fix(sandbox): pass no_change_timeout to exec_command to prevent 120s premature termination (#2685)

* fix(sandbox): pass no_change_timeout to exec_command to prevent 120s premature termination

  The agent_sandbox library's shell API defaults no_change_timeout to 120
  seconds. When AioSandbox.execute_command() called exec_command() without
  this parameter, commands producing no output for 120s would return with
  NO_CHANGE_TIMEOUT status even though the script was still running.

  Pass no_change_timeout=600 to all exec_command calls (matching the
  client-level HTTP timeout) so long-running commands are not cut short.

  Fixes #2668

* test(sandbox): add assertions for no_change_timeout in execute_command and list_dir

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/2f37bc72-0826-4443-a6ba-e5b78c22fb5a

Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>

.env.example

@@ -34,5 +34,14 @@ INFOQUEST_API_KEY=your-infoquest-api-key
 
 # GitHub API Token
 # GITHUB_TOKEN=your-github-token
+
+# Database (only needed when config.yaml has database.backend: postgres)
+# DATABASE_URL=postgresql://deerflow:password@localhost:5432/deerflow
+#
 # WECOM_BOT_ID=your-wecom-bot-id
 # WECOM_BOT_SECRET=your-wecom-bot-secret
+# DINGTALK_CLIENT_ID=your-dingtalk-client-id
+# DINGTALK_CLIENT_SECRET=your-dingtalk-client-secret
+
+# Set to "false" to disable Swagger UI, ReDoc, and OpenAPI schema in production
+# GATEWAY_ENABLE_DOCS=false

.github/workflows/e2e-tests.yml

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

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

@@ -0,0 +1,43 @@
+name: Frontend Unit Tests
+
+on:
+  push:
+    branches: [ 'main' ]
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+
+concurrency:
+  group: frontend-unit-tests-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  frontend-unit-tests:
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+
+      - name: Enable Corepack
+        run: corepack enable
+
+      - name: Use pinned pnpm version
+        run: corepack prepare pnpm@10.26.2 --activate
+
+      - name: Install frontend dependencies
+        working-directory: frontend
+        run: pnpm install --frozen-lockfile
+
+      - name: Run unit tests of frontend
+        working-directory: frontend
+        run: make test

.gitignore

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

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  # Backend: ruff lint + format via uv (uses the same ruff version as backend deps)
+  - repo: local
+    hooks:
+      - id: ruff
+        name: ruff lint
+        entry: bash -c 'cd backend && uv run ruff check --fix "${@/#backend\//}"' --
+        language: system
+        types_or: [python]
+        files: ^backend/
+      - id: ruff-format
+        name: ruff format
+        entry: bash -c 'cd backend && uv run ruff format "${@/#backend\//}"' --
+        language: system
+        types_or: [python]
+        files: ^backend/
+
+  # Frontend: eslint + prettier (must run from frontend/ for node_modules resolution)
+  - repo: local
+    hooks:
+      - id: frontend-eslint
+        name: eslint (frontend)
+        entry: bash -c 'cd frontend && npx eslint --fix "${@/#frontend\//}"' --
+        language: system
+        types_or: [javascript, tsx, ts]
+        files: ^frontend/
+
+      - id: frontend-prettier
+        name: prettier (frontend)
+        entry: bash -c 'cd frontend && npx prettier --write "${@/#frontend\//}"' --
+        language: system
+        files: ^frontend/
+        types_or: [javascript, tsx, ts, json, css]

CONTRIBUTING.md

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

Makefile

@@ -1,6 +1,6 @@
 # DeerFlow - Unified Development Environment
 
-.PHONY: help config config-upgrade check install setup doctor dev dev-pro dev-daemon dev-daemon-pro start start-pro start-daemon start-daemon-pro stop up up-pro down clean docker-init docker-start docker-start-pro docker-stop docker-logs docker-logs-frontend docker-logs-gateway
+.PHONY: help config config-upgrade check install setup doctor dev dev-daemon start start-daemon stop up down clean docker-init docker-start docker-stop docker-logs docker-logs-frontend docker-logs-gateway
 
 BASH ?= bash
 BACKEND_UV_RUN = cd backend && uv run
@@ -23,28 +23,22 @@ help:
 	@echo "  make config          - Generate local config files (aborts if config already exists)"
 	@echo "  make config-upgrade  - Merge new fields from config.example.yaml into config.yaml"
 	@echo "  make check           - Check if all required tools are installed"
-	@echo "  make install         - Install all dependencies (frontend + backend)"
+	@echo "  make install         - Install all dependencies (frontend + backend + pre-commit hooks)"
 	@echo "  make setup-sandbox   - Pre-pull sandbox container image (recommended)"
 	@echo "  make dev             - Start all services in development mode (with hot-reloading)"
-	@echo "  make dev-pro         - Start in dev + Gateway mode (experimental, no LangGraph server)"
 	@echo "  make dev-daemon      - Start dev services in background (daemon mode)"
-	@echo "  make dev-daemon-pro  - Start dev daemon + Gateway mode (experimental)"
 	@echo "  make start           - Start all services in production mode (optimized, no hot-reloading)"
-	@echo "  make start-pro       - Start in prod + Gateway mode (experimental)"
 	@echo "  make start-daemon    - Start prod services in background (daemon mode)"
-	@echo "  make start-daemon-pro - Start prod daemon + Gateway mode (experimental)"
 	@echo "  make stop            - Stop all running services"
 	@echo "  make clean           - Clean up processes and temporary files"
 	@echo ""
 	@echo "Docker Production Commands:"
 	@echo "  make up              - Build and start production Docker services (localhost:2026)"
-	@echo "  make up-pro          - Build and start production Docker in Gateway mode (experimental)"
 	@echo "  make down            - Stop and remove production Docker containers"
 	@echo ""
 	@echo "Docker Development Commands:"
 	@echo "  make docker-init     - Pull the sandbox image"
 	@echo "  make docker-start    - Start Docker services (mode-aware from config.yaml, localhost:2026)"
-	@echo "  make docker-start-pro - Start Docker in Gateway mode (experimental, no LangGraph container)"
 	@echo "  make docker-stop     - Stop Docker development services"
 	@echo "  make docker-logs     - View Docker development logs"
 	@echo "  make docker-logs-frontend - View Docker frontend logs"
@@ -73,6 +67,8 @@ install:
 	@cd backend && uv sync
 	@echo "Installing frontend dependencies..."
 	@cd frontend && pnpm install
+	@echo "Installing pre-commit hooks..."
+	@$(BACKEND_UV_RUN) --with pre-commit pre-commit install
 	@echo "✓ All dependencies installed"
 	@echo ""
 	@echo "=========================================="
@@ -99,7 +95,7 @@ setup-sandbox:
 	echo ""; \
 	if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
 		echo "Detected Apple Container on macOS, pulling image..."; \
-		container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
+		container image pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
 	fi; \
 	if command -v docker >/dev/null 2>&1; then \
 		echo "Pulling image using Docker..."; \
@@ -121,41 +117,21 @@ dev:
 	@$(PYTHON) ./scripts/check.py
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev
 
-# Start all services in dev + Gateway mode (experimental: agent runtime embedded in Gateway)
-dev-pro:
-	@$(PYTHON) ./scripts/check.py
-	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway
-
 # Start all services in production mode (with optimizations)
 start:
 	@$(PYTHON) ./scripts/check.py
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod
 
-# Start all services in prod + Gateway mode (experimental)
-start-pro:
-	@$(PYTHON) ./scripts/check.py
-	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway
-
 # Start all services in daemon mode (background)
 dev-daemon:
 	@$(PYTHON) ./scripts/check.py
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --daemon
 
-# Start daemon + Gateway mode (experimental)
-dev-daemon-pro:
-	@$(PYTHON) ./scripts/check.py
-	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway --daemon
-
 # Start prod services in daemon mode (background)
 start-daemon:
 	@$(PYTHON) ./scripts/check.py
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --daemon
 
-# Start prod daemon + Gateway mode (experimental)
-start-daemon-pro:
-	@$(PYTHON) ./scripts/check.py
-	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway --daemon
-
 # Stop all services
 stop:
 	@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --stop
@@ -180,10 +156,6 @@ docker-init:
 docker-start:
 	@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start
 
-# Start Docker in Gateway mode (experimental)
-docker-start-pro:
-	@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start --gateway
-
 # Stop Docker development environment
 docker-stop:
 	@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh stop
@@ -206,10 +178,6 @@ docker-logs-gateway:
 up:
 	@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh
 
-# Build and start production services in Gateway mode
-up-pro:
-	@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh --gateway
-
 # Stop and remove production containers
 down:
 	@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh down

README.md

@@ -243,9 +243,6 @@ make up     # Build images and start all production services
 make down   # Stop and remove containers
 ```
 
-> [!NOTE]
-> The LangGraph agent server currently runs via `langgraph dev` (the open-source CLI server).
-
 Access: http://localhost:2026
 
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
@@ -254,7 +251,7 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
 
 If you prefer running services locally:
 
-Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root (can be overridden via `DEER_FLOW_CONFIG_PATH`). Run `make doctor` to verify your setup before starting.
+Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root. Set `DEER_FLOW_PROJECT_ROOT` to define that root explicitly, or `DEER_FLOW_CONFIG_PATH` to point at a specific config file. Runtime state defaults to `.deer-flow` under the project root and can be moved with `DEER_FLOW_HOME`; skills default to `skills/` under the project root and can be moved with `DEER_FLOW_SKILLS_PATH`. Run `make doctor` to verify your setup before starting.
 On Windows, run the local development flow from Git Bash. Native `cmd.exe` and PowerShell shells are not supported for the bash-based service scripts, and WSL is not guaranteed because some scripts rely on Git for Windows utilities such as `cygpath`.
 
 1. **Check prerequisites**:
@@ -264,7 +261,7 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
 
 2. **Install dependencies**:
    ```bash
-   make install  # Install backend + frontend dependencies
+   make install  # Install backend + frontend dependencies + pre-commit hooks
    ```
 
 3. **(Optional) Pre-pull sandbox image**:
@@ -289,53 +286,31 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
 
 #### Startup Modes
 
-DeerFlow supports multiple startup modes across two dimensions:
+DeerFlow runs the agent runtime inside the Gateway API. Development mode enables hot-reload; production mode uses a pre-built frontend.
-
-- **Dev / Prod** — dev enables hot-reload; prod uses pre-built frontend
-- **Standard / Gateway** — standard uses a separate LangGraph server (4 processes); Gateway mode (experimental) embeds the agent runtime in the Gateway API (3 processes)
 
 | | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
 |---|---|---|---|---|
 | **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
-| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
 | **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
-| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
 
 | Action | Local | Docker Dev | Docker Prod |
 |---|---|---|---|
 | **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
 | **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
 
-> **Gateway mode** eliminates the LangGraph server process — the Gateway API handles agent execution directly via async tasks, managing its own concurrency.
+Gateway owns `/api/langgraph/*` and translates those public LangGraph-compatible paths to its native `/api/*` routers behind nginx.
-
-#### Why Gateway Mode?
-
-In standard mode, DeerFlow runs a dedicated [LangGraph Platform](https://langchain-ai.github.io/langgraph/) server alongside the Gateway API. This architecture works well but has trade-offs:
-
-| | Standard Mode | Gateway Mode |
-|---|---|---|
-| **Architecture** | Gateway (REST API) + LangGraph (agent runtime) | Gateway embeds agent runtime |
-| **Concurrency** | `--n-jobs-per-worker` per worker (requires license) | `--workers` × async tasks (no per-worker cap) |
-| **Containers / Processes** | 4 (frontend, gateway, langgraph, nginx) | 3 (frontend, gateway, nginx) |
-| **Resource usage** | Higher (two Python runtimes) | Lower (single Python runtime) |
-| **LangGraph Platform license** | Required for production images | Not required |
-| **Cold start** | Slower (two services to initialize) | Faster |
-
-Both modes are functionally equivalent — the same agents, tools, and skills work in either mode.
 
 #### Docker Production Deployment
 
-`deploy.sh` supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
+`deploy.sh` supports building and starting separately:
 
 ```bash
 # One-step (build + start)
-deploy.sh                    # standard mode (default)
+deploy.sh
-deploy.sh --gateway          # gateway mode
 
-# Two-step (build once, start with any mode)
+# Two-step (build once, start later)
 deploy.sh build              # build all images
-deploy.sh start              # start in standard mode
+deploy.sh start              # start pre-built images
-deploy.sh start --gateway    # start in gateway mode
 
 # Stop
 deploy.sh down
@@ -370,13 +345,14 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when
 | Feishu / Lark | WebSocket | Moderate |
 | WeChat | Tencent iLink (long-polling) | Moderate |
 | WeCom | WebSocket | Moderate |
+| DingTalk | Stream Push (WebSocket) | Moderate |
 
 **Configuration in `config.yaml`:**
 
 ```yaml
 channels:
-  # LangGraph Server URL (default: http://localhost:2024)
+  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
-  langgraph_url: http://localhost:2024
+  langgraph_url: http://localhost:8001/api
   # Gateway API URL (default: http://localhost:8001)
   gateway_url: http://localhost:8001
 
@@ -439,11 +415,19 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
+
+  dingtalk:
+    enabled: true
+    client_id: $DINGTALK_CLIENT_ID             # Client ID of your DingTalk application
+    client_secret: $DINGTALK_CLIENT_SECRET     # Client Secret of your DingTalk application
+    allowed_users: []                          # empty = allow all
+    card_template_id: ""                       # Optional: AI Card template ID for streaming typewriter effect
 ```
 
 Notes:
 - `assistant_id: lead_agent` calls the default LangGraph assistant directly.
 - If `assistant_id` is set to a custom agent name, DeerFlow still routes through `lead_agent` and injects that value as `agent_name`, so the custom agent's SOUL/config takes effect for IM channels.
+- IM channel workers call Gateway's LangGraph-compatible API internally and automatically attach process-local internal auth plus the CSRF cookie/header pair required for thread and run creation.
 
 Set the corresponding API keys in your `.env` file:
 
@@ -466,6 +450,10 @@ WECHAT_ILINK_BOT_ID=your_ilink_bot_id
 # WeCom
 WECOM_BOT_ID=your_bot_id
 WECOM_BOT_SECRET=your_bot_secret
+
+# DingTalk
+DINGTALK_CLIENT_ID=your_client_id
+DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Telegram Setup**
@@ -504,7 +492,15 @@ WECOM_BOT_SECRET=your_bot_secret
 4. Make sure backend dependencies include `wecom-aibot-python-sdk`. The channel uses a WebSocket long connection and does not require a public callback URL.
 5. The current integration supports inbound text, image, and file messages. Final images/files generated by the agent are also sent back to the WeCom conversation.
 
-When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://langgraph:2024` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
+**DingTalk Setup**
+
+1. Create a DingTalk application in the [DingTalk Developer Console](https://open.dingtalk.com/) and enable **Robot** capability.
+2. Set the message receiving mode to **Stream Mode** in the robot configuration page.
+3. Copy the `Client ID` and `Client Secret`, set `DINGTALK_CLIENT_ID` and `DINGTALK_CLIENT_SECRET` in `.env`, and enable the channel in `config.yaml`.
+4. *(Optional)* To enable streaming AI Card replies (typewriter effect), create an **AI Card** template on the [DingTalk Card Platform](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), then set `card_template_id` in `config.yaml` to the template ID. You also need to apply for the `Card.Streaming.Write` and `Card.Instance.Write` permissions.
+
+
+When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://gateway:8001/api` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
 
 **Commands**
 
@@ -658,6 +654,8 @@ This is the difference between a chatbot with tool access and an agent with an a
 
 **Summarization**: Within a session, DeerFlow manages context aggressively — summarizing completed sub-tasks, offloading intermediate results to the filesystem, compressing what's no longer immediately relevant. This lets it stay sharp across long, multi-step tasks without blowing the context window.
 
+**Strict Tool-Call Recovery**: When a provider or middleware interrupts a tool-call loop, DeerFlow now strips provider-level raw tool-call metadata on forced-stop assistant messages and injects placeholder tool results for dangling calls before the next model invocation. This keeps OpenAI-compatible reasoning models that strictly validate `tool_call_id` sequences from failing with malformed history errors.
+
 ### Long-Term Memory
 
 Most agents forget everything the moment a conversation ends. DeerFlow remembers.

README_fr.md

@@ -290,6 +290,7 @@ DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les ca
 | Telegram | Bot API (long-polling) | Facile |
 | Slack | Socket Mode | Modérée |
 | Feishu / Lark | WebSocket | Modérée |
+| DingTalk | Stream Push (WebSocket) | Modérée |
 
 **Configuration dans `config.yaml` :**
 
@@ -341,6 +342,13 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
+
+  dingtalk:
+    enabled: true
+    client_id: $DINGTALK_CLIENT_ID             # ClientId depuis DingTalk Open Platform
+    client_secret: $DINGTALK_CLIENT_SECRET     # ClientSecret depuis DingTalk Open Platform
+    allowed_users: []                          # vide = tout le monde autorisé
+    card_template_id: ""                       # Optionnel : ID de modèle AI Card pour l'effet machine à écrire en streaming
 ```
 
 Définissez les clés API correspondantes dans votre fichier `.env` :
@@ -356,6 +364,10 @@ SLACK_APP_TOKEN=xapp-...
 # Feishu / Lark
 FEISHU_APP_ID=cli_xxxx
 FEISHU_APP_SECRET=your_app_secret
+
+# DingTalk
+DINGTALK_CLIENT_ID=your_client_id
+DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Configuration Telegram**
@@ -378,6 +390,13 @@ FEISHU_APP_SECRET=your_app_secret
 3. Dans **Events**, abonnez-vous à `im.message.receive_v1` et sélectionnez le mode **Long Connection**.
 4. Copiez l'App ID et l'App Secret. Définissez `FEISHU_APP_ID` et `FEISHU_APP_SECRET` dans `.env` et activez le canal dans `config.yaml`.
 
+**Configuration DingTalk**
+
+1. Créez une application sur [DingTalk Open Platform](https://open.dingtalk.com/) et activez la capacité **Robot**.
+2. Dans la page de configuration du robot, définissez le mode de réception des messages sur **Stream**.
+3. Copiez le `Client ID` et le `Client Secret`. Définissez `DINGTALK_CLIENT_ID` et `DINGTALK_CLIENT_SECRET` dans `.env` et activez le canal dans `config.yaml`.
+4. *(Optionnel)* Pour activer les réponses en streaming AI Card (effet machine à écrire), créez un modèle **AI Card** sur la [plateforme de cartes DingTalk](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), puis définissez `card_template_id` dans `config.yaml` avec l'ID du modèle. Vous devez également demander les permissions `Card.Streaming.Write` et `Card.Instance.Write`.
+
 **Commandes**
 
 Une fois un canal connecté, vous pouvez interagir avec DeerFlow directement depuis le chat :

README_ja.md

@@ -243,6 +243,7 @@ DeerFlowはメッセージングアプリからのタスク受信をサポート
 | Telegram | Bot API（ロングポーリング） | 簡単 |
 | Slack | Socket Mode | 中程度 |
 | Feishu / Lark | WebSocket | 中程度 |
+| DingTalk | Stream Push（WebSocket） | 中程度 |
 
 **`config.yaml`での設定：**
 
@@ -294,6 +295,13 @@ channels:
           context:
             thinking_enabled: true
             subagent_enabled: true
+
+  dingtalk:
+    enabled: true
+    client_id: $DINGTALK_CLIENT_ID             # DingTalk Open PlatformのClientId
+    client_secret: $DINGTALK_CLIENT_SECRET     # DingTalk Open PlatformのClientSecret
+    allowed_users: []                          # 空 = 全員許可
+    card_template_id: ""                       # オプション：ストリーミングタイプライター効果用のAIカードテンプレートID
 ```
 
 対応するAPIキーを`.env`ファイルに設定します：
@@ -309,6 +317,10 @@ SLACK_APP_TOKEN=xapp-...
 # Feishu / Lark
 FEISHU_APP_ID=cli_xxxx
 FEISHU_APP_SECRET=your_app_secret
+
+# DingTalk
+DINGTALK_CLIENT_ID=your_client_id
+DINGTALK_CLIENT_SECRET=your_client_secret
 ```
 
 **Telegramのセットアップ**
@@ -331,6 +343,13 @@ FEISHU_APP_SECRET=your_app_secret
 3. **イベント**で`im.message.receive_v1`を購読し、**ロングコネクション**モードを選択。
 4. App IDとApp Secretをコピー。`.env`に`FEISHU_APP_ID`と`FEISHU_APP_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
 
+**DingTalkのセットアップ**
+
+1. [DingTalk Open Platform](https://open.dingtalk.com/)でアプリを作成し、**ロボット**機能を有効化します。
+2. ロボット設定ページでメッセージ受信モードを**Streamモード**に設定します。
+3. `Client ID`と`Client Secret`をコピー。`.env`に`DINGTALK_CLIENT_ID`と`DINGTALK_CLIENT_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
+4. *（オプション）* ストリーミングAIカード返信（タイプライター効果）を有効にするには、[DingTalkカードプラットフォーム](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card)で**AIカード**テンプレートを作成し、`config.yaml`の`card_template_id`にテンプレートIDを設定します。`Card.Streaming.Write` および `Card.Instance.Write` 権限の申請も必要です。
+
 **コマンド**
 
 チャネル接続後、チャットから直接DeerFlowと対話できます：

README_ru.md

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

README_zh.md

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

backend/CLAUDE.md

@@ -7,15 +7,13 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 DeerFlow is a LangGraph-based AI super agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution, persistent memory, subagent delegation, and extensible tool integration - all operating in per-thread isolated environments.
 
 **Architecture**:
-- **LangGraph Server** (port 2024): Agent runtime and workflow execution
+- **Gateway API** (port 8001): REST API plus embedded LangGraph-compatible agent runtime
-- **Gateway API** (port 8001): REST API for models, MCP, skills, memory, artifacts, uploads, and local thread cleanup
 - **Frontend** (port 3000): Next.js web interface
 - **Nginx** (port 2026): Unified reverse proxy entry point
 - **Provisioner** (port 8002, optional in Docker dev): Started only when sandbox is configured for provisioner/Kubernetes mode
 
-**Runtime Modes**:
+**Runtime**:
-- **Standard mode** (`make dev`): LangGraph Server handles agent execution as a separate process. 4 processes total.
+- `make dev`, Docker dev, and production all run the agent runtime in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Nginx exposes that runtime at `/api/langgraph/*` and rewrites it to Gateway's native `/api/*` routers.
-- **Gateway mode** (`make dev-pro`, experimental): Agent runtime embedded in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Service manages its own concurrency via async tasks. 3 processes total, no LangGraph Server.
 
 **Project Structure**:
 ```
@@ -25,7 +23,7 @@ deer-flow/
 ├── extensions_config.json      # MCP servers and skills configuration
 ├── backend/                    # Backend application (this directory)
 │   ├── Makefile               # Backend-only commands (dev, gateway, lint)
-│   ├── langgraph.json         # LangGraph server configuration
+│   ├── langgraph.json         # LangGraph Studio graph configuration
 │   ├── packages/
 │   │   └── harness/           # deerflow-harness package (import: deerflow.*)
 │   │       ├── pyproject.toml
@@ -83,16 +81,15 @@ When making code changes, you MUST update the relevant documentation:
 ```bash
 make check      # Check system requirements
 make install    # Install all dependencies (frontend + backend)
-make dev        # Start all services (LangGraph + Gateway + Frontend + Nginx), with config.yaml preflight
+make dev        # Start all services (Gateway + Frontend + Nginx), with config.yaml preflight
-make dev-pro    # Gateway mode (experimental): skip LangGraph, agent runtime embedded in Gateway
+make start      # Start production services locally
-make start-pro  # Production + Gateway mode (experimental)
 make stop       # Stop all services
 ```
 
