKEN_USAGE_ATTRIBUTION_KEY = "token_usage_attribution"
+
+
+def _string_arg(value: Any) -> str | None:
+    if isinstance(value, str):
+        normalized = value.strip()
+        return normalized or None
+    return None
+
+
+def _normalize_todos(value: Any) -> list[Todo]:
+    if not isinstance(value, list):
+        return []
+
+    normalized: list[Todo] = []
+    for item in value:
+        if not isinstance(item, dict):
+            continue
+
+        todo: Todo = {}
+        content = _string_arg(item.get("content"))
+        status = item.get("status")
+
+        if content is not None:
+            todo["content"] = content
+        if status in {"pending", "in_progress", "completed"}:
+            todo["status"] = status
+
+        normalized.append(todo)
+
+    return normalized
+
+
+def _todo_action_kind(previous: Todo | None, current: Todo) -> str:
+    status = current.get("status")
+    previous_content = previous.get("content") if previous else None
+    current_content = current.get("content")
+
+    if previous is None:
+        if status == "completed":
+            return "todo_complete"
+        if status == "in_progress":
+            return "todo_start"
+        return "todo_update"
+
+    if previous_content != current_content:
+        return "todo_update"
+
+    if status == "completed":
+        return "todo_complete"
+    if status == "in_progress":
+        return "todo_start"
+    return "todo_update"
+
+
+def _build_todo_actions(previous_todos: list[Todo], next_todos: list[Todo]) -> list[dict[str, Any]]:
+    # This is the single source of truth for precise write_todos token
+    # attribution. The frontend intentionally falls back to a generic
+    # "Update to-do list" label when this metadata is missing or malformed.
+    previous_by_content: dict[str, list[tuple[int, Todo]]] = defaultdict(list)
+    matched_previous_indices: set[int] = set()
+
+    for index, todo in enumerate(previous_todos):
+        content = todo.get("content")
+        if isinstance(content, str) and content:
+            previous_by_content[content].append((index, todo))
+
+    actions: list[dict[str, Any]] = []
+
+    for index, todo in enumerate(next_todos):
+        content = todo.get("content")
+        if not isinstance(content, str) or not content:
+            continue
+
+        previous_match: Todo | None = None
+        content_matches = previous_by_content.get(content)
+        if content_matches:
+            while content_matches and content_matches[0][0] in matched_previous_indices:
+                content_matches.pop(0)
+            if content_matches:
+                previous_index, previous_match = content_matches.pop(0)
+                matched_previous_indices.add(previous_index)
+
+        if previous_match is None and index < len(previous_todos) and index not in matched_previous_indices:
+            previous_match = previous_todos[index]
+            matched_previous_indices.add(index)
+
+        if previous_match is not None:
+            previous_content = previous_match.get("content")
+            previous_status = previous_match.get("status")
+            if previous_content == content and previous_status == todo.get("status"):
+                continue
+
+        actions.append(
+            {
+                "kind": _todo_action_kind(previous_match, todo),
+                "content": content,
+            }
+        )
+
+    for index, todo in enumerate(previous_todos):
+        if index in matched_previous_indices:
+            continue
+
+        content = todo.get("content")
+        if not isinstance(content, str) or not content:
+            continue
+
+        actions.append(
+            {
+                "kind": "todo_remove",
+                "content": content,
+            }
+        )
+
+    return actions
+
+
+def _describe_tool_call(tool_call: dict[str, Any], todos: list[Todo]) -> list[dict[str, Any]]:
+    name = _string_arg(tool_call.get("name")) or "unknown"
+    args = tool_call.get("args") if isinstance(tool_call.get("args"), dict) else {}
+    tool_call_id = _string_arg(tool_call.get("id"))
+
+    if name == "write_todos":
+        next_todos = _normalize_todos(args.get("todos"))
+        actions = _build_todo_actions(todos, next_todos)
+        if not actions:
+            return [
+                {
+                    "kind": "tool",
+                    "tool_name": name,
+                    "tool_call_id": tool_call_id,
+                }
+            ]
+        return [
+            {
+                **action,
+                "tool_call_id": tool_call_id,
+            }
+            for action in actions
+        ]
+
+    if name == "task":
+        return [
+            {
+                "kind": "subagent",
+                "description": _string_arg(args.get("description")),
+                "subagent_type": _string_arg(args.get("subagent_type")),
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name in {"web_search", "image_search"}:
+        query = _string_arg(args.get("query"))
+        return [
+            {
+                "kind": "search",
+                "tool_name": name,
+                "query": query,
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name == "present_files":
+        return [
+            {
+                "kind": "present_files",
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name == "ask_clarification":
+        return [
+            {
+                "kind": "clarification",
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    return [
+        {
+            "kind": "tool",
+            "tool_name": name,
+            "description": _string_arg(args.get("description")),
+            "tool_call_id": tool_call_id,
+        }
+    ]
+
+
+def _infer_step_kind(message: AIMessage, actions: list[dict[str, Any]]) -> str:
+    if actions:
+        first_kind = actions[0].get("kind")
+        if len(actions) == 1 and first_kind in {"todo_start", "todo_complete", "todo_update", "todo_remove"}:
+            return "todo_update"
+        if len(actions) == 1 and first_kind == "subagent":
+            return "subagent_dispatch"
+        return "tool_batch"
+
+    if message.content:
+        return "final_answer"
+    return "thinking"
+
+
+def _has_tool_call(message: AIMessage, tool_call_id: str) -> bool:
+    """Return True if the AIMessage contains a tool_call with the given id."""
+    for tc in message.tool_calls or []:
+        if isinstance(tc, dict):
+            if tc.get("id") == tool_call_id:
+                return True
+        elif hasattr(tc, "id") and tc.id == tool_call_id:
+            return True
+    return False
+
+
+def _build_attribution(message: AIMessage, todos: list[Todo]) -> dict[str, Any]:
+    tool_calls = getattr(message, "tool_calls", None) or []
+    actions: list[dict[str, Any]] = []
+    current_todos = list(todos)
+
+    for raw_tool_call in tool_calls:
+        if not isinstance(raw_tool_call, dict):
+            continue
+
+        described_actions = _describe_tool_call(raw_tool_call, current_todos)
+        actions.extend(described_actions)
+
+        if raw_tool_call.get("name") == "write_todos":
+            args = raw_tool_call.get("args") if isinstance(raw_tool_call.get("args"), dict) else {}
+            current_todos = _normalize_todos(args.get("todos"))
+
+    tool_call_ids: list[str] = []
+    for tool_call in tool_calls:
+        if not isinstance(tool_call, dict):
+            continue
+
+        tool_call_id = _string_arg(tool_call.get("id"))
+        if tool_call_id is not None:
+            tool_call_ids.append(tool_call_id)
+
+    return {
+        # Schema changes should remain additive where possible so older
+        # frontends can ignore unknown fields and fall back safely.
+        "version": 1,
+        "kind": _infer_step_kind(message, actions),
+        "shared_attribution": len(actions) > 1,
+        "tool_call_ids": tool_call_ids,
+        "actions": actions,
+    }
+

 class TokenUsageMiddleware(AgentMiddleware):
-    """Logs token usage from model response usage_metadata."""
+    """Logs token usage from model responses and annotates the AI step."""

-    @override
-    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return self._log_usage(state)
-
-    @override
-    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return self._log_usage(state)
-
-    def _log_usage(self, state: AgentState) -> None:
+    def _apply(self, state: AgentState) -> dict | None:
        messages = state.get("messages", [])
        if not messages:
            return None
+
+        # Annotate subagent token usage onto the AIMessage that dispatched it.
+        # When a task tool completes, its usage is cached by tool_call_id.  Detect
+        # the ToolMessage → search backward for the corresponding AIMessage → merge.
+        # Walk backward through consecutive ToolMessages before the new AIMessage
+        # so that multiple concurrent task tool calls all get their subagent tokens
+        # written back to the same dispatch message (merging into one update).
+        state_updates: dict[int, AIMessage] = {}
+        if len(messages) >= 2:
+            from deerflow.tools.builtins.task_tool import pop_cached_subagent_usage
+
+            idx = len(messages) - 2
+            while idx >= 0:
+                tool_msg = messages[idx]
+                if not isinstance(tool_msg, ToolMessage) or not tool_msg.tool_call_id:
+                    break
+
+                subagent_usage = pop_cached_subagent_usage(tool_msg.tool_call_id)
+                if subagent_usage:
+                    # Search backward from the ToolMessage to find the AIMessage
+                    # that dispatched it.  A single model response can dispatch
+                    # multiple task tool calls, so we can't assume a fixed offset.
+                    dispatch_idx = idx - 1
+                    while dispatch_idx >= 0:
+                        candidate = messages[dispatch_idx]
+                        if isinstance(candidate, AIMessage) and _has_tool_call(candidate, tool_msg.tool_call_id):
+                            # Accumulate into an existing update for the same
+                            # AIMessage (multiple task calls in one response),
+                            # or merge fresh from the original message.
+                            existing_update = state_updates.get(dispatch_idx)
+                            prev = existing_update.usage_metadata if existing_update else (getattr(candidate, "usage_metadata", None) or {})
+                            merged = {
+                                **prev,
+                                "input_tokens": prev.get("input_tokens", 0) + subagent_usage["input_tokens"],
+                                "output_tokens": prev.get("output_tokens", 0) + subagent_usage["output_tokens"],
+                                "total_tokens": prev.get("total_tokens", 0) + subagent_usage["total_tokens"],
+                            }
+                            state_updates[dispatch_idx] = candidate.model_copy(update={"usage_metadata": merged})
+                            break
+                        dispatch_idx -= 1
+                idx -= 1
+
        last = messages[-1]
+        if not isinstance(last, AIMessage):
+            if state_updates:
+                return {"messages": [state_updates[idx] for idx in sorted(state_updates)]}
+            return None
+
        usage = getattr(last, "usage_metadata", None)
        if usage:
+            input_token_details = usage.get("input_token_details") or {}
+            output_token_details = usage.get("output_token_details") or {}
+            detail_parts = []
+            if input_token_details:
+                detail_parts.append(f"input_token_details={input_token_details}")
+            if output_token_details:
+                detail_parts.append(f"output_token_details={output_token_details}")
+            detail_suffix = f" {' '.join(detail_parts)}" if detail_parts else ""
            logger.info(
-                "LLM token usage: input=%s output=%s total=%s",
+                "LLM token usage: input=%s output=%s total=%s%s",
                usage.get("input_tokens", "?"),
                usage.get("output_tokens", "?"),
                usage.get("total_tokens", "?"),
+                detail_suffix,
            )
-        return None
+
+        todos = state.get("todos") or []
+        attribution = _build_attribution(last, todos if isinstance(todos, list) else [])
+        additional_kwargs = dict(getattr(last, "additional_kwargs", {}) or {})
+
+        if additional_kwargs.get(TOKEN_USAGE_ATTRIBUTION_KEY) == attribution:
+            return {"messages": [state_updates[idx] for idx in sorted(state_updates)]} if state_updates else None
+
+        additional_kwargs[TOKEN_USAGE_ATTRIBUTION_KEY] = attribution
+        updated_msg = last.model_copy(update={"additional_kwargs": additional_kwargs})
+        state_updates[len(messages) - 1] = updated_msg
+        return {"messages": [state_updates[idx] for idx in sorted(state_updates)]}
+
+    @override
+    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._apply(state)
+
+    @override
+    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._apply(state)
@@ -0,0 +1,50 @@
+"""Helpers for keeping AIMessage tool-call metadata consistent."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.messages import AIMessage
+
+
+def _raw_tool_call_id(raw_tool_call: Any) -> str | None:
+    if not isinstance(raw_tool_call, dict):
+        return None
+
+    raw_id = raw_tool_call.get("id")
+    return raw_id if isinstance(raw_id, str) and raw_id else None
+
+
+def clone_ai_message_with_tool_calls(
+    message: AIMessage,
+    tool_calls: list[dict[str, Any]],
+    *,
+    content: Any | None = None,
+) -> AIMessage:
+    """Clone an AIMessage while keeping raw provider tool-call metadata in sync."""
+    kept_ids = {tc["id"] for tc in tool_calls if isinstance(tc.get("id"), str) and tc["id"]}
+
+    update: dict[str, Any] = {"tool_calls": tool_calls}
+    if content is not None:
+        update["content"] = content
+
+    additional_kwargs = dict(getattr(message, "additional_kwargs", {}) or {})
+    raw_tool_calls = additional_kwargs.get("tool_calls")
+    if isinstance(raw_tool_calls, list):
+        synced_raw_tool_calls = [raw_tc for raw_tc in raw_tool_calls if _raw_tool_call_id(raw_tc) in kept_ids]
+        if synced_raw_tool_calls:
+            additional_kwargs["tool_calls"] = synced_raw_tool_calls
+        else:
+            additional_kwargs.pop("tool_calls", None)
+
+    if not tool_calls:
+        additional_kwargs.pop("function_call", None)
+
+    update["additional_kwargs"] = additional_kwargs
+
+    response_metadata = dict(getattr(message, "response_metadata", {}) or {})
+    if not tool_calls and response_metadata.get("finish_reason") == "tool_calls":
+        response_metadata["finish_reason"] = "stop"
+    update["response_metadata"] = response_metadata
+
+    return message.model_copy(update=update)
@@ -264,25 +264,35 @@ class DeerFlowClient:
        return [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in tool_calls]

    @staticmethod
-    def _ai_text_event(msg_id: str | None, text: str, usage: dict | None) -> "StreamEvent":
-        """Build a ``messages-tuple`` AI text event, attaching usage when present."""
+    def _serialize_additional_kwargs(msg) -> dict[str, Any] | None:
+        """Copy message additional_kwargs when present."""
+        additional_kwargs = getattr(msg, "additional_kwargs", None)
+        if isinstance(additional_kwargs, dict) and additional_kwargs:
+            return dict(additional_kwargs)
+        return None
+
+    @staticmethod
+    def _ai_text_event(msg_id: str | None, text: str, usage: dict | None, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
+        """Build a ``messages-tuple`` AI text event."""
        data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
        if usage:
            data["usage_metadata"] = usage
+        if additional_kwargs:
+            data["additional_kwargs"] = additional_kwargs
        return StreamEvent(type="messages-tuple", data=data)

    @staticmethod
-    def _ai_tool_calls_event(msg_id: str | None, tool_calls) -> "StreamEvent":
+    def _ai_tool_calls_event(msg_id: str | None, tool_calls, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
        """Build a ``messages-tuple`` AI tool-calls event."""
-        return StreamEvent(
-            type="messages-tuple",
-            data={
+        data: dict[str, Any] = {
            "type": "ai",
            "content": "",
            "id": msg_id,
            "tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
-            },
-        )
+        }
+        if additional_kwargs:
+            data["additional_kwargs"] = additional_kwargs
+        return StreamEvent(type="messages-tuple", data=data)

    @staticmethod
    def _tool_message_event(msg: ToolMessage) -> "StreamEvent":
@@ -307,19 +317,30 @@ class DeerFlowClient:
                d["tool_calls"] = DeerFlowClient._serialize_tool_calls(msg.tool_calls)
            if getattr(msg, "usage_metadata", None):
                d["usage_metadata"] = msg.usage_metadata
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
            return d
        if isinstance(msg, ToolMessage):
-            return {
+            d = {
                "type": "tool",
                "content": DeerFlowClient._extract_text(msg.content),
                "name": getattr(msg, "name", None),
                "tool_call_id": getattr(msg, "tool_call_id", None),
                "id": getattr(msg, "id", None),
            }
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
        if isinstance(msg, HumanMessage):
-            return {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
+            d = {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
        if isinstance(msg, SystemMessage):
-            return {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
+            d = {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
        return {"type": "unknown", "content": str(msg), "id": getattr(msg, "id", None)}

    @staticmethod
@@ -542,6 +563,7 @@ class DeerFlowClient:
            - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str}
            - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str, "usage_metadata": {...}}
            - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
+            - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "additional_kwargs": {...}}
            - type="messages-tuple"  data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
            - type="end"             data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
        """
@@ -564,6 +586,7 @@ class DeerFlowClient:
        # in both the final ``messages`` chunk and the values snapshot —
        # count it only on whichever arrives first.
        counted_usage_ids: set[str] = set()
+        sent_additional_kwargs_by_id: dict[str, dict[str, Any]] = {}
        cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}

        def _account_usage(msg_id: str | None, usage: Any) -> dict | None:
@@ -593,6 +616,20 @@ class DeerFlowClient:
                "total_tokens": total_tokens,
            }