 **Backend directory** (for backend development only):
 ```bash
 make install    # Install backend dependencies
-make dev        # Run LangGraph server only (port 2024)
+make dev        # Run Gateway API with reload (port 8001)
 make gateway    # Run Gateway API only (port 8001)
 make test       # Run all backend tests
 make lint       # Lint with ruff
@@ -115,7 +112,7 @@ CI runs these regression tests for every pull request via [.github/workflows/bac
 The backend is split into two layers with a strict dependency direction:
 
 - **Harness** (`packages/harness/deerflow/`): Publishable agent framework package (`deerflow-harness`). Import prefix: `deerflow.*`. Contains agent orchestration, tools, sandbox, models, MCP, skills, config — everything needed to build and run agents.
-- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram).
+- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram, DingTalk).
 
 **Dependency rule**: App imports deerflow, but deerflow never imports app. This boundary is enforced by `tests/test_harness_boundary.py` which runs in CI.
 
@@ -156,20 +153,26 @@ from deerflow.config import get_app_config
 
 ### Middleware Chain
 
-Middlewares execute in strict order in `packages/harness/deerflow/agents/lead_agent/agent.py`:
+Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`_build_middlewares`):
 
-1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local `.deer-flow/threads/{thread_id}` directory
+1. **ThreadDataMiddleware** - Creates per-thread directories under the user's isolation scope (`backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); resolves `user_id` via `get_effective_user_id()` (falls back to `"default"` in no-auth mode); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local thread directory
 2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
 3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
-4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
+4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption), including raw provider tool-call payloads preserved only in `additional_kwargs["tool_calls"]`
-5. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
+5. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later middleware/tool stages run
-6. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
+6. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
-7. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
+7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
-8. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
-9. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
-10. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-11. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
+11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
-12. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
+12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+15. **DeferredToolFilterMiddleware** - Hides deferred tool schemas from the bound model until tool search is enabled (optional)
+16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
+17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
+18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
 
 ### Configuration System
 
@@ -202,7 +205,7 @@ Configuration priority:
 
 ### Gateway API (`app/gateway/`)
 
-FastAPI application on port 8001 with health check at `GET /health`.
+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).
 
 **Routers**:
 
@@ -216,6 +219,9 @@ FastAPI application on port 8001 with health check at `GET /health`.
 | **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
 | **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
 | **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized before JSON parsing |
+| **Thread Runs** (`/api/threads/{id}/runs`) | `POST /` - create background run; `POST /stream` - create + SSE stream; `POST /wait` - create + block; `GET /` - list runs; `GET /{rid}` - run details; `POST /{rid}/cancel` - cancel; `GET /{rid}/join` - join SSE; `GET /{rid}/messages` - paginated messages `{data, has_more}`; `GET /{rid}/events` - full event stream; `GET /../messages` - thread messages with feedback; `GET /../token-usage` - aggregate tokens |
+| **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.
 
@@ -229,7 +235,7 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 
 **Virtual Path System**:
 - Agent sees: `/mnt/user-data/{workspace,uploads,outputs}`, `/mnt/skills`
-- Physical: `backend/.deer-flow/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
+- Physical: `backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
 - Translation: `replace_virtual_path()` / `replace_virtual_paths_in_command()`
 - Detection: `is_local_sandbox()` checks `sandbox_id == "local"`
 
@@ -269,7 +275,7 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 - `invoke_acp_agent` - Invokes external ACP-compatible agents from `config.yaml`
 - ACP launchers must be real ACP adapters. The standard `codex` CLI is not ACP-compatible by itself; configure a wrapper such as `npx -y @zed-industries/codex-acp` or an installed `codex-acp` binary
 - Missing ACP executables now return an actionable error message instead of a raw `[Errno 2]`
-- Each ACP agent uses a per-thread workspace at `{base_dir}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
+- Each ACP agent uses a per-thread workspace at `{base_dir}/users/{user_id}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
 - `image_search/` - Image search via DuckDuckGo
 
 ### MCP System (`packages/harness/deerflow/mcp/`)
@@ -306,9 +312,10 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 
 ### IM Channels System (`app/channels/`)
 
-Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
+Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via the LangGraph Server.
 
-**Architecture**: Channels communicate with the LangGraph Server through `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side.
+
+**Architecture**: Channels communicate with Gateway through the `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side. The internal SDK client injects process-local internal auth plus a matching CSRF cookie/header pair so Gateway accepts state-changing thread/run requests from channel workers without relying on browser session cookies.
 
 **Components**:
 - `message_bus.py` - Async pub/sub hub (`InboundMessage` → queue → dispatcher; `OutboundMessage` → callbacks → channels)
@@ -316,40 +323,51 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
 - `manager.py` - Core dispatcher: creates threads via `client.threads.create()`, routes commands, keeps Slack/Telegram on `client.runs.wait()`, and uses `client.runs.stream(["messages-tuple", "values"])` for Feishu incremental outbound updates
 - `base.py` - Abstract `Channel` base class (start/stop/send lifecycle)
 - `service.py` - Manages lifecycle of all configured channels from `config.yaml`
-- `slack.py` / `feishu.py` / `telegram.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place)
+- `slack.py` / `feishu.py` / `telegram.py` / `dingtalk.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place; `dingtalk.py` optionally uses AI Card streaming for in-place updates when `card_template_id` is configured)
 
 **Message Flow**:
 1. External platform -> Channel impl -> `MessageBus.publish_inbound()`
 2. `ChannelManager._dispatch_loop()` consumes from queue
-3. For chat: look up/create thread on LangGraph Server
+3. For chat: look up/create thread through Gateway's LangGraph-compatible API
 4. Feishu chat: `runs.stream()` → accumulate AI text → publish multiple outbound updates (`is_final=False`) → publish final outbound (`is_final=True`)
 5. Slack/Telegram chat: `runs.wait()` → extract final response → publish outbound
 6. Feishu channel sends one running reply card up front, then patches the same card for each outbound update (card JSON sets `config.update_multi=true` for Feishu's patch API requirement)
-7. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
+7. DingTalk AI Card mode (when `card_template_id` configured): `runs.stream()` → create card with initial text → stream updates via `PUT /v1.0/card/streaming` → finalize on `is_final=True`. Falls back to `sampleMarkdown` if card creation or streaming fails
-8. Outbound → channel callbacks → platform reply
+8. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
+9. Outbound → channel callbacks → platform reply
 
 **Configuration** (`config.yaml` -> `channels`):
-- `langgraph_url` - LangGraph Server URL (default: `http://localhost:2024`)
+- `langgraph_url` - LangGraph-compatible Gateway API base URL (default: `http://localhost:8001/api`)
 - `gateway_url` - Gateway API URL for auxiliary commands (default: `http://localhost:8001`)
-- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://langgraph:2024` / `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
+- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://gateway:8001/api` for `langgraph_url` and `http://gateway:8001` for `gateway_url`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
-- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token)
+- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token), `dingtalk` (client_id, client_secret, optional `card_template_id` for AI Card streaming)
+
 
 ### Memory System (`packages/harness/deerflow/agents/memory/`)
 
 **Components**:
 - `updater.py` - LLM-based memory updates with fact extraction, whitespace-normalized fact deduplication (trims leading/trailing whitespace before comparing), and atomic file I/O
-- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time)
+- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time); captures `user_id` at enqueue time so it survives the `threading.Timer` boundary
 - `prompt.py` - Prompt templates for memory updates
+- `storage.py` - File-based storage with per-user isolation; cache keyed by `(user_id, agent_name)` tuple
 
-**Data Structure** (stored in `backend/.deer-flow/memory.json`):
+**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`
+- `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`
+
+**Data Structure** (stored in `{base_dir}/users/{user_id}/memory.json`):
 - **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
 - **History**: `recentMonths`, `earlierContext`, `longTermBackground`
 - **Facts**: Discrete facts with `id`, `content`, `category` (preference/knowledge/context/behavior/goal), `confidence` (0-1), `createdAt`, `source`
 
 **Workflow**:
-1. `MemoryMiddleware` filters messages (user inputs + final AI responses) and queues conversation
+1. `MemoryMiddleware` filters messages (user inputs + final AI responses), captures `user_id` via `get_effective_user_id()`, and queues conversation with the captured `user_id`
 2. Queue debounces (30s default), batches updates, deduplicates per-thread
-3. Background thread invokes LLM to extract context updates and facts
+3. Background thread invokes LLM to extract context updates and facts, using the stored `user_id` (not the contextvar, which is unavailable on timer threads)
 4. Applies updates atomically (temp file + rename) with cache invalidation, skipping duplicate fact content before append
 5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
 
@@ -357,7 +375,7 @@ Focused regression coverage for the updater lives in `backend/tests/test_memory_
 
 **Configuration** (`config.yaml` → `memory`):
 - `enabled` / `injection_enabled` - Master switches
-- `storage_path` - Path to memory.json
+- `storage_path` - Path to memory.json (absolute path opts out of per-user isolation)
 - `debounce_seconds` - Wait time before processing (default: 30)
 - `model_name` - LLM for updates (null = default model)
 - `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
@@ -392,9 +410,9 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
 
 `DeerFlowClient` provides direct in-process access to all DeerFlow capabilities without HTTP services. All return types align with the Gateway API response schemas, so consumer code works identically in HTTP and embedded modes.
 
-**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
+**Architecture**: Imports the same `deerflow` modules that Gateway API uses. Shares the same config files and data directories. No FastAPI dependency.
 
-**Agent Conversation** (replaces LangGraph Server):
+**Agent Conversation**:
 - `chat(message, thread_id)` — synchronous, accumulates streaming deltas per message-id and returns the final AI text
 - `stream(message, thread_id)` — subscribes to LangGraph `stream_mode=["values", "messages", "custom"]` and yields `StreamEvent`:
   - `"values"` — full state snapshot (title, messages, artifacts); AI text already delivered via `messages` mode is **not** re-synthesized here to avoid duplicate deliveries
@@ -457,20 +475,15 @@ This starts all services and makes the application available at `http://localhos
 | | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
 |---|---|---|---|---|
 | **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
-| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
 | **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
-| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
 
 | Action | Local | Docker Dev | Docker Prod |
 |---|---|---|---|
 | **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
 | **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
 
-Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
-
 **Nginx routing**:
-- Standard mode: `/api/langgraph/*` → LangGraph Server (2024)
+- `/api/langgraph/*` → Gateway embedded runtime (8001), rewritten to `/api/*`
-- Gateway mode: `/api/langgraph/*` → Gateway embedded runtime (8001) (via envsubst)
 - `/api/*` (other) → Gateway API (8001)
 - `/` (non-API) → Frontend (3000)
 
@@ -479,15 +492,11 @@ Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
 From the **backend** directory:
 
 ```bash
-# Terminal 1: LangGraph server
+# Gateway API
-make dev
-
-# Terminal 2: Gateway API
 make gateway
 ```
 
 Direct access (without nginx):
-- LangGraph: `http://localhost:2024`
 - Gateway: `http://localhost:8001`
 
 ### Frontend Configuration

backend/Dockerfile

@@ -13,6 +13,9 @@ FROM python:3.12-slim-bookworm AS builder
 ARG NODE_MAJOR=22
 ARG APT_MIRROR
 ARG UV_INDEX_URL
+# Optional extras to install (e.g. "postgres" for PostgreSQL support)
+# Usage: docker build --build-arg UV_EXTRAS=postgres ...
+ARG UV_EXTRAS
 
 # Optionally override apt mirror for restricted networks (e.g. APT_MIRROR=mirrors.aliyun.com)
 RUN if [ -n "${APT_MIRROR}" ]; then \
@@ -43,8 +46,9 @@ WORKDIR /app
 COPY backend ./backend
 
 # Install dependencies with cache mount
+# When UV_EXTRAS is set (e.g. "postgres"), installs optional dependencies.
 RUN --mount=type=cache,target=/root/.cache/uv \
-    sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync"
+    sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync ${UV_EXTRAS:+--extra $UV_EXTRAS}"
 
 # ── Stage 2: Dev ──────────────────────────────────────────────────────────────
 # Retains compiler toolchain from builder so startup-time `uv sync` can build

backend/Makefile

@@ -2,7 +2,7 @@ install:
 	uv sync
 
 dev:
-	uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
+	PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001 --reload
 
 gateway:
 	PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001

backend/app/channels/__init__.py

@@ -2,7 +2,7 @@
 
 Provides a pluggable channel system that connects external messaging platforms
 (Feishu/Lark, Slack, Telegram) to the DeerFlow agent via the ChannelManager,
-which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
+which uses ``langgraph-sdk`` to communicate with Gateway's LangGraph-compatible API.
 """
 
 from app.channels.base import Channel

backend/app/channels/base.py

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

backend/app/channels/dingtalk.py

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

backend/app/channels/feishu.py

@@ -13,6 +13,7 @@ from app.channels.base import Channel
 from app.channels.commands import KNOWN_CHANNEL_COMMANDS
 from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import get_sandbox_provider
 
 logger = logging.getLogger(__name__)
@@ -62,6 +63,10 @@ class FeishuChannel(Channel):
         self._GetMessageResourceRequest = None
         self._thread_lock = threading.Lock()
 
+    @property
+    def supports_streaming(self) -> bool:
+        return True
+
     async def start(self) -> None:
         if self._running:
             return
@@ -344,8 +349,9 @@ class FeishuChannel(Channel):
             return f"Failed to obtain the [{type}]"
 
         paths = get_paths()
-        paths.ensure_thread_dirs(thread_id)
+        user_id = get_effective_user_id()
-        uploads_dir = paths.sandbox_uploads_dir(thread_id).resolve()
+        paths.ensure_thread_dirs(thread_id, user_id=user_id)
+        uploads_dir = paths.sandbox_uploads_dir(thread_id, user_id=user_id).resolve()
 
         ext = "png" if type == "image" else "bin"
         raw_filename = getattr(response, "file_name", "") or f"feishu_{file_key[-12:]}.{ext}"

backend/app/channels/manager.py

@@ -1,4 +1,4 @@
-"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
+"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via Gateway."""
 
 from __future__ import annotations
 
@@ -17,10 +17,13 @@ from langgraph_sdk.errors import ConflictError
 from app.channels.commands import KNOWN_CHANNEL_COMMANDS
 from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 from app.channels.store import ChannelStore
+from app.gateway.csrf_middleware import CSRF_COOKIE_NAME, CSRF_HEADER_NAME, generate_csrf_token
+from app.gateway.internal_auth import create_internal_auth_headers
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_LANGGRAPH_URL = "http://localhost:2024"
+DEFAULT_LANGGRAPH_URL = "http://localhost:8001/api"
 DEFAULT_GATEWAY_URL = "http://localhost:8001"
 DEFAULT_ASSISTANT_ID = "lead_agent"
 CUSTOM_AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
@@ -35,6 +38,7 @@ STREAM_UPDATE_MIN_INTERVAL_SECONDS = 0.35
 THREAD_BUSY_MESSAGE = "This conversation is already processing another request. Please wait for it to finish and try again."
 
 CHANNEL_CAPABILITIES = {
+    "dingtalk": {"supports_streaming": False},
     "discord": {"supports_streaming": False},
     "feishu": {"supports_streaming": True},
     "slack": {"supports_streaming": False},
@@ -45,6 +49,13 @@ CHANNEL_CAPABILITIES = {
 
 InboundFileReader = Callable[[dict[str, Any], httpx.AsyncClient], Awaitable[bytes | None]]
 
+_METADATA_DROP_KEYS = frozenset({"raw_message", "ref_msg"})
+
+
+def _slim_metadata(meta: dict[str, Any]) -> dict[str, Any]:
+    """Return a shallow copy of *meta* with known-large keys removed."""
+    return {k: v for k, v in meta.items() if k not in _METADATA_DROP_KEYS}
+
 
 INBOUND_FILE_READERS: dict[str, InboundFileReader] = {}
 
@@ -342,14 +353,15 @@ def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedA
 
     attachments: list[ResolvedAttachment] = []
     paths = get_paths()
-    outputs_dir = paths.sandbox_outputs_dir(thread_id).resolve()
+    user_id = get_effective_user_id()
+    outputs_dir = paths.sandbox_outputs_dir(thread_id, user_id=user_id).resolve()
     for virtual_path in artifacts:
         # Security: only allow files from the agent outputs directory
         if not virtual_path.startswith(_OUTPUTS_VIRTUAL_PREFIX):
             logger.warning("[Manager] rejected non-outputs artifact path: %s", virtual_path)
             continue
         try:
-            actual = paths.resolve_virtual_path(thread_id, virtual_path)
+            actual = paths.resolve_virtual_path(thread_id, virtual_path, user_id=user_id)
             # Verify the resolved path is actually under the outputs directory
             # (guards against path-traversal even after prefix check)
             try:
@@ -507,7 +519,7 @@ class ChannelManager:
     """Core dispatcher that bridges IM channels to the DeerFlow agent.
 
     It reads from the MessageBus inbound queue, creates/reuses threads on
-    the LangGraph Server, sends messages via ``runs.wait``, and publishes
+    Gateway's LangGraph-compatible API, sends messages via ``runs.wait``, and publishes
     outbound responses back through the bus.
     """
 
@@ -532,12 +544,20 @@ class ChannelManager:
         self._default_session = _as_dict(default_session)
         self._channel_sessions = dict(channel_sessions or {})
         self._client = None  # lazy init — langgraph_sdk async client
+        self._csrf_token = generate_csrf_token()
         self._semaphore: asyncio.Semaphore | None = None
         self._running = False
         self._task: asyncio.Task | None = None
 
     @staticmethod
     def _channel_supports_streaming(channel_name: str) -> bool:
+        from .service import get_channel_service
+
+        service = get_channel_service()
+        if service:
+            channel = service.get_channel(channel_name)
+            if channel is not None:
+                return channel.supports_streaming
         return CHANNEL_CAPABILITIES.get(channel_name, {}).get("supports_streaming", False)
 
     def _resolve_session_layer(self, msg: InboundMessage) -> tuple[dict[str, Any], dict[str, Any]]:
@@ -584,7 +604,14 @@ class ChannelManager:
         if self._client is None:
             from langgraph_sdk import get_client
 
-            self._client = get_client(url=self._langgraph_url)
+            self._client = get_client(
+                url=self._langgraph_url,
+                headers={
+                    **create_internal_auth_headers(),
+                    CSRF_HEADER_NAME: self._csrf_token,
+                    "Cookie": f"{CSRF_COOKIE_NAME}={self._csrf_token}",
+                },
+            )
         return self._client
 
     # -- lifecycle ---------------------------------------------------------
@@ -667,7 +694,7 @@ class ChannelManager:
     # -- chat handling -----------------------------------------------------
 
     async def _create_thread(self, client, msg: InboundMessage) -> str:
-        """Create a new thread on the LangGraph Server and store the mapping."""
+        """Create a new thread through Gateway and store the mapping."""
         thread = await client.threads.create()
         thread_id = thread["thread_id"]
         self.store.set_thread_id(
@@ -677,7 +704,7 @@ class ChannelManager:
             topic_id=msg.topic_id,
             user_id=msg.user_id,
         )
-        logger.info("[Manager] new thread created on LangGraph Server: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
+        logger.info("[Manager] new thread created through Gateway: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
         return thread_id
 
     async def _handle_chat(self, msg: InboundMessage, extra_context: dict[str, Any] | None = None) -> None:
@@ -760,6 +787,7 @@ class ChannelManager:
             artifacts=artifacts,
             attachments=attachments,
             thread_ts=msg.thread_ts,
+            metadata=_slim_metadata(msg.metadata),
         )
         logger.info("[Manager] publishing outbound message to bus: channel=%s, chat_id=%s", msg.channel_name, msg.chat_id)
         await self.bus.publish_outbound(outbound)
@@ -821,6 +849,7 @@ class ChannelManager:
                         text=latest_text,
                         is_final=False,
                         thread_ts=msg.thread_ts,
+                        metadata=_slim_metadata(msg.metadata),
                     )
                 )
                 last_published_text = latest_text
@@ -865,6 +894,7 @@ class ChannelManager:
                     attachments=attachments,
                     is_final=True,
                     thread_ts=msg.thread_ts,
+                    metadata=_slim_metadata(msg.metadata),
                 )
             )
 
@@ -884,7 +914,7 @@ class ChannelManager:
             return
 
         if command == "new":
-            # Create a new thread on the LangGraph Server
+            # Create a new thread through Gateway
             client = self._get_client()
             thread = await client.threads.create()
             new_thread_id = thread["thread_id"]
@@ -923,6 +953,7 @@ class ChannelManager:
             thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
             text=reply,
             thread_ts=msg.thread_ts,
+            metadata=_slim_metadata(msg.metadata),
         )
         await self.bus.publish_outbound(outbound)
 
@@ -956,5 +987,6 @@ class ChannelManager:
             thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
             text=error_text,
             thread_ts=msg.thread_ts,
+            metadata=_slim_metadata(msg.metadata),
         )
         await self.bus.publish_outbound(outbound)

backend/app/channels/service.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from app.channels.base import Channel
 from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
@@ -13,8 +13,12 @@ from app.channels.store import ChannelStore
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 # Channel name → import path for lazy loading
 _CHANNEL_REGISTRY: dict[str, str] = {
+    "dingtalk": "app.channels.dingtalk:DingTalkChannel",
     "discord": "app.channels.discord:DiscordChannel",
     "feishu": "app.channels.feishu:FeishuChannel",
     "slack": "app.channels.slack:SlackChannel",
@@ -23,6 +27,17 @@ _CHANNEL_REGISTRY: dict[str, str] = {
     "wecom": "app.channels.wecom:WeComChannel",
 }
 
+# Keys that indicate a user has configured credentials for a channel.
+_CHANNEL_CREDENTIAL_KEYS: dict[str, list[str]] = {
+    "dingtalk": ["client_id", "client_secret"],
+    "discord": ["bot_token"],
+    "feishu": ["app_id", "app_secret"],
+    "slack": ["bot_token", "app_token"],
+    "telegram": ["bot_token"],
+    "wecom": ["bot_id", "bot_secret"],
+    "wechat": ["bot_token"],
+}
+
 _CHANNELS_LANGGRAPH_URL_ENV = "DEER_FLOW_CHANNELS_LANGGRAPH_URL"
 _CHANNELS_GATEWAY_URL_ENV = "DEER_FLOW_CHANNELS_GATEWAY_URL"
 
@@ -65,14 +80,15 @@ class ChannelService:
         self._running = False
 
     @classmethod
-    def from_app_config(cls) -> ChannelService:
+    def from_app_config(cls, app_config: AppConfig | None = None) -> ChannelService:
         """Create a ChannelService from the application config."""
-        from deerflow.config.app_config import get_app_config
+        if app_config is None:
+            from deerflow.config.app_config import get_app_config
 
-        config = get_app_config()
+            app_config = get_app_config()
         channels_config = {}
         # extra fields are allowed by AppConfig (extra="allow")
-        extra = config.model_extra or {}
+        extra = app_config.model_extra or {}
         if "channels" in extra:
             channels_config = extra["channels"]
         return cls(channels_config=channels_config)
@@ -88,7 +104,16 @@ class ChannelService:
             if not isinstance(channel_config, dict):
                 continue
             if not channel_config.get("enabled", False):
-                logger.info("Channel %s is disabled, skipping", name)
+                cred_keys = _CHANNEL_CREDENTIAL_KEYS.get(name, [])
+                has_creds = any(not isinstance(channel_config.get(k), bool) and channel_config.get(k) is not None and str(channel_config[k]).strip() for k in cred_keys)
+                if has_creds:
+                    logger.warning(
+                        "Channel '%s' has credentials configured but is disabled. Set enabled: true under channels.%s in config.yaml to activate it.",
+                        name,
+                        name,
+                    )
+                else:
+                    logger.info("Channel %s is disabled, skipping", name)
                 continue
 
             await self._start_channel(name, channel_config)
@@ -143,11 +168,16 @@ class ChannelService:
 
         try:
             channel = channel_cls(bus=self.bus, config=config)
-            await channel.start()
             self._channels[name] = channel
+            await channel.start()
+            if not channel.is_running:
+                self._channels.pop(name, None)
+                logger.error("Channel %s did not enter a running state after start()", name)
+                return False
             logger.info("Channel %s started", name)
             return True
         except Exception:
+            self._channels.pop(name, None)
             logger.exception("Failed to start channel %s", name)
             return False
 
@@ -182,12 +212,12 @@ def get_channel_service() -> ChannelService | None:
     return _channel_service
 
 
-async def start_channel_service() -> ChannelService:
+async def start_channel_service(app_config: AppConfig | None = None) -> ChannelService:
     """Create and start the global ChannelService from app config."""
     global _channel_service
     if _channel_service is not None:
         return _channel_service
-    _channel_service = ChannelService.from_app_config()
+    _channel_service = ChannelService.from_app_config(app_config)
     await _channel_service.start()
     return _channel_service
 

backend/app/channels/slack.py

@@ -16,13 +16,31 @@ logger = logging.getLogger(__name__)
 _slack_md_converter = SlackMarkdownConverter()
 
 
+def _normalize_allowed_users(allowed_users: Any) -> set[str]:
+    if allowed_users is None:
+        return set()
+    if isinstance(allowed_users, str):
+        values = [allowed_users]
+    elif isinstance(allowed_users, list | tuple | set):
+        values = allowed_users
+    else:
+        logger.warning(
+            "Slack allowed_users should be a list of Slack user IDs or a single Slack user ID string; treating %s as one string value",
+            type(allowed_users).__name__,
+        )
+        values = [allowed_users]
+    return {str(user_id) for user_id in values if str(user_id)}
+
+
 class SlackChannel(Channel):
     """Slack IM channel using Socket Mode (WebSocket, no public IP).
 
     Configuration keys (in ``config.yaml`` under ``channels.slack``):
         - ``bot_token``: Slack Bot User OAuth Token (xoxb-...).
         - ``app_token``: Slack App-Level Token (xapp-...) for Socket Mode.
-        - ``allowed_users``: (optional) List of allowed Slack user IDs. Empty = allow all.
+        - ``allowed_users``: (optional) List of allowed Slack user IDs, or a
+          single Slack user ID string as shorthand. Empty = allow all. Other
+          scalar values are treated as a single string with a warning.
     """
 
     def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -30,7 +48,7 @@ class SlackChannel(Channel):
         self._socket_client = None
         self._web_client = None
         self._loop: asyncio.AbstractEventLoop | None = None
-        self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}
+        self._allowed_users = _normalize_allowed_users(config.get("allowed_users", []))
 
     async def start(self) -> None:
         if self._running:

backend/app/channels/wecom.py

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

backend/app/gateway/app.py

@@ -1,16 +1,23 @@
+import asyncio
 import logging
+import os
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
+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.deps import langgraph_runtime
 from app.gateway.routers import (
     agents,
     artifacts,
     assistants_compat,
+    auth,
     channels,
+    feedback,
     mcp,
     memory,
     models,
@@ -21,9 +28,13 @@ from app.gateway.routers import (
     threads,
     uploads,
 )
-from deerflow.config.app_config import get_app_config
+from deerflow.config import app_config as deerflow_app_config
+from deerflow.config.app_config import apply_logging_level
 
-# Configure logging
+AppConfig = deerflow_app_config.AppConfig
+get_app_config = deerflow_app_config.get_app_config
+
+# Default logging; lifespan overrides from config.yaml log_level.
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -32,6 +43,120 @@ logging.basicConfig(
 
 logger = logging.getLogger(__name__)
 
+# Upper bound (seconds) each lifespan shutdown hook is allowed to run.
+# Bounds worker exit time so uvicorn's reload supervisor does not keep
+# firing signals into a worker that is stuck waiting for shutdown cleanup.
+_SHUTDOWN_HOOK_TIMEOUT_SECONDS = 5.0
+
+
+async def _ensure_admin_user(app: FastAPI) -> None:
+    """Startup hook: handle first boot and migrate orphan threads otherwise.
+
+    After admin creation, migrate orphan threads from the LangGraph
+    store (metadata.user_id unset) to the admin account. This is the
+    "no-auth → with-auth" upgrade path: users who ran DeerFlow without
+    authentication have existing LangGraph thread data that needs an
+    owner assigned.
+        First boot (no admin exists):
+            - Does NOT create any user accounts automatically.
+            - The operator must visit ``/setup`` to create the first admin.
+
+    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.
+
+    No SQL persistence migration is needed: the four user_id columns
+    (threads_meta, runs, run_events, feedback) only come into existence
+    alongside the auth module via create_all, so freshly created tables
+    never contain NULL-owner rows.
+    """
+    from sqlalchemy import select
+
+    from app.gateway.deps import get_local_provider
+    from deerflow.persistence.engine import get_session_factory
+    from deerflow.persistence.user.model import UserRow
+
+    try:
+        provider = get_local_provider()
+    except RuntimeError:
+        # Auth persistence may not be initialized in some test/boot paths.
+        # Skip admin migration work rather than failing gateway startup.
+        logger.warning("Auth persistence not ready; skipping admin bootstrap check")
+        return
+
+    sf = get_session_factory()
+    if sf is None:
+        return
+
+    admin_count = await provider.count_admin_users()
+
+    if admin_count == 0:
+        logger.info("=" * 60)
+        logger.info("  First boot detected — no admin account exists.")
+        logger.info("  Visit /setup to complete admin account creation.")
+        logger.info("=" * 60)
+        return
+
+    # Admin already exists — run orphan thread migration for any
+    # LangGraph thread metadata that pre-dates the auth module.
+    async with sf() as session:
+        stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
+        row = (await session.execute(stmt)).scalar_one_or_none()
+
+    if row is None:
+        return  # Should not happen (admin_count > 0 above), but be safe.
+
+    admin_id = str(row.id)
+
+    # LangGraph store orphan migration — non-fatal.
+    # This covers the "no-auth → with-auth" upgrade path for users
+    # whose existing LangGraph thread metadata has no user_id set.
+    store = getattr(app.state, "store", None)
+    if store is not None:
+        try:
+            migrated = await _migrate_orphaned_threads(store, admin_id)
+            if migrated:
+                logger.info("Migrated %d orphan LangGraph thread(s) to admin", migrated)
+        except Exception:
+            logger.exception("LangGraph thread migration failed (non-fatal)")
+
+
+async def _iter_store_items(store, namespace, *, page_size: int = 500):
+    """Paginated async iterator over a LangGraph store namespace.
+
+    Replaces the old hardcoded ``limit=1000`` call with a cursor-style
+    loop so that environments with more than one page of orphans do
+    not silently lose data. Terminates when a page is empty OR when a
+    short page arrives (indicating the last page).
+    """
+    offset = 0
+    while True:
+        batch = await store.asearch(namespace, limit=page_size, offset=offset)
+        if not batch:
+            return
+        for item in batch:
+            yield item
+        if len(batch) < page_size:
+            return
+        offset += page_size
+
+
+async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
+    """Migrate LangGraph store threads with no user_id to the given admin.
+
+    Uses cursor pagination so all orphans are migrated regardless of
+    count. Returns the number of rows migrated.
+    """
+    migrated = 0
+    async for item in _iter_store_items(store, ("threads",)):
+        metadata = item.value.get("metadata", {})
+        if not metadata.get("user_id"):
+            metadata["user_id"] = admin_user_id
+            item.value["metadata"] = metadata
+            await store.aput(("threads",), item.key, item.value)
+            migrated += 1
+    return migrated
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
@@ -39,7 +164,8 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
 
     # Load config and check necessary environment variables at startup
     try:
-        get_app_config()
+        app.state.config = get_app_config()
+        apply_logging_level(app.state.config.log_level)
         logger.info("Configuration loaded successfully")
     except Exception as e:
         error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -52,22 +178,34 @@ 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)
+        # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
+        await _ensure_admin_user(app)
+
         # Start IM channel service if any channels are configured
         try:
             from app.channels.service import start_channel_service
 
-            channel_service = await start_channel_service()
+            channel_service = await start_channel_service(app.state.config)
             logger.info("Channel service started: %s", channel_service.get_status())
         except Exception:
             logger.exception("No IM channels configured or channel service failed to start")
 
         yield
 
-        # Stop channel service on shutdown
+        # Stop channel service on shutdown (bounded to prevent worker hang)
         try:
             from app.channels.service import stop_channel_service
 
-            await stop_channel_service()
+            await asyncio.wait_for(
+                stop_channel_service(),
+                timeout=_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
+            )
+        except TimeoutError:
+            logger.warning(
+                "Channel service shutdown exceeded %.1fs; proceeding with worker exit.",
+                _SHUTDOWN_HOOK_TIMEOUT_SECONDS,
+            )
         except Exception:
             logger.exception("Failed to stop channel service")
 
@@ -80,6 +218,8 @@ def create_app() -> FastAPI:
     Returns:
         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}
 
     app = FastAPI(
         title="DeerFlow API Gateway",
@@ -104,9 +244,7 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
         """,
         version="0.1.0",
         lifespan=lifespan,
-        docs_url="/docs",
+        **docs_kwargs,
-        redoc_url="/redoc",
-        openapi_url="/openapi.json",
         openapi_tags=[
             {
                 "name": "models",
@@ -163,7 +301,31 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
         ],
     )
 
-    # CORS is handled by nginx - no need for FastAPI middleware
+    # Auth: reject unauthenticated requests to non-public paths (fail-closed safety net)
+    app.add_middleware(AuthMiddleware)
+
+    # 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
+        if cors_origins:
+            app.add_middleware(
+                CORSMiddleware,
+                allow_origins=cors_origins,
+                allow_credentials=True,
+                allow_methods=["*"],
+                allow_headers=["*"],
+            )
 
     # Include routers
     # Models API is mounted at /api/models
@@ -199,6 +361,12 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
     # Assistants compatibility API (LangGraph Platform stub)
     app.include_router(assistants_compat.router)
 
+    # Auth API is mounted at /api/v1/auth
+    app.include_router(auth.router)
+
+    # Feedback API is mounted at /api/threads/{thread_id}/runs/{run_id}/feedback
+    app.include_router(feedback.router)
+
     # Thread Runs API (LangGraph Platform-compatible runs lifecycle)
     app.include_router(thread_runs.router)
 

backend/app/gateway/auth/__init__.py

@@ -0,0 +1,42 @@
+"""Authentication module for DeerFlow.
+
+This module provides:
+- JWT-based authentication
+- Provider Factory pattern for extensible auth methods
+- UserRepository interface for storage backends (SQLite)
+"""
+
+from app.gateway.auth.config import AuthConfig, get_auth_config, set_auth_config
+from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse, TokenError
+from app.gateway.auth.jwt import TokenPayload, create_access_token, decode_token
+from app.gateway.auth.local_provider import LocalAuthProvider
+from app.gateway.auth.models import User, UserResponse
+from app.gateway.auth.password import hash_password, verify_password
+from app.gateway.auth.providers import AuthProvider
+from app.gateway.auth.repositories.base import UserRepository
+
+__all__ = [
+    # Config
+    "AuthConfig",
+    "get_auth_config",
+    "set_auth_config",
+    # Errors
+    "AuthErrorCode",
+    "AuthErrorResponse",
+    "TokenError",
+    # JWT
+    "TokenPayload",
+    "create_access_token",
+    "decode_token",
+    # Password
+    "hash_password",
+    "verify_password",
+    # Models
+    "User",
+    "UserResponse",
+    # Providers
+    "AuthProvider",
+    "LocalAuthProvider",
+    # Repository
+    "UserRepository",
+]

backend/app/gateway/auth/config.py

@@ -0,0 +1,57 @@
+"""Authentication configuration for DeerFlow."""
+
+import logging
+import os
+import secrets
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+
+class AuthConfig(BaseModel):
+    """JWT and auth-related configuration. Parsed once at startup.
+
+    Note: the ``users`` table now lives in the shared persistence
+    database managed by ``deerflow.persistence.engine``. The old
+    ``users_db_path`` config key has been removed — user storage is
+    configured through ``config.database`` like every other table.
+    """
+
+    jwt_secret: str = Field(
+        ...,
+        description="Secret key for JWT signing. MUST be set via AUTH_JWT_SECRET.",
+    )
+    token_expiry_days: int = Field(default=7, ge=1, le=30)
+    oauth_github_client_id: str | None = Field(default=None)
+    oauth_github_client_secret: str | None = Field(default=None)
+
+
+_auth_config: AuthConfig | None = None
+
+
+def get_auth_config() -> AuthConfig:
+    """Get the global AuthConfig instance. Parses from env on first call."""
+    global _auth_config
+    if _auth_config is None:
+        from dotenv import load_dotenv
+
+        load_dotenv()
+        jwt_secret = os.environ.get("AUTH_JWT_SECRET")
+        if not jwt_secret:
+            jwt_secret = secrets.token_urlsafe(32)
+            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. "
+                "For production, add AUTH_JWT_SECRET to your .env file: "
+                'python -c "import secrets; print(secrets.token_urlsafe(32))"'
+            )
+        _auth_config = AuthConfig(jwt_secret=jwt_secret)
+    return _auth_config
+
+
+def set_auth_config(config: AuthConfig) -> None:
+    """Set the global AuthConfig instance (for testing)."""
+    global _auth_config
+    _auth_config = config

backend/app/gateway/auth/credential_file.py

@@ -0,0 +1,48 @@
+"""Write initial admin credentials to a restricted file instead of logs.
+
+Logging secrets to stdout/stderr is a well-known CodeQL finding
+(py/clear-text-logging-sensitive-data) — in production those logs
+get collected into ELK/Splunk/etc and become a secret sprawl
+source. This helper writes the credential to a 0600 file that only
+the process user can read, and returns the path so the caller can
+log **the path** (not the password) for the operator to pick up.
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from deerflow.config.paths import get_paths
+
+_CREDENTIAL_FILENAME = "admin_initial_credentials.txt"
+
+
+def write_initial_credentials(email: str, password: str, *, label: str = "initial") -> Path:
+    """Write the admin email + password to ``{base_dir}/admin_initial_credentials.txt``.
+
+    The file is created **atomically** with mode 0600 via ``os.open``
+    so the password is never world-readable, even for the single syscall
+    window between ``write_text`` and ``chmod``.
+
+    ``label`` distinguishes "initial" (fresh creation) from "reset"
+    (password reset) in the file header so an operator picking up the
+    file after a restart can tell which event produced it.
+
+    Returns the absolute :class:`Path` to the file.
+    """
+    target = get_paths().base_dir / _CREDENTIAL_FILENAME
+    target.parent.mkdir(parents=True, exist_ok=True)
+
+    content = (
+        f"# DeerFlow admin {label} credentials\n# This file is generated on first boot or password reset.\n# Change the password after login via Settings -> Account,\n# then delete this file.\n#\nemail: {email}\npassword: {password}\n"
+    )
+
+    # Atomic 0600 create-or-truncate. O_TRUNC (not O_EXCL) so the
+    # reset-password path can rewrite an existing file without a
+    # separate unlink-then-create dance.
+    fd = os.open(target, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
+    with os.fdopen(fd, "w", encoding="utf-8") as fh:
+        fh.write(content)
+
+    return target.resolve()

backend/app/gateway/auth/errors.py

@@ -0,0 +1,45 @@
+"""Typed error definitions for auth module.
+
+AuthErrorCode: exhaustive enum of all auth failure conditions.
+TokenError: exhaustive enum of JWT decode failures.
+AuthErrorResponse: structured error payload for HTTP responses.
+"""
+
+from enum import StrEnum
+
+from pydantic import BaseModel
+
+
+class AuthErrorCode(StrEnum):
+    """Exhaustive list of auth error conditions."""
+
+    INVALID_CREDENTIALS = "invalid_credentials"
+    TOKEN_EXPIRED = "token_expired"
+    TOKEN_INVALID = "token_invalid"
+    USER_NOT_FOUND = "user_not_found"
+    EMAIL_ALREADY_EXISTS = "email_already_exists"
+    PROVIDER_NOT_FOUND = "provider_not_found"
+    NOT_AUTHENTICATED = "not_authenticated"
+    SYSTEM_ALREADY_INITIALIZED = "system_already_initialized"
+
+
+class TokenError(StrEnum):
+    """Exhaustive list of JWT decode failure reasons."""
+
+    EXPIRED = "expired"
+    INVALID_SIGNATURE = "invalid_signature"
+    MALFORMED = "malformed"
+
+
+class AuthErrorResponse(BaseModel):
+    """Structured error response — replaces bare `detail` strings."""
+
+    code: AuthErrorCode
+    message: str
+
+
+def token_error_to_code(err: TokenError) -> AuthErrorCode:
+    """Map TokenError to AuthErrorCode — single source of truth."""
+    if err == TokenError.EXPIRED:
+        return AuthErrorCode.TOKEN_EXPIRED
+    return AuthErrorCode.TOKEN_INVALID

backend/app/gateway/auth/jwt.py

@@ -0,0 +1,55 @@
+"""JWT token creation and verification."""
+
+from datetime import UTC, datetime, timedelta
+
+import jwt
+from pydantic import BaseModel
+
+from app.gateway.auth.config import get_auth_config
+from app.gateway.auth.errors import TokenError
+
+
+class TokenPayload(BaseModel):
+    """JWT token payload."""
+
+    sub: str  # user_id
+    exp: datetime
+    iat: datetime | None = None
+    ver: int = 0  # token_version — must match User.token_version
+
+
+def create_access_token(user_id: str, expires_delta: timedelta | None = None, token_version: int = 0) -> str:
+    """Create a JWT access token.
+
+    Args:
+        user_id: The user's UUID as string
+        expires_delta: Optional custom expiry, defaults to 7 days
+        token_version: User's current token_version for invalidation
+
+    Returns:
+        Encoded JWT string
+    """
+    config = get_auth_config()
+    expiry = expires_delta or timedelta(days=config.token_expiry_days)
+
+    now = datetime.now(UTC)
+    payload = {"sub": user_id, "exp": now + expiry, "iat": now, "ver": token_version}
+    return jwt.encode(payload, config.jwt_secret, algorithm="HS256")
+
+
+def decode_token(token: str) -> TokenPayload | TokenError:
+    """Decode and validate a JWT token.
+
+    Returns:
+        TokenPayload if valid, or a specific TokenError variant.
+    """
+    config = get_auth_config()
+    try:
+        payload = jwt.decode(token, config.jwt_secret, algorithms=["HS256"])
+        return TokenPayload(**payload)
+    except jwt.ExpiredSignatureError:
+        return TokenError.EXPIRED
+    except jwt.InvalidSignatureError:
+        return TokenError.INVALID_SIGNATURE
+    except jwt.PyJWTError:
+        return TokenError.MALFORMED

backend/app/gateway/auth/local_provider.py

@@ -0,0 +1,104 @@
+"""Local email/password authentication provider."""
+
+import logging
+
+from app.gateway.auth.models import User
+from app.gateway.auth.password import hash_password_async, needs_rehash, verify_password_async
+from app.gateway.auth.providers import AuthProvider
+from app.gateway.auth.repositories.base import UserRepository
+
+logger = logging.getLogger(__name__)
+
+
+class LocalAuthProvider(AuthProvider):
+    """Email/password authentication provider using local database."""
+
+    def __init__(self, repository: UserRepository):
+        """Initialize with a UserRepository.
+
+        Args:
+            repository: UserRepository implementation (SQLite)
+        """
+        self._repo = repository
+
+    async def authenticate(self, credentials: dict) -> User | None:
+        """Authenticate with email and password.
+
+        Args:
+            credentials: dict with 'email' and 'password' keys
+
+        Returns:
+            User if authentication succeeds, None otherwise
+        """
+        email = credentials.get("email")
+        password = credentials.get("password")
+
+        if not email or not password:
+            return None
+
+        user = await self._repo.get_user_by_email(email)
+        if user is None:
+            return None
+
+        if user.password_hash is None:
+            # OAuth user without local password
+            return None
+
+        if not await verify_password_async(password, user.password_hash):
+            return None
+
+        if needs_rehash(user.password_hash):
+            try:
+                user.password_hash = await hash_password_async(password)