+        def _unsent_additional_kwargs(msg_id: str | None, additional_kwargs: dict[str, Any] | None) -> dict[str, Any] | None:
+            if not additional_kwargs:
+                return None
+            if not msg_id:
+                return additional_kwargs
+
+            sent = sent_additional_kwargs_by_id.setdefault(msg_id, {})
+            delta = {key: value for key, value in additional_kwargs.items() if sent.get(key) != value}
+            if not delta:
+                return None
+
+            sent.update(delta)
+            return delta
+
        for item in self._agent.stream(
            state,
            config=config,
@@ -620,17 +657,31 @@ class DeerFlowClient:

                if isinstance(msg_chunk, AIMessage):
                    text = self._extract_text(msg_chunk.content)
+                    additional_kwargs = self._serialize_additional_kwargs(msg_chunk)
                    counted_usage = _account_usage(msg_id, msg_chunk.usage_metadata)
+                    sent_additional_kwargs = False

                    if text:
                        if msg_id:
                            streamed_ids.add(msg_id)
-                        yield self._ai_text_event(msg_id, text, counted_usage)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_text_event(
+                            msg_id,
+                            text,
+                            counted_usage,
+                            additional_kwargs_delta,
+                        )
+                        sent_additional_kwargs = bool(additional_kwargs_delta)

                    if msg_chunk.tool_calls:
                        if msg_id:
                            streamed_ids.add(msg_id)
-                        yield self._ai_tool_calls_event(msg_id, msg_chunk.tool_calls)
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_tool_calls_event(
+                            msg_id,
+                            msg_chunk.tool_calls,
+                            additional_kwargs_delta,
+                        )

                elif isinstance(msg_chunk, ToolMessage):
                    if msg_id:
@@ -653,17 +704,45 @@ class DeerFlowClient:
                if msg_id and msg_id in streamed_ids:
                    if isinstance(msg, AIMessage):
                        _account_usage(msg_id, getattr(msg, "usage_metadata", None))
+                        additional_kwargs = self._serialize_additional_kwargs(msg)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        if additional_kwargs_delta:
+                            # Metadata-only follow-up: ``messages-tuple`` has no
+                            # dedicated attribution event, so clients should
+                            # merge this empty-content AI event by message id
+                            # and ignore it for text rendering.
+                            yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
                    continue

                if isinstance(msg, AIMessage):
                    counted_usage = _account_usage(msg_id, msg.usage_metadata)
+                    additional_kwargs = self._serialize_additional_kwargs(msg)
+                    sent_additional_kwargs = False

                    if msg.tool_calls:
-                        yield self._ai_tool_calls_event(msg_id, msg.tool_calls)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_tool_calls_event(
+                            msg_id,
+                            msg.tool_calls,
+                            additional_kwargs_delta,
+                        )
+                        sent_additional_kwargs = bool(additional_kwargs_delta)

                    text = self._extract_text(msg.content)
                    if text:
-                        yield self._ai_text_event(msg_id, text, counted_usage)
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_text_event(
+                            msg_id,
+                            text,
+                            counted_usage,
+                            additional_kwargs_delta,
+                        )
+                    elif msg_id:
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        if not additional_kwargs_delta:
+                            continue
+                        # See the metadata-only follow-up convention above.
+                        yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)

                elif isinstance(msg, ToolMessage):
                    yield self._tool_message_event(msg)
@@ -84,8 +84,52 @@ class RemoteSandboxBackend(SandboxBackend):
        """
        return self._provisioner_discover(sandbox_id)

+    def list_running(self) -> list[SandboxInfo]:
+        """Return all sandboxes currently managed by the provisioner.
+
+        Calls ``GET /api/sandboxes`` so that ``AioSandboxProvider._reconcile_orphans()``
+        can adopt pods that were created by a previous process and were never
+        explicitly destroyed.
+        Without this, a process restart silently orphans all existing k8s Pods —
+        they stay running forever because the idle checker only
+        tracks in-process state.
+        """
+        return self._provisioner_list()
+
    # ── Provisioner API calls ─────────────────────────────────────────────

+    def _provisioner_list(self) -> list[SandboxInfo]:
+        """GET /api/sandboxes → list all running sandboxes."""
+        try:
+            resp = requests.get(f"{self._provisioner_url}/api/sandboxes", timeout=10)
+            resp.raise_for_status()
+            data = resp.json()
+            if not isinstance(data, dict):
+                logger.warning("Provisioner list_running returned non-dict payload: %r", type(data))
+                return []
+
+            sandboxes = data.get("sandboxes", [])
+            if not isinstance(sandboxes, list):
+                logger.warning("Provisioner list_running returned non-list sandboxes: %r", type(sandboxes))
+                return []
+
+            infos: list[SandboxInfo] = []
+            for sandbox in sandboxes:
+                if not isinstance(sandbox, dict):
+                    logger.warning("Provisioner list_running entry is not a dict: %r", type(sandbox))
+                    continue
+
+                sandbox_id = sandbox.get("sandbox_id")
+                sandbox_url = sandbox.get("sandbox_url")
+                if isinstance(sandbox_id, str) and sandbox_id and isinstance(sandbox_url, str) and sandbox_url:
+                    infos.append(SandboxInfo(sandbox_id=sandbox_id, sandbox_url=sandbox_url))
+
+            logger.info("Provisioner list_running: %d sandbox(es) found", len(infos))
+            return infos
+        except requests.RequestException as exc:
+            logger.warning("Provisioner list_running failed: %s", exc)
+            return []
+
    def _provisioner_create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
        """POST /api/sandboxes → create Pod + Service."""
        try:
@@ -0,0 +1,3 @@
+from .tools import web_search_tool
+
+__all__ = ["web_search_tool"]
@@ -0,0 +1,95 @@
+"""
+Web Search Tool - Search the web using Serper (Google Search API).
+
+Serper provides real-time Google Search results via a JSON API.
+An API key is required. Sign up at https://serper.dev to get one.
+"""
+
+import json
+import logging
+import os
+
+import httpx
+from langchain.tools import tool
+
+from deerflow.config import get_app_config
+
+logger = logging.getLogger(__name__)
+
+_SERPER_ENDPOINT = "https://google.serper.dev/search"
+_api_key_warned = False
+
+
+def _get_api_key() -> str | None:
+    config = get_app_config().get_tool_config("web_search")
+    if config is not None:
+        api_key = config.model_extra.get("api_key")
+        if isinstance(api_key, str) and api_key.strip():
+            return api_key
+    return os.getenv("SERPER_API_KEY")
+
+
+@tool("web_search", parse_docstring=True)
+def web_search_tool(query: str, max_results: int = 5) -> str:
+    """Search the web for information using Google Search via Serper.
+
+    Args:
+        query: Search keywords describing what you want to find. Be specific for better results.
+        max_results: Maximum number of search results to return. Default is 5.
+    """
+    global _api_key_warned
+
+    config = get_app_config().get_tool_config("web_search")
+    if config is not None and "max_results" in config.model_extra:
+        max_results = config.model_extra.get("max_results", max_results)
+
+    api_key = _get_api_key()
+    if not api_key:
+        if not _api_key_warned:
+            _api_key_warned = True
+            logger.warning("Serper API key is not set. Set SERPER_API_KEY in your environment or provide api_key in config.yaml. Sign up at https://serper.dev")
+        return json.dumps(
+            {"error": "SERPER_API_KEY is not configured", "query": query},
+            ensure_ascii=False,
+        )
+
+    headers = {
+        "X-API-KEY": api_key,
+        "Content-Type": "application/json",
+    }
+    payload = {"q": query, "num": max_results}
+
+    try:
+        with httpx.Client(timeout=30) as client:
+            response = client.post(_SERPER_ENDPOINT, headers=headers, json=payload)
+        response.raise_for_status()
+        data = response.json()
+    except httpx.HTTPStatusError as e:
+        logger.error(f"Serper API returned HTTP {e.response.status_code}: {e.response.text}")
+        return json.dumps(
+            {"error": f"Serper API error: HTTP {e.response.status_code}", "query": query},
+            ensure_ascii=False,
+        )
+    except Exception as e:
+        logger.error(f"Serper search failed: {type(e).__name__}: {e}")
+        return json.dumps({"error": str(e), "query": query}, ensure_ascii=False)
+
+    organic = data.get("organic", [])
+    if not organic:
+        return json.dumps({"error": "No results found", "query": query}, ensure_ascii=False)
+
+    normalized_results = [
+        {
+            "title": r.get("title", ""),
+            "url": r.get("link", ""),
+            "content": r.get("snippet", ""),
+        }
+        for r in organic[:max_results]
+    ]
+
+    output = {
+        "query": query,
+        "total_results": len(normalized_results),
+        "results": normalized_results,
+    }
+    return json.dumps(output, indent=2, ensure_ascii=False)
@@ -1,5 +1,6 @@
 from .app_config import get_app_config
 from .extensions_config import ExtensionsConfig, get_extensions_config
+from .loop_detection_config import LoopDetectionConfig
 from .memory_config import MemoryConfig, get_memory_config
 from .paths import Paths, get_paths
 from .skill_evolution_config import SkillEvolutionConfig
@@ -20,6 +21,7 @@ __all__ = [
    "SkillsConfig",
    "ExtensionsConfig",
    "get_extensions_config",
+    "LoopDetectionConfig",
    "MemoryConfig",
    "get_memory_config",
    "get_tracing_config",
@@ -1,13 +1,22 @@
-"""Configuration and loaders for custom agents."""
+"""Configuration and loaders for custom agents.
+
+Custom agents are stored per-user under ``{base_dir}/users/{user_id}/agents/{name}/``.
+A legacy shared layout at ``{base_dir}/agents/{name}/`` is still readable so that
+installations that pre-date user isolation continue to work until they run the
+``scripts/migrate_user_isolation.py`` migration. New writes always target the
+per-user layout.
+"""

 import logging
 import re
+from pathlib import Path
 from typing import Any

 import yaml
 from pydantic import BaseModel

 from deerflow.config.paths import get_paths
+from deerflow.runtime.user_context import get_effective_user_id

 logger = logging.getLogger(__name__)

@@ -40,14 +49,47 @@ class AgentConfig(BaseModel):
    skills: list[str] | None = None


-def load_agent_config(name: str | None) -> AgentConfig | None:
+def resolve_agent_dir(name: str, *, user_id: str | None = None) -> Path:
+    """Return the on-disk directory for an agent, preferring the per-user layout.
+
+    Resolution order:
+    1. ``{base_dir}/users/{user_id}/agents/{name}/`` (per-user, current layout).
+    2. ``{base_dir}/agents/{name}/`` (legacy shared layout — read-only fallback).
+
+    If neither exists, the per-user path is returned so callers that intend to
+    create the agent write into the new layout.
+
+    Args:
+        name: Validated agent name.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            request context (or ``"default"`` in no-auth mode).
+    """
+    paths = get_paths()
+    effective_user = user_id or get_effective_user_id()
+    user_path = paths.user_agent_dir(effective_user, name)
+    if user_path.exists():
+        return user_path
+
+    legacy_path = paths.agent_dir(name)
+    if legacy_path.exists():
+        return legacy_path
+
+    return user_path
+
+
+def load_agent_config(name: str | None, *, user_id: str | None = None) -> AgentConfig | None:
    """Load the custom or default agent's config from its directory.

+    Reads from the per-user layout first; falls back to the legacy shared layout
+    for installations that have not yet been migrated.
+
    Args:
        name: The agent name.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            current request context.

    Returns:
-        AgentConfig instance.
+        AgentConfig instance, or ``None`` if ``name`` is ``None``.

    Raises:
        FileNotFoundError: If the agent directory or config.yaml does not exist.
@@ -58,7 +100,7 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
        return None

    name = validate_agent_name(name)
-    agent_dir = get_paths().agent_dir(name)
+    agent_dir = resolve_agent_dir(name, user_id=user_id)
    config_file = agent_dir / "config.yaml"

    if not agent_dir.exists():
@@ -84,7 +126,7 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
    return AgentConfig(**data)


-def load_agent_soul(agent_name: str | None) -> str | None:
+def load_agent_soul(agent_name: str | None, *, user_id: str | None = None) -> str | None:
    """Read the SOUL.md file for a custom agent, if it exists.

    SOUL.md defines the agent's personality, values, and behavioral guardrails.
@@ -92,11 +134,16 @@ def load_agent_soul(agent_name: str | None) -> str | None:

    Args:
        agent_name: The name of the agent or None for the default agent.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            current request context.

    Returns:
        The SOUL.md content as a string, or None if the file does not exist.
    """
-    agent_dir = get_paths().agent_dir(agent_name) if agent_name else get_paths().base_dir
+    if agent_name:
+        agent_dir = resolve_agent_dir(agent_name, user_id=user_id)
+    else:
+        agent_dir = get_paths().base_dir
    soul_path = agent_dir / SOUL_FILENAME
    if not soul_path.exists():
        return None
@@ -104,32 +151,50 @@ def load_agent_soul(agent_name: str | None) -> str | None:
    return content or None


-def list_custom_agents() -> list[AgentConfig]:
+def list_custom_agents(*, user_id: str | None = None) -> list[AgentConfig]:
    """Scan the agents directory and return all valid custom agents.

+    Returns the union of agents in the per-user layout and the legacy shared
+    layout, so that pre-migration installations remain visible until they are
+    migrated. Per-user entries shadow legacy entries with the same name.
+
+    Args:
+        user_id: Owner whose agents to list. Defaults to the effective user
+            from the current request context.
+
    Returns:
        List of AgentConfig for each valid agent directory found.
    """
-    agents_dir = get_paths().agents_dir
-
-    if not agents_dir.exists():
-        return []
+    paths = get_paths()
+    effective_user = user_id or get_effective_user_id()

+    seen: set[str] = set()
    agents: list[AgentConfig] = []

-    for entry in sorted(agents_dir.iterdir()):
+    user_root = paths.user_agents_dir(effective_user)
+    legacy_root = paths.agents_dir
+
+    for root in (user_root, legacy_root):
+        if not root.exists():
+            continue
+        for entry in sorted(root.iterdir()):
            if not entry.is_dir():
                continue
-
+            if entry.name in seen:
+                continue
            config_file = entry / "config.yaml"
            if not config_file.exists():
                logger.debug(f"Skipping {entry.name}: no config.yaml")
                continue

            try:
-            agent_cfg = load_agent_config(entry.name)
+                agent_cfg = load_agent_config(entry.name, user_id=effective_user)
+                if agent_cfg is None:
+                    continue
                agents.append(agent_cfg)
+                seen.add(entry.name)
            except Exception as e:
                logger.warning(f"Skipping agent '{entry.name}': {e}")

+    agents.sort(key=lambda a: a.name)
    return agents
@@ -1,5 +1,6 @@
 import logging
 import os
+from collections.abc import Mapping
 from contextvars import ContextVar
 from pathlib import Path
 from typing import Any, Self
@@ -14,6 +15,7 @@ from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpo
 from deerflow.config.database_config import DatabaseConfig
 from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
+from deerflow.config.loop_detection_config import LoopDetectionConfig
 from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.run_events_config import RunEventsConfig
@@ -99,6 +101,7 @@ class AppConfig(BaseModel):
    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")
+    loop_detection: LoopDetectionConfig = Field(default_factory=LoopDetectionConfig, description="Loop detection middleware configuration")
    model_config = ConfigDict(extra="allow")
    database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
    run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
@@ -157,56 +160,54 @@ class AppConfig(BaseModel):
        config_data = cls.resolve_env_variables(config_data)
        cls._apply_database_defaults(config_data)

-        # Load title config if present
-        if "title" in config_data:
-            load_title_config_from_dict(config_data["title"])
-
-        # Load summarization config if present
-        if "summarization" in config_data:
-            load_summarization_config_from_dict(config_data["summarization"])
-
-        # Load memory config if present
-        if "memory" in config_data:
-            load_memory_config_from_dict(config_data["memory"])
-
-        # 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"])
-
-        # Load tool_search config if present
-        if "tool_search" in config_data:
-            load_tool_search_config_from_dict(config_data["tool_search"])
-
-        # Load guardrails config if present
-        if "guardrails" in config_data:
-            load_guardrails_config_from_dict(config_data["guardrails"])
-
        # Load 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"])
-
-        # Load stream bridge config if present
-        if "stream_bridge" in config_data:
-            load_stream_bridge_config_from_dict(config_data["stream_bridge"])
-
-        # Always refresh ACP agent config so removed entries do not linger across reloads.
-        load_acp_config_from_dict(config_data.get("acp_agents", {}))
-
        # Load extensions config separately (it's in a different file)
        extensions_config = ExtensionsConfig.from_file()
        config_data["extensions"] = extensions_config.model_dump()

        result = cls.model_validate(config_data)
+        acp_agents = cls._validate_acp_agents(config_data.get("acp_agents", {}))
+        cls._apply_singleton_configs(result, acp_agents)
        return result

+    @classmethod
+    def _validate_acp_agents(
+        cls,
+        config_data: Mapping[str, Mapping[str, object]] | None,
+    ) -> dict[str, ACPAgentConfig]:
+        if config_data is None:
+            config_data = {}
+        return {name: ACPAgentConfig(**cfg) for name, cfg in config_data.items()}
+
+    @classmethod
+    def _apply_singleton_configs(cls, config: Self, acp_agents: dict[str, ACPAgentConfig]) -> None:
+        from deerflow.config.checkpointer_config import get_checkpointer_config
+
+        previous_checkpointer_config = get_checkpointer_config()
+
+        load_title_config_from_dict(config.title.model_dump())
+        load_summarization_config_from_dict(config.summarization.model_dump())
+        load_memory_config_from_dict(config.memory.model_dump())
+        load_agents_api_config_from_dict(config.agents_api.model_dump())
+        load_subagents_config_from_dict(config.subagents.model_dump())
+        load_tool_search_config_from_dict(config.tool_search.model_dump())
+        load_guardrails_config_from_dict(config.guardrails.model_dump())
+        load_checkpointer_config_from_dict(config.checkpointer.model_dump() if config.checkpointer is not None else None)
+        load_stream_bridge_config_from_dict(config.stream_bridge.model_dump() if config.stream_bridge is not None else None)
+        load_acp_config_from_dict({name: agent.model_dump() for name, agent in acp_agents.items()})
+
+        if previous_checkpointer_config != config.checkpointer:
+            # These runtime singletons derive their backend from checkpointer config.
+            # Keep imports local to avoid cycles: both providers import get_app_config.
+            from deerflow.runtime.checkpointer import reset_checkpointer
+            from deerflow.runtime.store import reset_store
+
+            reset_checkpointer()
+            reset_store()
+
    @classmethod
    def _apply_database_defaults(cls, config_data: dict[str, Any]) -> None:
        """Apply config.yaml defaults for persistence when the section is absent."""
@@ -14,12 +14,13 @@ class CheckpointerConfig(BaseModel):
        description="Checkpointer backend type. "
        "'memory' is in-process only (lost on restart). "
        "'sqlite' persists to a local file (requires langgraph-checkpoint-sqlite). "
-        "'postgres' persists to PostgreSQL (requires langgraph-checkpoint-postgres)."
+        "'postgres' persists to PostgreSQL (install with deerflow-harness[postgres])."
    )
    connection_string: str | None = Field(
        default=None,
        description="Connection string for sqlite (file path) or postgres (DSN). "
-        "Required for sqlite and postgres types. "
+        "Optional for sqlite and defaults to 'store.db' when omitted. "
+        "Required for postgres. "
        "For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
        "For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
    )
@@ -40,7 +41,10 @@ def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
    _checkpointer_config = config


-def load_checkpointer_config_from_dict(config_dict: dict) -> None:
+def load_checkpointer_config_from_dict(config_dict: dict | None) -> None:
    """Load checkpointer configuration from a dictionary."""
    global _checkpointer_config
+    if config_dict is None:
+        _checkpointer_config = None
+        return
    _checkpointer_config = CheckpointerConfig(**config_dict)
@@ -0,0 +1,73 @@
+"""Configuration for loop detection middleware."""
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class ToolFreqOverride(BaseModel):
+    """Per-tool frequency threshold override.
+
+    Can be higher or lower than the global defaults. Commonly used to raise
+    thresholds for high-frequency tools like bash in batch workflows (e.g.
+    RNA-seq pipelines) without weakening protection on every other tool.
+    """
+
+    warn: int = Field(ge=1)
+    hard_limit: int = Field(ge=1)
+
+    @model_validator(mode="after")
+    def _validate(self) -> "ToolFreqOverride":
+        if self.hard_limit < self.warn:
+            raise ValueError("hard_limit must be >= warn")
+        return self
+
+
+class LoopDetectionConfig(BaseModel):
+    """Configuration for repetitive tool-call loop detection."""
+
+    enabled: bool = Field(
+        default=True,
+        description="Whether to enable repetitive tool-call loop detection",
+    )
+    warn_threshold: int = Field(
+        default=3,
+        ge=1,
+        description="Number of identical tool-call sets before injecting a warning",
+    )
+    hard_limit: int = Field(
+        default=5,
+        ge=1,
+        description="Number of identical tool-call sets before forcing a stop",
+    )
+    window_size: int = Field(
+        default=20,
+        ge=1,
+        description="Number of recent tool-call sets to track per thread",
+    )
+    max_tracked_threads: int = Field(
+        default=100,
+        ge=1,
+        description="Maximum number of thread histories to keep in memory",
+    )
+    tool_freq_warn: int = Field(
+        default=30,
+        ge=1,
+        description="Number of calls to the same tool type before injecting a frequency warning",
+    )
+    tool_freq_hard_limit: int = Field(
+        default=50,
+        ge=1,
+        description="Number of calls to the same tool type before forcing a stop",
+    )
+    tool_freq_overrides: dict[str, ToolFreqOverride] = Field(
+        default_factory=dict,
+        description=("Per-tool overrides for tool_freq_warn / tool_freq_hard_limit, keyed by tool name. Values can be higher or lower than the global defaults. Commonly used to raise thresholds for high-frequency tools like bash."),
+    )
+
+    @model_validator(mode="after")
+    def validate_thresholds(self) -> "LoopDetectionConfig":
+        """Ensure hard stop cannot happen before the warning threshold."""
+        if self.hard_limit < self.warn_threshold:
+            raise ValueError("hard_limit must be greater than or equal to warn_threshold")
+        if self.tool_freq_hard_limit < self.tool_freq_warn:
+            raise ValueError("tool_freq_hard_limit must be greater than or equal to tool_freq_warn")
+        return self
@@ -132,15 +132,20 @@ class Paths:

    @property
    def agents_dir(self) -> Path:
-        """Root directory for all custom agents: `{base_dir}/agents/`."""
+        """Legacy root for shared (pre user-isolation) custom agents: `{base_dir}/agents/`.
+
+        New code should use :meth:`user_agents_dir` instead. This property remains
+        only as a read-side fallback for installations that have not yet run the
+        ``migrate_user_isolation.py`` script.
+        """
        return self.base_dir / "agents"

    def agent_dir(self, name: str) -> Path:
-        """Directory for a specific agent: `{base_dir}/agents/{name}/`."""
+        """Legacy per-agent directory (no user isolation): `{base_dir}/agents/{name}/`."""
        return self.agents_dir / name.lower()

    def agent_memory_file(self, name: str) -> Path:
-        """Per-agent memory file: `{base_dir}/agents/{name}/memory.json`."""
+        """Legacy per-agent memory file: `{base_dir}/agents/{name}/memory.json`."""
        return self.agent_dir(name) / "memory.json"

    def user_dir(self, user_id: str) -> Path:
@@ -151,9 +156,17 @@ class Paths:
        """Per-user memory file: `{base_dir}/users/{user_id}/memory.json`."""
        return self.user_dir(user_id) / "memory.json"

+    def user_agents_dir(self, user_id: str) -> Path:
+        """Per-user root for that user's custom agents: `{base_dir}/users/{user_id}/agents/`."""
+        return self.user_dir(user_id) / "agents"
+
+    def user_agent_dir(self, user_id: str, agent_name: str) -> Path:
+        """Per-user per-agent directory: `{base_dir}/users/{user_id}/agents/{name}/`."""
+        return self.user_agents_dir(user_id) / agent_name.lower()
+
    def user_agent_memory_file(self, user_id: str, agent_name: str) -> Path:
        """Per-user per-agent memory: `{base_dir}/users/{user_id}/agents/{name}/memory.json`."""
-        return self.user_dir(user_id) / "agents" / agent_name.lower() / "memory.json"
+        return self.user_agent_dir(user_id, agent_name) / "memory.json"

    def thread_dir(self, thread_id: str, *, user_id: str | None = None) -> Path:
        """
@@ -6,6 +6,13 @@ from pydantic import BaseModel, Field
 from deerflow.config.runtime_paths import project_root, resolve_path


+def _legacy_skills_candidates() -> tuple[Path, ...]:
+    """Return source-tree skills locations for monorepo compatibility."""
+    backend_dir = Path(__file__).resolve().parents[4]
+    repo_root = backend_dir.parent
+    return (repo_root / "skills",)
+
+
 class SkillsConfig(BaseModel):
    """Configuration for skills system"""

@@ -15,7 +22,7 @@ class SkillsConfig(BaseModel):
    )
    path: str | None = Field(
        default=None,
-        description="Path to skills directory. If not specified, defaults to skills under the caller project root.",
+        description=("Path to skills directory. If not specified, defaults to `skills` under the caller project root, falling back to the legacy repo-root location for monorepo compatibility."),
    )
    container_path: str = Field(
        default="/mnt/skills",
@@ -26,15 +33,30 @@ class SkillsConfig(BaseModel):
        """
        Get the resolved skills directory path.

-        Returns:
-            Path to the skills directory
+        Resolution order:
+            1. Explicit ``path`` field
+            2. ``DEER_FLOW_SKILLS_PATH`` environment variable
+            3. ``skills`` under the caller project root (``project_root()``)
+            4. Legacy repo-root candidates for monorepo compatibility (``_legacy_skills_candidates``)
+
+        When none of (3) or (4) exist on disk, the project-root default is returned so callers
+        can still surface a stable "no skills" location without raising.
        """
        if self.path:
            # Use configured path (can be absolute or relative to project root)
            return resolve_path(self.path)
        if env_path := os.getenv("DEER_FLOW_SKILLS_PATH"):
            return resolve_path(env_path)
-        return project_root() / "skills"
+
+        project_default = project_root() / "skills"
+        if project_default.is_dir():
+            return project_default
+
+        for candidate in _legacy_skills_candidates():
+            if candidate.is_dir():
+                return candidate
+
+        return project_default

    def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
        """
@@ -40,7 +40,10 @@ def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
    _stream_bridge_config = config


-def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
+def load_stream_bridge_config_from_dict(config_dict: dict | None) -> None:
    """Load stream bridge configuration from a dictionary."""
    global _stream_bridge_config
+    if config_dict is None:
+        _stream_bridge_config = None
+        return
    _stream_bridge_config = StreamBridgeConfig(**config_dict)
@@ -179,9 +179,3 @@ def load_subagents_config_from_dict(config_dict: dict) -> None:
            overrides_summary or "none",
            custom_agents_names or "none",
        )
-    else:
-        logger.info(
-            "Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
-            _subagents_config.timeout_seconds,
-            _subagents_config.max_turns,
-        )
@@ -4,4 +4,4 @@ from pydantic import BaseModel, Field
 class TokenUsageConfig(BaseModel):
    """Configuration for token usage tracking."""

-    enabled: bool = Field(default=False, description="Enable token usage tracking middleware")
+    enabled: bool = Field(default=True, description="Enable token usage tracking middleware")
@@ -1,11 +1,6 @@
 """Load MCP tools using langchain-mcp-adapters."""

-import asyncio
-import atexit
-import concurrent.futures
 import logging
-from collections.abc import Callable
-from typing import Any

 from langchain_core.tools import BaseTool

@@ -13,46 +8,10 @@ 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
+from deerflow.tools.sync import make_sync_tool_wrapper

 logger = logging.getLogger(__name__)

-# Global thread pool for sync tool invocation in async environments
-_SYNC_TOOL_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=10, thread_name_prefix="mcp-sync-tool")
-
-# Register shutdown hook for the global executor
-atexit.register(lambda: _SYNC_TOOL_EXECUTOR.shutdown(wait=False))
-
-
-def _make_sync_tool_wrapper(coro: Callable[..., Any], tool_name: str) -> Callable[..., Any]:
-    """Build a synchronous wrapper for an asynchronous tool coroutine.
-
-    Args:
-        coro: The tool's asynchronous coroutine.
-        tool_name: Name of the tool (for logging).
-
-    Returns:
-        A synchronous function that correctly handles nested event loops.
-    """
-
-    def sync_wrapper(*args: Any, **kwargs: Any) -> Any:
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = None
-
-        try:
-            if loop is not None and loop.is_running():
-                # Use global executor to avoid nested loop issues and improve performance
-                future = _SYNC_TOOL_EXECUTOR.submit(asyncio.run, coro(*args, **kwargs))
-                return future.result()
-            else:
-                return asyncio.run(coro(*args, **kwargs))
-        except Exception as e:
-            logger.error(f"Error invoking MCP tool '{tool_name}' via sync wrapper: {e}", exc_info=True)
-            raise
-
-    return sync_wrapper
-

 async def get_mcp_tools() -> list[BaseTool]:
    """Get all tools from enabled MCP servers.
@@ -126,7 +85,7 @@ async def get_mcp_tools() -> list[BaseTool]:
        # Patch tools to support sync invocation, as deerflow client streams synchronously
        for tool in tools:
            if getattr(tool, "func", None) is None and getattr(tool, "coroutine", None) is not None:
-                tool.func = _make_sync_tool_wrapper(tool.coroutine, tool.name)
+                tool.func = make_sync_tool_wrapper(tool.coroutine, tool.name)

        return tools

@@ -196,6 +196,10 @@ class ClaudeChatModel(ChatAnthropic):
        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.
+
+        The system prompt is expected to be fully static (no per-user memory or
+        current date).  Dynamic context is injected per-turn via
+        DynamicContextMiddleware as a <system-reminder> in the first HumanMessage.
        """
        MAX_CACHE_BREAKPOINTS = 4

@@ -27,6 +27,34 @@ from deerflow.models.credential_loader import CodexCliCredential, load_codex_cli
 logger = logging.getLogger(__name__)

 CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
+
+
+def _build_usage_metadata(oai_usage: dict) -> dict:
+    """Convert Codex/Responses API usage dict to LangChain usage_metadata format.
+
+    Maps OpenAI Responses API token usage fields to the dict structure that
+    LangChain AIMessage.usage_metadata expects. This avoids depending on
+    langchain_openai private helpers like ``_create_usage_metadata_responses``.
+    """
+    input_tokens = oai_usage.get("input_tokens", 0)
+    output_tokens = oai_usage.get("output_tokens", 0)
+    total_tokens = oai_usage.get("total_tokens", input_tokens + output_tokens)
+    metadata: dict = {
+        "input_tokens": input_tokens,
+        "output_tokens": output_tokens,
+        "total_tokens": total_tokens,
+    }
+    input_details = oai_usage.get("input_tokens_details") or {}
+    output_details = oai_usage.get("output_tokens_details") or {}
+    cache_read = input_details.get("cached_tokens")
+    if cache_read is not None:
+        metadata["input_token_details"] = {"cache_read": cache_read}
+    reasoning = output_details.get("reasoning_tokens")
+    if reasoning is not None:
+        metadata["output_token_details"] = {"reasoning": reasoning}
+    return metadata
+
+
 MAX_RETRIES = 3


@@ -346,6 +374,7 @@ class CodexChatModel(BaseChatModel):
                )

        usage = response.get("usage", {})
+        usage_metadata = _build_usage_metadata(usage) if usage else None
        additional_kwargs = {}
        if reasoning_content:
            additional_kwargs["reasoning_content"] = reasoning_content
@@ -355,6 +384,7 @@ class CodexChatModel(BaseChatModel):
            tool_calls=tool_calls if tool_calls else [],
            invalid_tool_calls=invalid_tool_calls,
            additional_kwargs=additional_kwargs,
+            usage_metadata=usage_metadata,
            response_metadata={
                "model": response.get("model", self.model),
                "usage": usage,
@@ -81,7 +81,16 @@ async def init_engine(
        try:
            import asyncpg  # noqa: F401
        except ImportError:
-            raise ImportError("database.backend is set to 'postgres' but asyncpg is not installed.\nInstall it with:\n    uv sync --extra postgres\nOr switch to backend: sqlite in config.yaml for single-node deployment.") from None
+            raise ImportError(
+                "database.backend is set to 'postgres' but asyncpg is not installed.\n"
+                "Install it with:\n"
+                "    cd backend && uv sync --all-packages --extra postgres\n"
+                "On the next `make dev` the postgres extra is auto-detected from\n"
+                "config.yaml (database.backend: postgres) and reinstalled, so it\n"
+                "will not be wiped again. Set UV_EXTRAS=postgres in .env to opt in\n"
+                "explicitly. Or switch to backend: sqlite in config.yaml for\n"
+                "single-node deployment."
+            ) from None