+                await self._repo.update_user(user)
+            except Exception:
+                # Rehash is an opportunistic upgrade; a transient DB error must not
+                # prevent an otherwise-valid login from succeeding.
+                logger.warning("Failed to rehash password for user %s; login will still succeed", user.email, exc_info=True)
+
+        return user
+
+    async def get_user(self, user_id: str) -> User | None:
+        """Get user by ID."""
+        return await self._repo.get_user_by_id(user_id)
+
+    async def create_user(self, email: str, password: str | None = None, system_role: str = "user", needs_setup: bool = False) -> User:
+        """Create a new local user.
+
+        Args:
+            email: User email address
+            password: Plain text password (will be hashed)
+            system_role: Role to assign ("admin" or "user")
+            needs_setup: If True, user must complete setup on first login
+
+        Returns:
+            Created User instance
+        """
+        password_hash = await hash_password_async(password) if password else None
+        user = User(
+            email=email,
+            password_hash=password_hash,
+            system_role=system_role,
+            needs_setup=needs_setup,
+        )
+        return await self._repo.create_user(user)
+
+    async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
+        """Get user by OAuth provider and ID."""
+        return await self._repo.get_user_by_oauth(provider, oauth_id)
+
+    async def count_users(self) -> int:
+        """Return total number of registered users."""
+        return await self._repo.count_users()
+
+    async def count_admin_users(self) -> int:
+        """Return number of admin users."""
+        return await self._repo.count_admin_users()
+
+    async def update_user(self, user: User) -> User:
+        """Update an existing user."""
+        return await self._repo.update_user(user)
+
+    async def get_user_by_email(self, email: str) -> User | None:
+        """Get user by email."""
+        return await self._repo.get_user_by_email(email)

backend/app/gateway/auth/models.py

@@ -0,0 +1,41 @@
+"""User Pydantic models for authentication."""
+
+from datetime import UTC, datetime
+from typing import Literal
+from uuid import UUID, uuid4
+
+from pydantic import BaseModel, ConfigDict, EmailStr, Field
+
+
+def _utc_now() -> datetime:
+    """Return current UTC time (timezone-aware)."""
+    return datetime.now(UTC)
+
+
+class User(BaseModel):
+    """Internal user representation."""
+
+    model_config = ConfigDict(from_attributes=True)
+
+    id: UUID = Field(default_factory=uuid4, description="Primary key")
+    email: EmailStr = Field(..., description="Unique email address")
+    password_hash: str | None = Field(None, description="bcrypt hash, nullable for OAuth users")
+    system_role: Literal["admin", "user"] = Field(default="user")
+    created_at: datetime = Field(default_factory=_utc_now)
+
+    # OAuth linkage (optional)
+    oauth_provider: str | None = Field(None, description="e.g. 'github', 'google'")
+    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")
+    token_version: int = Field(default=0, description="Incremented on password change to invalidate old JWTs")
+
+
+class UserResponse(BaseModel):
+    """Response model for user info endpoint."""
+
+    id: str
+    email: str
+    system_role: Literal["admin", "user"]
+    needs_setup: bool = False

backend/app/gateway/auth/password.py

@@ -0,0 +1,81 @@
+"""Password hashing utilities with versioned hash format.
+
+Hash format: ``$dfv<N>$<bcrypt_hash>`` where ``<N>`` is the version.
+
+- **v1** (legacy): ``bcrypt(password)`` — plain bcrypt, susceptible to
+  72-byte silent truncation.
+- **v2** (current): ``bcrypt(b64(sha256(password)))`` — SHA-256 pre-hash
+  avoids the 72-byte truncation limit so the full password contributes
+  to the hash.
+
+Verification auto-detects the version and falls back to v1 for hashes
+without a prefix, so existing deployments upgrade transparently on next
+login.
+"""
+
+import asyncio
+import base64
+import hashlib
+
+import bcrypt
+
+_CURRENT_VERSION = 2
+_PREFIX_V2 = "$dfv2$"
+_PREFIX_V1 = "$dfv1$"
+
+
+def _pre_hash_v2(password: str) -> bytes:
+    """SHA-256 pre-hash to bypass bcrypt's 72-byte limit."""
+    return base64.b64encode(hashlib.sha256(password.encode("utf-8")).digest())
+
+
+def hash_password(password: str) -> str:
+    """Hash a password (current version: v2 — SHA-256 + bcrypt)."""
+    raw = bcrypt.hashpw(_pre_hash_v2(password), bcrypt.gensalt()).decode("utf-8")
+    return f"{_PREFIX_V2}{raw}"
+
+
+def verify_password(plain_password: str, hashed_password: str) -> bool:
+    """Verify a password, auto-detecting the hash version.
+
+    Accepts v2 (``$dfv2$…``), v1 (``$dfv1$…``), and bare bcrypt hashes
+    (treated as v1 for backward compatibility with pre-versioning data).
+    """
+    try:
+        if hashed_password.startswith(_PREFIX_V2):
+            bcrypt_hash = hashed_password[len(_PREFIX_V2) :]
+            return bcrypt.checkpw(_pre_hash_v2(plain_password), bcrypt_hash.encode("utf-8"))
+
+        if hashed_password.startswith(_PREFIX_V1):
+            bcrypt_hash = hashed_password[len(_PREFIX_V1) :]
+        else:
+            bcrypt_hash = hashed_password
+
+        return bcrypt.checkpw(plain_password.encode("utf-8"), bcrypt_hash.encode("utf-8"))
+    except ValueError:
+        # bcrypt raises ValueError for malformed or corrupt hashes (e.g., invalid salt).
+        # Fail closed rather than crashing the request.
+        return False
+
+
+def needs_rehash(hashed_password: str) -> bool:
+    """Return True if the hash uses an older version and should be rehashed."""
+    return not hashed_password.startswith(_PREFIX_V2)
+
+
+async def hash_password_async(password: str) -> str:
+    """Hash a password using bcrypt (non-blocking).
+
+    Wraps the blocking bcrypt operation in a thread pool to avoid
+    blocking the event loop during password hashing.
+    """
+    return await asyncio.to_thread(hash_password, password)
+
+
+async def verify_password_async(plain_password: str, hashed_password: str) -> bool:
+    """Verify a password against its hash (non-blocking).
+
+    Wraps the blocking bcrypt operation in a thread pool to avoid
+    blocking the event loop during password verification.
+    """
+    return await asyncio.to_thread(verify_password, plain_password, hashed_password)

backend/app/gateway/auth/providers.py

@@ -0,0 +1,24 @@
+"""Auth provider abstraction."""
+
+from abc import ABC, abstractmethod
+
+
+class AuthProvider(ABC):
+    """Abstract base class for authentication providers."""
+
+    @abstractmethod
+    async def authenticate(self, credentials: dict) -> "User | None":
+        """Authenticate user with given credentials.
+
+        Returns User if authentication succeeds, None otherwise.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def get_user(self, user_id: str) -> "User | None":
+        """Retrieve user by ID."""
+        raise NotImplementedError
+
+
+# Import User at runtime to avoid circular imports
+from app.gateway.auth.models import User  # noqa: E402

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

@@ -0,0 +1,102 @@
+"""User repository interface for abstracting database operations."""
+
+from abc import ABC, abstractmethod
+
+from app.gateway.auth.models import User
+
+
+class UserNotFoundError(LookupError):
+    """Raised when a user repository operation targets a non-existent row.
+
+    Subclass of :class:`LookupError` so callers that already catch
+    ``LookupError`` for "missing entity" can keep working unchanged,
+    while specific call sites can pin to this class to distinguish
+    "concurrent delete during update" from other lookups.
+    """
+
+
+class UserRepository(ABC):
+    """Abstract interface for user data storage.
+
+    Implement this interface to support different storage backends
+    (SQLite)
+    """
+
+    @abstractmethod
+    async def create_user(self, user: User) -> User:
+        """Create a new user.
+
+        Args:
+            user: User object to create
+
+        Returns:
+            Created User with ID assigned
+
+        Raises:
+            ValueError: If email already exists
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def get_user_by_id(self, user_id: str) -> User | None:
+        """Get user by ID.
+
+        Args:
+            user_id: User UUID as string
+
+        Returns:
+            User if found, None otherwise
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def get_user_by_email(self, email: str) -> User | None:
+        """Get user by email.
+
+        Args:
+            email: User email address
+
+        Returns:
+            User if found, None otherwise
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def update_user(self, user: User) -> User:
+        """Update an existing user.
+
+        Args:
+            user: User object with updated fields
+
+        Returns:
+            Updated User
+
+        Raises:
+            UserNotFoundError: If no row exists for ``user.id``. This is
+                a hard failure (not a no-op) so callers cannot mistake a
+                concurrent-delete race for a successful update.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def count_users(self) -> int:
+        """Return total number of registered users."""
+        raise NotImplementedError
+
+    @abstractmethod
+    async def count_admin_users(self) -> int:
+        """Return number of users with system_role == 'admin'."""
+        raise NotImplementedError
+
+    @abstractmethod
+    async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
+        """Get user by OAuth provider and ID.
+
+        Args:
+            provider: OAuth provider name (e.g. 'github', 'google')
+            oauth_id: User ID from the OAuth provider
+
+        Returns:
+            User if found, None otherwise
+        """
+        raise NotImplementedError

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

@@ -0,0 +1,127 @@
+"""SQLAlchemy-backed UserRepository implementation.
+
+Uses the shared async session factory from
+``deerflow.persistence.engine`` — the ``users`` table lives in the
+same database as ``threads_meta``, ``runs``, ``run_events``, and
+``feedback``.
+
+Constructor takes the session factory directly (same pattern as the
+other four repositories in ``deerflow.persistence.*``). Callers
+construct this after ``init_engine_from_config()`` has run.
+"""
+
+from __future__ import annotations
+
+from datetime import UTC
+from uuid import UUID
+
+from sqlalchemy import func, select
+from sqlalchemy.exc import IntegrityError
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
+
+from app.gateway.auth.models import User
+from app.gateway.auth.repositories.base import UserNotFoundError, UserRepository
+from deerflow.persistence.user.model import UserRow
+
+
+class SQLiteUserRepository(UserRepository):
+    """Async user repository backed by the shared SQLAlchemy engine."""
+
+    def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
+        self._sf = session_factory
+
+    # ── Converters ────────────────────────────────────────────────────
+
+    @staticmethod
+    def _row_to_user(row: UserRow) -> User:
+        return User(
+            id=UUID(row.id),
+            email=row.email,
+            password_hash=row.password_hash,
+            system_role=row.system_role,  # type: ignore[arg-type]
+            # SQLite loses tzinfo on read; reattach UTC so downstream
+            # code can compare timestamps reliably.
+            created_at=row.created_at if row.created_at.tzinfo else row.created_at.replace(tzinfo=UTC),
+            oauth_provider=row.oauth_provider,
+            oauth_id=row.oauth_id,
+            needs_setup=row.needs_setup,
+            token_version=row.token_version,
+        )
+
+    @staticmethod
+    def _user_to_row(user: User) -> UserRow:
+        return UserRow(
+            id=str(user.id),
+            email=user.email,
+            password_hash=user.password_hash,
+            system_role=user.system_role,
+            created_at=user.created_at,
+            oauth_provider=user.oauth_provider,
+            oauth_id=user.oauth_id,
+            needs_setup=user.needs_setup,
+            token_version=user.token_version,
+        )
+
+    # ── CRUD ──────────────────────────────────────────────────────────
+
+    async def create_user(self, user: User) -> User:
+        """Insert a new user. Raises ``ValueError`` on duplicate email."""
+        row = self._user_to_row(user)
+        async with self._sf() as session:
+            session.add(row)
+            try:
+                await session.commit()
+            except IntegrityError as exc:
+                await session.rollback()
+                raise ValueError(f"Email already registered: {user.email}") from exc
+        return user
+
+    async def get_user_by_id(self, user_id: str) -> User | None:
+        async with self._sf() as session:
+            row = await session.get(UserRow, user_id)
+            return self._row_to_user(row) if row is not None else None
+
+    async def get_user_by_email(self, email: str) -> User | None:
+        stmt = select(UserRow).where(UserRow.email == email)
+        async with self._sf() as session:
+            result = await session.execute(stmt)
+            row = result.scalar_one_or_none()
+            return self._row_to_user(row) if row is not None else None
+
+    async def update_user(self, user: User) -> User:
+        async with self._sf() as session:
+            row = await session.get(UserRow, str(user.id))
+            if row is None:
+                # Hard fail on concurrent delete: callers (reset_admin,
+                # password change handlers, _ensure_admin_user) all
+                # fetched the user just before this call, so a missing
+                # row here means the row vanished underneath us. Silent
+                # success would let the caller log "password reset" for
+                # a row that no longer exists.
+                raise UserNotFoundError(f"User {user.id} no longer exists")
+            row.email = user.email
+            row.password_hash = user.password_hash
+            row.system_role = user.system_role
+            row.oauth_provider = user.oauth_provider
+            row.oauth_id = user.oauth_id
+            row.needs_setup = user.needs_setup
+            row.token_version = user.token_version
+            await session.commit()
+        return user
+
+    async def count_users(self) -> int:
+        stmt = select(func.count()).select_from(UserRow)
+        async with self._sf() as session:
+            return await session.scalar(stmt) or 0
+
+    async def count_admin_users(self) -> int:
+        stmt = select(func.count()).select_from(UserRow).where(UserRow.system_role == "admin")
+        async with self._sf() as session:
+            return await session.scalar(stmt) or 0
+
+    async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
+        stmt = select(UserRow).where(UserRow.oauth_provider == provider, UserRow.oauth_id == oauth_id)
+        async with self._sf() as session:
+            result = await session.execute(stmt)
+            row = result.scalar_one_or_none()
+            return self._row_to_user(row) if row is not None else None

backend/app/gateway/auth/reset_admin.py

@@ -0,0 +1,91 @@
+"""CLI tool to reset an admin password.
+
+Usage:
+    python -m app.gateway.auth.reset_admin
+    python -m app.gateway.auth.reset_admin --email admin@example.com
+
+Writes the new password to ``.deer-flow/admin_initial_credentials.txt``
+(mode 0600) instead of printing it, so CI / log aggregators never see
+the cleartext secret.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import secrets
+import sys
+
+from sqlalchemy import select
+
+from app.gateway.auth.credential_file import write_initial_credentials
+from app.gateway.auth.password import hash_password
+from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
+from deerflow.persistence.user.model import UserRow
+
+
+async def _run(email: str | None) -> int:
+    from deerflow.config import get_app_config
+    from deerflow.persistence.engine import (
+        close_engine,
+        get_session_factory,
+        init_engine_from_config,
+    )
+
+    config = get_app_config()
+    await init_engine_from_config(config.database)
+    try:
+        sf = get_session_factory()
+        if sf is None:
+            print("Error: persistence engine not available (check config.database).", file=sys.stderr)
+            return 1
+
+        repo = SQLiteUserRepository(sf)
+
+        if email:
+            user = await repo.get_user_by_email(email)
+        else:
+            # Find first admin via direct SELECT — repository does not
+            # expose a "first admin" helper and we do not want to add
+            # one just for this CLI.
+            async with sf() as session:
+                stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
+                row = (await session.execute(stmt)).scalar_one_or_none()
+            if row is None:
+                user = None
+            else:
+                user = await repo.get_user_by_id(row.id)
+
+        if user is None:
+            if email:
+                print(f"Error: user '{email}' not found.", file=sys.stderr)
+            else:
+                print("Error: no admin user found.", file=sys.stderr)
+            return 1
+
+        new_password = secrets.token_urlsafe(16)
+        user.password_hash = hash_password(new_password)
+        user.token_version += 1
+        user.needs_setup = True
+        await repo.update_user(user)
+
+        cred_path = write_initial_credentials(user.email, new_password, label="reset")
+        print(f"Password reset for: {user.email}")
+        print(f"Credentials written to: {cred_path} (mode 0600)")
+        print("Next login will require setup (new email + password).")
+        return 0
+    finally:
+        await close_engine()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Reset admin password")
+    parser.add_argument("--email", help="Admin email (default: first admin found)")
+    args = parser.parse_args()
+
+    exit_code = asyncio.run(_run(args.email))
+    sys.exit(exit_code)
+
+
+if __name__ == "__main__":
+    main()

backend/app/gateway/auth_middleware.py

@@ -0,0 +1,126 @@
+"""Global authentication middleware — fail-closed safety net.
+
+Rejects unauthenticated requests to non-public paths with 401. When a
+request passes the cookie check, resolves the JWT payload to a real
+``User`` object and stamps it into both ``request.state.user`` and the
+``deerflow.runtime.user_context`` contextvar so that repository-layer
+owner filtering works automatically via the sentinel pattern.
+
+Fine-grained permission checks remain in authz.py decorators.
+"""
+
+from collections.abc import Callable
+
+from fastapi import HTTPException, Request, Response
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.responses import JSONResponse
+from starlette.types import ASGIApp
+
+from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
+from app.gateway.authz import _ALL_PERMISSIONS, AuthContext
+from app.gateway.internal_auth import INTERNAL_AUTH_HEADER_NAME, get_internal_user, is_valid_internal_auth_token
+from deerflow.runtime.user_context import reset_current_user, set_current_user
+
+# Paths that never require authentication.
+_PUBLIC_PATH_PREFIXES: tuple[str, ...] = (
+    "/health",
+    "/docs",
+    "/redoc",
+    "/openapi.json",
+)
+
+# Exact auth paths that are public (login/register/status check).
+# /api/v1/auth/me, /api/v1/auth/change-password etc. are NOT public.
+_PUBLIC_EXACT_PATHS: frozenset[str] = frozenset(
+    {
+        "/api/v1/auth/login/local",
+        "/api/v1/auth/register",
+        "/api/v1/auth/logout",
+        "/api/v1/auth/setup-status",
+        "/api/v1/auth/initialize",
+    }
+)
+
+
+def _is_public(path: str) -> bool:
+    stripped = path.rstrip("/")
+    if stripped in _PUBLIC_EXACT_PATHS:
+        return True
+    return any(path.startswith(prefix) for prefix in _PUBLIC_PATH_PREFIXES)
+
+
+class AuthMiddleware(BaseHTTPMiddleware):
+    """Strict auth gate: reject requests without a valid session.
+
+    Two-stage check for non-public paths:
+
+    1. Cookie presence — return 401 NOT_AUTHENTICATED if missing
+    2. JWT validation via ``get_optional_user_from_request`` — return 401
+       TOKEN_INVALID if the token is absent, malformed, expired, or the
+       signed user does not exist / is stale
+
+    On success, stamps ``request.state.user`` and the
+    ``deerflow.runtime.user_context`` contextvar so that repository-layer
+    owner filters work downstream without every route needing a
+    ``@require_auth`` decorator. Routes that need per-resource
+    authorization (e.g. "user A cannot read user B's thread by guessing
+    the URL") should additionally use ``@require_permission(...,
+    owner_check=True)`` for explicit enforcement — but authentication
+    itself is fully handled here.
+    """
+
+    def __init__(self, app: ASGIApp) -> None:
+        super().__init__(app)
+
+    async def dispatch(self, request: Request, call_next: Callable) -> Response:
+        if _is_public(request.url.path):
+            return await call_next(request)
+
+        internal_user = None
+        if is_valid_internal_auth_token(request.headers.get(INTERNAL_AUTH_HEADER_NAME)):
+            internal_user = get_internal_user()
+
+        # Non-public path: require session cookie
+        if internal_user is None and not request.cookies.get("access_token"):
+            return JSONResponse(
+                status_code=401,
+                content={
+                    "detail": AuthErrorResponse(
+                        code=AuthErrorCode.NOT_AUTHENTICATED,
+                        message="Authentication required",
+                    ).model_dump()
+                },
+            )
+
+        # Strict JWT validation: reject junk/expired tokens with 401
+        # right here instead of silently passing through. This closes
+        # the "junk cookie bypass" gap (AUTH_TEST_PLAN test 7.5.8):
+        # without this, non-isolation routes like /api/models would
+        # accept any cookie-shaped string as authentication.
+        #
+        # We call the *strict* resolver so that fine-grained error
+        # codes (token_expired, token_invalid, user_not_found, …)
+        # propagate from AuthErrorCode, not get flattened into one
+        # generic code. BaseHTTPMiddleware doesn't let HTTPException
+        # bubble up, so we catch and render it as JSONResponse here.
+        from app.gateway.deps import get_current_user_from_request
+
+        if internal_user is not None:
+            user = internal_user
+        else:
+            try:
+                user = await get_current_user_from_request(request)
+            except HTTPException as exc:
+                return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
+
+        # Stamp both request.state.user (for the contextvar pattern)
+        # and request.state.auth (so @require_permission's "auth is
+        # None" branch short-circuits instead of running the entire
+        # JWT-decode + DB-lookup pipeline a second time per request).
+        request.state.user = user
+        request.state.auth = AuthContext(user=user, permissions=_ALL_PERMISSIONS)
+        token = set_current_user(user)
+        try:
+            return await call_next(request)
+        finally:
+            reset_current_user(token)

backend/app/gateway/authz.py

@@ -0,0 +1,301 @@
+"""Authorization decorators and context for DeerFlow.
+
+Inspired by LangGraph Auth system: https://github.com/langchain-ai/langgraph/blob/main/libs/sdk-py/langgraph_sdk/auth/__init__.py
+
+**Usage:**
+
+1. Use ``@require_auth`` on routes that need authentication
+2. Use ``@require_permission("resource", "action", filter_key=...)`` for permission checks
+3. The decorator chain processes from bottom to top
+
+**Example:**
+
+    @router.get("/{thread_id}")
+    @require_auth
+    @require_permission("threads", "read", owner_check=True)
+    async def get_thread(thread_id: str, request: Request):
+        # User is authenticated and has threads:read permission
+        ...
+
+**Permission Model:**
+
+- threads:read   - View thread
+- threads:write  - Create/update thread
+- threads:delete - Delete thread
+- runs:create   - Run agent
+- runs:read     - View run
+- runs:cancel   - Cancel run
+"""
+
+from __future__ import annotations
+
+import functools
+import inspect
+from collections.abc import Callable
+from types import SimpleNamespace
+from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar
+
+from fastapi import HTTPException, Request
+
+if TYPE_CHECKING:
+    from app.gateway.auth.models import User
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+
+# Permission constants
+class Permissions:
+    """Permission constants for resource:action format."""
+
+    # Threads
+    THREADS_READ = "threads:read"
+    THREADS_WRITE = "threads:write"
+    THREADS_DELETE = "threads:delete"
+
+    # Runs
+    RUNS_CREATE = "runs:create"
+    RUNS_READ = "runs:read"
+    RUNS_CANCEL = "runs:cancel"
+
+
+class AuthContext:
+    """Authentication context for the current request.
+
+    Stored in request.state.auth after require_auth decoration.
+
+    Attributes:
+        user: The authenticated user, or None if anonymous
+        permissions: List of permission strings (e.g., "threads:read")
+    """
+
+    __slots__ = ("user", "permissions")
+
+    def __init__(self, user: User | None = None, permissions: list[str] | None = None):
+        self.user = user
+        self.permissions = permissions or []
+
+    @property
+    def is_authenticated(self) -> bool:
+        """Check if user is authenticated."""
+        return self.user is not None
+
+    def has_permission(self, resource: str, action: str) -> bool:
+        """Check if context has permission for resource:action.
+
+        Args:
+            resource: Resource name (e.g., "threads")
+            action: Action name (e.g., "read")
+
+        Returns:
+            True if user has permission
+        """
+        permission = f"{resource}:{action}"
+        return permission in self.permissions
+
+    def require_user(self) -> User:
+        """Get user or raise 401.
+
+        Raises:
+            HTTPException 401 if not authenticated
+        """
+        if not self.user:
+            raise HTTPException(status_code=401, detail="Authentication required")
+        return self.user
+
+
+def get_auth_context(request: Request) -> AuthContext | None:
+    """Get AuthContext from request state."""
+    return getattr(request.state, "auth", None)
+
+
+_ALL_PERMISSIONS: list[str] = [
+    Permissions.THREADS_READ,
+    Permissions.THREADS_WRITE,
+    Permissions.THREADS_DELETE,
+    Permissions.RUNS_CREATE,
+    Permissions.RUNS_READ,
+    Permissions.RUNS_CANCEL,
+]
+
+
+def _make_test_request_stub() -> Any:
+    """Create a minimal request-like object for direct unit calls.
+
+    Used when decorated route handlers are invoked without FastAPI's
+    request injection. Includes fields accessed by auth helpers.
+    """
+    return SimpleNamespace(state=SimpleNamespace(), cookies={}, _deerflow_test_bypass_auth=True)
+
+
+async def _authenticate(request: Request) -> AuthContext:
+    """Authenticate request and return AuthContext.
+
+    Delegates to deps.get_optional_user_from_request() for the JWT→User pipeline.
+    Returns AuthContext with user=None for anonymous requests.
+    """
+    from app.gateway.deps import get_optional_user_from_request
+
+    user = await get_optional_user_from_request(request)
+    if user is None:
+        return AuthContext(user=None, permissions=[])
+
+    # In future, permissions could be stored in user record
+    return AuthContext(user=user, permissions=_ALL_PERMISSIONS)
+
+
+def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
+    """Decorator that authenticates the request and enforces authentication.
+
+    Independently raises HTTP 401 for unauthenticated requests, regardless of
+    whether ``AuthMiddleware`` is present in the ASGI stack. Sets the resolved
+    ``AuthContext`` on ``request.state.auth`` for downstream handlers.
+
+    Must be placed ABOVE other decorators (executes after them).
+
+    Usage:
+        @router.get("/{thread_id}")
+        @require_auth  # Bottom decorator (executes first after permission check)
+        @require_permission("threads", "read")
+        async def get_thread(thread_id: str, request: Request):
+            auth: AuthContext = request.state.auth
+            ...
+
+    Raises:
+        HTTPException: 401 if the request is unauthenticated.
+        ValueError: If 'request' parameter is missing.
+    """
+
+    @functools.wraps(func)
+    async def wrapper(*args: Any, **kwargs: Any) -> Any:
+        request = kwargs.get("request")
+        if request is None:
+            # Unit tests may call decorated handlers directly without a
+            # FastAPI Request object. Inject a minimal request stub when
+            # the wrapped function declares `request`.
+            if "request" in inspect.signature(func).parameters:
+                kwargs["request"] = _make_test_request_stub()
+            else:
+                raise ValueError("require_auth decorator requires 'request' parameter")
+            request = kwargs["request"]
+
+        if getattr(request, "_deerflow_test_bypass_auth", False):
+            return await func(*args, **kwargs)
+
+        # Authenticate and set context
+        auth_context = await _authenticate(request)
+        request.state.auth = auth_context
+
+        if not auth_context.is_authenticated:
+            raise HTTPException(status_code=401, detail="Authentication required")
+
+        return await func(*args, **kwargs)
+
+    return wrapper
+
+
+def require_permission(
+    resource: str,
+    action: str,
+    owner_check: bool = False,
+    require_existing: bool = False,
+) -> Callable[[Callable[P, T]], Callable[P, T]]:
+    """Decorator that checks permission for resource:action.
+
+    Must be used AFTER @require_auth.
+
+    Args:
+        resource: Resource name (e.g., "threads", "runs")
+        action: Action name (e.g., "read", "write", "delete")
+        owner_check: If True, validates that the current user owns the resource.
+                     Requires 'thread_id' path parameter and performs ownership check.
+        require_existing: Only meaningful with ``owner_check=True``. If True, a
+                          missing ``threads_meta`` row counts as a denial (404)
+                          instead of "untracked legacy thread, allow". Use on
+                          **destructive / mutating** routes (DELETE, PATCH,
+                          state-update) so a deleted thread can't be re-targeted
+                          by another user via the missing-row code path.
+
+    Usage:
+        # Read-style: legacy untracked threads are allowed
+        @require_permission("threads", "read", owner_check=True)
+        async def get_thread(thread_id: str, request: Request):
+            ...
+
+        # Destructive: thread row MUST exist and be owned by caller
+        @require_permission("threads", "delete", owner_check=True, require_existing=True)
+        async def delete_thread(thread_id: str, request: Request):
+            ...
+
+    Raises:
+        HTTPException 401: If authentication required but user is anonymous
+        HTTPException 403: If user lacks permission
+        HTTPException 404: If owner_check=True but user doesn't own the thread
+        ValueError: If owner_check=True but 'thread_id' parameter is missing
+    """
+
+    def decorator(func: Callable[P, T]) -> Callable[P, T]:
+        @functools.wraps(func)
+        async def wrapper(*args: Any, **kwargs: Any) -> Any:
+            request = kwargs.get("request")
+            if request is None:
+                # Unit tests may call decorated route handlers directly without
+                # constructing a FastAPI Request object. Inject a minimal stub
+                # when the wrapped function declares `request`.
+                if "request" in inspect.signature(func).parameters:
+                    kwargs["request"] = _make_test_request_stub()
+                else:
+                    return await func(*args, **kwargs)
+                request = kwargs["request"]
+
+            if getattr(request, "_deerflow_test_bypass_auth", False):
+                return await func(*args, **kwargs)
+
+            auth: AuthContext = getattr(request.state, "auth", None)
+            if auth is None:
+                auth = await _authenticate(request)
+                request.state.auth = auth
+
+            if not auth.is_authenticated:
+                raise HTTPException(status_code=401, detail="Authentication required")
+
+            # Check permission
+            if not auth.has_permission(resource, action):
+                raise HTTPException(
+                    status_code=403,
+                    detail=f"Permission denied: {resource}:{action}",
+                )
+
+            # Owner check for thread-specific resources.
+            #
+            # 2.0-rc moved thread metadata into the SQL persistence layer
+            # (``threads_meta`` table). We verify ownership via
+            # ``ThreadMetaStore.check_access``: it returns True for
+            # missing rows (untracked legacy thread) and for rows whose
+            # ``user_id`` is NULL (shared / pre-auth data), so this is
+            # strict-deny rather than strict-allow — only an *existing*
+            # row with a *different* user_id triggers 404.
+            if owner_check:
+                thread_id = kwargs.get("thread_id")
+                if thread_id is None:
+                    raise ValueError("require_permission with owner_check=True requires 'thread_id' parameter")
+
+                from app.gateway.deps import get_thread_store
+
+                thread_store = get_thread_store(request)
+                allowed = await thread_store.check_access(
+                    thread_id,
+                    str(auth.user.id),
+                    require_existing=require_existing,
+                )
+                if not allowed:
+                    raise HTTPException(
+                        status_code=404,
+                        detail=f"Thread {thread_id} not found",
+                    )
+
+            return await func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator

backend/app/gateway/config.py

@@ -9,6 +9,7 @@ class GatewayConfig(BaseModel):
     host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
     port: int = Field(default=8001, description="Port to bind the gateway server")
     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")
 
 
 _gateway_config: GatewayConfig | None = None
@@ -23,5 +24,6 @@ def get_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

backend/app/gateway/csrf_middleware.py

@@ -0,0 +1,113 @@
+"""CSRF protection middleware for FastAPI.
+
+Per RFC-001:
+State-changing operations require CSRF protection.
+"""
+
+import secrets
+from collections.abc import Callable
+
+from fastapi import Request, Response
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.responses import JSONResponse
+from starlette.types import ASGIApp
+
+CSRF_COOKIE_NAME = "csrf_token"
+CSRF_HEADER_NAME = "X-CSRF-Token"
+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"
+
+
+def generate_csrf_token() -> str:
+    """Generate a secure random CSRF token."""
+    return secrets.token_urlsafe(CSRF_TOKEN_LENGTH)
+
+
+def should_check_csrf(request: Request) -> bool:
+    """Determine if a request needs CSRF validation.
+
+    CSRF is checked for state-changing methods (POST, PUT, DELETE, PATCH).
+    GET, HEAD, OPTIONS, and TRACE are exempt per RFC 7231.
+    """
+    if request.method not in ("POST", "PUT", "DELETE", "PATCH"):
+        return False
+
+    path = request.url.path.rstrip("/")
+    # Exempt /api/v1/auth/me endpoint