    if backend == "sqlite":
        import os
@@ -0,0 +1,195 @@
+"""Dialect-aware JSON value matching for SQLAlchemy (SQLite + PostgreSQL)."""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from typing import Any
+
+from sqlalchemy import BigInteger, Float, String, bindparam
+from sqlalchemy.ext.compiler import compiles
+from sqlalchemy.sql.compiler import SQLCompiler
+from sqlalchemy.sql.expression import ColumnElement
+from sqlalchemy.sql.visitors import InternalTraversal
+from sqlalchemy.types import Boolean, TypeEngine
+
+# Key is interpolated into compiled SQL; restrict charset to prevent injection.
+_KEY_CHARSET_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
+
+# Allowed value types for metadata filter values (same set accepted by JsonMatch).
+ALLOWED_FILTER_VALUE_TYPES: tuple[type, ...] = (type(None), bool, int, float, str)
+
+# SQLite raises an overflow when binding values outside signed 64-bit range;
+# PostgreSQL overflows during BIGINT cast. Reject at validation time instead.
+_INT64_MIN = -(2**63)
+_INT64_MAX = 2**63 - 1
+
+
+def validate_metadata_filter_key(key: object) -> bool:
+    """Return True if *key* is safe for use as a JSON metadata filter key.
+
+    A key is "safe" when it is a string matching ``[A-Za-z0-9_-]+``. The
+    charset is restricted because the key is interpolated into the
+    compiled SQL path expression (``$."<key>"`` / ``->`` literal), so any
+    laxer pattern would open a SQL/JSONPath injection surface.
+    """
+    return isinstance(key, str) and bool(_KEY_CHARSET_RE.match(key))
+
+
+def validate_metadata_filter_value(value: object) -> bool:
+    """Return True if *value* is an allowed type for a JSON metadata filter.
+
+    Matches the set of types ``_build_clause`` knows how to compile into
+    a dialect-portable predicate. Anything else (list/dict/bytes/...) is
+    intentionally rejected rather than silently coerced via ``str()`` —
+    silent coercion would (a) produce wrong matches and (b) break
+    SQLAlchemy's ``inherit_cache`` invariant when ``value`` is unhashable.
+
+    Integer values are additionally restricted to the signed 64-bit range
+    ``[-2**63, 2**63 - 1]``: SQLite overflows when binding larger values
+    and PostgreSQL overflows during the ``BIGINT`` cast.
+    """
+    if not isinstance(value, ALLOWED_FILTER_VALUE_TYPES):
+        return False
+    if isinstance(value, int) and not isinstance(value, bool):
+        if not (_INT64_MIN <= value <= _INT64_MAX):
+            return False
+    return True
+
+
+class JsonMatch(ColumnElement):
+    """Dialect-portable ``column[key] == value`` for JSON columns.
+
+    Compiles to ``json_type``/``json_extract`` on SQLite and
+    ``json_typeof``/``->>`` on PostgreSQL, with type-safe comparison
+    that distinguishes bool vs int and NULL vs missing key.
+
+    *key* must be a single literal key matching ``[A-Za-z0-9_-]+``.
+    *value* must be one of: ``None``, ``bool``, ``int`` (signed 64-bit), ``float``, ``str``.
+    """
+
+    inherit_cache = True
+    type = Boolean()
+    _is_implicitly_boolean = True
+
+    _traverse_internals = [
+        ("column", InternalTraversal.dp_clauseelement),
+        ("key", InternalTraversal.dp_string),
+        ("value", InternalTraversal.dp_plain_obj),
+    ]
+
+    def __init__(self, column: ColumnElement, key: str, value: object) -> None:
+        if not validate_metadata_filter_key(key):
+            raise ValueError(f"JsonMatch key must match {_KEY_CHARSET_RE.pattern!r}; got: {key!r}")
+        if not validate_metadata_filter_value(value):
+            if isinstance(value, int) and not isinstance(value, bool):
+                raise TypeError(f"JsonMatch int value out of signed 64-bit range [-2**63, 2**63-1]: {value!r}")
+            raise TypeError(f"JsonMatch value must be None, bool, int, float, or str; got: {type(value).__name__!r}")
+        self.column = column
+        self.key = key
+        self.value = value
+        super().__init__()
+
+
+@dataclass(frozen=True)
+class _Dialect:
+    """Per-dialect names used when emitting JSON type/value comparisons."""
+
+    null_type: str
+    num_types: tuple[str, ...]
+    num_cast: str
+    int_types: tuple[str, ...]
+    int_cast: str
+    # None for SQLite where json_type already returns 'integer'/'real';
+    # regex literal for PostgreSQL where json_typeof returns 'number' for
+    # both ints and floats, so an extra guard prevents CAST errors on floats.
+    int_guard: str | None
+    string_type: str
+    bool_type: str | None
+
+
+_SQLITE = _Dialect(
+    null_type="null",
+    num_types=("integer", "real"),
+    num_cast="REAL",
+    int_types=("integer",),
+    int_cast="INTEGER",
+    int_guard=None,
+    string_type="text",
+    bool_type=None,
+)
+
+_PG = _Dialect(
+    null_type="null",
+    num_types=("number",),
+    num_cast="DOUBLE PRECISION",
+    int_types=("number",),
+    int_cast="BIGINT",
+    int_guard="'^-?[0-9]+$'",
+    string_type="string",
+    bool_type="boolean",
+)
+
+
+def _bind(compiler: SQLCompiler, value: object, sa_type: TypeEngine[Any], **kw: Any) -> str:
+    param = bindparam(None, value, type_=sa_type)
+    return compiler.process(param, **kw)
+
+
+def _type_check(typeof: str, types: tuple[str, ...]) -> str:
+    if len(types) == 1:
+        return f"{typeof} = '{types[0]}'"
+    quoted = ", ".join(f"'{t}'" for t in types)
+    return f"{typeof} IN ({quoted})"
+
+
+def _build_clause(compiler: SQLCompiler, typeof: str, extract: str, value: object, dialect: _Dialect, **kw: Any) -> str:
+    if value is None:
+        return f"{typeof} = '{dialect.null_type}'"
+    if isinstance(value, bool):
+        # bool check must precede int check — bool is a subclass of int in Python
+        bool_str = "true" if value else "false"
+        if dialect.bool_type is None:
+            return f"{typeof} = '{bool_str}'"
+        return f"({typeof} = '{dialect.bool_type}' AND {extract} = '{bool_str}')"
+    if isinstance(value, int):
+        bp = _bind(compiler, value, BigInteger(), **kw)
+        if dialect.int_guard:
+            # CASE prevents CAST error when json_typeof = 'number' also matches floats
+            return f"(CASE WHEN {_type_check(typeof, dialect.int_types)} AND {extract} ~ {dialect.int_guard} THEN CAST({extract} AS {dialect.int_cast}) END = {bp})"
+        return f"({_type_check(typeof, dialect.int_types)} AND CAST({extract} AS {dialect.int_cast}) = {bp})"
+    if isinstance(value, float):
+        bp = _bind(compiler, value, Float(), **kw)
+        return f"({_type_check(typeof, dialect.num_types)} AND CAST({extract} AS {dialect.num_cast}) = {bp})"
+    bp = _bind(compiler, str(value), String(), **kw)
+    return f"({typeof} = '{dialect.string_type}' AND {extract} = {bp})"
+
+
+@compiles(JsonMatch, "sqlite")
+def _compile_sqlite(element: JsonMatch, compiler: SQLCompiler, **kw: Any) -> str:
+    if not validate_metadata_filter_key(element.key):
+        raise ValueError(f"Key escaped validation: {element.key!r}")
+    col = compiler.process(element.column, **kw)
+    path = f'$."{element.key}"'
+    typeof = f"json_type({col}, '{path}')"
+    extract = f"json_extract({col}, '{path}')"
+    return _build_clause(compiler, typeof, extract, element.value, _SQLITE, **kw)
+
+
+@compiles(JsonMatch, "postgresql")
+def _compile_pg(element: JsonMatch, compiler: SQLCompiler, **kw: Any) -> str:
+    if not validate_metadata_filter_key(element.key):
+        raise ValueError(f"Key escaped validation: {element.key!r}")
+    col = compiler.process(element.column, **kw)
+    typeof = f"json_typeof({col} -> '{element.key}')"
+    extract = f"({col} ->> '{element.key}')"
+    return _build_clause(compiler, typeof, extract, element.value, _PG, **kw)
+
+
+@compiles(JsonMatch)
+def _compile_default(element: JsonMatch, compiler: SQLCompiler, **kw: Any) -> str:
+    raise NotImplementedError(f"JsonMatch supports only sqlite and postgresql; got dialect: {compiler.dialect.name}")
+
+
+def json_match(column: ColumnElement, key: str, value: object) -> JsonMatch:
+    return JsonMatch(column, key, value)
@@ -23,6 +23,18 @@ class RunRepository(RunStore):
    def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
        self._sf = session_factory

+    @staticmethod
+    def _normalize_model_name(model_name: str | None) -> str | None:
+        """Normalize model_name for storage: strip whitespace, truncate to 128 chars."""
+        if model_name is None:
+            return None
+        if not isinstance(model_name, str):
+            model_name = str(model_name)
+        normalized = model_name.strip()
+        if len(normalized) > 128:
+            normalized = normalized[:128]
+        return normalized
+
    @staticmethod
    def _safe_json(obj: Any) -> Any:
        """Ensure obj is JSON-serializable. Falls back to model_dump() or str()."""
@@ -70,6 +82,7 @@ class RunRepository(RunStore):
        thread_id,
        assistant_id=None,
        user_id: str | None | _AutoSentinel = AUTO,
+        model_name: str | None = None,
        status="pending",
        multitask_strategy="reject",
        metadata=None,
@@ -85,6 +98,7 @@ class RunRepository(RunStore):
            thread_id=thread_id,
            assistant_id=assistant_id,
            user_id=resolved_user_id,
+            model_name=self._normalize_model_name(model_name),
            status=status,
            multitask_strategy=multitask_strategy,
            metadata_json=self._safe_json(metadata) or {},
@@ -209,10 +223,11 @@ class RunRepository(RunStore):
        """Aggregate token usage via a single SQL GROUP BY query."""
        _completed = RunRow.status.in_(("success", "error"))
        _thread = RunRow.thread_id == thread_id
+        model_name = func.coalesce(RunRow.model_name, "unknown")

        stmt = (
            select(
-                func.coalesce(RunRow.model_name, "unknown").label("model"),
+                model_name.label("model"),
                func.count().label("runs"),
                func.coalesce(func.sum(RunRow.total_tokens), 0).label("total_tokens"),
                func.coalesce(func.sum(RunRow.total_input_tokens), 0).label("total_input_tokens"),
@@ -222,7 +237,7 @@ class RunRepository(RunStore):
                func.coalesce(func.sum(RunRow.middleware_tokens), 0).label("middleware"),
            )
            .where(_thread, _completed)
-            .group_by(func.coalesce(RunRow.model_name, "unknown"))
+            .group_by(model_name)
        )

        async with self._sf() as session:
@@ -4,7 +4,7 @@ from __future__ import annotations

 from typing import TYPE_CHECKING

-from deerflow.persistence.thread_meta.base import ThreadMetaStore
+from deerflow.persistence.thread_meta.base import InvalidMetadataFilterError, ThreadMetaStore
 from deerflow.persistence.thread_meta.memory import MemoryThreadMetaStore
 from deerflow.persistence.thread_meta.model import ThreadMetaRow
 from deerflow.persistence.thread_meta.sql import ThreadMetaRepository
@@ -14,6 +14,7 @@ if TYPE_CHECKING:
    from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker

 __all__ = [
+    "InvalidMetadataFilterError",
    "MemoryThreadMetaStore",
    "ThreadMetaRepository",
    "ThreadMetaRow",
@@ -15,10 +15,15 @@ three-state semantics (see :mod:`deerflow.runtime.user_context`):
 from __future__ import annotations

 import abc
+from typing import Any

 from deerflow.runtime.user_context import AUTO, _AutoSentinel


+class InvalidMetadataFilterError(ValueError):
+    """Raised when all client-supplied metadata filter keys are rejected."""
+
+
 class ThreadMetaStore(abc.ABC):
    @abc.abstractmethod
    async def create(
@@ -40,12 +45,12 @@ class ThreadMetaStore(abc.ABC):
    async def search(
        self,
        *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
        status: str | None = None,
        limit: int = 100,
        offset: int = 0,
        user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
        pass

    @abc.abstractmethod
@@ -7,13 +7,13 @@ router for thread records.

 from __future__ import annotations

-import time
 from typing import Any

 from langgraph.store.base import BaseStore

 from deerflow.persistence.thread_meta.base import ThreadMetaStore
 from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id
+from deerflow.utils.time import coerce_iso, now_iso

 THREADS_NS: tuple[str, ...] = ("threads",)

@@ -48,7 +48,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
        metadata: dict | None = None,
    ) -> dict:
        resolved_user_id = resolve_user_id(user_id, method_name="MemoryThreadMetaStore.create")
-        now = time.time()
+        now = now_iso()
        record: dict[str, Any] = {
            "thread_id": thread_id,
            "assistant_id": assistant_id,
@@ -69,12 +69,12 @@ class MemoryThreadMetaStore(ThreadMetaStore):
    async def search(
        self,
        *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
        status: str | None = None,
        limit: int = 100,
        offset: int = 0,
        user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
        resolved_user_id = resolve_user_id(user_id, method_name="MemoryThreadMetaStore.search")
        filter_dict: dict[str, Any] = {}
        if metadata:
@@ -106,7 +106,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
        if record is None:
            return
        record["display_name"] = display_name
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
        await self._store.aput(THREADS_NS, thread_id, record)

    async def update_status(self, thread_id: str, status: str, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -114,7 +114,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
        if record is None:
            return
        record["status"] = status
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
        await self._store.aput(THREADS_NS, thread_id, record)

    async def update_metadata(self, thread_id: str, metadata: dict, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -124,7 +124,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
        merged = dict(record.get("metadata") or {})
        merged.update(metadata)
        record["metadata"] = merged
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
        await self._store.aput(THREADS_NS, thread_id, record)

    async def delete(self, thread_id: str, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -144,6 +144,8 @@ class MemoryThreadMetaStore(ThreadMetaStore):
            "display_name": val.get("display_name"),
            "status": val.get("status", "idle"),
            "metadata": val.get("metadata", {}),
-            "created_at": str(val.get("created_at", "")),
-            "updated_at": str(val.get("updated_at", "")),
+            # ``coerce_iso`` heals legacy unix-second values written by
+            # earlier Gateway versions that called ``str(time.time())``.
+            "created_at": coerce_iso(val.get("created_at", "")),
+            "updated_at": coerce_iso(val.get("updated_at", "")),
        }
@@ -2,16 +2,20 @@

 from __future__ import annotations

+import logging
 from datetime import UTC, datetime
 from typing import Any

 from sqlalchemy import select, update
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker

-from deerflow.persistence.thread_meta.base import ThreadMetaStore
+from deerflow.persistence.json_compat import json_match
+from deerflow.persistence.thread_meta.base import InvalidMetadataFilterError, ThreadMetaStore
 from deerflow.persistence.thread_meta.model import ThreadMetaRow
 from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id

+logger = logging.getLogger(__name__)
+

 class ThreadMetaRepository(ThreadMetaStore):
    def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
@@ -20,7 +24,7 @@ class ThreadMetaRepository(ThreadMetaStore):
    @staticmethod
    def _row_to_dict(row: ThreadMetaRow) -> dict[str, Any]:
        d = row.to_dict()
-        d["metadata"] = d.pop("metadata_json", {})
+        d["metadata"] = d.pop("metadata_json", None) or {}
        for key in ("created_at", "updated_at"):
            val = d.get(key)
            if isinstance(val, datetime):
@@ -104,35 +108,39 @@ class ThreadMetaRepository(ThreadMetaStore):
    async def search(
        self,
        *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
        status: str | None = None,
        limit: int = 100,
        offset: int = 0,
        user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
        """Search threads with optional metadata and status filters.

        Owner filter is enforced by default: caller must be in a user
        context. Pass ``user_id=None`` to bypass (migration/CLI).
        """
        resolved_user_id = resolve_user_id(user_id, method_name="ThreadMetaRepository.search")
-        stmt = select(ThreadMetaRow).order_by(ThreadMetaRow.updated_at.desc())
+        stmt = select(ThreadMetaRow).order_by(ThreadMetaRow.updated_at.desc(), ThreadMetaRow.thread_id.desc())
        if resolved_user_id is not None:
            stmt = stmt.where(ThreadMetaRow.user_id == resolved_user_id)
        if status:
            stmt = stmt.where(ThreadMetaRow.status == status)

        if metadata:
-            # When metadata filter is active, fetch a larger window and filter
-            # in Python. TODO(Phase 2): use JSON DB operators (Postgres @>,
-            # SQLite json_extract) for server-side filtering.
-            stmt = stmt.limit(limit * 5 + offset)
-            async with self._sf() as session:
-                result = await session.execute(stmt)
-                rows = [self._row_to_dict(r) for r in result.scalars()]
-            rows = [r for r in rows if all(r.get("metadata", {}).get(k) == v for k, v in metadata.items())]
-            return rows[offset : offset + limit]
-        else:
+            applied = 0
+            for key, value in metadata.items():
+                try:
+                    stmt = stmt.where(json_match(ThreadMetaRow.metadata_json, key, value))
+                    applied += 1
+                except (ValueError, TypeError) as exc:
+                    logger.warning("Skipping metadata filter key %s: %s", ascii(key), exc)
+            if applied == 0:
+                # Comma-separated plain string (no list repr / nested
+                # quoting) so the 400 detail surfaced by the Gateway is
+                # easy for clients to read. Sorted for determinism.
+                rejected_keys = ", ".join(sorted(str(k) for k in metadata))
+                raise InvalidMetadataFilterError(f"All metadata filter keys were rejected as unsafe: {rejected_keys}")
+
        stmt = stmt.limit(limit).offset(offset)
        async with self._sf() as session:
            result = await session.execute(stmt)
@@ -36,7 +36,9 @@ logger = logging.getLogger(__name__)
 # ---------------------------------------------------------------------------

 SQLITE_INSTALL = "langgraph-checkpoint-sqlite is required for the SQLite checkpointer. Install it with: uv add langgraph-checkpoint-sqlite"
-POSTGRES_INSTALL = "langgraph-checkpoint-postgres is required for the PostgreSQL checkpointer. Install it with: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool"
+POSTGRES_INSTALL = (
+    "langgraph-checkpoint-postgres is required for the PostgreSQL checkpointer. Install the package extra with: pip install 'deerflow-harness[postgres]' (or use: uv sync --all-packages --extra postgres when developing locally)"
+)
 POSTGRES_CONN_REQUIRED = "checkpointer.connection_string is required for the postgres backend"

 # ---------------------------------------------------------------------------
@@ -9,8 +9,9 @@ from __future__ import annotations
 import json
 import logging
 from datetime import UTC, datetime
+from typing import Any

-from sqlalchemy import delete, func, select
+from sqlalchemy import delete, func, select, text
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker

 from deerflow.persistence.models.run_event import RunEventRow
@@ -33,20 +34,21 @@ class DbRunEventStore(RunEventStore):
        if isinstance(val, datetime):
            d["created_at"] = val.isoformat()
        d.pop("id", None)
-        # Restore dict content that was JSON-serialized on write
+        # Restore structured content that was JSON-serialized on write.
        raw = d.get("content", "")
-        if isinstance(raw, str) and d.get("metadata", {}).get("content_is_dict"):
+        metadata = d.get("metadata", {})
+        if isinstance(raw, str) and (metadata.get("content_is_json") or metadata.get("content_is_dict")):
            try:
                d["content"] = json.loads(raw)
            except (json.JSONDecodeError, ValueError):
-                # Content looked like JSON (content_is_dict flag) but failed to parse;
+                # Content looked like JSON but failed to parse;
                # keep the raw string as-is.
                logger.debug("Failed to deserialize content as JSON for event seq=%s", d.get("seq"))
        return d

-    def _truncate_trace(self, category: str, content: str | dict, metadata: dict | None) -> tuple[str | dict, dict]:
+    def _truncate_trace(self, category: str, content: Any, metadata: dict | None) -> tuple[Any, dict]:
        if category == "trace":
-            text = json.dumps(content, default=str, ensure_ascii=False) if isinstance(content, dict) else content
+            text = content if isinstance(content, str) else json.dumps(content, default=str, ensure_ascii=False)
            encoded = text.encode("utf-8")
            if len(encoded) > self._max_trace_content:
                # Truncate by bytes, then decode back (may cut a multi-byte char, so use errors="ignore")
@@ -54,6 +56,18 @@ class DbRunEventStore(RunEventStore):
                metadata = {**(metadata or {}), "content_truncated": True, "original_byte_length": len(encoded)}
        return content, metadata or {}

+    @staticmethod
+    def _content_to_db(content: Any, metadata: dict | None) -> tuple[str, dict]:
+        metadata = metadata or {}
+        if isinstance(content, str):
+            return content, metadata
+
+        db_content = json.dumps(content, default=str, ensure_ascii=False)
+        metadata = {**metadata, "content_is_json": True}
+        if isinstance(content, dict):
+            metadata["content_is_dict"] = True
+        return db_content, metadata
+
    @staticmethod
    def _user_id_from_context() -> str | None:
        """Soft read of user_id from contextvar for write paths.
@@ -72,6 +86,28 @@ class DbRunEventStore(RunEventStore):
        user = get_current_user()
        return str(user.id) if user is not None else None

+    @staticmethod
+    async def _max_seq_for_thread(session: AsyncSession, thread_id: str) -> int | None:
+        """Return the current max seq while serializing writers per thread.
+
+        PostgreSQL rejects ``SELECT max(...) FOR UPDATE`` because aggregate
+        results are not lockable rows. As a release-safe workaround, take a
+        transaction-level advisory lock keyed by thread_id before reading the
+        aggregate. Other dialects keep the existing row-locking statement.
+        """
+        stmt = select(func.max(RunEventRow.seq)).where(RunEventRow.thread_id == thread_id)
+        bind = session.get_bind()
+        dialect_name = bind.dialect.name if bind is not None else ""
+
+        if dialect_name == "postgresql":
+            await session.execute(
+                text("SELECT pg_advisory_xact_lock(hashtext(CAST(:thread_id AS text))::bigint)"),
+                {"thread_id": thread_id},
+            )
+            return await session.scalar(stmt)
+
+        return await session.scalar(stmt.with_for_update())
+
    async def put(self, *, thread_id, run_id, event_type, category, content="", metadata=None, created_at=None):  # noqa: D401
        """Write a single event — low-frequency path only.

@@ -82,18 +118,11 @@ class DbRunEventStore(RunEventStore):
        the initial ``human_message`` event (once per run).
        """
        content, metadata = self._truncate_trace(category, content, metadata)
-        if isinstance(content, dict):
-            db_content = json.dumps(content, default=str, ensure_ascii=False)
-            metadata = {**(metadata or {}), "content_is_dict": True}
-        else:
-            db_content = content
+        db_content, metadata = self._content_to_db(content, metadata)
        user_id = self._user_id_from_context()
        async with self._sf() as session:
            async with session.begin():
-                # Use FOR UPDATE to serialize seq assignment within a thread.
-                # NOTE: with_for_update() on aggregates is a no-op on SQLite;
-                # the UNIQUE(thread_id, seq) constraint catches races there.
-                max_seq = await session.scalar(select(func.max(RunEventRow.seq)).where(RunEventRow.thread_id == thread_id).with_for_update())
+                max_seq = await self._max_seq_for_thread(session, thread_id)
                seq = (max_seq or 0) + 1
                row = RunEventRow(
                    thread_id=thread_id,
@@ -116,10 +145,8 @@ class DbRunEventStore(RunEventStore):
        async with self._sf() as session:
            async with session.begin():
                # Get max seq for the thread (assume all events in batch belong to same thread).
-                # NOTE: with_for_update() on aggregates is a no-op on SQLite;
-                # the UNIQUE(thread_id, seq) constraint catches races there.
                thread_id = events[0]["thread_id"]
-                max_seq = await session.scalar(select(func.max(RunEventRow.seq)).where(RunEventRow.thread_id == thread_id).with_for_update())
+                max_seq = await self._max_seq_for_thread(session, thread_id)
                seq = max_seq or 0
                rows = []
                for e in events:
@@ -128,11 +155,7 @@ class DbRunEventStore(RunEventStore):
                    category = e.get("category", "trace")
                    metadata = e.get("metadata")
                    content, metadata = self._truncate_trace(category, content, metadata)
-                    if isinstance(content, dict):
-                        db_content = json.dumps(content, default=str, ensure_ascii=False)
-                        metadata = {**(metadata or {}), "content_is_dict": True}
-                    else:
-                        db_content = content
+                    db_content, metadata = self._content_to_db(content, metadata)
                    row = RunEventRow(
                        thread_id=e["thread_id"],
                        run_id=e["run_id"],
@@ -20,12 +20,13 @@ from __future__ import annotations
 import asyncio
 import logging
 import time
+from collections.abc import Mapping
 from datetime import UTC, datetime
 from typing import TYPE_CHECKING, Any, cast
 from uuid import UUID

 from langchain_core.callbacks import BaseCallbackHandler
-from langchain_core.messages import AnyMessage, BaseMessage, HumanMessage, ToolMessage
+from langchain_core.messages import AIMessage, AnyMessage, BaseMessage, HumanMessage, ToolMessage
 from langgraph.types import Command

 if TYPE_CHECKING:
@@ -63,6 +64,16 @@ class RunJournal(BaseCallbackHandler):
        self._total_tokens = 0
        self._llm_call_count = 0

+        # Caller-bucketed token accumulators
+        self._lead_agent_tokens = 0
+        self._subagent_tokens = 0
+        self._middleware_tokens = 0
+
+        # Dedup: LangChain may fire on_llm_end multiple times for the same run_id
+        self._counted_llm_run_ids: set[str] = set()
+        self._counted_external_source_ids: set[str] = set()
+        self._counted_message_llm_run_ids: set[str] = set()
+
        # Convenience fields
        self._last_ai_msg: str | None = None
        self._first_human_msg: str | None = None
@@ -77,6 +88,50 @@ class RunJournal(BaseCallbackHandler):

    # -- Lifecycle callbacks --

+    @staticmethod
+    def _message_text(message: BaseMessage) -> str:
+        """Extract displayable text from a message's mixed content shape."""
+        content = getattr(message, "content", None)
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            parts: list[str] = []
+            for block in content:
+                if isinstance(block, str):
+                    parts.append(block)
+                elif isinstance(block, Mapping):
+                    text = block.get("text")
+                    if isinstance(text, str):
+                        parts.append(text)
+                    else:
+                        nested = block.get("content")
+                        if isinstance(nested, str):
+                            parts.append(nested)
+            return "".join(parts)
+        if isinstance(content, Mapping):
+            for key in ("text", "content"):
+                value = content.get(key)
+                if isinstance(value, str):
+                    return value
+
+        text = getattr(message, "text", None)
+        if isinstance(text, str):
+            return text
+        return ""
+
+    def _record_message_summary(self, message: BaseMessage, *, caller: str | None = None) -> None:
+        """Update run-level convenience fields for persisted run rows."""
+        self._msg_count += 1
+
+        # ``last_ai_message`` should represent the lead agent's user-facing
+        # answer. Middleware/subagent model calls and empty tool-call-only
+        # AI messages must not overwrite the last useful assistant text.
+        is_ai_message = isinstance(message, AIMessage) or getattr(message, "type", None) == "ai"
+        if is_ai_message and (caller is None or caller == "lead_agent"):
+            text = self._message_text(message).strip()
+            if text:
+                self._last_ai_msg = text[:2000]
+
    def on_chain_start(
        self,
        serialized: dict[str, Any],
@@ -155,6 +210,7 @@ class RunJournal(BaseCallbackHandler):
                            content=m.model_dump(),
                            metadata={"caller": caller},
                        )
+                        self._record_message_summary(m, caller=caller)
                        break
                if self._first_human_msg:
                    break
@@ -213,20 +269,34 @@ class RunJournal(BaseCallbackHandler):
                    "llm_call_index": call_index,
                },
            )
+            if rid not in self._counted_message_llm_run_ids:
+                self._record_message_summary(message, caller=caller)

-            # Token accumulation
+            # Token accumulation (dedup by langchain run_id to avoid double-counting
+            # when the callback fires more than once for the same response)
            if self._track_tokens:
                input_tk = usage_dict.get("input_tokens", 0) or 0
                output_tk = usage_dict.get("output_tokens", 0) or 0
                total_tk = usage_dict.get("total_tokens", 0) or 0
                if total_tk == 0:
                    total_tk = input_tk + output_tk
-                if total_tk > 0:
+                if total_tk > 0 and rid not in self._counted_llm_run_ids:
+                    self._counted_llm_run_ids.add(rid)
                    self._total_input_tokens += input_tk
                    self._total_output_tokens += output_tk
                    self._total_tokens += total_tk
                    self._llm_call_count += 1

+                    if caller.startswith("subagent:"):
+                        self._subagent_tokens += total_tk
+                    elif caller.startswith("middleware:"):
+                        self._middleware_tokens += total_tk
+                    else:
+                        self._lead_agent_tokens += total_tk
+
+        if messages:
+            self._counted_message_llm_run_ids.add(str(run_id))
+
    def on_llm_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
        self._llm_start_times.pop(str(run_id), None)
        self._put(event_type="llm.error", category="trace", content=str(error))
@@ -242,12 +312,14 @@ class RunJournal(BaseCallbackHandler):
            if isinstance(output, ToolMessage):
                msg = cast(ToolMessage, output)
                self._put(event_type="llm.tool.result", category="message", content=msg.model_dump())
+                self._record_message_summary(msg)
            elif isinstance(output, Command):
                cmd = cast(Command, output)
                messages = cmd.update.get("messages", [])
                for message in messages:
                    if isinstance(message, BaseMessage):
                        self._put(event_type="llm.tool.result", category="message", content=message.model_dump())
+                        self._record_message_summary(message)
                    else:
                        logger.warning(f"on_tool_end {run_id}: command update message is not BaseMessage: {type(message)}")
            else:
@@ -330,6 +402,49 @@ class RunJournal(BaseCallbackHandler):

    # -- Public methods (called by worker) --

+    def record_external_llm_usage_records(
+        self,
+        records: list[dict[str, int | str]],
+    ) -> None:
+        """Record token usage from external sources (e.g., subagents).
+
+        Each record should contain:
+            source_run_id: Unique identifier to prevent double-counting
+            caller: Caller tag (e.g. "subagent:general-purpose")
+            input_tokens: Input token count
+            output_tokens: Output token count
+            total_tokens: Total token count (computed from input+output if 0/missing)
+        """
+        if not self._track_tokens:
+            return
+        for record in records:
+            source_id = str(record.get("source_run_id", ""))
+            if not source_id:
+                continue
+            if source_id in self._counted_external_source_ids:
+                continue
+
+            total_tk = record.get("total_tokens", 0) or 0
+            if total_tk <= 0:
+                input_tk = record.get("input_tokens", 0) or 0
+                output_tk = record.get("output_tokens", 0) or 0
+                total_tk = input_tk + output_tk
+            if total_tk <= 0:
+                continue
+
+            self._counted_external_source_ids.add(source_id)
+            self._total_input_tokens += record.get("input_tokens", 0) or 0
+            self._total_output_tokens += record.get("output_tokens", 0) or 0
+            self._total_tokens += total_tk
+
+            caller = str(record.get("caller", ""))
+            if caller.startswith("subagent:"):
+                self._subagent_tokens += total_tk
+            elif caller.startswith("middleware:"):
+                self._middleware_tokens += total_tk
+            else:
+                self._lead_agent_tokens += total_tk
+
    def set_first_human_message(self, content: str) -> None:
        """Record the first human message for convenience fields."""
        self._first_human_msg = content[:2000] if content else None
@@ -376,6 +491,9 @@ class RunJournal(BaseCallbackHandler):
            "total_output_tokens": self._total_output_tokens,
            "total_tokens": self._total_tokens,
            "llm_call_count": self._llm_call_count,
+            "lead_agent_tokens": self._lead_agent_tokens,
+            "subagent_tokens": self._subagent_tokens,
+            "middleware_tokens": self._middleware_tokens,
            "message_count": self._msg_count,
            "last_ai_message": self._last_ai_msg,
            "first_human_message": self._first_human_msg,
@@ -6,9 +6,10 @@ import asyncio
 import logging
 import uuid
 from dataclasses import dataclass, field
-from datetime import UTC, datetime
 from typing import TYPE_CHECKING

+from deerflow.utils.time import now_iso as _now_iso
+
 from .schemas import DisconnectMode, RunStatus

 if TYPE_CHECKING:
@@ -17,10 +18,6 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)


-def _now_iso() -> str:
-    return datetime.now(UTC).isoformat()
-
-
@dataclass
 class RunRecord:
    """Mutable record for a single run."""
@@ -39,6 +36,7 @@ class RunRecord:
    abort_event: asyncio.Event = field(default_factory=asyncio.Event, repr=False)
    abort_action: str = "interrupt"
    error: str | None = None
+    model_name: str | None = None


 class RunManager:
@@ -68,6 +66,7 @@ class RunManager:
                metadata=record.metadata or {},
                kwargs=record.kwargs or {},
                created_at=record.created_at,
+                model_name=record.model_name,
            )
        except Exception:
            logger.warning("Failed to persist run %s to store", record.run_id, exc_info=True)
@@ -140,6 +139,18 @@ class RunManager:
                logger.warning("Failed to persist status update for run %s", run_id, exc_info=True)
        logger.info("Run %s -> %s", run_id, status.value)

+    async def update_model_name(self, run_id: str, model_name: str | None) -> None:
+        """Update the model name for a run."""
+        async with self._lock:
+            record = self._runs.get(run_id)
+            if record is None:
+                logger.warning("update_model_name called for unknown run %s", run_id)
+                return
+            record.model_name = model_name
+            record.updated_at = _now_iso()
+        await self._persist_to_store(record)
+        logger.info("Run %s model_name=%s", run_id, model_name)
+
    async def cancel(self, run_id: str, *, action: str = "interrupt") -> bool:
        """Request cancellation of a run.

@@ -174,6 +185,7 @@ class RunManager:
        metadata: dict | None = None,
        kwargs: dict | None = None,
        multitask_strategy: str = "reject",
+        model_name: str | None = None,
    ) -> RunRecord:
        """Atomically check for inflight runs and create a new one.

@@ -224,6 +236,7 @@ class RunManager:
                kwargs=kwargs or {},
                created_at=now,
                updated_at=now,
+                model_name=model_name,
            )
            self._runs[run_id] = record

@@ -23,6 +23,7 @@ class RunStore(abc.ABC):
        thread_id: str,
        assistant_id: str | None = None,
        user_id: str | None = None,
+        model_name: str | None = None,
        status: str = "pending",
        multitask_strategy: str = "reject",
        metadata: dict[str, Any] | None = None,
@@ -22,6 +22,7 @@ class MemoryRunStore(RunStore):
        thread_id,
        assistant_id=None,
        user_id=None,
+        model_name=None,
        status="pending",
        multitask_strategy="reject",
        metadata=None,
@@ -35,6 +36,7 @@ class MemoryRunStore(RunStore):
            "thread_id": thread_id,
            "assistant_id": assistant_id,
            "user_id": user_id,
+            "model_name": model_name,
            "status": status,
            "multitask_strategy": multitask_strategy,
            "metadata": metadata or {},
@@ -23,6 +23,8 @@ from dataclasses import dataclass, field
 from functools import lru_cache
 from typing import TYPE_CHECKING, Any, Literal, cast

+from langgraph.checkpoint.base import empty_checkpoint
+
 if TYPE_CHECKING:
    from langchain_core.messages import HumanMessage

@@ -228,6 +230,17 @@ async def run_agent(
        else:
            agent = agent_factory(config=runnable_config)

+        # Capture the effective (resolved) model name from the agent's metadata.
+        # _resolve_model_name in agent.py may return the default model if the
+        # requested name is not in the allowlist — this update ensures the
+        # persisted model_name reflects the actual model used.
+        if record.model_name is not None:
+            resolved = getattr(agent, "metadata", {}) or {}
+            if isinstance(resolved, dict):
+                effective = resolved.get("model_name")
+                if effective and effective != record.model_name:
+                    await run_manager.update_model_name(record.run_id, effective)
+
        # 4. Attach checkpointer and store
        if checkpointer is not None:
            agent.checkpointer = checkpointer
@@ -442,6 +455,12 @@ async def _rollback_to_pre_run_checkpoint(
    if checkpoint_to_restore.get("id") is None:
        logger.warning("Run %s rollback skipped: pre-run checkpoint has no checkpoint id", run_id)
        return
+    restore_marker = _new_checkpoint_marker()
+    checkpoint_to_restore = {
+        **checkpoint_to_restore,
+        "id": restore_marker["id"],
+        "ts": restore_marker["ts"],
+    }
    metadata = pre_run_snapshot.get("metadata", {})
    metadata_to_restore = metadata if isinstance(metadata, dict) else {}
    raw_checkpoint_ns = pre_run_snapshot.get("checkpoint_ns")
@@ -493,6 +512,11 @@ async def _rollback_to_pre_run_checkpoint(
        )


+def _new_checkpoint_marker() -> dict[str, str]:
+    marker = empty_checkpoint()
+    return {"id": marker["id"], "ts": marker["ts"]}
+
+
 def _lg_mode_to_sse_event(mode: str) -> str:
    """Map LangGraph internal stream_mode name to SSE event name.