+    if path == "/api/v1/auth/me":
+        return False
+    return True
+
+
+_AUTH_EXEMPT_PATHS: frozenset[str] = frozenset(
+    {
+        "/api/v1/auth/login/local",
+        "/api/v1/auth/logout",
+        "/api/v1/auth/register",
+        "/api/v1/auth/initialize",
+    }
+)
+
+
+def is_auth_endpoint(request: Request) -> bool:
+    """Check if the request is to an auth endpoint.
+
+    Auth endpoints don't need CSRF validation on first call (no token).
+    """
+    return request.url.path.rstrip("/") in _AUTH_EXEMPT_PATHS
+
+
+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:
+        _is_auth = is_auth_endpoint(request)
+
+        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)
+
+            if not cookie_token or not header_token:
+                return JSONResponse(
+                    status_code=403,
+                    content={"detail": "CSRF token missing. Include X-CSRF-Token header."},
+                )
+
+            if not secrets.compare_digest(cookie_token, header_token):
+                return JSONResponse(
+                    status_code=403,
+                    content={"detail": "CSRF token mismatch."},
+                )
+
+        response = await call_next(request)
+
+        # For auth endpoints that set up session, also set CSRF cookie
+        if _is_auth and request.method == "POST":
+            # Generate a new CSRF token for the session
+            csrf_token = generate_csrf_token()
+            is_https = is_secure_request(request)
+            response.set_cookie(
+                key=CSRF_COOKIE_NAME,
+                value=csrf_token,
+                httponly=False,  # Must be JS-readable for Double Submit Cookie pattern
+                secure=is_https,
+                samesite="strict",
+            )
+
+        return response
+
+
+def get_csrf_token(request: Request) -> str | None:
+    """Get the CSRF token from the current request's cookies.
+
+    This is useful for server-side rendering where you need to embed
+    token in forms or headers.
+    """
+    return request.cookies.get(CSRF_COOKIE_NAME)

backend/app/gateway/deps.py

@@ -8,12 +8,34 @@ Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
 
 from __future__ import annotations
 
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncGenerator, Callable
 from contextlib import AsyncExitStack, asynccontextmanager
+from typing import TYPE_CHECKING, TypeVar, cast
 
 from fastapi import FastAPI, HTTPException, Request
+from langgraph.types import Checkpointer
 
-from deerflow.runtime import RunManager, StreamBridge
+from deerflow.config.app_config import AppConfig
+from deerflow.persistence.feedback import FeedbackRepository
+from deerflow.runtime import RunContext, RunManager, StreamBridge
+from deerflow.runtime.events.store.base import RunEventStore
+from deerflow.runtime.runs.store.base import RunStore
+
+if TYPE_CHECKING:
+    from app.gateway.auth.local_provider import LocalAuthProvider
+    from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
+    from deerflow.persistence.thread_meta.base import ThreadMetaStore
+
+
+T = TypeVar("T")
+
+
+def get_config(request: Request) -> AppConfig:
+    """Return the app-scoped ``AppConfig`` stored on ``app.state``."""
+    config = getattr(request.app.state, "config", None)
+    if config is None:
+        raise HTTPException(status_code=503, detail="Configuration not available")
+    return config
 
 
 @asynccontextmanager
@@ -25,15 +47,54 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
         async with langgraph_runtime(app):
             yield
     """
-    from deerflow.agents.checkpointer.async_provider import make_checkpointer
+    from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
     from deerflow.runtime import make_store, make_stream_bridge
+    from deerflow.runtime.checkpointer.async_provider import make_checkpointer
+    from deerflow.runtime.events.store import make_run_event_store
 
     async with AsyncExitStack() as stack:
-        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
+        config = getattr(app.state, "config", None)
-        app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
+        if config is None:
-        app.state.store = await stack.enter_async_context(make_store())
+            raise RuntimeError("langgraph_runtime() requires app.state.config to be initialized")
-        app.state.run_manager = RunManager()
+
-        yield
+        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
+
+        # Initialize persistence engine BEFORE checkpointer so that
+        # auto-create-database logic runs first (postgres backend).
+        await init_engine_from_config(config.database)
+
+        app.state.checkpointer = await stack.enter_async_context(make_checkpointer(config))
+        app.state.store = await stack.enter_async_context(make_store(config))
+
+        # Initialize repositories — one get_session_factory() call for all.
+        sf = get_session_factory()
+        if sf is not None:
+            from deerflow.persistence.feedback import FeedbackRepository
+            from deerflow.persistence.run import RunRepository
+
+            app.state.run_store = RunRepository(sf)
+            app.state.feedback_repo = FeedbackRepository(sf)
+        else:
+            from deerflow.runtime.runs.store.memory import MemoryRunStore
+
+            app.state.run_store = MemoryRunStore()
+            app.state.feedback_repo = None
+
+        from deerflow.persistence.thread_meta import make_thread_store
+
+        app.state.thread_store = make_thread_store(sf, app.state.store)
+
+        # Run event store (has its own factory with config-driven backend selection)
+        run_events_config = getattr(config, "run_events", None)
+        app.state.run_event_store = make_run_event_store(run_events_config)
+
+        # RunManager with store backing for persistence
+        app.state.run_manager = RunManager(store=app.state.run_store)
+
+        try:
+            yield
+        finally:
+            await close_engine()
 
 
 # ---------------------------------------------------------------------------
@@ -41,30 +102,144 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
 # ---------------------------------------------------------------------------
 
 
-def get_stream_bridge(request: Request) -> StreamBridge:
+def _require(attr: str, label: str) -> Callable[[Request], T]:
-    """Return the global :class:`StreamBridge`, or 503."""
+    """Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
-    bridge = getattr(request.app.state, "stream_bridge", None)
+
-    if bridge is None:
+    def dep(request: Request) -> T:
-        raise HTTPException(status_code=503, detail="Stream bridge not available")
+        val = getattr(request.app.state, attr, None)
-    return bridge
+        if val is None:
+            raise HTTPException(status_code=503, detail=f"{label} not available")
+        return cast(T, val)
+
+    dep.__name__ = dep.__qualname__ = f"get_{attr}"
+    return dep
 
 
-def get_run_manager(request: Request) -> RunManager:
+get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
-    """Return the global :class:`RunManager`, or 503."""
+get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
-    mgr = getattr(request.app.state, "run_manager", None)
+get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
-    if mgr is None:
+get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
-        raise HTTPException(status_code=503, detail="Run manager not available")
+get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
-    return mgr
+get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
-
-
-def get_checkpointer(request: Request):
-    """Return the global checkpointer, or 503."""
-    cp = getattr(request.app.state, "checkpointer", None)
-    if cp is None:
-        raise HTTPException(status_code=503, detail="Checkpointer not available")
-    return cp
 
 
 def get_store(request: Request):
     """Return the global store (may be ``None`` if not configured)."""
     return getattr(request.app.state, "store", None)
+
+
+def get_thread_store(request: Request) -> ThreadMetaStore:
+    """Return the thread metadata store (SQL or memory-backed)."""
+    val = getattr(request.app.state, "thread_store", None)
+    if val is None:
+        raise HTTPException(status_code=503, detail="Thread metadata store not available")
+    return val
+
+
+def get_run_context(request: Request) -> RunContext:
+    """Build a :class:`RunContext` from ``app.state`` singletons.
+
+    Returns a *base* context with infrastructure dependencies.
+    """
+    config = get_config(request)
+    return RunContext(
+        checkpointer=get_checkpointer(request),
+        store=get_store(request),
+        event_store=get_run_event_store(request),
+        run_events_config=getattr(config, "run_events", None),
+        thread_store=get_thread_store(request),
+        app_config=config,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Auth helpers (used by authz.py and auth middleware)
+# ---------------------------------------------------------------------------
+
+# Cached singletons to avoid repeated instantiation per request
+_cached_local_provider: LocalAuthProvider | None = None
+_cached_repo: SQLiteUserRepository | None = None
+
+
+def get_local_provider() -> LocalAuthProvider:
+    """Get or create the cached LocalAuthProvider singleton.
+
+    Must be called after ``init_engine_from_config()`` — the shared
+    session factory is required to construct the user repository.
+    """
+    global _cached_local_provider, _cached_repo
+    if _cached_repo is None:
+        from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
+        from deerflow.persistence.engine import get_session_factory
+
+        sf = get_session_factory()
+        if sf is None:
+            raise RuntimeError("get_local_provider() called before init_engine_from_config(); cannot access users table")
+        _cached_repo = SQLiteUserRepository(sf)
+    if _cached_local_provider is None:
+        from app.gateway.auth.local_provider import LocalAuthProvider
+
+        _cached_local_provider = LocalAuthProvider(repository=_cached_repo)
+    return _cached_local_provider
+
+
+async def get_current_user_from_request(request: Request):
+    """Get the current authenticated user from the request cookie.
+
+    Raises HTTPException 401 if not authenticated.
+    """
+    from app.gateway.auth import decode_token
+    from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse, TokenError, token_error_to_code
+
+    access_token = request.cookies.get("access_token")
+    if not access_token:
+        raise HTTPException(
+            status_code=401,
+            detail=AuthErrorResponse(code=AuthErrorCode.NOT_AUTHENTICATED, message="Not authenticated").model_dump(),
+        )
+
+    payload = decode_token(access_token)
+    if isinstance(payload, TokenError):
+        raise HTTPException(
+            status_code=401,
+            detail=AuthErrorResponse(code=token_error_to_code(payload), message=f"Token error: {payload.value}").model_dump(),
+        )
+
+    provider = get_local_provider()
+    user = await provider.get_user(payload.sub)
+    if user is None:
+        raise HTTPException(
+            status_code=401,
+            detail=AuthErrorResponse(code=AuthErrorCode.USER_NOT_FOUND, message="User not found").model_dump(),
+        )
+
+    # Token version mismatch → password was changed, token is stale
+    if user.token_version != payload.ver:
+        raise HTTPException(
+            status_code=401,
+            detail=AuthErrorResponse(code=AuthErrorCode.TOKEN_INVALID, message="Token revoked (password changed)").model_dump(),
+        )
+
+    return user
+
+
+async def get_optional_user_from_request(request: Request):
+    """Get optional authenticated user from request.
+
+    Returns None if not authenticated.
+    """
+    try:
+        return await get_current_user_from_request(request)
+    except HTTPException:
+        return None
+
+
+async def get_current_user(request: Request) -> str | None:
+    """Extract user_id from request cookie, or None if not authenticated.
+
+    Thin adapter that returns the string id for callers that only need
+    identification (e.g., ``feedback.py``). Full-user callers should use
+    ``get_current_user_from_request`` or ``get_optional_user_from_request``.
+    """
+    user = await get_optional_user_from_request(request)
+    return str(user.id) if user else None

backend/app/gateway/internal_auth.py

@@ -0,0 +1,26 @@
+"""Process-local authentication for Gateway internal callers."""
+
+from __future__ import annotations
+
+import secrets
+from types import SimpleNamespace
+
+from deerflow.runtime.user_context import DEFAULT_USER_ID
+
+INTERNAL_AUTH_HEADER_NAME = "X-DeerFlow-Internal-Token"
+_INTERNAL_AUTH_TOKEN = secrets.token_urlsafe(32)
+
+
+def create_internal_auth_headers() -> dict[str, str]:
+    """Return headers that authenticate same-process Gateway internal calls."""
+    return {INTERNAL_AUTH_HEADER_NAME: _INTERNAL_AUTH_TOKEN}
+
+
+def is_valid_internal_auth_token(token: str | None) -> bool:
+    """Return True when *token* matches the process-local internal token."""
+    return bool(token) and secrets.compare_digest(token, _INTERNAL_AUTH_TOKEN)
+
+
+def get_internal_user():
+    """Return the synthetic user used for trusted internal channel calls."""
+    return SimpleNamespace(id=DEFAULT_USER_ID, system_role="internal")

backend/app/gateway/langgraph_auth.py

@@ -0,0 +1,106 @@
+"""LangGraph Server 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.
+
+Two layers:
+  1. @auth.authenticate — validates JWT cookie, extracts user_id,
+     and enforces CSRF on state-changing methods (POST/PUT/DELETE/PATCH)
+  2. @auth.on — returns metadata filter so each user only sees own threads
+"""
+
+import secrets
+
+from langgraph_sdk import Auth
+
+from app.gateway.auth.errors import TokenError
+from app.gateway.auth.jwt import decode_token
+from app.gateway.deps import get_local_provider
+
+auth = Auth()
+
+# Methods that require CSRF validation (state-changing per RFC 7231).
+_CSRF_METHODS = frozenset({"POST", "PUT", "DELETE", "PATCH"})
+
+
+def _check_csrf(request) -> None:
+    """Enforce Double Submit Cookie CSRF check for state-changing requests.
+
+    Mirrors Gateway's CSRFMiddleware logic so that LangGraph routes
+    proxied directly by nginx have the same CSRF protection.
+    """
+    method = getattr(request, "method", "") or ""
+    if method.upper() not in _CSRF_METHODS:
+        return
+
+    cookie_token = request.cookies.get("csrf_token")
+    header_token = request.headers.get("x-csrf-token")
+
+    if not cookie_token or not header_token:
+        raise Auth.exceptions.HTTPException(
+            status_code=403,
+            detail="CSRF token missing. Include X-CSRF-Token header.",
+        )
+
+    if not secrets.compare_digest(cookie_token, header_token):
+        raise Auth.exceptions.HTTPException(
+            status_code=403,
+            detail="CSRF token mismatch.",
+        )
+
+
+@auth.authenticate
+async def authenticate(request):
+    """Validate the session cookie, decode JWT, and check token_version.
+
+    Same validation chain as Gateway's get_current_user_from_request:
+      cookie → decode JWT → DB lookup → token_version match
+    Also enforces CSRF on state-changing methods.
+    """
+    # CSRF check before authentication so forged cross-site requests
+    # are rejected early, even if the cookie carries a valid JWT.
+    _check_csrf(request)
+
+    token = request.cookies.get("access_token")
+    if not token:
+        raise Auth.exceptions.HTTPException(
+            status_code=401,
+            detail="Not authenticated",
+        )
+
+    payload = decode_token(token)
+    if isinstance(payload, TokenError):
+        raise Auth.exceptions.HTTPException(
+            status_code=401,
+            detail="Invalid token",
+        )
+
+    user = await get_local_provider().get_user(payload.sub)
+    if user is None:
+        raise Auth.exceptions.HTTPException(
+            status_code=401,
+            detail="User not found",
+        )
+    if user.token_version != payload.ver:
+        raise Auth.exceptions.HTTPException(
+            status_code=401,
+            detail="Token revoked (password changed)",
+        )
+
+    return payload.sub
+
+
+@auth.on
+async def add_owner_filter(ctx: Auth.types.AuthContext, value: dict):
+    """Inject user_id metadata on writes; filter by user_id on reads.
+
+    Gateway stores thread ownership as ``metadata.user_id``.
+    This handler ensures LangGraph Server enforces the same isolation.
+    """
+    # On create/update: stamp user_id into metadata
+    metadata = value.setdefault("metadata", {})
+    metadata["user_id"] = ctx.user.identity
+
+    # Return filter dict — LangGraph applies it to search/read/delete
+    return {"user_id": ctx.user.identity}

backend/app/gateway/path_utils.py

@@ -5,6 +5,7 @@ from pathlib import Path
 from fastapi import HTTPException
 
 from deerflow.config.paths import get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 
 
 def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
@@ -22,7 +23,7 @@ def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
         HTTPException: If the path is invalid or outside allowed directories.
     """
     try:
-        return get_paths().resolve_virtual_path(thread_id, virtual_path)
+        return get_paths().resolve_virtual_path(thread_id, virtual_path, user_id=get_effective_user_id())
     except ValueError as e:
         status = 403 if "traversal" in str(e) else 400
         raise HTTPException(status_code=status, detail=str(e))

backend/app/gateway/routers/agents.py

@@ -8,6 +8,7 @@ import yaml
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
 
+from deerflow.config.agents_api_config import get_agents_api_config
 from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
 from deerflow.config.paths import get_paths
 
@@ -24,6 +25,7 @@ class AgentResponse(BaseModel):
     description: str = Field(default="", description="Agent description")
     model: str | None = Field(default=None, description="Optional model override")
     tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
+    skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all, []=none)")
     soul: str | None = Field(default=None, description="SOUL.md content")
 
 
@@ -40,6 +42,7 @@ class AgentCreateRequest(BaseModel):
     description: str = Field(default="", description="Agent description")
     model: str | None = Field(default=None, description="Optional model override")
     tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
+    skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all enabled, []=none)")
     soul: str = Field(default="", description="SOUL.md content — agent personality and behavioral guardrails")
 
 
@@ -49,6 +52,7 @@ class AgentUpdateRequest(BaseModel):
     description: str | None = Field(default=None, description="Updated description")
     model: str | None = Field(default=None, description="Updated model override")
     tool_groups: list[str] | None = Field(default=None, description="Updated tool group whitelist")
+    skills: list[str] | None = Field(default=None, description="Updated skill whitelist (None=all, []=none)")
     soul: str | None = Field(default=None, description="Updated SOUL.md content")
 
 
@@ -73,6 +77,15 @@ def _normalize_agent_name(name: str) -> str:
     return name.lower()
 
 
+def _require_agents_api_enabled() -> None:
+    """Reject access unless the custom-agent management API is explicitly enabled."""
+    if not get_agents_api_config().enabled:
+        raise HTTPException(
+            status_code=403,
+            detail=("Custom-agent management API is disabled. Set agents_api.enabled=true to expose agent and user-profile routes over HTTP."),
+        )
+
+
 def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
     """Convert AgentConfig to AgentResponse."""
     soul: str | None = None
@@ -84,6 +97,7 @@ def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False
         description=agent_cfg.description,
         model=agent_cfg.model,
         tool_groups=agent_cfg.tool_groups,
+        skills=agent_cfg.skills,
         soul=soul,
     )
 
@@ -100,6 +114,8 @@ async def list_agents() -> AgentsListResponse:
     Returns:
         List of all custom agents with their metadata and soul content.
     """
+    _require_agents_api_enabled()
+
     try:
         agents = list_custom_agents()
         return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
@@ -125,6 +141,7 @@ async def check_agent_name(name: str) -> dict:
     Raises:
         HTTPException: 422 if the name is invalid.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     normalized = _normalize_agent_name(name)
     available = not get_paths().agent_dir(normalized).exists()
@@ -149,6 +166,7 @@ async def get_agent(name: str) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -181,6 +199,7 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     Raises:
         HTTPException: 409 if agent already exists, 422 if name is invalid.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(request.name)
     normalized_name = _normalize_agent_name(request.name)
 
@@ -200,6 +219,8 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
             config_data["model"] = request.model
         if request.tool_groups is not None:
             config_data["tool_groups"] = request.tool_groups
+        if request.skills is not None:
+            config_data["skills"] = request.skills
 
         config_file = agent_dir / "config.yaml"
         with open(config_file, "w", encoding="utf-8") as f:
@@ -243,6 +264,7 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -255,21 +277,32 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
 
     try:
         # Update config if any config fields changed
-        config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])
+        # Use model_fields_set to distinguish "field omitted" from "explicitly set to null".
+        # This is critical for skills where None means "inherit all" (not "don't change").
+        fields_set = request.model_fields_set
+        config_changed = bool(fields_set & {"description", "model", "tool_groups", "skills"})
 
         if config_changed:
             updated: dict = {
                 "name": agent_cfg.name,
-                "description": request.description if request.description is not None else agent_cfg.description,
+                "description": request.description if "description" in fields_set else agent_cfg.description,
             }
-            new_model = request.model if request.model is not None else agent_cfg.model
+            new_model = request.model if "model" in fields_set else agent_cfg.model
             if new_model is not None:
                 updated["model"] = new_model
 
-            new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
+            new_tool_groups = request.tool_groups if "tool_groups" in fields_set else agent_cfg.tool_groups
             if new_tool_groups is not None:
                 updated["tool_groups"] = new_tool_groups
 
+            # skills: None = inherit all, [] = no skills, ["a","b"] = whitelist
+            if "skills" in fields_set:
+                new_skills = request.skills
+            else:
+                new_skills = agent_cfg.skills
+            if new_skills is not None:
+                updated["skills"] = new_skills
+
             config_file = agent_dir / "config.yaml"
             with open(config_file, "w", encoding="utf-8") as f:
                 yaml.dump(updated, f, default_flow_style=False, allow_unicode=True)
@@ -315,6 +348,8 @@ async def get_user_profile() -> UserProfileResponse:
     Returns:
         UserProfileResponse with content=None if USER.md does not exist yet.
     """
+    _require_agents_api_enabled()
+
     try:
         user_md_path = get_paths().user_md_file
         if not user_md_path.exists():
@@ -341,6 +376,8 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     Returns:
         UserProfileResponse with the saved content.
     """
+    _require_agents_api_enabled()
+
     try:
         paths = get_paths()
         paths.base_dir.mkdir(parents=True, exist_ok=True)
@@ -367,6 +404,7 @@ async def delete_agent(name: str) -> None:
     Raises:
         HTTPException: 404 if agent not found.
     """
+    _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 

backend/app/gateway/routers/artifacts.py

@@ -7,6 +7,7 @@ from urllib.parse import quote
 from fastapi import APIRouter, HTTPException, Request
 from fastapi.responses import FileResponse, PlainTextResponse, Response
 
+from app.gateway.authz import require_permission
 from app.gateway.path_utils import resolve_thread_virtual_path
 
 logger = logging.getLogger(__name__)
@@ -81,6 +82,7 @@ def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> byte
     summary="Get Artifact File",
     description="Retrieve an artifact file generated by the AI agent. Text and binary files can be viewed inline, while active web content is always downloaded.",
 )
+@require_permission("threads", "read", owner_check=True)
 async def get_artifact(thread_id: str, path: str, request: Request, download: bool = False) -> Response:
     """Get an artifact file by its path.
 

backend/app/gateway/routers/auth.py

@@ -0,0 +1,493 @@
+"""Authentication endpoints."""
+
+import logging
+import os
+import time
+from ipaddress import ip_address, ip_network
+
+from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
+from fastapi.security import OAuth2PasswordRequestForm
+from pydantic import BaseModel, EmailStr, Field, field_validator
+
+from app.gateway.auth import (
+    UserResponse,
+    create_access_token,
+)
+from app.gateway.auth.config import get_auth_config
+from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
+from app.gateway.csrf_middleware import is_secure_request
+from app.gateway.deps import get_current_user_from_request, get_local_provider
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/auth", tags=["auth"])
+
+
+# ── Request/Response Models ──────────────────────────────────────────────
+
+
+class LoginResponse(BaseModel):
+    """Response model for login — token only lives in HttpOnly cookie."""
+
+    expires_in: int  # seconds
+    needs_setup: bool = False
+
+
+# Top common-password blocklist. Drawn from the public SecLists "10k worst
+# passwords" set, lowercased + length>=8 only (shorter ones already fail
+# the min_length check). Kept tight on purpose: this is the **lower bound**
+# defense, not a full HIBP / passlib check, and runs in-process per request.
+_COMMON_PASSWORDS: frozenset[str] = frozenset(
+    {
+        "password",
+        "password1",
+        "password12",
+        "password123",
+        "password1234",
+        "12345678",
+        "123456789",
+        "1234567890",
+        "qwerty12",
+        "qwertyui",
+        "qwerty123",
+        "abc12345",
+        "abcd1234",
+        "iloveyou",
+        "letmein1",
+        "welcome1",
+        "welcome123",
+        "admin123",
+        "administrator",
+        "passw0rd",
+        "p@ssw0rd",
+        "monkey12",
+        "trustno1",
+        "sunshine",
+        "princess",
+        "football",
+        "baseball",
+        "superman",
+        "batman123",
+        "starwars",
+        "dragon123",
+        "master123",
+        "shadow12",
+        "michael1",
+        "jennifer",
+        "computer",
+    }
+)
+
+
+def _password_is_common(password: str) -> bool:
+    """Case-insensitive blocklist check.
+
+    Lowercases the input so trivial mutations like ``Password`` /
+    ``PASSWORD`` are also rejected. Does not normalize digit substitutions
+    (``p@ssw0rd`` is included as a literal entry instead) — keeping the
+    rule cheap and predictable.
+    """
+    return password.lower() in _COMMON_PASSWORDS
+
+
+def _validate_strong_password(value: str) -> str:
+    """Pydantic field-validator body shared by Register + ChangePassword.
+
+    Constraint = function, not type-level mixin. The two request models
+    have no "is-a" relationship; they only share the password-strength
+    rule. Lifting it into a free function lets each model bind it via
+    ``@field_validator(field_name)`` without inheritance gymnastics.
+    """
+    if _password_is_common(value):
+        raise ValueError("Password is too common; choose a stronger password.")
+    return value
+
+
+class RegisterRequest(BaseModel):
+    """Request model for user registration."""
+
+    email: EmailStr
+    password: str = Field(..., min_length=8)
+
+    _strong_password = field_validator("password")(classmethod(lambda cls, v: _validate_strong_password(v)))
+
+
+class ChangePasswordRequest(BaseModel):
+    """Request model for password change (also handles setup flow)."""
+
+    current_password: str
+    new_password: str = Field(..., min_length=8)
+    new_email: EmailStr | None = None
+
+    _strong_password = field_validator("new_password")(classmethod(lambda cls, v: _validate_strong_password(v)))
+
+
+class MessageResponse(BaseModel):
+    """Generic message response."""
+
+    message: str
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────
+
+
+def _set_session_cookie(response: Response, token: str, request: Request) -> None:
+    """Set the access_token HttpOnly cookie on the response."""
+    config = get_auth_config()
+    is_https = is_secure_request(request)
+    response.set_cookie(
+        key="access_token",
+        value=token,
+        httponly=True,
+        secure=is_https,
+        samesite="lax",
+        max_age=config.token_expiry_days * 24 * 3600 if is_https else None,
+    )
+
+
+# ── Rate Limiting ────────────────────────────────────────────────────────
+# In-process dict — not shared across workers.
+#
+# **Limitation**: with multi-worker deployments (e.g., gunicorn -w N), each
+# worker maintains its own lockout table, so an attacker effectively gets
+# N × _MAX_LOGIN_ATTEMPTS guesses before being locked out everywhere. For
+# production multi-worker setups, replace this with a shared store (Redis,
+# database-backed counter) to enforce a true per-IP limit.
+
+_MAX_LOGIN_ATTEMPTS = 5
+_LOCKOUT_SECONDS = 300  # 5 minutes
+
+# ip → (fail_count, lock_until_timestamp)
+_login_attempts: dict[str, tuple[int, float]] = {}
+
+
+def _trusted_proxies() -> list:
+    """Parse ``AUTH_TRUSTED_PROXIES`` env var into a list of ip_network objects.
+
+    Comma-separated CIDR or single-IP entries. Empty / unset = no proxy is
+    trusted (direct mode). Invalid entries are skipped with a logger warning.
+    Read live so env-var overrides take effect immediately and tests can
+    ``monkeypatch.setenv`` without poking a module-level cache.
+    """
+    raw = os.getenv("AUTH_TRUSTED_PROXIES", "").strip()
+    if not raw:
+        return []
+    nets = []
+    for entry in raw.split(","):
+        entry = entry.strip()
+        if not entry:
+            continue
+        try:
+            nets.append(ip_network(entry, strict=False))
+        except ValueError:
+            logger.warning("AUTH_TRUSTED_PROXIES: ignoring invalid entry %r", entry)
+    return nets
+
+
+def _get_client_ip(request: Request) -> str:
+    """Extract the real client IP for rate limiting.
+
+    Trust model:
+
+    - The TCP peer (``request.client.host``) is always the baseline. It is
+      whatever the kernel reports as the connecting socket — unforgeable
+      by the client itself.
+    - ``X-Real-IP`` is **only** honored if the TCP peer is in the
+      ``AUTH_TRUSTED_PROXIES`` allowlist (set via env var, comma-separated
+      CIDR or single IPs). When set, the gateway is assumed to be behind a
+      reverse proxy (nginx, Cloudflare, ALB, …) that overwrites
+      ``X-Real-IP`` with the original client address.
+    - With no ``AUTH_TRUSTED_PROXIES`` set, ``X-Real-IP`` is silently
+      ignored — closing the bypass where any client could rotate the
+      header to dodge per-IP rate limits in dev / direct-gateway mode.
+
+    ``X-Forwarded-For`` is intentionally NOT used because it is naturally
+    client-controlled at the *first* hop and the trust chain is harder to
+    audit per-request.
+    """
+    peer_host = request.client.host if request.client else None
+
+    trusted = _trusted_proxies()
+    if trusted and peer_host:
+        try:
+            peer_ip = ip_address(peer_host)
+            if any(peer_ip in net for net in trusted):
+                real_ip = request.headers.get("x-real-ip", "").strip()
+                if real_ip:
+                    return real_ip
+        except ValueError:
+            # peer_host wasn't a parseable IP (e.g. "unknown") — fall through
+            pass
+
+    return peer_host or "unknown"
+
+
+def _check_rate_limit(ip: str) -> None:
+    """Raise 429 if the IP is currently locked out."""
+    record = _login_attempts.get(ip)
+    if record is None:
+        return
+    fail_count, lock_until = record
+    if fail_count >= _MAX_LOGIN_ATTEMPTS:
+        if time.time() < lock_until:
+            raise HTTPException(
+                status_code=429,
+                detail="Too many login attempts. Try again later.",
+            )
+        del _login_attempts[ip]
+
+
+_MAX_TRACKED_IPS = 10000
+
+
+def _record_login_failure(ip: str) -> None:
+    """Record a failed login attempt for the given IP."""
+    # Evict expired lockouts when dict grows too large
+    if len(_login_attempts) >= _MAX_TRACKED_IPS:
+        now = time.time()
+        expired = [k for k, (c, t) in _login_attempts.items() if c >= _MAX_LOGIN_ATTEMPTS and now >= t]
+        for k in expired:
+            del _login_attempts[k]
+        # If still too large, evict cheapest-to-lose half: below-threshold
+        # IPs (lock_until=0.0) sort first, then earliest-expiring lockouts.
+        if len(_login_attempts) >= _MAX_TRACKED_IPS:
+            by_time = sorted(_login_attempts.items(), key=lambda kv: kv[1][1])
+            for k, _ in by_time[: len(by_time) // 2]:
+                del _login_attempts[k]
+
+    record = _login_attempts.get(ip)
+    if record is None:
+        _login_attempts[ip] = (1, 0.0)
+    else:
+        new_count = record[0] + 1
+        lock_until = time.time() + _LOCKOUT_SECONDS if new_count >= _MAX_LOGIN_ATTEMPTS else 0.0
+        _login_attempts[ip] = (new_count, lock_until)
+
+
+def _record_login_success(ip: str) -> None:
+    """Clear failure counter for the given IP on successful login."""
+    _login_attempts.pop(ip, None)
+
+
+# ── Endpoints ─────────────────────────────────────────────────────────────
+
+
+@router.post("/login/local", response_model=LoginResponse)
+async def login_local(
+    request: Request,
+    response: Response,
+    form_data: OAuth2PasswordRequestForm = Depends(),
+):
+    """Local email/password login."""
+    client_ip = _get_client_ip(request)
+    _check_rate_limit(client_ip)
+
+    user = await get_local_provider().authenticate({"email": form_data.username, "password": form_data.password})
+
+    if user is None:
+        _record_login_failure(client_ip)
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="Incorrect email or password").model_dump(),
+        )
+
+    _record_login_success(client_ip)
+    token = create_access_token(str(user.id), token_version=user.token_version)
+    _set_session_cookie(response, token, request)
+
+    return LoginResponse(
+        expires_in=get_auth_config().token_expiry_days * 24 * 3600,
+        needs_setup=user.needs_setup,
+    )
+
+
+@router.post("/register", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
+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.
+    Auto-login by setting the session cookie.
+    """
+    try:
+        user = await get_local_provider().create_user(email=body.email, password=body.password, system_role="user")
+    except ValueError:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=AuthErrorResponse(code=AuthErrorCode.EMAIL_ALREADY_EXISTS, message="Email already registered").model_dump(),
+        )
+
+    token = create_access_token(str(user.id), token_version=user.token_version)