@@ -36,7 +36,9 @@ logger = logging.getLogger(__name__)
 # ---------------------------------------------------------------------------

 SQLITE_STORE_INSTALL = "langgraph-checkpoint-sqlite is required for the SQLite store. Install it with: uv add langgraph-checkpoint-sqlite"
-POSTGRES_STORE_INSTALL = "langgraph-checkpoint-postgres is required for the PostgreSQL store. Install it with: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool"
+POSTGRES_STORE_INSTALL = (
+    "langgraph-checkpoint-postgres is required for the PostgreSQL store. Install the package extra with: pip install 'deerflow-harness[postgres]' (or use: uv sync --all-packages --extra postgres when developing locally)"
+)
 POSTGRES_CONN_REQUIRED = "checkpointer.connection_string is required for the postgres backend"

 # ---------------------------------------------------------------------------
@@ -109,6 +109,34 @@ def get_effective_user_id() -> str:
    return str(user.id)


+def resolve_runtime_user_id(runtime: object | None) -> str:
+    """Single source of truth for a tool/middleware's effective user_id.
+
+    Resolution order (most authoritative first):
+      1. ``runtime.context["user_id"]`` — set by ``inject_authenticated_user_context``
+         in the gateway from the auth-validated ``request.state.user``. This is
+         the only source that survives boundaries where the contextvar may have
+         been lost (background tasks scheduled outside the request task,
+         worker pools that don't copy_context, future cross-process drivers).
+      2. The ``_current_user`` ContextVar — set by the auth middleware at
+         request entry. Reliable for in-task work; copied by ``asyncio``
+         child tasks and by ``ContextThreadPoolExecutor``.
+      3. ``DEFAULT_USER_ID`` — last-resort fallback so unauthenticated
+         CLI / migration / test paths keep working without raising.
+
+    Tools that persist user-scoped state (custom agents, memory, uploads)
+    MUST call this instead of ``get_effective_user_id()`` directly so they
+    benefit from the runtime.context channel that ``setup_agent`` already
+    relies on.
+    """
+    context = getattr(runtime, "context", None)
+    if isinstance(context, dict):
+        ctx_user_id = context.get("user_id")
+        if ctx_user_id:
+            return str(ctx_user_id)
+    return get_effective_user_id()
+
+
 # ---------------------------------------------------------------------------
 # Sentinel-based user_id resolution
 # ---------------------------------------------------------------------------
@@ -42,6 +42,13 @@ class LocalSandbox(Sandbox):
        """Return whether the selected shell is cmd.exe."""
        return LocalSandbox._shell_name(shell) in {"cmd", "cmd.exe"}

+    @staticmethod
+    def _is_msys_shell(shell: str) -> bool:
+        """Return whether the selected shell is a Git Bash/MSYS shell."""
+        normalized = shell.replace("\\", "/").lower()
+        shell_name = LocalSandbox._shell_name(shell)
+        return shell_name in {"sh.exe", "bash.exe"} and any(part in normalized for part in ("/git/", "/mingw", "/msys"))
+
    @staticmethod
    def _find_first_available_shell(candidates: tuple[str, ...]) -> str | None:
        """Return the first executable shell path or command found from candidates."""
@@ -303,12 +310,19 @@ class LocalSandbox(Sandbox):
        shell = self._get_shell()

        if os.name == "nt":
+            env = None
            if self._is_powershell(shell):
                args = [shell, "-NoProfile", "-Command", resolved_command]
            elif self._is_cmd_shell(shell):
                args = [shell, "/c", resolved_command]
            else:
                args = [shell, "-c", resolved_command]
+                if self._is_msys_shell(shell):
+                    env = {
+                        **os.environ,
+                        "MSYS_NO_PATHCONV": "1",
+                        "MSYS2_ARG_CONV_EXCL": "*",
+                    }

            result = subprocess.run(
                args,
@@ -316,6 +330,7 @@ class LocalSandbox(Sandbox):
                capture_output=True,
                text=True,
                timeout=600,
+                env=env,
            )
        else:
            args = [shell, "-c", resolved_command]
@@ -119,3 +119,13 @@ class LocalSandboxProvider(SandboxProvider):
        # For Docker-based providers (e.g., AioSandboxProvider), cleanup
        # happens at application shutdown via the shutdown() method.
        pass
+
+    def reset(self) -> None:
+        # reset_sandbox_provider() must also clear the module singleton.
+        global _singleton
+        _singleton = None
+
+    def shutdown(self) -> None:
+        # LocalSandboxProvider has no extra resources beyond the shared
+        # singleton, so shutdown uses the same cleanup path as reset.
+        self.reset()
@@ -37,6 +37,10 @@ class SandboxProvider(ABC):
        """
        pass

+    def reset(self) -> None:
+        """Clear cached state that survives provider instance replacement."""
+        pass
+

 _default_sandbox_provider: SandboxProvider | None = None

@@ -65,10 +69,17 @@ def reset_sandbox_provider() -> None:
    The next call to `get_sandbox_provider()` will create a new instance.
    Useful for testing or when switching configurations.

+    Providers can override `reset()` to clear any module-level state they keep
+    alive across instances (for example, `LocalSandboxProvider`'s cached
+    `LocalSandbox` singleton). Without it, config/mount changes would not take
+    effect on the next acquire().
+
    Note: If the provider has active sandboxes, they will be orphaned.
    Use `shutdown_sandbox_provider()` for proper cleanup.
    """
    global _default_sandbox_provider
+    if _default_sandbox_provider is not None:
+        _default_sandbox_provider.reset()
        _default_sandbox_provider = None


@@ -3,10 +3,9 @@ import re
 import shlex
 from pathlib import Path

-from langchain.tools import ToolRuntime, tool
-from langgraph.typing import ContextT
+from langchain.tools import tool

-from deerflow.agents.thread_state import ThreadDataState, ThreadState
+from deerflow.agents.thread_state import ThreadDataState
 from deerflow.config import get_app_config
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX
 from deerflow.sandbox.exceptions import (
@@ -19,6 +18,7 @@ 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.tools.types import Runtime

 _ABSOLUTE_PATH_PATTERN = re.compile(r"(?<![:\w])(?<!:/)/(?:[^\s\"'`;&|<>()]+)")
 _FILE_URL_PATTERN = re.compile(r"\bfile://\S+", re.IGNORECASE)
@@ -419,7 +419,7 @@ def _join_path_preserving_style(base: str, relative: str) -> str:
    return f"{stripped_base}{separator}{normalized_relative}"


-def _sanitize_error(error: Exception, runtime: "ToolRuntime[ContextT, ThreadState] | None" = None) -> str:
+def _sanitize_error(error: Exception, runtime: Runtime | None = None) -> str:
    """Sanitize an error message to avoid leaking host filesystem paths.

    In local-sandbox mode, resolved host paths in the error string are masked
@@ -994,7 +994,7 @@ def _apply_cwd_prefix(command: str, thread_data: ThreadDataState | None) -> str:
    return command


-def get_thread_data(runtime: ToolRuntime[ContextT, ThreadState] | None) -> ThreadDataState | None:
+def get_thread_data(runtime: Runtime | None) -> ThreadDataState | None:
    """Extract thread_data from runtime state."""
    if runtime is None:
        return None
@@ -1003,7 +1003,7 @@ def get_thread_data(runtime: ToolRuntime[ContextT, ThreadState] | None) -> Threa
    return runtime.state.get("thread_data")


-def is_local_sandbox(runtime: ToolRuntime[ContextT, ThreadState] | None) -> bool:
+def is_local_sandbox(runtime: Runtime | None) -> bool:
    """Check if the current sandbox is a local sandbox.

    Path replacement is only needed for local sandbox since aio sandbox
@@ -1019,7 +1019,7 @@ def is_local_sandbox(runtime: ToolRuntime[ContextT, ThreadState] | None) -> bool
    return sandbox_state.get("sandbox_id") == "local"


-def sandbox_from_runtime(runtime: ToolRuntime[ContextT, ThreadState] | None = None) -> Sandbox:
+def sandbox_from_runtime(runtime: Runtime | None = None) -> Sandbox:
    """Extract sandbox instance from tool runtime.

    DEPRECATED: Use ensure_sandbox_initialized() for lazy initialization support.
@@ -1048,7 +1048,7 @@ def sandbox_from_runtime(runtime: ToolRuntime[ContextT, ThreadState] | None = No
    return sandbox


-def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | None = None) -> Sandbox:
+def ensure_sandbox_initialized(runtime: Runtime | None = None) -> Sandbox:
    """Ensure sandbox is initialized, acquiring lazily if needed.

    On first call, acquires a sandbox from the provider and stores it in runtime state.
@@ -1107,7 +1107,7 @@ def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | Non
    return sandbox


-def ensure_thread_directories_exist(runtime: ToolRuntime[ContextT, ThreadState] | None) -> None:
+def ensure_thread_directories_exist(runtime: Runtime | None) -> None:
    """Ensure thread data directories (workspace, uploads, outputs) exist.

    This function is called lazily when any sandbox tool is first used.
@@ -1221,7 +1221,7 @@ def _truncate_ls_output(output: str, max_chars: int) -> str:


@tool("bash", parse_docstring=True)
-def bash_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, command: str) -> str:
+def bash_tool(runtime: Runtime, description: str, command: str) -> str:
    """Execute a bash command in a Linux environment.


@@ -1270,7 +1270,7 @@ def bash_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, com


@tool("ls", parse_docstring=True)
-def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path: str) -> str:
+def ls_tool(runtime: Runtime, description: str, path: str) -> str:
    """List the contents of a directory up to 2 levels deep in tree format.

    Args:
@@ -1318,7 +1318,7 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:

@tool("glob", parse_docstring=True)
 def glob_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    pattern: str,
    path: str,
@@ -1368,7 +1368,7 @@ def glob_tool(

@tool("grep", parse_docstring=True)
 def grep_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    pattern: str,
    path: str,
@@ -1438,7 +1438,7 @@ def grep_tool(

@tool("read_file", parse_docstring=True)
 def read_file_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    path: str,
    start_line: int | None = None,
@@ -1493,18 +1493,19 @@ def read_file_tool(

@tool("write_file", parse_docstring=True)
 def write_file_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    path: str,
    content: str,
    append: bool = False,
 ) -> str:
-    """Write text content to a file.
+    """Write text content to a file. By default this overwrites the target file; set append to true to add content to the end without replacing existing content.

    Args:
        description: Explain why you are writing to this file in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
        path: The **absolute** path to the file to write to. ALWAYS PROVIDE THIS PARAMETER SECOND.
        content: The content to write to the file. ALWAYS PROVIDE THIS PARAMETER THIRD.
+        append: Whether to append content to the end of the file instead of overwriting it. Defaults to false.
    """
    try:
        sandbox = ensure_sandbox_initialized(runtime)
@@ -1533,7 +1534,7 @@ def write_file_tool(

@tool("str_replace", parse_docstring=True)
 def str_replace_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    path: str,
    old_str: str,
@@ -9,6 +9,29 @@ from .types import SKILL_MD_FILE, Skill, SkillCategory
 logger = logging.getLogger(__name__)


+def parse_allowed_tools(raw: object, skill_file: Path) -> list[str] | None:
+    """Parse the optional allowed-tools frontmatter field.
+
+    Returns None when the field is omitted. Returns a list when the field is a
+    YAML sequence of strings, including an empty list for explicit no-tool
+    skills. Raises ValueError for malformed values.
+    """
+    if raw is None:
+        return None
+    if not isinstance(raw, list):
+        raise ValueError(f"allowed-tools in {skill_file} must be a list of strings")
+
+    allowed_tools: list[str] = []
+    for item in raw:
+        if not isinstance(item, str):
+            raise ValueError(f"allowed-tools in {skill_file} must contain only strings")
+        tool_name = item.strip()
+        if not tool_name:
+            raise ValueError(f"allowed-tools in {skill_file} cannot contain empty tool names")
+        allowed_tools.append(tool_name)
+    return allowed_tools
+
+
 def parse_skill_file(skill_file: Path, category: SkillCategory, relative_path: Path | None = None) -> Skill | None:
    """Parse a SKILL.md file and extract metadata.

@@ -64,6 +87,12 @@ def parse_skill_file(skill_file: Path, category: SkillCategory, relative_path: P
        if license_text is not None:
            license_text = str(license_text).strip() or None

+        try:
+            allowed_tools = parse_allowed_tools(metadata.get("allowed-tools"), skill_file)
+        except ValueError as exc:
+            logger.error("Invalid allowed-tools in %s: %s", skill_file, exc)
+            return None
+
        return Skill(
            name=name,
            description=description,
@@ -72,6 +101,7 @@ def parse_skill_file(skill_file: Path, category: SkillCategory, relative_path: P
            skill_file=skill_file,
            relative_path=relative_path or Path(skill_file.parent.name),
            category=category,
+            allowed_tools=allowed_tools,
            enabled=True,  # Actual state comes from the extensions config file.
        )

@@ -0,0 +1,44 @@
+import logging
+from typing import Protocol
+
+from deerflow.skills.types import Skill
+
+logger = logging.getLogger(__name__)
+
+
+class NamedTool(Protocol):
+    name: str
+
+
+def allowed_tool_names_for_skills(skills: list[Skill]) -> set[str] | None:
+    """Return the union of explicit skill allowed-tools declarations.
+
+    None means legacy allow-all behavior. It is returned only when no loaded
+    skill declares allowed-tools. Once any skill declares the field, legacy
+    skills without the field contribute no tools instead of disabling the
+    explicit restrictions from other skills.
+    """
+    if not skills:
+        return None
+
+    allowed: set[str] = set()
+    has_explicit_declaration = False
+    for skill in skills:
+        if skill.allowed_tools is None:
+            continue
+        has_explicit_declaration = True
+        if not skill.allowed_tools:
+            logger.info("Skill %s declared empty allowed-tools", skill.name)
+        allowed.update(skill.allowed_tools)
+
+    if not has_explicit_declaration:
+        return None
+    return allowed
+
+
+def filter_tools_by_skill_allowed_tools[ToolT: NamedTool](tools: list[ToolT], skills: list[Skill]) -> list[ToolT]:
+    allowed = allowed_tool_names_for_skills(skills)
+    if allowed is None:
+        return tools
+
+    return [tool for tool in tools if tool.name in allowed]
@@ -27,6 +27,7 @@ class Skill:
    skill_file: Path
    relative_path: Path  # Relative path from category root to skill directory
    category: SkillCategory  # 'public' or 'custom'
+    allowed_tools: list[str] | None = None
    enabled: bool = False  # Whether this skill is enabled

    @property
@@ -8,6 +8,7 @@ from pathlib import Path

 import yaml

+from deerflow.skills.parser import parse_allowed_tools
 from deerflow.skills.types import SKILL_MD_FILE

 # Allowed properties in SKILL.md frontmatter
@@ -84,4 +85,9 @@ def _validate_skill_frontmatter(skill_dir: Path) -> tuple[bool, str, str | None]
        if len(description) > 1024:
            return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters.", None

+    try:
+        parse_allowed_tools(frontmatter.get("allowed-tools"), skill_md)
+    except ValueError as e:
+        return False, str(e).replace(str(skill_md), SKILL_MD_FILE), None
+
    return True, "Skill is valid!", name
@@ -26,7 +26,7 @@ class SubagentConfig:

    name: str
    description: str
-    system_prompt: str
+    system_prompt: str | None = None
    tools: list[str] | None = None
    disallowed_tools: list[str] | None = field(default_factory=lambda: ["task"])
    skills: list[str] | None = None
@@ -23,7 +23,10 @@ from deerflow.agents.thread_state import SandboxState, ThreadDataState, ThreadSt
 from deerflow.config import get_app_config
 from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model
+from deerflow.skills.tool_policy import filter_tools_by_skill_allowed_tools
+from deerflow.skills.types import Skill
 from deerflow.subagents.config import SubagentConfig, resolve_subagent_model_name
+from deerflow.subagents.token_collector import SubagentTokenCollector

 logger = logging.getLogger(__name__)

@@ -68,6 +71,8 @@ class SubagentResult:
    started_at: datetime | None = None
    completed_at: datetime | None = None
    ai_messages: list[dict[str, Any]] | None = None
+    token_usage_records: list[dict[str, int | str]] = field(default_factory=list)
+    usage_reported: bool = False
    cancel_event: threading.Event = field(default_factory=threading.Event, repr=False)

    def __post_init__(self):
@@ -260,16 +265,16 @@ class SubagentExecutor:
        # Generate trace_id if not provided (for top-level calls)
        self.trace_id = trace_id or str(uuid.uuid4())[:8]

-        # Filter tools based on config
-        self.tools = _filter_tools(
+        self._base_tools = _filter_tools(
            tools,
            config.tools,
            config.disallowed_tools,
        )
+        self.tools = self._base_tools

        logger.info(f"[trace={self.trace_id}] SubagentExecutor initialized: {config.name} with {len(self.tools)} tools")

-    def _create_agent(self):
+    def _create_agent(self, tools: list[BaseTool] | None = None):
        """Create the agent instance."""
        app_config = self.app_config or get_app_config()
        if self.model_name is None:
@@ -281,28 +286,18 @@ class SubagentExecutor:
        # Reuse shared middleware composition with lead agent.
        middlewares = build_subagent_runtime_middlewares(app_config=app_config, model_name=self.model_name, lazy_init=True)

+        # system_prompt is included in initial state messages (see _build_initial_state)
+        # to avoid multiple SystemMessages which some LLM APIs don't support.
        return create_agent(
            model=model,
-            tools=self.tools,
+            tools=tools if tools is not None else self.tools,
            middleware=middlewares,
-            system_prompt=self.config.system_prompt,
+            system_prompt=None,
            state_schema=ThreadState,
        )

-    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.
-        """
+    async def _load_skills(self) -> list[Skill]:
+        """Load enabled skill metadata based on config.skills."""
        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 []
@@ -316,8 +311,8 @@ class SubagentExecutor:
            all_skills = await asyncio.to_thread(storage.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 []
+            logger.exception(f"[trace={self.trace_id}] Failed to load skills for subagent {self.config.name}")
+            raise

        if not all_skills:
            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} no enabled skills found")
@@ -326,10 +321,26 @@ class SubagentExecutor:
        # 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
+            return [s for s in all_skills if s.name in allowed]
+        return all_skills

+    def _apply_skill_allowed_tools(self, skills: list[Skill]) -> list[BaseTool]:
+        return filter_tools_by_skill_allowed_tools(self._base_tools, skills)
+
+    async def _load_skill_messages(self, skills: list[Skill]) -> 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 not skills:
            return []

@@ -347,21 +358,34 @@ class SubagentExecutor:

        return messages

-    async def _build_initial_state(self, task: str) -> dict[str, Any]:
+    async def _build_initial_state(self, task: str) -> tuple[dict[str, Any], list[BaseTool]]:
        """Build the initial state for agent execution.

        Args:
            task: The task description.

        Returns:
-            Initial state dictionary.
+            Initial state dictionary and tools filtered by loaded skill metadata.
        """
-        # 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)
+        # Load skills as conversation items (Codex pattern)
+        skills = await self._load_skills()
+        filtered_tools = self._apply_skill_allowed_tools(skills)
+        skill_messages = await self._load_skill_messages(skills)
+
+        # Combine system_prompt and skills into a single SystemMessage.
+        # Some LLM APIs reject multiple SystemMessages with
+        # "System message must be at the beginning."
+        system_parts: list[str] = []
+        if self.config.system_prompt:
+            system_parts.append(self.config.system_prompt)
+        for skill_msg in skill_messages:
+            system_parts.append(skill_msg.content)
+
+        messages: list[Any] = []
+        if system_parts:
+            messages.append(SystemMessage(content="\n\n".join(system_parts)))
+
        # Then the actual task
        messages.append(HumanMessage(content=task))

@@ -375,7 +399,7 @@ class SubagentExecutor:
        if self.thread_data is not None:
            state["thread_data"] = self.thread_data

-        return state
+        return state, filtered_tools

    async def _aexecute(self, task: str, result_holder: SubagentResult | None = None) -> SubagentResult:
        """Execute a task asynchronously.
@@ -404,13 +428,20 @@ class SubagentExecutor:
            ai_messages = []
            result.ai_messages = ai_messages

+        collector: SubagentTokenCollector | None = None
        try:
-            agent = self._create_agent()
-            state = await self._build_initial_state(task)
+            state, filtered_tools = await self._build_initial_state(task)
+            agent = self._create_agent(filtered_tools)
+
+            # Token collector for subagent LLM calls
+            collector_caller = f"subagent:{self.config.name}"
+            collector = SubagentTokenCollector(caller=collector_caller)

            # Build config with thread_id for sandbox access and recursion limit
            run_config: RunnableConfig = {
                "recursion_limit": self.config.max_turns,
+                "callbacks": [collector],
+                "tags": [collector_caller],
            }
            context: dict[str, Any] = {}
            if self.thread_id:
@@ -433,6 +464,8 @@ class SubagentExecutor:
                        result.status = SubagentStatus.CANCELLED
                        result.error = "Cancelled by user"
                        result.completed_at = datetime.now()
+                if collector is not None:
+                    result.token_usage_records = collector.snapshot_records()
                return result

            async for chunk in agent.astream(state, config=run_config, context=context, stream_mode="values"):  # type: ignore[arg-type]
@@ -447,6 +480,7 @@ class SubagentExecutor:
                            result.status = SubagentStatus.CANCELLED
                            result.error = "Cancelled by user"
                            result.completed_at = datetime.now()
+                    result.token_usage_records = collector.snapshot_records()
                    return result

                final_state = chunk
@@ -473,6 +507,7 @@ class SubagentExecutor:
                            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} captured AI message #{len(ai_messages)}")

            logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} completed async execution")
+            result.token_usage_records = collector.snapshot_records()

            if final_state is None:
                logger.warning(f"[trace={self.trace_id}] Subagent {self.config.name} no final state")
@@ -552,6 +587,8 @@ class SubagentExecutor:
            result.status = SubagentStatus.FAILED
            result.error = str(e)
            result.completed_at = datetime.now()
+            if collector is not None:
+                result.token_usage_records = collector.snapshot_records()

        return result

@@ -0,0 +1,63 @@
+"""Callback handler that collects LLM token usage within a subagent.
+
+Each subagent execution creates its own collector. After the subagent
+finishes, the collected records are transferred to the parent RunJournal
+via :meth:`RunJournal.record_external_llm_usage_records`.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.callbacks import BaseCallbackHandler
+
+
+class SubagentTokenCollector(BaseCallbackHandler):
+    """Lightweight callback handler that collects LLM token usage within a subagent."""
+
+    def __init__(self, caller: str):
+        super().__init__()
+        self.caller = caller
+        self._records: list[dict[str, int | str]] = []
+        self._counted_run_ids: set[str] = set()
+
+    def on_llm_end(
+        self,
+        response: Any,
+        *,
+        run_id: Any,
+        tags: list[str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        rid = str(run_id)
+        if rid in self._counted_run_ids:
+            return
+
+        for generation in response.generations:
+            for gen in generation:
+                if not hasattr(gen, "message"):
+                    continue
+                usage = getattr(gen.message, "usage_metadata", None)
+                usage_dict = dict(usage) if usage else {}
+                input_tk = usage_dict.get("input_tokens", 0) or 0
+                output_tk = usage_dict.get("output_tokens", 0) or 0
+                total_tk = usage_dict.get("total_tokens", 0) or 0
+                if total_tk <= 0:
+                    total_tk = input_tk + output_tk
+                if total_tk <= 0:
+                    continue
+                self._counted_run_ids.add(rid)
+                self._records.append(
+                    {
+                        "source_run_id": rid,
+                        "caller": self.caller,
+                        "input_tokens": input_tk,
+                        "output_tokens": output_tk,
+                        "total_tokens": total_tk,
+                    }
+                )
+                return
+
+    def snapshot_records(self) -> list[dict[str, int | str]]:
+        """Return a copy of the accumulated usage records."""
+        return list(self._records)
@@ -2,10 +2,12 @@ from .clarification_tool import ask_clarification_tool
 from .present_file_tool import present_file_tool
 from .setup_agent_tool import setup_agent
 from .task_tool import task_tool
+from .update_agent_tool import update_agent
 from .view_image_tool import view_image_tool

 __all__ = [
    "setup_agent",
+    "update_agent",
    "present_file_tool",
    "ask_clarification_tool",
    "view_image_tool",
@@ -1,20 +1,19 @@
 from pathlib import Path
 from typing import Annotated

-from langchain.tools import InjectedToolCallId, ToolRuntime, tool
+from langchain.tools import InjectedToolCallId, tool
 from langchain_core.messages import ToolMessage
 from langgraph.config import get_config
 from langgraph.types import Command
-from langgraph.typing import ContextT

-from deerflow.agents.thread_state import ThreadState
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
+from deerflow.tools.types import Runtime

 OUTPUTS_VIRTUAL_PREFIX = f"{VIRTUAL_PATH_PREFIX}/outputs"


-def _get_thread_id(runtime: ToolRuntime[ContextT, ThreadState]) -> str | None:
+def _get_thread_id(runtime: Runtime) -> str | None:
    """Resolve the current thread id from runtime context or RunnableConfig."""
    thread_id = runtime.context.get("thread_id") if runtime.context else None
    if thread_id:
@@ -32,7 +31,7 @@ def _get_thread_id(runtime: ToolRuntime[ContextT, ThreadState]) -> str | None:


 def _normalize_presented_filepath(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    filepath: str,
 ) -> str:
    """Normalize a presented file path to the `/mnt/user-data/outputs/*` contract.
@@ -83,7 +82,7 @@ def _normalize_presented_filepath(

@tool("present_files", parse_docstring=True)
 def present_file_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    filepaths: list[str],
    tool_call_id: Annotated[str, InjectedToolCallId],
 ) -> Command:
@@ -3,20 +3,21 @@ import logging
 import yaml
 from langchain_core.messages import ToolMessage
 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
+from deerflow.runtime.user_context import resolve_runtime_user_id
+from deerflow.tools.types import Runtime

 logger = logging.getLogger(__name__)


-@tool
+@tool(parse_docstring=True)
 def setup_agent(
    soul: str,
    description: str,
-    runtime: ToolRuntime,
+    runtime: Runtime,
    skills: list[str] | None = None,
 ) -> Command:
    """Setup the custom DeerFlow agent.
@@ -34,7 +35,14 @@ def setup_agent(
    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
+        if agent_name:
+            # Custom agents are persisted under the current user's bucket so
+            # different users do not see each other's agents.
+            user_id = resolve_runtime_user_id(runtime)
+            agent_dir = paths.user_agent_dir(user_id, agent_name)
+        else:
+            # Default agent (no agent_name): SOUL.md lives at the global base dir.
+            agent_dir = paths.base_dir
        is_new_dir = not agent_dir.exists()
        agent_dir.mkdir(parents=True, exist_ok=True)

@@ -6,11 +6,9 @@ import uuid
 from dataclasses import replace
 from typing import TYPE_CHECKING, Annotated, Any, cast

-from langchain.tools import InjectedToolCallId, ToolRuntime, tool
+from langchain.tools import InjectedToolCallId, tool
 from langgraph.config import get_stream_writer
-from langgraph.typing import ContextT

-from deerflow.agents.thread_state import ThreadState
 from deerflow.config import get_app_config
 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
@@ -21,12 +19,132 @@ from deerflow.subagents.executor import (
    get_background_task_result,
    request_cancel_background_task,
 )
+from deerflow.tools.types import Runtime

 if TYPE_CHECKING:
    from deerflow.config.app_config import AppConfig

 logger = logging.getLogger(__name__)

+# Cache subagent token usage by tool_call_id so TokenUsageMiddleware can
+# write it back to the triggering AIMessage's usage_metadata.
+_subagent_usage_cache: dict[str, dict[str, int]] = {}
+
+
+def _token_usage_cache_enabled(app_config: "AppConfig | None") -> bool:
+    if app_config is None:
+        try:
+            app_config = get_app_config()
+        except FileNotFoundError:
+            return False
+    return bool(getattr(getattr(app_config, "token_usage", None), "enabled", False))
+
+
+def _cache_subagent_usage(tool_call_id: str, usage: dict | None, *, enabled: bool = True) -> None:
+    if enabled and usage:
+        _subagent_usage_cache[tool_call_id] = usage
+
+
+def pop_cached_subagent_usage(tool_call_id: str) -> dict | None:
+    return _subagent_usage_cache.pop(tool_call_id, None)
+
+
+def _is_subagent_terminal(result: Any) -> bool:
+    """Return whether a background subagent result is safe to clean up."""
+    return result.status in {SubagentStatus.COMPLETED, SubagentStatus.FAILED, SubagentStatus.CANCELLED, SubagentStatus.TIMED_OUT} or getattr(result, "completed_at", None) is not None
+
+
+async def _await_subagent_terminal(task_id: str, max_polls: int) -> Any | None:
+    """Poll until the background subagent reaches a terminal status or we run out of polls."""
+    for _ in range(max_polls):
+        result = get_background_task_result(task_id)
+        if result is None:
+            return None
+        if _is_subagent_terminal(result):
+            return result
+        await asyncio.sleep(5)
+    return None
+
+
+async def _deferred_cleanup_subagent_task(task_id: str, trace_id: str, max_polls: int) -> None:
+    """Keep polling a cancelled subagent until it can be safely removed."""
+    cleanup_poll_count = 0
+    while True:
+        result = get_background_task_result(task_id)
+        if result is None:
+            return
+        if _is_subagent_terminal(result):
+            cleanup_background_task(task_id)
+            return
+        if cleanup_poll_count >= max_polls:
+            logger.warning(f"[trace={trace_id}] Deferred cleanup for task {task_id} timed out after {cleanup_poll_count} polls")
+            return
+        await asyncio.sleep(5)
+        cleanup_poll_count += 1
+
+
+def _log_cleanup_failure(cleanup_task: asyncio.Task[None], *, trace_id: str, task_id: str) -> None:
+    if cleanup_task.cancelled():
+        return
+
+    exc = cleanup_task.exception()
+    if exc is not None:
+        logger.error(f"[trace={trace_id}] Deferred cleanup failed for task {task_id}: {exc}")
+
+
+def _schedule_deferred_subagent_cleanup(task_id: str, trace_id: str, max_polls: int) -> None:
+    logger.debug(f"[trace={trace_id}] Scheduling deferred cleanup for cancelled task {task_id}")
+    cleanup_task = asyncio.create_task(_deferred_cleanup_subagent_task(task_id, trace_id, max_polls))
+    cleanup_task.add_done_callback(lambda task: _log_cleanup_failure(task, trace_id=trace_id, task_id=task_id))
+
+
+def _find_usage_recorder(runtime: Any) -> Any | None:
+    """Find a callback handler with ``record_external_llm_usage_records`` in the runtime config."""
+    if runtime is None:
+        return None
+    config = getattr(runtime, "config", None)
+    if not isinstance(config, dict):
+        return None
+    callbacks = config.get("callbacks", [])
+    if not callbacks:
+        return None
+    for cb in callbacks:
+        if hasattr(cb, "record_external_llm_usage_records"):
+            return cb
+    return None
+
+
+def _summarize_usage(records: list[dict] | None) -> dict | None:
+    """Summarize token usage records into a compact dict for SSE events."""
+    if not records:
+        return None
+    return {
+        "input_tokens": sum(r.get("input_tokens", 0) or 0 for r in records),
+        "output_tokens": sum(r.get("output_tokens", 0) or 0 for r in records),
+        "total_tokens": sum(r.get("total_tokens", 0) or 0 for r in records),
+    }
+
+
+def _report_subagent_usage(runtime: Any, result: Any) -> None:
+    """Report subagent token usage to the parent RunJournal, if available.
+
+    Each subagent task must be reported only once (guarded by usage_reported).
+    """
+    if getattr(result, "usage_reported", True):
+        return
+    records = getattr(result, "token_usage_records", None) or []
+    if not records:
+        return
+    journal = _find_usage_recorder(runtime)
+    if journal is None:
+        logger.debug("No usage recorder found in runtime callbacks — subagent token usage not recorded")
+        return
+    try:
+        journal.record_external_llm_usage_records(records)
+        result.usage_reported = True
+    except Exception:
+        logger.warning("Failed to report subagent token usage", exc_info=True)
+

 def _get_runtime_app_config(runtime: Any) -> "AppConfig | None":
    context = getattr(runtime, "context", None)
@@ -50,12 +168,11 @@ def _merge_skill_allowlists(parent: list[str] | None, child: list[str] | None) -

@tool("task", parse_docstring=True)
 async def task_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    description: str,
    prompt: str,
    subagent_type: str,
    tool_call_id: Annotated[str, InjectedToolCallId],
-    max_turns: int | None = None,
 ) -> str:
    """Delegate a task to a specialized subagent that runs in its own context.

@@ -91,9 +208,9 @@ async def task_tool(
        description: A short (3-5 word) description of the task for logging/display. ALWAYS PROVIDE THIS PARAMETER FIRST.
        prompt: The task description for the subagent. Be specific and clear about what needs to be done. ALWAYS PROVIDE THIS PARAMETER SECOND.
        subagent_type: The type of subagent to use. ALWAYS PROVIDE THIS PARAMETER THIRD.
-        max_turns: Optional maximum number of agent turns. Defaults to subagent's configured max.
    """
    runtime_app_config = _get_runtime_app_config(runtime)
+    cache_token_usage = _token_usage_cache_enabled(runtime_app_config)
    available_subagent_names = get_available_subagent_names(app_config=runtime_app_config) if runtime_app_config is not None else get_available_subagent_names()

    # Get subagent configuration
@@ -113,9 +230,6 @@ async def task_tool(
    # each subagent loads its own skills based on config, injected as conversation items).
    # No longer appended to system_prompt here.