+    _set_session_cookie(response, token, request)
+
+    return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role)
+
+
+@router.post("/logout", response_model=MessageResponse)
+async def logout(request: Request, response: Response):
+    """Logout current user by clearing the cookie."""
+    response.delete_cookie(key="access_token", secure=is_secure_request(request), samesite="lax")
+    return MessageResponse(message="Successfully logged out")
+
+
+@router.post("/change-password", response_model=MessageResponse)
+async def change_password(request: Request, response: Response, body: ChangePasswordRequest):
+    """Change password for the currently authenticated user.
+
+    Also handles the first-boot setup flow:
+    - If new_email is provided, updates email (checks uniqueness)
+    - If user.needs_setup is True and new_email is given, clears needs_setup
+    - Always increments token_version to invalidate old sessions
+    - Re-issues session cookie with new token_version
+    """
+    from app.gateway.auth.password import hash_password_async, verify_password_async
+
+    user = await get_current_user_from_request(request)
+
+    if user.password_hash is None:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="OAuth users cannot change password").model_dump())
+
+    if not await verify_password_async(body.current_password, user.password_hash):
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="Current password is incorrect").model_dump())
+
+    provider = get_local_provider()
+
+    # Update email if provided
+    if body.new_email is not None:
+        existing = await provider.get_user_by_email(body.new_email)
+        if existing and str(existing.id) != str(user.id):
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.EMAIL_ALREADY_EXISTS, message="Email already in use").model_dump())
+        user.email = body.new_email
+
+    # Update password + bump version
+    user.password_hash = await hash_password_async(body.new_password)
+    user.token_version += 1
+
+    # Clear setup flag if this is the setup flow
+    if user.needs_setup and body.new_email is not None:
+        user.needs_setup = False
+
+    await provider.update_user(user)
+
+    # Re-issue cookie with new token_version
+    token = create_access_token(str(user.id), token_version=user.token_version)
+    _set_session_cookie(response, token, request)
+
+    return MessageResponse(message="Password changed successfully")
+
+
+@router.get("/me", response_model=UserResponse)
+async def get_me(request: Request):
+    """Get current authenticated user info."""
+    user = await get_current_user_from_request(request)
+    return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role, needs_setup=user.needs_setup)
+
+
+_SETUP_STATUS_COOLDOWN: dict[str, float] = {}
+_SETUP_STATUS_COOLDOWN_SECONDS = 60
+_MAX_TRACKED_SETUP_STATUS_IPS = 10000
+
+
+@router.get("/setup-status")
+async def setup_status(request: Request):
+    """Check if an admin account exists. Returns needs_setup=True when no admin exists."""
+    client_ip = _get_client_ip(request)
+    now = time.time()
+    last_check = _SETUP_STATUS_COOLDOWN.get(client_ip, 0)
+    elapsed = now - last_check
+    if elapsed < _SETUP_STATUS_COOLDOWN_SECONDS:
+        retry_after = max(1, int(_SETUP_STATUS_COOLDOWN_SECONDS - elapsed))
+        raise HTTPException(
+            status_code=status.HTTP_429_TOO_MANY_REQUESTS,
+            detail="Setup status check is rate limited",
+            headers={"Retry-After": str(retry_after)},
+        )
+    # Evict stale entries when dict grows too large to bound memory usage.
+    if len(_SETUP_STATUS_COOLDOWN) >= _MAX_TRACKED_SETUP_STATUS_IPS:
+        cutoff = now - _SETUP_STATUS_COOLDOWN_SECONDS
+        stale = [k for k, t in _SETUP_STATUS_COOLDOWN.items() if t < cutoff]
+        for k in stale:
+            del _SETUP_STATUS_COOLDOWN[k]
+        # If still too large after evicting expired entries, remove oldest half.
+        if len(_SETUP_STATUS_COOLDOWN) >= _MAX_TRACKED_SETUP_STATUS_IPS:
+            by_time = sorted(_SETUP_STATUS_COOLDOWN.items(), key=lambda kv: kv[1])
+            for k, _ in by_time[: len(by_time) // 2]:
+                del _SETUP_STATUS_COOLDOWN[k]
+    _SETUP_STATUS_COOLDOWN[client_ip] = now
+    admin_count = await get_local_provider().count_admin_users()
+    return {"needs_setup": admin_count == 0}
+
+
+class InitializeAdminRequest(BaseModel):
+    """Request model for first-boot admin account creation."""
+
+    email: EmailStr
+    password: str = Field(..., min_length=8)
+
+    _strong_password = field_validator("password")(classmethod(lambda cls, v: _validate_strong_password(v)))
+
+
+@router.post("/initialize", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
+async def initialize_admin(request: Request, response: Response, body: InitializeAdminRequest):
+    """Create the first admin account on initial system setup.
+
+    Only callable when no admin exists. Returns 409 Conflict if an admin
+    already exists.
+
+    On success, the admin account is created with ``needs_setup=False`` and
+    the session cookie is set.
+    """
+    admin_count = await get_local_provider().count_admin_users()
+    if admin_count > 0:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
+        )
+
+    try:
+        user = await get_local_provider().create_user(email=body.email, password=body.password, system_role="admin", needs_setup=False)
+    except ValueError:
+        # DB unique-constraint race: another concurrent request beat us.
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
+        )
+
+    token = create_access_token(str(user.id), token_version=user.token_version)
+    _set_session_cookie(response, token, request)
+
+    return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role)
+
+
+# ── OAuth Endpoints (Future/Placeholder) ─────────────────────────────────
+
+
+@router.get("/oauth/{provider}")
+async def oauth_login(provider: str):
+    """Initiate OAuth login flow.
+
+    Redirects to the OAuth provider's authorization URL.
+    Currently a placeholder - requires OAuth provider implementation.
+    """
+    if provider not in ["github", "google"]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"Unsupported OAuth provider: {provider}",
+        )
+
+    raise HTTPException(
+        status_code=status.HTTP_501_NOT_IMPLEMENTED,
+        detail="OAuth login not yet implemented",
+    )
+
+
+@router.get("/callback/{provider}")
+async def oauth_callback(provider: str, code: str, state: str):
+    """OAuth callback endpoint.
+
+    Handles the OAuth provider's callback after user authorization.
+    Currently a placeholder.
+    """
+    raise HTTPException(
+        status_code=status.HTTP_501_NOT_IMPLEMENTED,
+        detail="OAuth callback not yet implemented",
+    )

backend/app/gateway/routers/feedback.py

@@ -0,0 +1,188 @@
+"""Feedback endpoints — create, list, stats, delete.
+
+Allows users to submit thumbs-up/down feedback on runs,
+optionally scoped to a specific message.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from fastapi import APIRouter, HTTPException, Request
+from pydantic import BaseModel, Field
+
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_current_user, get_feedback_repo, get_run_store
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/api/threads", tags=["feedback"])
+
+
+# ---------------------------------------------------------------------------
+# Request / response models
+# ---------------------------------------------------------------------------
+
+
+class FeedbackCreateRequest(BaseModel):
+    rating: int = Field(..., description="Feedback rating: +1 (positive) or -1 (negative)")
+    comment: str | None = Field(default=None, description="Optional text feedback")
+    message_id: str | None = Field(default=None, description="Optional: scope feedback to a specific message")
+
+
+class FeedbackUpsertRequest(BaseModel):
+    rating: int = Field(..., description="Feedback rating: +1 (positive) or -1 (negative)")
+    comment: str | None = Field(default=None, description="Optional text feedback")
+
+
+class FeedbackResponse(BaseModel):
+    feedback_id: str
+    run_id: str
+    thread_id: str
+    user_id: str | None = None
+    message_id: str | None = None
+    rating: int
+    comment: str | None = None
+    created_at: str = ""
+
+
+class FeedbackStatsResponse(BaseModel):
+    run_id: str
+    total: int = 0
+    positive: int = 0
+    negative: int = 0
+
+
+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+
+
+@router.put("/{thread_id}/runs/{run_id}/feedback", response_model=FeedbackResponse)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
+async def upsert_feedback(
+    thread_id: str,
+    run_id: str,
+    body: FeedbackUpsertRequest,
+    request: Request,
+) -> dict[str, Any]:
+    """Create or update feedback for a run (idempotent)."""
+    if body.rating not in (1, -1):
+        raise HTTPException(status_code=400, detail="rating must be +1 or -1")
+
+    user_id = await get_current_user(request)
+
+    run_store = get_run_store(request)
+    run = await run_store.get(run_id)
+    if run is None:
+        raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
+    if run.get("thread_id") != thread_id:
+        raise HTTPException(status_code=404, detail=f"Run {run_id} not found in thread {thread_id}")
+
+    feedback_repo = get_feedback_repo(request)
+    return await feedback_repo.upsert(
+        run_id=run_id,
+        thread_id=thread_id,
+        rating=body.rating,
+        user_id=user_id,
+        comment=body.comment,
+    )
+
+
+@router.delete("/{thread_id}/runs/{run_id}/feedback")
+@require_permission("threads", "delete", owner_check=True, require_existing=True)
+async def delete_run_feedback(
+    thread_id: str,
+    run_id: str,
+    request: Request,
+) -> dict[str, bool]:
+    """Delete the current user's feedback for a run."""
+    user_id = await get_current_user(request)
+    feedback_repo = get_feedback_repo(request)
+    deleted = await feedback_repo.delete_by_run(
+        thread_id=thread_id,
+        run_id=run_id,
+        user_id=user_id,
+    )
+    if not deleted:
+        raise HTTPException(status_code=404, detail="No feedback found for this run")
+    return {"success": True}
+
+
+@router.post("/{thread_id}/runs/{run_id}/feedback", response_model=FeedbackResponse)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
+async def create_feedback(
+    thread_id: str,
+    run_id: str,
+    body: FeedbackCreateRequest,
+    request: Request,
+) -> dict[str, Any]:
+    """Submit feedback (thumbs-up/down) for a run."""
+    if body.rating not in (1, -1):
+        raise HTTPException(status_code=400, detail="rating must be +1 or -1")
+
+    user_id = await get_current_user(request)
+
+    # Validate run exists and belongs to thread
+    run_store = get_run_store(request)
+    run = await run_store.get(run_id)
+    if run is None:
+        raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
+    if run.get("thread_id") != thread_id:
+        raise HTTPException(status_code=404, detail=f"Run {run_id} not found in thread {thread_id}")
+
+    feedback_repo = get_feedback_repo(request)
+    return await feedback_repo.create(
+        run_id=run_id,
+        thread_id=thread_id,
+        rating=body.rating,
+        user_id=user_id,
+        message_id=body.message_id,
+        comment=body.comment,
+    )
+
+
+@router.get("/{thread_id}/runs/{run_id}/feedback", response_model=list[FeedbackResponse])
+@require_permission("threads", "read", owner_check=True)
+async def list_feedback(
+    thread_id: str,
+    run_id: str,
+    request: Request,
+) -> list[dict[str, Any]]:
+    """List all feedback for a run."""
+    feedback_repo = get_feedback_repo(request)
+    return await feedback_repo.list_by_run(thread_id, run_id)
+
+
+@router.get("/{thread_id}/runs/{run_id}/feedback/stats", response_model=FeedbackStatsResponse)
+@require_permission("threads", "read", owner_check=True)
+async def feedback_stats(
+    thread_id: str,
+    run_id: str,
+    request: Request,
+) -> dict[str, Any]:
+    """Get aggregated feedback stats (positive/negative counts) for a run."""
+    feedback_repo = get_feedback_repo(request)
+    return await feedback_repo.aggregate_by_run(thread_id, run_id)
+
+
+@router.delete("/{thread_id}/runs/{run_id}/feedback/{feedback_id}")
+@require_permission("threads", "delete", owner_check=True, require_existing=True)
+async def delete_feedback(
+    thread_id: str,
+    run_id: str,
+    feedback_id: str,
+    request: Request,
+) -> dict[str, bool]:
+    """Delete a feedback record."""
+    feedback_repo = get_feedback_repo(request)
+    # Verify feedback belongs to the specified thread/run before deleting
+    existing = await feedback_repo.get(feedback_id)
+    if existing is None:
+        raise HTTPException(status_code=404, detail=f"Feedback {feedback_id} not found")
+    if existing.get("thread_id") != thread_id or existing.get("run_id") != run_id:
+        raise HTTPException(status_code=404, detail=f"Feedback {feedback_id} not found in run {run_id}")
+    deleted = await feedback_repo.delete(feedback_id)
+    if not deleted:
+        raise HTTPException(status_code=404, detail=f"Feedback {feedback_id} not found")
+    return {"success": True}

backend/app/gateway/routers/memory.py

@@ -13,6 +13,7 @@ from deerflow.agents.memory.updater import (
     update_memory_fact,
 )
 from deerflow.config.memory_config import get_memory_config
+from deerflow.runtime.user_context import get_effective_user_id
 
 router = APIRouter(prefix="/api", tags=["memory"])
 
@@ -147,7 +148,7 @@ async def get_memory() -> MemoryResponse:
         }
         ```
     """
-    memory_data = get_memory_data()
+    memory_data = get_memory_data(user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -167,7 +168,7 @@ async def reload_memory() -> MemoryResponse:
     Returns:
         The reloaded memory data.
     """
-    memory_data = reload_memory_data()
+    memory_data = reload_memory_data(user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -181,7 +182,7 @@ async def reload_memory() -> MemoryResponse:
 async def clear_memory() -> MemoryResponse:
     """Clear all persisted memory data."""
     try:
-        memory_data = clear_memory_data()
+        memory_data = clear_memory_data(user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
 
@@ -202,6 +203,7 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
             content=request.content,
             category=request.category,
             confidence=request.confidence,
+            user_id=get_effective_user_id(),
         )
     except ValueError as exc:
         raise _map_memory_fact_value_error(exc) from exc
@@ -221,7 +223,7 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
 async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
     """Delete a single fact from memory by fact id."""
     try:
-        memory_data = delete_memory_fact(fact_id)
+        memory_data = delete_memory_fact(fact_id, user_id=get_effective_user_id())
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
     except OSError as exc:
@@ -245,6 +247,7 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
             content=request.content,
             category=request.category,
             confidence=request.confidence,
+            user_id=get_effective_user_id(),
         )
     except ValueError as exc:
         raise _map_memory_fact_value_error(exc) from exc
@@ -265,7 +268,7 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
 )
 async def export_memory() -> MemoryResponse:
     """Export the current memory data."""
-    memory_data = get_memory_data()
+    memory_data = get_memory_data(user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -279,7 +282,7 @@ async def export_memory() -> MemoryResponse:
 async def import_memory(request: MemoryResponse) -> MemoryResponse:
     """Import and persist memory data."""
     try:
-        memory_data = import_memory_data(request.model_dump())
+        memory_data = import_memory_data(request.model_dump(), user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
 
@@ -337,7 +340,7 @@ async def get_memory_status() -> MemoryStatusResponse:
         Combined memory configuration and current data.
     """
     config = get_memory_config()
-    memory_data = get_memory_data()
+    memory_data = get_memory_data(user_id=get_effective_user_id())
 
     return MemoryStatusResponse(
         config=MemoryConfigResponse(

backend/app/gateway/routers/models.py

@@ -1,7 +1,8 @@
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
-from deerflow.config import get_app_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 
 router = APIRouter(prefix="/api", tags=["models"])
 
@@ -17,10 +18,17 @@ class ModelResponse(BaseModel):
     supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")
 
 
+class TokenUsageResponse(BaseModel):
+    """Token usage display configuration."""
+
+    enabled: bool = Field(default=False, description="Whether token usage display is enabled")
+
+
 class ModelsListResponse(BaseModel):
     """Response model for listing all models."""
 
     models: list[ModelResponse]
+    token_usage: TokenUsageResponse
 
 
 @router.get(
@@ -29,14 +37,14 @@ class ModelsListResponse(BaseModel):
     summary="List All Models",
     description="Retrieve a list of all available AI models configured in the system.",
 )
-async def list_models() -> ModelsListResponse:
+async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResponse:
     """List all available models from configuration.
 
     Returns model information suitable for frontend display,
     excluding sensitive fields like API keys and internal configuration.
 
     Returns:
-        A list of all configured models with their metadata.
+        A list of all configured models with their metadata and token usage display settings.
 
     Example Response:
         ```json
@@ -44,21 +52,27 @@ async def list_models() -> ModelsListResponse:
             "models": [
                 {
                     "name": "gpt-4",
+                    "model": "gpt-4",
                     "display_name": "GPT-4",
                     "description": "OpenAI GPT-4 model",
-                    "supports_thinking": false
+                    "supports_thinking": false,
+                    "supports_reasoning_effort": false
                 },
                 {
                     "name": "claude-3-opus",
+                    "model": "claude-3-opus",
                     "display_name": "Claude 3 Opus",
                     "description": "Anthropic Claude 3 Opus model",
-                    "supports_thinking": true
+                    "supports_thinking": true,
+                    "supports_reasoning_effort": false
                 }
-            ]
+            ],
+            "token_usage": {
+                "enabled": true
+            }
         }
         ```
     """
-    config = get_app_config()
     models = [
         ModelResponse(
             name=model.name,
@@ -70,7 +84,10 @@ async def list_models() -> ModelsListResponse:
         )
         for model in config.models
     ]
-    return ModelsListResponse(models=models)
+    return ModelsListResponse(
+        models=models,
+        token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
+    )
 
 
 @router.get(
@@ -79,7 +96,7 @@ async def list_models() -> ModelsListResponse:
     summary="Get Model Details",
     description="Retrieve detailed information about a specific AI model by its name.",
 )
-async def get_model(model_name: str) -> ModelResponse:
+async def get_model(model_name: str, config: AppConfig = Depends(get_config)) -> ModelResponse:
     """Get a specific model by name.
 
     Args:
@@ -101,7 +118,6 @@ async def get_model(model_name: str) -> ModelResponse:
         }
         ```
     """
-    config = get_app_config()
     model = config.get_model_config(model_name)
     if model is None:
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")

backend/app/gateway/routers/runs.py

@@ -11,10 +11,11 @@ import asyncio
 import logging
 import uuid
 
-from fastapi import APIRouter, Request
+from fastapi import APIRouter, HTTPException, Query, Request
 from fastapi.responses import StreamingResponse
 
-from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_checkpointer, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
 from app.gateway.routers.thread_runs import RunCreateRequest
 from app.gateway.services import sse_consumer, start_run
 from deerflow.runtime import serialize_channel_values
@@ -85,3 +86,58 @@ async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
         logger.exception("Failed to fetch final state for run %s", record.run_id)
 
     return {"status": record.status.value, "error": record.error}
+
+
+# ---------------------------------------------------------------------------
+# Run-scoped read endpoints
+# ---------------------------------------------------------------------------
+
+
+async def _resolve_run(run_id: str, request: Request) -> dict:
+    """Fetch run by run_id with user ownership check. Raises 404 if not found."""
+    run_store = get_run_store(request)
+    record = await run_store.get(run_id)  # user_id=AUTO filters by contextvar
+    if record is None:
+        raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
+    return record
+
+
+@router.get("/{run_id}/messages")
+@require_permission("runs", "read")
+async def run_messages(
+    run_id: str,
+    request: Request,
+    limit: int = Query(default=50, le=200, ge=1),
+    before_seq: int | None = Query(default=None),
+    after_seq: int | None = Query(default=None),
+) -> dict:
+    """Return paginated messages for a run (cursor-based).
+
+    Pagination:
+    - after_seq: messages with seq > after_seq (forward)
+    - before_seq: messages with seq < before_seq (backward)
+    - neither: latest messages
+
+    Response: { data: [...], has_more: bool }
+    """
+    run = await _resolve_run(run_id, request)
+    event_store = get_run_event_store(request)
+    rows = await event_store.list_messages_by_run(
+        run["thread_id"],
+        run_id,
+        limit=limit + 1,
+        before_seq=before_seq,
+        after_seq=after_seq,
+    )
+    has_more = len(rows) > limit
+    data = rows[:limit] if has_more else rows
+    return {"data": data, "has_more": has_more}
+
+
+@router.get("/{run_id}/feedback")
+@require_permission("runs", "read")
+async def run_feedback(run_id: str, request: Request) -> list[dict]:
+    """Return all feedback for a run."""
+    run = await _resolve_run(run_id, request)
+    feedback_repo = get_feedback_repo(request)
+    return await feedback_repo.list_by_run(run["thread_id"], run_id)

backend/app/gateway/routers/skills.py

@@ -1,29 +1,20 @@
 import json
 import logging
-import shutil
 from pathlib import Path
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
+from app.gateway.deps import get_config
 from app.gateway.path_utils import resolve_thread_virtual_path
 from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
+from deerflow.config.app_config import AppConfig
 from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
-from deerflow.skills import Skill, load_skills
+from deerflow.skills import Skill
-from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
+from deerflow.skills.installer import SkillAlreadyExistsError
-from deerflow.skills.manager import (
-    append_history,
-    atomic_write,
-    custom_skill_exists,
-    ensure_custom_skill_is_editable,
-    get_custom_skill_dir,
-    get_custom_skill_file,
-    get_skill_history_file,
-    read_custom_skill_content,
-    read_history,
-    validate_skill_markdown_content,
-)
 from deerflow.skills.security_scanner import scan_skill_content
+from deerflow.skills.storage import get_or_new_skill_storage
+from deerflow.skills.types import SKILL_MD_FILE, SkillCategory
 
 logger = logging.getLogger(__name__)
 
@@ -36,7 +27,7 @@ class SkillResponse(BaseModel):
     name: str = Field(..., description="Name of the skill")
     description: str = Field(..., description="Description of what the skill does")
     license: str | None = Field(None, description="License information")
-    category: str = Field(..., description="Category of the skill (public or custom)")
+    category: SkillCategory = Field(..., description="Category of the skill (public or custom)")
     enabled: bool = Field(default=True, description="Whether this skill is enabled")
 
 
@@ -100,9 +91,9 @@ def _skill_to_response(skill: Skill) -> SkillResponse:
     summary="List All Skills",
     description="Retrieve a list of all available skills from both public and custom directories.",
 )
-async def list_skills() -> SkillsListResponse:
+async def list_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
         return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
     except Exception as e:
         logger.error(f"Failed to load skills: {e}", exc_info=True)
@@ -115,10 +106,10 @@ async def list_skills() -> SkillsListResponse:
     summary="Install Skill",
     description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
 )
-async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
+async def install_skill(request: SkillInstallRequest, config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
     try:
         skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
-        result = install_skill_from_archive(skill_file_path)
+        result = await get_or_new_skill_storage(app_config=config).ainstall_skill_from_archive(skill_file_path)
         await refresh_skills_system_prompt_cache_async()
         return SkillInstallResponse(**result)
     except FileNotFoundError as e:
@@ -135,9 +126,9 @@ async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
 
 
 @router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
-async def list_custom_skills() -> SkillsListResponse:
+async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
+        skills = [skill for skill in get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False) if skill.category == SkillCategory.CUSTOM]
         return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
     except Exception as e:
         logger.error("Failed to list custom skills: %s", e, exc_info=True)
@@ -145,13 +136,14 @@ async def list_custom_skills() -> SkillsListResponse:
 
 
 @router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
-async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
+async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skill = next((s for s in skills if s.name == skill_name and s.category == SkillCategory.CUSTOM), None)
         if skill is None:
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
+        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=get_or_new_skill_storage(app_config=config).read_custom_skill(skill_name))
     except HTTPException:
         raise
     except Exception as e:
@@ -160,30 +152,31 @@ async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
 
 
 @router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
-async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
+async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
     try:
-        ensure_custom_skill_is_editable(skill_name)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        validate_skill_markdown_content(skill_name, request.content)
+        storage = get_or_new_skill_storage(app_config=config)
-        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
+        storage.ensure_custom_skill_is_editable(skill_name)
+        storage.validate_skill_markdown_content(skill_name, request.content)
+        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
         if scan.decision == "block":
             raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
-        skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
+        prev_content = storage.read_custom_skill(skill_name)
-        prev_content = skill_file.read_text(encoding="utf-8")
+        storage.write_custom_skill(skill_name, SKILL_MD_FILE, request.content)
-        atomic_write(skill_file, request.content)
+        storage.append_history(
-        append_history(
             skill_name,
             {
                 "action": "human_edit",
                 "author": "human",
                 "thread_id": None,
-                "file_path": "SKILL.md",
+                "file_path": SKILL_MD_FILE,
                 "prev_content": prev_content,
                 "new_content": request.content,
                 "scanner": {"decision": scan.decision, "reason": scan.reason},
             },
         )
         await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name)
+        return await get_custom_skill(skill_name, config)
     except HTTPException:
         raise
     except FileNotFoundError as e:
@@ -196,24 +189,22 @@ async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest
 
 
 @router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
-async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
+async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> dict[str, bool]:
     try:
-        ensure_custom_skill_is_editable(skill_name)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
-        skill_dir = get_custom_skill_dir(skill_name)
+        storage = get_or_new_skill_storage(app_config=config)
-        prev_content = read_custom_skill_content(skill_name)
+        storage.delete_custom_skill(
-        append_history(
             skill_name,
-            {
+            history_meta={
                 "action": "human_delete",
                 "author": "human",
                 "thread_id": None,
-                "file_path": "SKILL.md",
+                "file_path": SKILL_MD_FILE,
-                "prev_content": prev_content,
+                "prev_content": None,
                 "new_content": None,
                 "scanner": {"decision": "allow", "reason": "Deletion requested."},
             },
         )
-        shutil.rmtree(skill_dir)
         await refresh_skills_system_prompt_cache_async()
         return {"success": True}
     except FileNotFoundError as e:
@@ -226,11 +217,13 @@ async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
 
 
 @router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
-async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
+async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
     try:
-        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        storage = get_or_new_skill_storage(app_config=config)
+        if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillHistoryResponse(history=read_history(skill_name))
+        return CustomSkillHistoryResponse(history=storage.read_history(skill_name))
     except HTTPException:
         raise
     except Exception as e:
@@ -239,38 +232,39 @@ async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryRespons
 
 
 @router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
-async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
+async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
     try:
-        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
+        storage = get_or_new_skill_storage(app_config=config)
+        if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        history = read_history(skill_name)
+        history = storage.read_history(skill_name)
         if not history:
             raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
         record = history[request.history_index]
         target_content = record.get("prev_content")
         if target_content is None:
             raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
-        validate_skill_markdown_content(skill_name, target_content)
+        storage.validate_skill_markdown_content(skill_name, target_content)
-        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
+        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
-        skill_file = get_custom_skill_file(skill_name)
+        skill_file = storage.get_custom_skill_file(skill_name)
         current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
         history_entry = {
             "action": "rollback",
             "author": "human",
             "thread_id": None,
-            "file_path": "SKILL.md",
+            "file_path": SKILL_MD_FILE,
             "prev_content": current_content,
             "new_content": target_content,
             "rollback_from_ts": record.get("ts"),
             "scanner": {"decision": scan.decision, "reason": scan.reason},
         }
         if scan.decision == "block":
-            append_history(skill_name, history_entry)
+            storage.append_history(skill_name, history_entry)
             raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
-        atomic_write(skill_file, target_content)
+        storage.write_custom_skill(skill_name, SKILL_MD_FILE, target_content)
-        append_history(skill_name, history_entry)
+        storage.append_history(skill_name, history_entry)
         await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name)
+        return await get_custom_skill(skill_name, config)
     except HTTPException:
         raise
     except IndexError:
@@ -290,9 +284,10 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest)
     summary="Get Skill Details",
     description="Retrieve detailed information about a specific skill by its name.",
 )
-async def get_skill(skill_name: str) -> SkillResponse:
+async def get_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> SkillResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -312,9 +307,10 @@ async def get_skill(skill_name: str) -> SkillResponse:
     summary="Update Skill",
     description="Update a skill's enabled status by modifying the extensions_config.json file.",
 )
-async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
+async def update_skill(skill_name: str, request: SkillUpdateRequest, config: AppConfig = Depends(get_config)) -> SkillResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -340,7 +336,7 @@ async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillRes
         reload_extensions_config()
         await refresh_skills_system_prompt_cache_async()
 
-        skills = load_skills(enabled_only=False)
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
         updated_skill = next((s for s in skills if s.name == skill_name), None)
 
         if updated_skill is None:

backend/app/gateway/routers/suggestions.py

@@ -1,10 +1,13 @@
 import json
 import logging
 
-from fastapi import APIRouter
+from fastapi import APIRouter, Depends, Request
 from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import BaseModel, Field
 
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -98,12 +101,18 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
     summary="Generate Follow-up Questions",
     description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
 )
-async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> SuggestionsResponse:
+@require_permission("threads", "read", owner_check=True)
-    if not request.messages:
+async def generate_suggestions(
+    thread_id: str,
+    body: SuggestionsRequest,
+    request: Request,
+    config: AppConfig = Depends(get_config),
+) -> SuggestionsResponse:
+    if not body.messages:
         return SuggestionsResponse(suggestions=[])
 
-    n = request.n
+    n = body.n
-    conversation = _format_conversation(request.messages)
+    conversation = _format_conversation(body.messages)
     if not conversation:
         return SuggestionsResponse(suggestions=[])
 
@@ -120,8 +129,8 @@ async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> S
     user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
 
     try:
-        model = create_chat_model(name=request.model_name, thinking_enabled=False)
+        model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=config)
-        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
+        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []
         cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]

backend/app/gateway/routers/thread_runs.py