-    if max_turns is not None:
-        overrides["max_turns"] = max_turns
-
    # Extract parent context from runtime
    sandbox_state = None
    thread_data = None
@@ -232,23 +346,32 @@ async def task_tool(
                last_message_count = current_message_count

            # Check if task completed, failed, or timed out
+            usage = _summarize_usage(getattr(result, "token_usage_records", None))
            if result.status == SubagentStatus.COMPLETED:
-                writer({"type": "task_completed", "task_id": task_id, "result": result.result})
+                _cache_subagent_usage(tool_call_id, usage, enabled=cache_token_usage)
+                _report_subagent_usage(runtime, result)
+                writer({"type": "task_completed", "task_id": task_id, "result": result.result, "usage": usage})
                logger.info(f"[trace={trace_id}] Task {task_id} completed after {poll_count} polls")
                cleanup_background_task(task_id)
                return f"Task Succeeded. Result: {result.result}"
            elif result.status == SubagentStatus.FAILED:
-                writer({"type": "task_failed", "task_id": task_id, "error": result.error})
+                _cache_subagent_usage(tool_call_id, usage, enabled=cache_token_usage)
+                _report_subagent_usage(runtime, result)
+                writer({"type": "task_failed", "task_id": task_id, "error": result.error, "usage": usage})
                logger.error(f"[trace={trace_id}] Task {task_id} failed: {result.error}")
                cleanup_background_task(task_id)
                return f"Task failed. Error: {result.error}"
            elif result.status == SubagentStatus.CANCELLED:
-                writer({"type": "task_cancelled", "task_id": task_id, "error": result.error})
+                _cache_subagent_usage(tool_call_id, usage, enabled=cache_token_usage)
+                _report_subagent_usage(runtime, result)
+                writer({"type": "task_cancelled", "task_id": task_id, "error": result.error, "usage": usage})
                logger.info(f"[trace={trace_id}] Task {task_id} cancelled: {result.error}")
                cleanup_background_task(task_id)
                return "Task cancelled by user."
            elif result.status == SubagentStatus.TIMED_OUT:
-                writer({"type": "task_timed_out", "task_id": task_id, "error": result.error})
+                _cache_subagent_usage(tool_call_id, usage, enabled=cache_token_usage)
+                _report_subagent_usage(runtime, result)
+                writer({"type": "task_timed_out", "task_id": task_id, "error": result.error, "usage": usage})
                logger.warning(f"[trace={trace_id}] Task {task_id} timed out: {result.error}")
                cleanup_background_task(task_id)
                return f"Task timed out. Error: {result.error}"
@@ -266,43 +389,34 @@ async def task_tool(
            if poll_count > max_poll_count:
                timeout_minutes = config.timeout_seconds // 60
                logger.error(f"[trace={trace_id}] Task {task_id} polling timed out after {poll_count} polls (should have been caught by thread pool timeout)")
-                writer({"type": "task_timed_out", "task_id": task_id})
+                _report_subagent_usage(runtime, result)
+                usage = _summarize_usage(getattr(result, "token_usage_records", None))
+                _cache_subagent_usage(tool_call_id, usage, enabled=cache_token_usage)
+                writer({"type": "task_timed_out", "task_id": task_id, "usage": usage})
                return f"Task polling timed out after {timeout_minutes} minutes. This may indicate the background task is stuck. Status: {result.status.value}"
    except asyncio.CancelledError:
        # Signal the background subagent thread to stop cooperatively.
-        # Without this, the thread (running in ThreadPoolExecutor with its
-        # own event loop via asyncio.run) would continue executing even
-        # after the parent task is cancelled.
        request_cancel_background_task(task_id)

-        async def cleanup_when_done() -> None:
-            max_cleanup_polls = max_poll_count
-            cleanup_poll_count = 0
+        # Wait (shielded) for the subagent to reach a terminal state so the
+        # final token usage snapshot is reported to the parent RunJournal
+        # before the parent worker persists get_completion_data().
+        terminal_result = None
+        try:
+            terminal_result = await asyncio.shield(_await_subagent_terminal(task_id, max_poll_count))
+        except asyncio.CancelledError:
+            pass

-            while True:
-                result = get_background_task_result(task_id)
-                if result is None:
-                    return
-
-                if result.status in {SubagentStatus.COMPLETED, SubagentStatus.FAILED, SubagentStatus.CANCELLED, SubagentStatus.TIMED_OUT} or getattr(result, "completed_at", None) is not None:
+        # Report whatever the subagent collected (even if we timed out).
+        final_result = terminal_result or get_background_task_result(task_id)
+        if final_result is not None:
+            _report_subagent_usage(runtime, final_result)
+        if final_result is not None and _is_subagent_terminal(final_result):
            cleanup_background_task(task_id)
-                    return
-
-                if cleanup_poll_count > max_cleanup_polls:
-                    logger.warning(f"[trace={trace_id}] Deferred cleanup for task {task_id} timed out after {cleanup_poll_count} polls")
-                    return
-
-                await asyncio.sleep(5)
-                cleanup_poll_count += 1
-
-        def log_cleanup_failure(cleanup_task: asyncio.Task[None]) -> None:
-            if cleanup_task.cancelled():
-                return
-
-            exc = cleanup_task.exception()
-            if exc is not None:
-                logger.error(f"[trace={trace_id}] Deferred cleanup failed for task {task_id}: {exc}")
-
-        logger.debug(f"[trace={trace_id}] Scheduling deferred cleanup for cancelled task {task_id}")
-        asyncio.create_task(cleanup_when_done()).add_done_callback(log_cleanup_failure)
+        else:
+            _schedule_deferred_subagent_cleanup(task_id, trace_id, max_poll_count)
+        _subagent_usage_cache.pop(tool_call_id, None)
+        raise
+    except Exception:
+        _subagent_usage_cache.pop(tool_call_id, None)
        raise
@@ -0,0 +1,245 @@
+"""update_agent tool — let a custom agent persist updates to its own SOUL.md / config.
+
+Bound to the lead agent only when ``runtime.context['agent_name']`` is set
+(i.e. inside an existing custom agent's chat). The default agent does not see
+this tool, and the bootstrap flow continues to use ``setup_agent`` for the
+initial creation handshake.
+
+The tool writes back to ``{base_dir}/users/{user_id}/agents/{agent_name}/{config.yaml,SOUL.md}``
+so an agent created by one user is never visible to (or mutable by) another.
+Writes are staged into temp files first; both files are renamed into place only
+after both temp files are successfully written, so a partial failure cannot leave
+config.yaml updated while SOUL.md still holds stale content.
+"""
+
+from __future__ import annotations
+
+import logging
+import tempfile
+from pathlib import Path
+from typing import Any
+
+import yaml
+from langchain_core.messages import ToolMessage
+from langchain_core.tools import tool
+from langgraph.types import Command
+
+from deerflow.config.agents_config import load_agent_config, validate_agent_name
+from deerflow.config.app_config import get_app_config
+from deerflow.config.paths import get_paths
+from deerflow.runtime.user_context import resolve_runtime_user_id
+from deerflow.tools.types import Runtime
+
+logger = logging.getLogger(__name__)
+
+
+def _stage_temp(path: Path, text: str) -> Path:
+    """Write ``text`` into a sibling temp file and return its path.
+
+    The caller is responsible for ``Path.replace``-ing the temp into the target
+    once every staged file is ready, or for unlinking it on failure.
+    """
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fd = tempfile.NamedTemporaryFile(
+        mode="w",
+        dir=path.parent,
+        suffix=".tmp",
+        delete=False,
+        encoding="utf-8",
+    )
+    try:
+        fd.write(text)
+        fd.flush()
+        fd.close()
+        return Path(fd.name)
+    except BaseException:
+        fd.close()
+        Path(fd.name).unlink(missing_ok=True)
+        raise
+
+
+def _cleanup_temps(temps: list[Path]) -> None:
+    """Best-effort removal of staged temp files."""
+    for tmp in temps:
+        try:
+            tmp.unlink(missing_ok=True)
+        except OSError:
+            logger.debug("Failed to clean up temp file %s", tmp, exc_info=True)
+
+
+@tool(parse_docstring=True)
+def update_agent(
+    runtime: Runtime,
+    soul: str | None = None,
+    description: str | None = None,
+    skills: list[str] | None = None,
+    tool_groups: list[str] | None = None,
+    model: str | None = None,
+) -> Command:
+    """Persist updates to the current custom agent's SOUL.md and config.yaml.
+
+    Use this when the user asks to refine the agent's identity, description,
+    skill whitelist, tool-group whitelist, or default model. Only the fields
+    you explicitly pass are updated; omitted fields keep their existing values.
+
+    Pass ``soul`` as the FULL replacement SOUL.md content — there is no patch
+    semantics, so always start from the current SOUL and apply your edits.
+
+    Pass ``skills=[]`` to disable all skills for this agent. Omit ``skills``
+    entirely to keep the existing whitelist.
+
+    Args:
+        soul: Optional full replacement SOUL.md content.
+        description: Optional new one-line description.
+        skills: Optional skill whitelist. ``[]`` = no skills, omit = unchanged.
+        tool_groups: Optional tool-group whitelist. ``[]`` = empty, omit = unchanged.
+        model: Optional model override (must match a configured model name).
+
+    Returns:
+        Command with a ToolMessage describing the result. Changes take effect
+        on the next user turn (when the lead agent is rebuilt with the fresh
+        SOUL.md and config.yaml).
+    """
+    tool_call_id = runtime.tool_call_id
+    agent_name_raw: str | None = runtime.context.get("agent_name") if runtime.context else None
+
+    def _err(message: str) -> Command:
+        return Command(update={"messages": [ToolMessage(content=f"Error: {message}", tool_call_id=tool_call_id)]})
+
+    if soul is None and description is None and skills is None and tool_groups is None and model is None:
+        return _err("No fields provided. Pass at least one of: soul, description, skills, tool_groups, model.")
+
+    try:
+        agent_name = validate_agent_name(agent_name_raw)
+    except ValueError as e:
+        return _err(str(e))
+
+    if not agent_name:
+        return _err("update_agent is only available inside a custom agent's chat. There is no agent_name in the current runtime context, so there is nothing to update. If you are inside the bootstrap flow, use setup_agent instead.")
+
+    # Resolve the active user so that updates only affect this user's agent.
+    # ``resolve_runtime_user_id`` prefers ``runtime.context["user_id"]`` (set by
+    # the gateway from the auth-validated request) and falls back to the
+    # contextvar, then DEFAULT_USER_ID. This matches setup_agent so a user
+    # creating an agent and later refining it always touches the same files,
+    # even if the contextvar gets lost across an async/thread boundary
+    # (issue #2782 / #2862 class of bugs).
+    user_id = resolve_runtime_user_id(runtime)
+
+    # Reject an unknown ``model`` *before* touching the filesystem. Otherwise
+    # ``_resolve_model_name`` silently falls back to the default at runtime
+    # and the user sees confusing repeated warnings on every later turn.
+    if model is not None and get_app_config().get_model_config(model) is None:
+        return _err(f"Unknown model '{model}'. Pass a model name that exists in config.yaml's models section.")
+
+    paths = get_paths()
+    agent_dir = paths.user_agent_dir(user_id, agent_name)
+    if not agent_dir.exists() and paths.agent_dir(agent_name).exists():
+        return _err(f"Agent '{agent_name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating.")
+
+    try:
+        existing_cfg = load_agent_config(agent_name, user_id=user_id)
+    except FileNotFoundError:
+        return _err(f"Agent '{agent_name}' does not exist for the current user. Use setup_agent to create a new agent first.")
+    except ValueError as e:
+        return _err(f"Agent '{agent_name}' has an unreadable config: {e}")
+
+    if existing_cfg is None:
+        return _err(f"Agent '{agent_name}' could not be loaded.")
+
+    updated_fields: list[str] = []
+
+    # Force the on-disk ``name`` to match the directory we are writing into,
+    # even if ``existing_cfg.name`` had drifted (e.g. from manual yaml edits).
+    config_data: dict[str, Any] = {"name": agent_name}
+    new_description = description if description is not None else existing_cfg.description
+    config_data["description"] = new_description
+    if description is not None and description != existing_cfg.description:
+        updated_fields.append("description")
+
+    new_model = model if model is not None else existing_cfg.model
+    if new_model is not None:
+        config_data["model"] = new_model
+    if model is not None and model != existing_cfg.model:
+        updated_fields.append("model")
+
+    new_tool_groups = tool_groups if tool_groups is not None else existing_cfg.tool_groups
+    if new_tool_groups is not None:
+        config_data["tool_groups"] = new_tool_groups
+    if tool_groups is not None and tool_groups != existing_cfg.tool_groups:
+        updated_fields.append("tool_groups")
+
+    new_skills = skills if skills is not None else existing_cfg.skills
+    if new_skills is not None:
+        config_data["skills"] = new_skills
+    if skills is not None and skills != existing_cfg.skills:
+        updated_fields.append("skills")
+
+    config_changed = bool({"description", "model", "tool_groups", "skills"} & set(updated_fields))
+
+    # Stage every file we intend to rewrite into a temp sibling. Only after
+    # *all* temp files exist do we rename them into place — so a failure on
+    # SOUL.md cannot leave config.yaml already replaced.
+    pending: list[tuple[Path, Path]] = []
+    staged_temps: list[Path] = []
+
+    try:
+        agent_dir.mkdir(parents=True, exist_ok=True)
+
+        if config_changed:
+            yaml_text = yaml.dump(config_data, default_flow_style=False, allow_unicode=True, sort_keys=False)
+            config_target = agent_dir / "config.yaml"
+            config_tmp = _stage_temp(config_target, yaml_text)
+            staged_temps.append(config_tmp)
+            pending.append((config_tmp, config_target))
+
+        if soul is not None:
+            soul_target = agent_dir / "SOUL.md"
+            soul_tmp = _stage_temp(soul_target, soul)
+            staged_temps.append(soul_tmp)
+            pending.append((soul_tmp, soul_target))
+            updated_fields.append("soul")
+
+        # Commit phase. ``Path.replace`` is atomic per file on POSIX/NTFS and
+        # the staging step above means any earlier failure has already been
+        # reported. The remaining failure mode is a crash *between* two
+        # ``replace`` calls, which is reported via the partial-write error
+        # branch below so the caller knows which files are now on disk.
+        committed: list[Path] = []
+        try:
+            for tmp, target in pending:
+                tmp.replace(target)
+                committed.append(target)
+        except Exception as e:
+            _cleanup_temps([t for t, _ in pending if t not in committed])
+            if committed:
+                logger.error(
+                    "[update_agent] Partial write for agent '%s' (user=%s): committed=%s, failed during rename: %s",
+                    agent_name,
+                    user_id,
+                    [p.name for p in committed],
+                    e,
+                    exc_info=True,
+                )
+                return _err(f"Partial update for agent '{agent_name}': {[p.name for p in committed]} were updated, but the rest failed ({e}). Re-run update_agent to retry the remaining fields.")
+            raise
+
+    except Exception as e:
+        _cleanup_temps(staged_temps)
+        logger.error("[update_agent] Failed to update agent '%s' (user=%s): %s", agent_name, user_id, e, exc_info=True)
+        return _err(f"Failed to update agent '{agent_name}': {e}")
+
+    if not updated_fields:
+        return Command(update={"messages": [ToolMessage(content=f"No changes applied to agent '{agent_name}'. The provided values matched the existing config.", tool_call_id=tool_call_id)]})
+
+    logger.info("[update_agent] Updated agent '%s' (user=%s) fields: %s", agent_name, user_id, updated_fields)
+    return Command(
+        update={
+            "messages": [
+                ToolMessage(
+                    content=(f"Agent '{agent_name}' updated successfully. Changed: {', '.join(updated_fields)}. The new configuration takes effect on the next user turn."),
+                    tool_call_id=tool_call_id,
+                )
+            ]
+        }
+    )
@@ -3,13 +3,13 @@ import mimetypes
 from pathlib import Path
 from typing import Annotated

-from langchain.tools import InjectedToolCallId, ToolRuntime, tool
+from langchain.tools import InjectedToolCallId, tool
 from langchain_core.messages import ToolMessage
 from langgraph.types import Command
-from langgraph.typing import ContextT

-from deerflow.agents.thread_state import ThreadDataState, ThreadState
+from deerflow.agents.thread_state import ThreadDataState
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX
+from deerflow.tools.types import Runtime

 _ALLOWED_IMAGE_VIRTUAL_ROOTS = (
    f"{VIRTUAL_PATH_PREFIX}/workspace",
@@ -48,7 +48,7 @@ def _sanitize_image_error(error: Exception, thread_data: ThreadDataState | None)

@tool("view_image", parse_docstring=True)
 def view_image_tool(
-    runtime: ToolRuntime[ContextT, ThreadState],
+    runtime: Runtime,
    image_path: str,
    tool_call_id: Annotated[str, InjectedToolCallId],
 ) -> Command:
--- a/Show More
+++ b/Show More