@@ -19,7 +19,8 @@ from fastapi import APIRouter, HTTPException, Query, Request
 from fastapi.responses import Response, StreamingResponse
 from pydantic import BaseModel, Field
 
-from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_checkpointer, get_current_user, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
 from app.gateway.services import sse_consumer, start_run
 from deerflow.runtime import RunRecord, serialize_channel_values
 
@@ -92,6 +93,7 @@ def _record_to_response(record: RunRecord) -> RunResponse:
 
 
 @router.post("/{thread_id}/runs", response_model=RunResponse)
+@require_permission("runs", "create", owner_check=True, require_existing=True)
 async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -> RunResponse:
     """Create a background run (returns immediately)."""
     record = await start_run(body, thread_id, request)
@@ -99,6 +101,7 @@ async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -
 
 
 @router.post("/{thread_id}/runs/stream")
+@require_permission("runs", "create", owner_check=True, require_existing=True)
 async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -> StreamingResponse:
     """Create a run and stream events via SSE.
 
@@ -126,6 +129,7 @@ async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -
 
 
 @router.post("/{thread_id}/runs/wait", response_model=dict)
+@require_permission("runs", "create", owner_check=True, require_existing=True)
 async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
     """Create a run and block until it completes, returning the final state."""
     record = await start_run(body, thread_id, request)
@@ -151,6 +155,7 @@ async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) ->
 
 
 @router.get("/{thread_id}/runs", response_model=list[RunResponse])
+@require_permission("runs", "read", owner_check=True)
 async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
     """List all runs for a thread."""
     run_mgr = get_run_manager(request)
@@ -159,6 +164,7 @@ async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
 
 
 @router.get("/{thread_id}/runs/{run_id}", response_model=RunResponse)
+@require_permission("runs", "read", owner_check=True)
 async def get_run(thread_id: str, run_id: str, request: Request) -> RunResponse:
     """Get details of a specific run."""
     run_mgr = get_run_manager(request)
@@ -169,6 +175,7 @@ async def get_run(thread_id: str, run_id: str, request: Request) -> RunResponse:
 
 
 @router.post("/{thread_id}/runs/{run_id}/cancel")
+@require_permission("runs", "cancel", owner_check=True, require_existing=True)
 async def cancel_run(
     thread_id: str,
     run_id: str,
@@ -206,6 +213,7 @@ async def cancel_run(
 
 
 @router.get("/{thread_id}/runs/{run_id}/join")
+@require_permission("runs", "read", owner_check=True)
 async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingResponse:
     """Join an existing run's SSE stream."""
     bridge = get_stream_bridge(request)
@@ -226,6 +234,7 @@ async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingRe
 
 
 @router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
+@require_permission("runs", "read", owner_check=True)
 async def stream_existing_run(
     thread_id: str,
     run_id: str,
@@ -265,3 +274,104 @@ async def stream_existing_run(
             "X-Accel-Buffering": "no",
         },
     )
+
+
+# ---------------------------------------------------------------------------
+# Messages / Events / Token usage endpoints
+# ---------------------------------------------------------------------------
+
+
+@router.get("/{thread_id}/messages")
+@require_permission("runs", "read", owner_check=True)
+async def list_thread_messages(
+    thread_id: str,
+    request: Request,
+    limit: int = Query(default=50, le=200),
+    before_seq: int | None = Query(default=None),
+    after_seq: int | None = Query(default=None),
+) -> list[dict]:
+    """Return displayable messages for a thread (across all runs), with feedback attached."""
+    event_store = get_run_event_store(request)
+    messages = await event_store.list_messages(thread_id, limit=limit, before_seq=before_seq, after_seq=after_seq)
+
+    # Attach feedback to the last AI message of each run
+    feedback_repo = get_feedback_repo(request)
+    user_id = await get_current_user(request)
+    feedback_map = await feedback_repo.list_by_thread_grouped(thread_id, user_id=user_id)
+
+    # Find the last ai_message per run_id
+    last_ai_per_run: dict[str, int] = {}  # run_id -> index in messages list
+    for i, msg in enumerate(messages):
+        if msg.get("event_type") == "ai_message":
+            last_ai_per_run[msg["run_id"]] = i
+
+    # Attach feedback field
+    last_ai_indices = set(last_ai_per_run.values())
+    for i, msg in enumerate(messages):
+        if i in last_ai_indices:
+            run_id = msg["run_id"]
+            fb = feedback_map.get(run_id)
+            msg["feedback"] = (
+                {
+                    "feedback_id": fb["feedback_id"],
+                    "rating": fb["rating"],
+                    "comment": fb.get("comment"),
+                }
+                if fb
+                else None
+            )
+        else:
+            msg["feedback"] = None
+
+    return messages
+
+
+@router.get("/{thread_id}/runs/{run_id}/messages")
+@require_permission("runs", "read", owner_check=True)
+async def list_run_messages(
+    thread_id: str,
+    run_id: str,
+    request: Request,
+    limit: int = Query(default=50, le=200, ge=1),
+    before_seq: int | None = Query(default=None),
+    after_seq: int | None = Query(default=None),
+) -> dict:
+    """Return paginated messages for a specific run.
+
+    Response: { data: [...], has_more: bool }
+    """
+    event_store = get_run_event_store(request)
+    rows = await event_store.list_messages_by_run(
+        thread_id,
+        run_id,
+        limit=limit + 1,
+        before_seq=before_seq,
+        after_seq=after_seq,
+    )
+    has_more = len(rows) > limit
+    data = rows[:limit] if has_more else rows
+    return {"data": data, "has_more": has_more}
+
+
+@router.get("/{thread_id}/runs/{run_id}/events")
+@require_permission("runs", "read", owner_check=True)
+async def list_run_events(
+    thread_id: str,
+    run_id: str,
+    request: Request,
+    event_types: str | None = Query(default=None),
+    limit: int = Query(default=500, le=2000),
+) -> list[dict]:
+    """Return the full event stream for a run (debug/audit)."""
+    event_store = get_run_event_store(request)
+    types = event_types.split(",") if event_types else None
+    return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)
+
+
+@router.get("/{thread_id}/token-usage")
+@require_permission("threads", "read", owner_check=True)
+async def thread_token_usage(thread_id: str, request: Request) -> dict:
+    """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}

backend/app/gateway/routers/threads.py

@@ -18,23 +18,35 @@ import uuid
 from typing import Any
 
 from fastapi import APIRouter, HTTPException, Request
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 
-from app.gateway.deps import get_checkpointer, get_store
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_checkpointer
+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
-# ---------------------------------------------------------------------------
-# Store namespace
-# ---------------------------------------------------------------------------
-
-THREADS_NS: tuple[str, ...] = ("threads",)
-"""Namespace used by the Store for thread metadata records."""
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api/threads", tags=["threads"])
 
 
+# Metadata keys that the server controls; clients are not allowed to set
+# them. Pydantic ``@field_validator("metadata")`` strips them on every
+# inbound model below so a malicious client cannot reflect a forged
+# owner identity through the API surface. Defense-in-depth — the
+# row-level invariant is still ``threads_meta.user_id`` populated from
+# the auth contextvar; this list closes the metadata-blob echo gap.
+_SERVER_RESERVED_METADATA_KEYS: frozenset[str] = frozenset({"owner_id", "user_id"})
+
+
+def _strip_reserved_metadata(metadata: dict[str, Any] | None) -> dict[str, Any]:
+    """Return ``metadata`` with server-controlled keys removed."""
+    if not metadata:
+        return metadata or {}
+    return {k: v for k, v in metadata.items() if k not in _SERVER_RESERVED_METADATA_KEYS}
+
+
 # ---------------------------------------------------------------------------
 # Response / request models
 # ---------------------------------------------------------------------------
@@ -63,8 +75,11 @@ class ThreadCreateRequest(BaseModel):
     """Request body for creating a thread."""
 
     thread_id: str | None = Field(default=None, description="Optional thread ID (auto-generated if omitted)")
+    assistant_id: str | None = Field(default=None, description="Associate thread with an assistant")
     metadata: dict[str, Any] = Field(default_factory=dict, description="Initial metadata")
 
+    _strip_reserved = field_validator("metadata")(classmethod(lambda cls, v: _strip_reserved_metadata(v)))
+
 
 class ThreadSearchRequest(BaseModel):
     """Request body for searching threads."""
@@ -93,6 +108,8 @@ class ThreadPatchRequest(BaseModel):
 
     metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata to merge")
 
+    _strip_reserved = field_validator("metadata")(classmethod(lambda cls, v: _strip_reserved_metadata(v)))
+
 
 class ThreadStateUpdateRequest(BaseModel):
     """Request body for updating thread state (human-in-the-loop resume)."""
@@ -126,70 +143,25 @@ class ThreadHistoryRequest(BaseModel):
 # ---------------------------------------------------------------------------
 
 
-def _delete_thread_data(thread_id: str, paths: Paths | None = None) -> ThreadDeleteResponse:
+def _delete_thread_data(thread_id: str, paths: Paths | None = None, *, user_id: str | None = None) -> ThreadDeleteResponse:
     """Delete local persisted filesystem data for a thread."""
     path_manager = paths or get_paths()
     try:
-        path_manager.delete_thread_dir(thread_id)
+        path_manager.delete_thread_dir(thread_id, user_id=user_id)
     except ValueError as exc:
         raise HTTPException(status_code=422, detail=str(exc)) from exc
     except FileNotFoundError:
         # Not critical — thread data may not exist on disk
-        logger.debug("No local thread data to delete for %s", thread_id)
+        logger.debug("No local thread data to delete for %s", sanitize_log_param(thread_id))
         return ThreadDeleteResponse(success=True, message=f"No local data for {thread_id}")
     except Exception as exc:
-        logger.exception("Failed to delete thread data for %s", thread_id)
+        logger.exception("Failed to delete thread data for %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to delete local thread data.") from exc
 
-    logger.info("Deleted local thread data for %s", thread_id)
+    logger.info("Deleted local thread data for %s", sanitize_log_param(thread_id))
     return ThreadDeleteResponse(success=True, message=f"Deleted local thread data for {thread_id}")
 
 
-async def _store_get(store, thread_id: str) -> dict | None:
-    """Fetch a thread record from the Store; returns ``None`` if absent."""
-    item = await store.aget(THREADS_NS, thread_id)
-    return item.value if item is not None else None
-
-
-async def _store_put(store, record: dict) -> None:
-    """Write a thread record to the Store."""
-    await store.aput(THREADS_NS, record["thread_id"], record)
-
-
-async def _store_upsert(store, thread_id: str, *, metadata: dict | None = None, values: dict | None = None) -> None:
-    """Create or refresh a thread record in the Store.
-
-    On creation the record is written with ``status="idle"``.  On update only
-    ``updated_at`` (and optionally ``metadata`` / ``values``) are changed so
-    that existing fields are preserved.
-
-    ``values`` carries the agent-state snapshot exposed to the frontend
-    (currently just ``{"title": "..."}``).
-    """
-    now = time.time()
-    existing = await _store_get(store, thread_id)
-    if existing is None:
-        await _store_put(
-            store,
-            {
-                "thread_id": thread_id,
-                "status": "idle",
-                "created_at": now,
-                "updated_at": now,
-                "metadata": metadata or {},
-                "values": values or {},
-            },
-        )
-    else:
-        val = dict(existing)
-        val["updated_at"] = now
-        if metadata:
-            val.setdefault("metadata", {}).update(metadata)
-        if values:
-            val.setdefault("values", {}).update(values)
-        await _store_put(store, val)
-
-
 def _derive_thread_status(checkpoint_tuple) -> str:
     """Derive thread status from checkpoint metadata."""
     if checkpoint_tuple is None:
@@ -215,22 +187,18 @@ def _derive_thread_status(checkpoint_tuple) -> str:
 
 
 @router.delete("/{thread_id}", response_model=ThreadDeleteResponse)
+@require_permission("threads", "delete", owner_check=True, require_existing=True)
 async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteResponse:
     """Delete local persisted filesystem data for a thread.
 
     Cleans DeerFlow-managed thread directories, removes checkpoint data,
-    and removes the thread record from the Store.
+    and removes the thread_meta row from the configured ThreadMetaStore
+    (sqlite or memory).
     """
-    # Clean local filesystem
+    from app.gateway.deps import get_thread_store
-    response = _delete_thread_data(thread_id)
 
-    # Remove from Store (best-effort)
+    # Clean local filesystem
-    store = get_store(request)
+    response = _delete_thread_data(thread_id, user_id=get_effective_user_id())
-    if store is not None:
-        try:
-            await store.adelete(THREADS_NS, thread_id)
-        except Exception:
-            logger.debug("Could not delete store record for thread %s (not critical)", thread_id)
 
     # Remove checkpoints (best-effort)
     checkpointer = getattr(request.app.state, "checkpointer", None)
@@ -239,7 +207,15 @@ async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteRe
             if hasattr(checkpointer, "adelete_thread"):
                 await checkpointer.adelete_thread(thread_id)
         except Exception:
-            logger.debug("Could not delete checkpoints for thread %s (not critical)", thread_id)
+            logger.debug("Could not delete checkpoints for thread %s (not critical)", sanitize_log_param(thread_id))
+
+    # Remove thread_meta row (best-effort) — required for sqlite backend
+    # so the deleted thread no longer appears in /threads/search.
+    try:
+        thread_store = get_thread_store(request)
+        await thread_store.delete(thread_id)
+    except Exception:
+        logger.debug("Could not delete thread_meta for %s (not critical)", sanitize_log_param(thread_id))
 
     return response
 
@@ -248,43 +224,40 @@ async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteRe
 async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadResponse:
     """Create a new thread.
 
-    The thread record is written to the Store (for fast listing) and an
+    Writes a thread_meta record (so the thread appears in /threads/search)
-    empty checkpoint is written to the checkpointer (for state reads).
+    and an empty checkpoint (so state endpoints work immediately).
     Idempotent: returns the existing record when ``thread_id`` already exists.
     """
-    store = get_store(request)
+    from app.gateway.deps import get_thread_store
+
     checkpointer = get_checkpointer(request)
+    thread_store = get_thread_store(request)
     thread_id = body.thread_id or str(uuid.uuid4())
     now = time.time()
+    # ``body.metadata`` is already stripped of server-reserved keys by
+    # ``ThreadCreateRequest._strip_reserved`` — see the model definition.
 
-    # Idempotency: return existing record from Store when already present
+    # Idempotency: return existing record when already present
-    if store is not None:
+    existing_record = await thread_store.get(thread_id)
-        existing_record = await _store_get(store, thread_id)
+    if existing_record is not None:
-        if existing_record is not None:
+        return ThreadResponse(
-            return ThreadResponse(
+            thread_id=thread_id,
-                thread_id=thread_id,
+            status=existing_record.get("status", "idle"),
-                status=existing_record.get("status", "idle"),
+            created_at=str(existing_record.get("created_at", "")),
-                created_at=str(existing_record.get("created_at", "")),
+            updated_at=str(existing_record.get("updated_at", "")),
-                updated_at=str(existing_record.get("updated_at", "")),
+            metadata=existing_record.get("metadata", {}),
-                metadata=existing_record.get("metadata", {}),
+        )
-            )
 
-    # Write thread record to Store
+    # Write thread_meta so the thread appears in /threads/search immediately
-    if store is not None:
+    try:
-        try:
+        await thread_store.create(
-            await _store_put(
+            thread_id,
-                store,
+            assistant_id=getattr(body, "assistant_id", None),
-                {
+            metadata=body.metadata,
-                    "thread_id": thread_id,
+        )
-                    "status": "idle",
+    except Exception:
-                    "created_at": now,
+        logger.exception("Failed to write thread_meta for %s", sanitize_log_param(thread_id))
-                    "updated_at": now,
+        raise HTTPException(status_code=500, detail="Failed to create thread")
-                    "metadata": body.metadata,
-                },
-            )
-        except Exception:
-            logger.exception("Failed to write thread %s to store", thread_id)
-            raise HTTPException(status_code=500, detail="Failed to create thread")
 
     # Write an empty checkpoint so state endpoints work immediately
     config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
@@ -301,10 +274,10 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
         }
         await checkpointer.aput(config, empty_checkpoint(), ckpt_metadata, {})
     except Exception:
-        logger.exception("Failed to create checkpoint for thread %s", thread_id)
+        logger.exception("Failed to create checkpoint for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to create thread")
 
-    logger.info("Thread created: %s", thread_id)
+    logger.info("Thread created: %s", sanitize_log_param(thread_id))
     return ThreadResponse(
         thread_id=thread_id,
         status="idle",
@@ -318,166 +291,91 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
 async def search_threads(body: ThreadSearchRequest, request: Request) -> list[ThreadResponse]:
     """Search and list threads.
 
-    Two-phase approach:
+    Delegates to the configured ThreadMetaStore implementation
-
+    (SQL-backed for sqlite/postgres, Store-backed for memory mode).
-    **Phase 1 — Store (fast path, O(threads))**: returns threads that were
-    created or run through this Gateway.  Store records are tiny metadata
-    dicts so fetching all of them at once is cheap.
-
-    **Phase 2 — Checkpointer supplement (lazy migration)**: threads that
-    were created directly by LangGraph Server (and therefore absent from the
-    Store) are discovered here by iterating the shared checkpointer.  Any
-    newly found thread is immediately written to the Store so that the next
-    search skips Phase 2 for that thread — the Store converges to a full
-    index over time without a one-shot migration job.
     """
-    store = get_store(request)
+    from app.gateway.deps import get_thread_store
-    checkpointer = get_checkpointer(request)
 
-    # -----------------------------------------------------------------------
+    repo = get_thread_store(request)
-    # Phase 1: Store
+    rows = await repo.search(
-    # -----------------------------------------------------------------------
+        metadata=body.metadata or None,
-    merged: dict[str, ThreadResponse] = {}
+        status=body.status,
-
+        limit=body.limit,
-    if store is not None:
+        offset=body.offset,
-        try:
+    )
-            items = await store.asearch(THREADS_NS, limit=10_000)
+    return [
-        except Exception:
+        ThreadResponse(
-            logger.warning("Store search failed — falling back to checkpointer only", exc_info=True)
+            thread_id=r["thread_id"],
-            items = []
+            status=r.get("status", "idle"),
-
+            created_at=r.get("created_at", ""),
-        for item in items:
+            updated_at=r.get("updated_at", ""),
-            val = item.value
+            metadata=r.get("metadata", {}),
-            merged[val["thread_id"]] = ThreadResponse(
+            values={"title": r["display_name"]} if r.get("display_name") else {},
-                thread_id=val["thread_id"],
+            interrupts={},
-                status=val.get("status", "idle"),
+        )
-                created_at=str(val.get("created_at", "")),
+        for r in rows
-                updated_at=str(val.get("updated_at", "")),
+    ]
-                metadata=val.get("metadata", {}),
-                values=val.get("values", {}),
-            )
-
-    # -----------------------------------------------------------------------
-    # Phase 2: Checkpointer supplement
-    # Discovers threads not yet in the Store (e.g. created by LangGraph
-    # Server) and lazily migrates them so future searches skip this phase.
-    # -----------------------------------------------------------------------
-    try:
-        async for checkpoint_tuple in checkpointer.alist(None):
-            cfg = getattr(checkpoint_tuple, "config", {})
-            thread_id = cfg.get("configurable", {}).get("thread_id")
-            if not thread_id or thread_id in merged:
-                continue
-
-            # Skip sub-graph checkpoints (checkpoint_ns is non-empty for those)
-            if cfg.get("configurable", {}).get("checkpoint_ns", ""):
-                continue
-
-            ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
-            # Strip LangGraph internal keys from the user-visible metadata dict
-            user_meta = {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")}
-
-            # Extract state values (title) from the checkpoint's channel_values
-            checkpoint_data = getattr(checkpoint_tuple, "checkpoint", {}) or {}
-            channel_values = checkpoint_data.get("channel_values", {})
-            ckpt_values = {}
-            if title := channel_values.get("title"):
-                ckpt_values["title"] = title
-
-            thread_resp = ThreadResponse(
-                thread_id=thread_id,
-                status=_derive_thread_status(checkpoint_tuple),
-                created_at=str(ckpt_meta.get("created_at", "")),
-                updated_at=str(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
-                metadata=user_meta,
-                values=ckpt_values,
-            )
-            merged[thread_id] = thread_resp
-
-            # Lazy migration — write to Store so the next search finds it there
-            if store is not None:
-                try:
-                    await _store_upsert(store, thread_id, metadata=user_meta, values=ckpt_values or None)
-                except Exception:
-                    logger.debug("Failed to migrate thread %s to store (non-fatal)", thread_id)
-    except Exception:
-        logger.exception("Checkpointer scan failed during thread search")
-        # Don't raise — return whatever was collected from Store + partial scan
-
-    # -----------------------------------------------------------------------
-    # Phase 3: Filter → sort → paginate
-    # -----------------------------------------------------------------------
-    results = list(merged.values())
-
-    if body.metadata:
-        results = [r for r in results if all(r.metadata.get(k) == v for k, v in body.metadata.items())]
-
-    if body.status:
-        results = [r for r in results if r.status == body.status]
-
-    results.sort(key=lambda r: r.updated_at, reverse=True)
-    return results[body.offset : body.offset + body.limit]
 
 
 @router.patch("/{thread_id}", response_model=ThreadResponse)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
 async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Request) -> ThreadResponse:
     """Merge metadata into a thread record."""
-    store = get_store(request)
+    from app.gateway.deps import get_thread_store
-    if store is None:
-        raise HTTPException(status_code=503, detail="Store not available")
 
-    record = await _store_get(store, thread_id)
+    thread_store = get_thread_store(request)
+    record = await thread_store.get(thread_id)
     if record is None:
         raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
 
-    now = time.time()
+    # ``body.metadata`` already stripped by ``ThreadPatchRequest._strip_reserved``.
-    updated = dict(record)
-    updated.setdefault("metadata", {}).update(body.metadata)
-    updated["updated_at"] = now
-
     try:
-        await _store_put(store, updated)
+        await thread_store.update_metadata(thread_id, body.metadata)
     except Exception:
-        logger.exception("Failed to patch thread %s", thread_id)
+        logger.exception("Failed to patch thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to update thread")
 
+    # Re-read to get the merged metadata + refreshed updated_at
+    record = await thread_store.get(thread_id) or record
     return ThreadResponse(
         thread_id=thread_id,
-        status=updated.get("status", "idle"),
+        status=record.get("status", "idle"),
-        created_at=str(updated.get("created_at", "")),
+        created_at=str(record.get("created_at", "")),
-        updated_at=str(now),
+        updated_at=str(record.get("updated_at", "")),
-        metadata=updated.get("metadata", {}),
+        metadata=record.get("metadata", {}),
     )
 
 
 @router.get("/{thread_id}", response_model=ThreadResponse)
+@require_permission("threads", "read", owner_check=True)
 async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
     """Get thread info.
 
-    Reads metadata from the Store and derives the accurate execution
+    Reads metadata from the ThreadMetaStore and derives the accurate
-    status from the checkpointer.  Falls back to the checkpointer alone
+    execution status from the checkpointer.  Falls back to the checkpointer
-    for threads that pre-date Store adoption (backward compat).
+    alone for threads that pre-date ThreadMetaStore adoption (backward compat).
     """
-    store = get_store(request)
+    from app.gateway.deps import get_thread_store
+
+    thread_store = get_thread_store(request)
     checkpointer = get_checkpointer(request)
 
-    record: dict | None = None
+    record: dict | None = await thread_store.get(thread_id)
-    if store is not None:
-        record = await _store_get(store, thread_id)
 
     # Derive accurate status from the checkpointer
     config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
     try:
         checkpoint_tuple = await checkpointer.aget_tuple(config)
     except Exception:
-        logger.exception("Failed to get checkpoint for thread %s", thread_id)
+        logger.exception("Failed to get checkpoint for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to get thread")
 
     if record is None and checkpoint_tuple is None:
         raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
 
-    # If the thread exists in the checkpointer but not the store (e.g. legacy
+    # If the thread exists in the checkpointer but not in thread_meta (e.g.
-    # data), synthesize a minimal store record from the checkpoint metadata.
+    # legacy data created before thread_meta adoption), synthesize a minimal
+    # record from the checkpoint metadata.
     if record is None and checkpoint_tuple is not None:
         ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
         record = {
@@ -505,7 +403,9 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
     )
 
 
+# ---------------------------------------------------------------------------
 @router.get("/{thread_id}/state", response_model=ThreadStateResponse)
+@require_permission("threads", "read", owner_check=True)
 async def get_thread_state(thread_id: str, request: Request) -> ThreadStateResponse:
     """Get the latest state snapshot for a thread.
 
@@ -518,7 +418,7 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
     try:
         checkpoint_tuple = await checkpointer.aget_tuple(config)
     except Exception:
-        logger.exception("Failed to get state for thread %s", thread_id)
+        logger.exception("Failed to get state for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to get thread state")
 
     if checkpoint_tuple is None:
@@ -542,8 +442,10 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
     next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
     tasks = [{"id": getattr(t, "id", ""), "name": getattr(t, "name", "")} for t in tasks_raw]
 
+    values = serialize_channel_values(channel_values)
+
     return ThreadStateResponse(
-        values=serialize_channel_values(channel_values),
+        values=values,
         next=next_tasks,
         metadata=metadata,
         checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
@@ -555,15 +457,19 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
 
 
 @router.post("/{thread_id}/state", response_model=ThreadStateResponse)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
 async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, request: Request) -> ThreadStateResponse:
     """Update thread state (e.g. for human-in-the-loop resume or title rename).
 
     Writes a new checkpoint that merges *body.values* into the latest
-    channel values, then syncs any updated ``title`` field back to the Store
+    channel values, then syncs any updated ``title`` field through the
-    so that ``/threads/search`` reflects the change immediately.
+    ThreadMetaStore abstraction so that ``/threads/search`` reflects the
+    change immediately in both sqlite and memory backends.
     """
+    from app.gateway.deps import get_thread_store
+
     checkpointer = get_checkpointer(request)
-    store = get_store(request)
+    thread_store = get_thread_store(request)
 
     # checkpoint_ns must be present in the config for aput — default to ""
     # (the root graph namespace).  checkpoint_id is optional; omitting it
@@ -580,7 +486,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
     try:
         checkpoint_tuple = await checkpointer.aget_tuple(read_config)
     except Exception:
-        logger.exception("Failed to get state for thread %s", thread_id)
+        logger.exception("Failed to get state for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to get thread state")
 
     if checkpoint_tuple is None:
@@ -614,19 +520,22 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
     try:
         new_config = await checkpointer.aput(write_config, checkpoint, metadata, {})
     except Exception:
-        logger.exception("Failed to update state for thread %s", thread_id)
+        logger.exception("Failed to update state for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to update thread state")
 
     new_checkpoint_id: str | None = None
     if isinstance(new_config, dict):
         new_checkpoint_id = new_config.get("configurable", {}).get("checkpoint_id")
 
-    # Sync title changes to the Store so /threads/search reflects them immediately.
+    # Sync title changes through the ThreadMetaStore abstraction so /threads/search
-    if store is not None and body.values and "title" in body.values:
+    # reflects them immediately in both sqlite and memory backends.
-        try:
+    if body.values and "title" in body.values:
-            await _store_upsert(store, thread_id, values={"title": body.values["title"]})
+        new_title = body.values["title"]
-        except Exception:
+        if new_title:  # Skip empty strings and None
-            logger.debug("Failed to sync title to store for thread %s (non-fatal)", thread_id)
+            try:
+                await thread_store.update_display_name(thread_id, new_title)
+            except Exception:
+                logger.debug("Failed to sync title to thread_meta for %s (non-fatal)", sanitize_log_param(thread_id))
 
     return ThreadStateResponse(
         values=serialize_channel_values(channel_values),
@@ -638,8 +547,16 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
 
 
 @router.post("/{thread_id}/history", response_model=list[HistoryEntry])
+@require_permission("threads", "read", owner_check=True)
 async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request: Request) -> list[HistoryEntry]:
-    """Get checkpoint history for a thread."""
+    """Get checkpoint history for a thread.
+
+    Messages are read from the checkpointer's channel values (the
+    authoritative source) and serialized via
+    :func:`~deerflow.runtime.serialization.serialize_channel_values`.
+    Only the latest (first) checkpoint carries the ``messages`` key to
+    avoid duplicating them across every entry.
+    """
     checkpointer = get_checkpointer(request)
 
     config: dict[str, Any] = {"configurable": {"thread_id": thread_id}}
@@ -647,6 +564,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
         config["configurable"]["checkpoint_id"] = body.before
 
     entries: list[HistoryEntry] = []
+    is_latest_checkpoint = True
     try:
         async for checkpoint_tuple in checkpointer.alist(config, limit=body.limit):
             ckpt_config = getattr(checkpoint_tuple, "config", {})
@@ -661,22 +579,42 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
 
             channel_values = checkpoint.get("channel_values", {})
 
+            # Build values from checkpoint channel_values
+            values: dict[str, Any] = {}
+            if title := channel_values.get("title"):
+                values["title"] = title
+            if thread_data := channel_values.get("thread_data"):
+                values["thread_data"] = thread_data
+
+            # Attach messages only to the latest checkpoint entry.
+            if is_latest_checkpoint:
+                messages = channel_values.get("messages")
+                if messages:
+                    values["messages"] = serialize_channel_values({"messages": messages}).get("messages", [])
+            is_latest_checkpoint = False
+
             # Derive next tasks
             tasks_raw = getattr(checkpoint_tuple, "tasks", []) or []
             next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
 
+            # Strip LangGraph internal keys from metadata
+            user_meta = {k: v for k, v in metadata.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")}
+            # Keep step for ordering context
+            if "step" in metadata:
+                user_meta["step"] = metadata["step"]
+
             entries.append(
                 HistoryEntry(
                     checkpoint_id=checkpoint_id,
                     parent_checkpoint_id=parent_id,
-                    metadata=metadata,
+                    metadata=user_meta,
-                    values=serialize_channel_values(channel_values),
+                    values=values,
                     created_at=str(metadata.get("created_at", "")),
                     next=next_tasks,
                 )
             )
     except Exception:
-        logger.exception("Failed to get history for thread %s", thread_id)
+        logger.exception("Failed to get history for thread %s", sanitize_log_param(thread_id))
         raise HTTPException(status_code=500, detail="Failed to get thread history")
 
     return entries

backend/app/gateway/routers/uploads.py

@@ -4,11 +4,15 @@ import logging
 import os
 import stat
 
-from fastapi import APIRouter, File, HTTPException, UploadFile
+from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
 from pydantic import BaseModel
 
+from app.gateway.authz import require_permission
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
-from deerflow.sandbox.sandbox_provider import get_sandbox_provider
+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,
     delete_file_safe,
@@ -26,6 +30,11 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
 
+UPLOAD_CHUNK_SIZE = 8192
+DEFAULT_MAX_FILES = 10
+DEFAULT_MAX_FILE_SIZE = 50 * 1024 * 1024
+DEFAULT_MAX_TOTAL_SIZE = 100 * 1024 * 1024
+
 
 class UploadResponse(BaseModel):
     """Response model for file upload."""
@@ -35,6 +44,14 @@ class UploadResponse(BaseModel):
     message: str
 
 
+class UploadLimits(BaseModel):
+    """Application-level upload limits exposed to clients."""
+
+    max_files: int
+    max_file_size: int
+    max_total_size: int
+
+
 def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
     """Ensure uploaded files remain writable when mounted into non-local sandboxes.
 
@@ -53,25 +70,124 @@ def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
     os.chmod(file_path, writable_mode, **chmod_kwargs)
 
 
+def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
+    return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
+
+
+def _get_uploads_config_value(app_config: AppConfig, key: str, default: object) -> object:
+    """Read a value from the uploads config, supporting dict and attribute access."""
+    uploads_cfg = getattr(app_config, "uploads", None)
+    if isinstance(uploads_cfg, dict):
+        return uploads_cfg.get(key, default)
+    return getattr(uploads_cfg, key, default)
+
+
+def _get_upload_limit(app_config: AppConfig, key: str, default: int, *, legacy_key: str | None = None) -> int:
+    try:
+        value = _get_uploads_config_value(app_config, key, None)
+        if value is None and legacy_key is not None:
+            value = _get_uploads_config_value(app_config, legacy_key, None)
+        if value is None:
+            value = default
+        limit = int(value)
+        if limit <= 0:
+            raise ValueError
+        return limit
+    except Exception:
+        logger.warning("Invalid uploads.%s value; falling back to %d", key, default)
+        return default
+
+
+def _get_upload_limits(app_config: AppConfig) -> UploadLimits:
+    return UploadLimits(
+        max_files=_get_upload_limit(app_config, "max_files", DEFAULT_MAX_FILES, legacy_key="max_file_count"),
+        max_file_size=_get_upload_limit(app_config, "max_file_size", DEFAULT_MAX_FILE_SIZE, legacy_key="max_single_file_size"),
+        max_total_size=_get_upload_limit(app_config, "max_total_size", DEFAULT_MAX_TOTAL_SIZE),
+    )
+
+
+def _cleanup_uploaded_paths(paths: list[os.PathLike[str] | str]) -> None:
+    for path in reversed(paths):
+        try:
+            os.unlink(path)
+        except FileNotFoundError:
+            pass
+        except Exception:
+            logger.warning("Failed to clean up upload path after rejected request: %s", path, exc_info=True)
+
+
+async def _write_upload_file_streaming(
+    file: UploadFile,
+    file_path: os.PathLike[str] | str,
+    *,
+    display_filename: str,
+    max_single_file_size: int,
+    max_total_size: int,
+    total_size: int,
+) -> tuple[int, int]:
+    file_size = 0
+    with open(file_path, "wb") as output:
+        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
+            file_size += len(chunk)
+            total_size += len(chunk)
+            if file_size > max_single_file_size:
+                raise HTTPException(status_code=413, detail=f"File too large: {display_filename}")
+            if total_size > max_total_size:
+                raise HTTPException(status_code=413, detail="Total upload size too large")
+            output.write(chunk)
+    return file_size, total_size
+
+
+def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
+    """Return whether automatic host-side document conversion is enabled.
+
+    The secure default is disabled unless an operator explicitly opts in via
+    uploads.auto_convert_documents in config.yaml.
+    """
+    try:
+        raw = _get_uploads_config_value(app_config, "auto_convert_documents", False)
+        if isinstance(raw, str):
+            return raw.strip().lower() in {"1", "true", "yes", "on"}
+        return bool(raw)
+    except Exception:
+        return False
+
+
 @router.post("", response_model=UploadResponse)
+@require_permission("threads", "write", owner_check=True, require_existing=False)
 async def upload_files(
     thread_id: str,
+    request: Request,
     files: list[UploadFile] = File(...),
+    config: AppConfig = Depends(get_config),
 ) -> UploadResponse:
     """Upload multiple files to a thread's uploads directory."""
     if not files:
         raise HTTPException(status_code=400, detail="No files provided")
 
+    limits = _get_upload_limits(config)
+    if len(files) > limits.max_files:
+        raise HTTPException(status_code=413, detail=f"Too many files: maximum is {limits.max_files}")
+
     try:
         uploads_dir = ensure_uploads_dir(thread_id)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
-    sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
+    sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
     uploaded_files = []
+    written_paths = []
+    sandbox_sync_targets = []
+    total_size = 0
 
     sandbox_provider = get_sandbox_provider()
-    sandbox_id = sandbox_provider.acquire(thread_id)
+    sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
-    sandbox = sandbox_provider.get(sandbox_id)
+    sandbox = None
+    if sync_to_sandbox:
+        sandbox_id = sandbox_provider.acquire(thread_id)
+        sandbox = sandbox_provider.get(sandbox_id)
+        if sandbox is None:
+            raise HTTPException(status_code=500, detail="Failed to acquire sandbox")
+    auto_convert_documents = _auto_convert_documents_enabled(config)
 
     for file in files:
         if not file.filename:
@@ -84,35 +200,41 @@ async def upload_files(
             continue
 
         try:
-            content = await file.read()
             file_path = uploads_dir / safe_filename
-            file_path.write_bytes(content)
+            written_paths.append(file_path)
+            file_size, total_size = await _write_upload_file_streaming(
+                file,
+                file_path,
+                display_filename=safe_filename,
+                max_single_file_size=limits.max_file_size,
+                max_total_size=limits.max_total_size,
+                total_size=total_size,
+            )
 
             virtual_path = upload_virtual_path(safe_filename)
 
-            if sandbox_id != "local":
+            if sync_to_sandbox:
-                _make_file_sandbox_writable(file_path)
+                sandbox_sync_targets.append((file_path, virtual_path))
-                sandbox.update_file(virtual_path, content)
 
             file_info = {
                 "filename": safe_filename,
-                "size": str(len(content)),
+                "size": str(file_size),
                 "path": str(sandbox_uploads / safe_filename),
                 "virtual_path": virtual_path,
                 "artifact_url": upload_artifact_url(thread_id, safe_filename),
             }
 
-            logger.info(f"Saved file: {safe_filename} ({len(content)} bytes) to {file_info['path']}")
+            logger.info(f"Saved file: {safe_filename} ({file_size} bytes) to {file_info['path']}")
 
             file_ext = file_path.suffix.lower()
-            if file_ext in CONVERTIBLE_EXTENSIONS:
+            if auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
                 md_path = await convert_file_to_markdown(file_path)
                 if md_path:
+                    written_paths.append(md_path)
                     md_virtual_path = upload_virtual_path(md_path.name)
 
-                    if sandbox_id != "local":
+                    if sync_to_sandbox:
-                        _make_file_sandbox_writable(md_path)
+                        sandbox_sync_targets.append((md_path, md_virtual_path))
-                        sandbox.update_file(md_virtual_path, md_path.read_bytes())
 
                     file_info["markdown_file"] = md_path.name
                     file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
@@ -121,10 +243,19 @@ async def upload_files(
 
             uploaded_files.append(file_info)
 
+        except HTTPException as e:
+            _cleanup_uploaded_paths(written_paths)
+            raise e
         except Exception as e:
             logger.error(f"Failed to upload {file.filename}: {e}")
+            _cleanup_uploaded_paths(written_paths)
             raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
 
+    if sync_to_sandbox:
+        for file_path, virtual_path in sandbox_sync_targets:
+            _make_file_sandbox_writable(file_path)
+            sandbox.update_file(virtual_path, file_path.read_bytes())
+
     return UploadResponse(
         success=True,
         files=uploaded_files,
@@ -132,8 +263,20 @@ async def upload_files(
     )
 
 
+@router.get("/limits", response_model=UploadLimits)
+@require_permission("threads", "read", owner_check=True)
+async def get_upload_limits(
+    thread_id: str,
+    request: Request,
+    config: AppConfig = Depends(get_config),
+) -> UploadLimits:
+    """Return upload limits used by the gateway for this thread."""
+    return _get_upload_limits(config)
+
+
 @router.get("/list", response_model=dict)
-async def list_uploaded_files(thread_id: str) -> dict:
+@require_permission("threads", "read", owner_check=True)
+async def list_uploaded_files(thread_id: str, request: Request) -> dict:
     """List all files in a thread's uploads directory."""
     try:
         uploads_dir = get_uploads_dir(thread_id)
@@ -143,7 +286,7 @@ async def list_uploaded_files(thread_id: str) -> dict:
     enrich_file_listing(result, thread_id)
 
     # Gateway additionally includes the sandbox-relative path.
-    sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
+    sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
     for f in result["files"]:
         f["path"] = str(sandbox_uploads / f["filename"])
 
@@ -151,7 +294,8 @@ async def list_uploaded_files(thread_id: str) -> dict:
 
 
 @router.delete("/{filename}")
-async def delete_uploaded_file(thread_id: str, filename: str) -> dict:
+@require_permission("threads", "delete", owner_check=True, require_existing=True)
+async def delete_uploaded_file(thread_id: str, filename: str, request: Request) -> dict:
     """Delete a file from a thread's uploads directory."""
     try:
         uploads_dir = get_uploads_dir(thread_id)

backend/app/gateway/services.py

@@ -11,13 +11,14 @@ import asyncio
 import json
 import logging
 import re
-import time
+from collections.abc import Mapping
 from typing import Any
 
 from fastapi import HTTPException, Request
 from langchain_core.messages import HumanMessage
 
-from app.gateway.deps import get_checkpointer, get_run_manager, get_store, get_stream_bridge
+from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
+from app.gateway.utils import sanitize_log_param
 from deerflow.runtime import (
     END_SENTINEL,
     HEARTBEAT_SENTINEL,
@@ -97,13 +98,52 @@ def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
 _DEFAULT_ASSISTANT_ID = "lead_agent"
 
 
+# Whitelist of run-context keys that the langgraph-compat layer forwards from
+# ``body.context`` into the run config. ``config["context"]`` exists in
+# LangGraph >=0.6, but these values must be written to both ``configurable``
+# (for legacy ``_get_runtime_config`` consumers) and ``context`` because
+# LangGraph >=1.1.9 no longer makes ``ToolRuntime.context`` fall back to
+# ``configurable`` for consumers like ``setup_agent``.
+_CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
+    {
+        "model_name",
+        "mode",
+        "thinking_enabled",
+        "reasoning_effort",
+        "is_plan_mode",
+        "subagent_enabled",
+        "max_concurrent_subagents",
+        "agent_name",
+        "is_bootstrap",
+    }
+)
+
+
+def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, Any] | None) -> None:
+    """Merge whitelisted keys from ``body.context`` into both ``config['configurable']``
+    and ``config['context']`` so they are visible to legacy configurable readers and
+    to LangGraph ``ToolRuntime.context`` consumers (e.g. the ``setup_agent`` tool —
+    see issue #2677)."""
+    if not context:
+        return
+    configurable = config.setdefault("configurable", {})
+    runtime_context = config.setdefault("context", {})
+    for key in _CONTEXT_CONFIGURABLE_KEYS:
+        if key in context:
+            if isinstance(configurable, dict):
+                configurable.setdefault(key, context[key])
+            if isinstance(runtime_context, dict):
+                runtime_context.setdefault(key, context[key])
+
+
 def resolve_agent_factory(assistant_id: str | None):
     """Resolve the agent factory callable from config.
 
     Custom agents are implemented as ``lead_agent`` + an ``agent_name``
-    injected into ``configurable`` — see :func:`build_run_config`.  All
+    injected into ``configurable`` or ``context`` — see
-    ``assistant_id`` values therefore map to the same factory; the routing
+    :func:`build_run_config`.  All ``assistant_id`` values therefore map to the
-    happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
+    same factory; the routing happens inside ``make_lead_agent`` when it reads
+    ``cfg["agent_name"]``.
     """
     from deerflow.agents.lead_agent.agent import make_lead_agent
 
@@ -120,10 +160,12 @@ def build_run_config(
     """Build a RunnableConfig dict for the agent.
 
     When *assistant_id* refers to a custom agent (anything other than
-    ``"lead_agent"`` / ``None``), the name is forwarded as
+    ``"lead_agent"`` / ``None``), the name is forwarded as ``agent_name`` in
-    ``configurable["agent_name"]``.  ``make_lead_agent`` reads this key to
+    whichever runtime options container is active: ``context`` for
-    load the matching ``agents/<name>/SOUL.md`` and per-agent config —
+    LangGraph >= 0.6.0 requests, otherwise ``configurable``.
-    without it the agent silently runs as the default lead agent.
+    ``make_lead_agent`` reads this key to load the matching
+    ``agents/<name>/SOUL.md`` and per-agent config — without it the agent
+    silently runs as the default lead agent.
 
     This mirrors the channel manager's ``_resolve_run_params`` logic so that
     the LangGraph Platform-compatible HTTP API and the IM channel path behave
@@ -142,7 +184,14 @@ def build_run_config(
                     thread_id,
                     list(request_config.get("configurable", {}).keys()),
                 )
-            config["context"] = request_config["context"]
+            context_value = request_config["context"]
+            if context_value is None:
+                context = {}
+            elif isinstance(context_value, Mapping):
+                context = dict(context_value)
+            else:
+                raise ValueError("request config 'context' must be a mapping or null.")
+            config["context"] = context
         else:
             configurable = {"thread_id": thread_id}
             configurable.update(request_config.get("configurable", {}))
@@ -154,13 +203,19 @@ def build_run_config(
         config["configurable"] = {"thread_id": thread_id}
 
     # Inject custom agent name when the caller specified a non-default assistant.
-    # Honour an explicit configurable["agent_name"] in the request if already set.
+    # Honour an explicit agent_name in the active runtime options container.
-    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
+    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID:
-        if "agent_name" not in config["configurable"]:
+        normalized = assistant_id.strip().lower().replace("_", "-")
-            normalized = assistant_id.strip().lower().replace("_", "-")
+        if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
-            if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
+            raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
-                raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
+        if "configurable" in config:
-            config["configurable"]["agent_name"] = normalized
+            target = config["configurable"]
+        elif "context" in config:
+            target = config["context"]
+        else:
+            target = config.setdefault("configurable", {})
+        if target is not None and "agent_name" not in target:
+            target["agent_name"] = normalized
     if metadata:
         config.setdefault("metadata", {}).update(metadata)
     return config
@@ -171,71 +226,6 @@ def build_run_config(
 # ---------------------------------------------------------------------------
 
 
-async def _upsert_thread_in_store(store, thread_id: str, metadata: dict | None) -> None:
-    """Create or refresh the thread record in the Store.
-
-    Called from :func:`start_run` so that threads created via the stateless
-    ``/runs/stream`` endpoint (which never calls ``POST /threads``) still
-    appear in ``/threads/search`` results.
-    """
-    # Deferred import to avoid circular import with the threads router module.
-    from app.gateway.routers.threads import _store_upsert
-
-    try:
-        await _store_upsert(store, thread_id, metadata=metadata)
-    except Exception:
-        logger.warning("Failed to upsert thread %s in store (non-fatal)", thread_id)
-
-
-async def _sync_thread_title_after_run(
-    run_task: asyncio.Task,
-    thread_id: str,
-    checkpointer: Any,
-    store: Any,
-) -> None:
-    """Wait for *run_task* to finish, then persist the generated title to the Store.
-
-    TitleMiddleware writes the generated title to the LangGraph agent state
-    (checkpointer) but the Gateway's Store record is not updated automatically.
-    This coroutine closes that gap by reading the final checkpoint after the
-    run completes and syncing ``values.title`` into the Store record so that
-    subsequent ``/threads/search`` responses include the correct title.
-
-    Runs as a fire-and-forget :func:`asyncio.create_task`; failures are
-    logged at DEBUG level and never propagate.
-    """
-    # Wait for the background run task to complete (any outcome).
-    # asyncio.wait does not propagate task exceptions — it just returns
-    # when the task is done, cancelled, or failed.
-    await asyncio.wait({run_task})
-
-    # Deferred import to avoid circular import with the threads router module.
-    from app.gateway.routers.threads import _store_get, _store_put
-
-    try:
-        ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
-        ckpt_tuple = await checkpointer.aget_tuple(ckpt_config)
-        if ckpt_tuple is None:
-            return
-
-        channel_values = ckpt_tuple.checkpoint.get("channel_values", {})
-        title = channel_values.get("title")
-        if not title:
-            return
-
-        existing = await _store_get(store, thread_id)
-        if existing is None:
-            return
-
-        updated = dict(existing)
-        updated.setdefault("values", {})["title"] = title
-        updated["updated_at"] = time.time()
-        await _store_put(store, updated)
-        logger.debug("Synced title %r for thread %s", title, thread_id)
-    except Exception:
-        logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id, exc_info=True)
-
-
 async def start_run(
     body: Any,
     thread_id: str,
@@ -255,8 +245,7 @@ async def start_run(
     """
     bridge = get_stream_bridge(request)
     run_mgr = get_run_manager(request)
-    checkpointer = get_checkpointer(request)
+    run_ctx = get_run_context(request)
-    store = get_store(request)
 
     disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
 
@@ -274,35 +263,31 @@ async def start_run(
     except UnsupportedStrategyError as exc:
         raise HTTPException(status_code=501, detail=str(exc)) from exc
 
-    # Ensure the thread is visible in /threads/search, even for threads that
+    # Upsert thread metadata so the thread appears in /threads/search,
-    # were never explicitly created via POST /threads (e.g. stateless runs).
+    # even for threads that were never explicitly created via POST /threads
-    store = get_store(request)
+    # (e.g. stateless runs).
-    if store is not None:
+    try:
-        await _upsert_thread_in_store(store, thread_id, body.metadata)
+        existing = await run_ctx.thread_store.get(thread_id)
+        if existing is None:
+            await run_ctx.thread_store.create(
+                thread_id,
+                assistant_id=body.assistant_id,
+                metadata=body.metadata,
+            )
+        else:
+            await run_ctx.thread_store.update_status(thread_id, "running")
+    except Exception:
+        logger.warning("Failed to upsert thread_meta for %s (non-fatal)", sanitize_log_param(thread_id))
 
     agent_factory = resolve_agent_factory(body.assistant_id)
     graph_input = normalize_input(body.input)
     config = build_run_config(thread_id, body.config, body.metadata, assistant_id=body.assistant_id)
 
-    # Merge DeerFlow-specific context overrides into configurable.
+    # Merge DeerFlow-specific context overrides into both ``configurable`` and ``context``.
     # The ``context`` field is a custom extension for the langgraph-compat layer
     # that carries agent configuration (model_name, thinking_enabled, etc.).
     # Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
-    context = getattr(body, "context", None)
+    merge_run_context_overrides(config, getattr(body, "context", None))
-    if context:
-        _CONTEXT_CONFIGURABLE_KEYS = {
-            "model_name",
-            "mode",
-            "thinking_enabled",
-            "reasoning_effort",
-            "is_plan_mode",
-            "subagent_enabled",
-            "max_concurrent_subagents",
-        }
-        configurable = config.setdefault("configurable", {})
-        for key in _CONTEXT_CONFIGURABLE_KEYS:
-            if key in context:
-                configurable.setdefault(key, context[key])
 
     stream_modes = normalize_stream_modes(body.stream_mode)
 
@@ -311,8 +296,7 @@ async def start_run(
             bridge,
             run_mgr,
             record,
-            checkpointer=checkpointer,
+            ctx=run_ctx,
-            store=store,
             agent_factory=agent_factory,
             graph_input=graph_input,
             config=config,
@@ -324,11 +308,9 @@ async def start_run(
     )
     record.task = task
 
-    # After the run completes, sync the title generated by TitleMiddleware from
+    # Title sync is handled by worker.py's finally block which reads the
-    # the checkpointer into the Store record so that /threads/search returns the
+    # title from the checkpoint and calls thread_store.update_display_name
-    # correct title instead of an empty values dict.
+    # after the run completes.
-    if store is not None:
-        asyncio.create_task(_sync_thread_title_after_run(task, thread_id, checkpointer, store))
 
     return record
 

backend/app/gateway/utils.py

@@ -0,0 +1,6 @@
+"""Shared utility helpers for the Gateway layer."""
+
+
+def sanitize_log_param(value: str) -> str:
+    """Strip control characters to prevent log injection."""
+    return value.replace("\n", "").replace("\r", "").replace("\x00", "")

backend/debug.py

@@ -19,24 +19,70 @@ import asyncio
 import logging
 
 from dotenv import load_dotenv
-from langchain_core.messages import HumanMessage
 
-from deerflow.agents import make_lead_agent
+try:
+    from prompt_toolkit import PromptSession
+    from prompt_toolkit.history import InMemoryHistory
+
+    _HAS_PROMPT_TOOLKIT = True
+except ImportError:
+    _HAS_PROMPT_TOOLKIT = False
 
 load_dotenv()
 
-logging.basicConfig(
+_LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    level=logging.INFO,
+_LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+
-    datefmt="%Y-%m-%d %H:%M:%S",
+
-)
+def _setup_logging(log_level: int = logging.INFO) -> None:
+    """Route logs to ``debug.log`` using *log_level* for the initial root/file setup.
+
+    This configures the root logger and the ``debug.log`` file handler so logs do
+    not print on the interactive console. It is idempotent: any pre-existing
+    handlers on the root logger (e.g. installed by ``logging.basicConfig`` in
+    transitively imported modules) are removed so the debug session output only
+    lands in ``debug.log``.
+
+    Note: later config-driven logging adjustments may change named logger
+    verbosity without raising the root logger or file-handler thresholds set
+    here, so the eventual contents of ``debug.log`` may not be filtered solely by
+    this function's ``log_level`` argument.
+    """
+    root = logging.root
+    for h in list(root.handlers):
+        root.removeHandler(h)
+        h.close()
+    root.setLevel(log_level)
+
+    file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
+    file_handler.setLevel(log_level)
+    file_handler.setFormatter(logging.Formatter(_LOG_FMT, datefmt=_LOG_DATEFMT))
+    root.addHandler(file_handler)
 
 
 async def main():
+    # Install file logging first so warnings emitted while loading config do not
+    # leak onto the interactive terminal via Python's lastResort handler.
+    _setup_logging()
+
+    from deerflow.config import get_app_config
+    from deerflow.config.app_config import apply_logging_level
+
+    app_config = get_app_config()
+    apply_logging_level(app_config.log_level)
+
+    # Delay the rest of the deerflow imports until *after* logging is installed
+    # so that any import-time side effects (e.g. deerflow.agents starts a
+    # background skill-loader thread on import) emit logs to debug.log instead
+    # of leaking onto the interactive terminal via Python's lastResort handler.
+    from langchain_core.messages import HumanMessage
+    from langgraph.runtime import Runtime
+
+    from deerflow.agents import make_lead_agent
+    from deerflow.mcp import initialize_mcp_tools
+
     # Initialize MCP tools at startup
     try:
-        from deerflow.mcp import initialize_mcp_tools
-
         await initialize_mcp_tools()
     except Exception as e:
         print(f"Warning: Failed to initialize MCP tools: {e}")
@@ -52,16 +98,27 @@ async def main():
         }
     }
 
+    runtime = Runtime(context={"thread_id": config["configurable"]["thread_id"]})
+    config["configurable"]["__pregel_runtime"] = runtime
+
     agent = make_lead_agent(config)
 
+    session = PromptSession(history=InMemoryHistory()) if _HAS_PROMPT_TOOLKIT else None
+
     print("=" * 50)
     print("Lead Agent Debug Mode")
     print("Type 'quit' or 'exit' to stop")
+    print(f"Logs: debug.log (log_level={app_config.log_level})")
+    if not _HAS_PROMPT_TOOLKIT:
+        print("Tip: `uv sync --group dev` to enable arrow-key & history support")
     print("=" * 50)
 
     while True:
         try:
-            user_input = input("\nYou: ").strip()
+            if session:
+                user_input = (await session.prompt_async("\nYou: ")).strip()
+            else:
+                user_input = input("\nYou: ").strip()
             if not user_input:
                 continue
             if user_input.lower() in ("quit", "exit"):
@@ -70,15 +127,15 @@ async def main():
 
             # Invoke the agent
             state = {"messages": [HumanMessage(content=user_input)]}
-            result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
+            result = await agent.ainvoke(state, config=config)
 
             # Print the response
             if result.get("messages"):
                 last_message = result["messages"][-1]
                 print(f"\nAgent: {last_message.content}")
 
-        except KeyboardInterrupt:
+        except (KeyboardInterrupt, EOFError):
-            print("\nInterrupted. Goodbye!")
+            print("\nGoodbye!")
             break
         except Exception as e:
             print(f"\nError: {e}")

backend/docs/ARCHITECTURE.md

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

backend/docs/AUTH_TEST_DOCKER_GAP.md

@@ -0,0 +1,77 @@
+# Docker Test Gap (Section 七 7.4)
+
+This file documents the only **un-executed** test cases from
+`backend/docs/AUTH_TEST_PLAN.md` after the full release validation pass.
+
+## Why this gap exists
+
+The release validation environment (sg_dev: `10.251.229.92`) **does not have
+a Docker daemon installed**. The TC-DOCKER cases are container-runtime
+behavior tests that need an actual Docker engine to spin up
+`docker/docker-compose.yaml` services.
+
+```bash
+$ ssh sg_dev "which docker; docker --version"
+# (empty)
+# bash: docker: command not found
+```
+
+All other test plan sections were executed against either:
+- The local dev box (Mac, all services running locally), or
+- The deployed sg_dev instance (gateway + frontend + nginx via SSH tunnel)
+
+## Cases not executed
+
+| 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-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-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
+
+The **auth-relevant** behavior in each Docker case is already exercised by
+the test cases that ran on sg_dev or local:
+
+| Docker case | Auth behavior covered by |
+|---|---|
+| 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-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
+
+Anyone with `docker` + `docker compose` installed can reproduce the gap by
+running the test plan section verbatim. Pre-flight:
+
+```bash
+# Required on the host
+docker --version           # >=24.x
+docker compose version     # plugin >=2.x
+
+# Required env var (otherwise sessions reset on every container restart)
+echo "AUTH_JWT_SECRET=$(python3 -c 'import secrets; print(secrets.token_urlsafe(32))')" \
+  >> .env
+
+# Optional: pin DEER_FLOW_HOME to a stable host path
+echo "DEER_FLOW_HOME=$HOME/deer-flow-data" >> .env
+```
+
+Then run TC-DOCKER-01..06 from the test plan as written.
+
+## Decision log
+
+- **Not blocking the release.** The auth-relevant behavior in every Docker
+  case has an already-validated equivalent on bare metal. The gap is purely
+  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 old "grep 'Password:' in docker logs" expectation would have failed
+  silently and given a false sense of coverage.

backend/docs/AUTH_UPGRADE.md

@@ -0,0 +1,129 @@
+# Authentication Upgrade Guide
+
+DeerFlow 内置了认证模块。本文档面向从无认证版本升级的用户。
+
+## 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
+- 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
+
+## 升级步骤
+
+### 1. 更新代码
+
+```bash
+git pull origin main
+cd backend && make install
+```
+
+### 2. 首次启动
+
+```bash
+make dev
+```
+
+控制台会输出：
+
+```
+============================================================
+  Admin account created on first boot
+  Email:    admin@deerflow.dev
+  Password: aB3xK9mN_pQ7rT2w
+  Change it after login: Settings → Account
+============================================================
+```
+
+如果未登录就重启了服务，不用担心——只要 setup 未完成，每次启动都会重置密码并重新打印到控制台。
+
+### 3. 登录
+
+访问 `http://localhost:2026/login`，使用控制台输出的邮箱和密码登录。
+
+### 4. 修改密码
+
+登录后进入 Settings → Account → Change Password。
+
+### 5. 添加用户（可选）
+
+其他用户通过 `/login` 页面注册，自动获得 **user** 角色。每个用户只能看到自己的对话。
+
+## 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` |
+| 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 |
+| HTTPS 自适应 | 检测 `x-forwarded-proto`，自动设置 `Secure` cookie 标志 |
+
+## 常见操作
+
+### 忘记密码
+
+```bash
+cd backend
+
+# 重置 admin 密码
+python -m app.gateway.auth.reset_admin
+
+# 重置指定用户密码
+python -m app.gateway.auth.reset_admin --email user@example.com
+```
+
+会输出新的随机密码。
+
+### 完全重置
+
+删除用户数据库，重启后自动创建新 admin：
+
+```bash
+rm -f backend/.deer-flow/users.db
+# 重启服务，控制台输出新密码
+```
+
+## 数据存储
+
+| 文件 | 内容 |
+|------|------|
+| `.deer-flow/users.db` | SQLite 用户数据库（密码哈希、角色） |
+| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥（未设置时自动生成临时密钥，重启后 session 失效） |
+
+### 生产环境建议
+
+```bash
+# 生成持久化 JWT 密钥，避免重启后所有用户需重新登录
+python -c "import secrets; print(secrets.token_urlsafe(32))"
+# 将输出添加到 .env：
+# AUTH_JWT_SECRET=<生成的密钥>
+```
+
+## API 端点
+
+| 端点 | 方法 | 说明 |
+|------|------|------|
+| `/api/v1/auth/login/local` | POST | 邮箱密码登录（OAuth2 form） |
+| `/api/v1/auth/register` | POST | 注册新用户（user 角色） |
+| `/api/v1/auth/logout` | POST | 登出（清除 cookie） |
+| `/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 |
+
+## 兼容性
+
+- **标准模式**（`make dev`）：完全兼容，admin 自动创建
+- **Gateway 模式**（`make dev-pro`）：完全兼容
+- **Docker 部署**：完全兼容，`.deer-flow/users.db` 需持久化卷挂载
+- **IM 渠道**（Feishu/Slack/Telegram）：通过 LangGraph SDK 通信，不经过认证层
+- **DeerFlowClient**（嵌入式）：不经过 HTTP，不受认证影响
+
+## 故障排查
+
+| 症状 | 原因 | 解决 |
+|------|------|------|
+| 启动后没看到密码 | admin 已存在（非首次启动） | 用 `reset_admin` 重置，或删 `users.db` |
+| 登录后 POST 返回 403 | CSRF token 缺失 | 确认前端已更新 |
+| 重启后需要重新登录 | `AUTH_JWT_SECRET` 未持久化 | 在 `.env` 中设置固定密钥 |

backend/docs/CONFIGURATION.md

@@ -259,6 +259,8 @@ sandbox:
 
 When you configure `sandbox.mounts`, DeerFlow exposes those `container_path` values in the agent prompt so the agent can discover and operate on mounted directories directly instead of assuming everything must live under `/mnt/user-data`.
 
+For bare-metal Docker sandbox runs that use localhost, DeerFlow binds the sandbox HTTP port to `127.0.0.1` by default so it is not exposed on every host interface. Docker-outside-of-Docker deployments that connect through `host.docker.internal` keep the broad legacy bind for compatibility. Set `DEER_FLOW_SANDBOX_BIND_HOST` explicitly if your deployment needs a different bind address.
+
 ### Skills
 
 Configure the skills directory for specialized workflows:
@@ -319,11 +321,16 @@ models:
 - `DEEPSEEK_API_KEY` - DeepSeek API key
 - `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
 - `TAVILY_API_KEY` - Tavily search API key
+- `DEER_FLOW_PROJECT_ROOT` - Project root for relative runtime paths
 - `DEER_FLOW_CONFIG_PATH` - Custom config file path
+- `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Custom extensions config file path
+- `DEER_FLOW_HOME` - Runtime state directory (defaults to `.deer-flow` under the project root)
+- `DEER_FLOW_SKILLS_PATH` - Skills directory when `skills.path` is omitted
+- `GATEWAY_ENABLE_DOCS` - Set to `false` to disable Swagger UI (`/docs`), ReDoc (`/redoc`), and OpenAPI schema (`/openapi.json`) endpoints (default: `true`)
 
 ## Configuration Location
 
-The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
+The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`). Set `DEER_FLOW_PROJECT_ROOT` when the process may start from another working directory, or set `DEER_FLOW_CONFIG_PATH` to point at a specific file.
 
 ## Configuration Priority
 
@@ -331,12 +338,12 @@ DeerFlow searches for configuration in this order:
 
 1. Path specified in code via `config_path` argument
 2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
-3. `config.yaml` in current working directory (typically `backend/` when running)
+3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or under the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
-4. `config.yaml` in parent directory (project root: `deer-flow/`)
+4. Legacy backend/repository-root locations for monorepo compatibility
 
 ## Best Practices
 
-1. **Place `config.yaml` in project root** - Not in `backend/` directory
+1. **Place `config.yaml` in project root** - Set `DEER_FLOW_PROJECT_ROOT` if the runtime starts elsewhere
 2. **Never commit `config.yaml`** - It's already in `.gitignore`
 3. **Use environment variables for secrets** - Don't hardcode API keys
 4. **Keep `config.example.yaml` updated** - Document all new options
@@ -347,7 +354,7 @@ DeerFlow searches for configuration in this order:
 
 ### "Config file not found"
 - Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
-- The backend searches parent directory by default, so root location is preferred
+- If the runtime starts outside the project root, set `DEER_FLOW_PROJECT_ROOT`
 - Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
 
 ### "Invalid API key"
@@ -357,7 +364,7 @@ DeerFlow searches for configuration in this order:
 ### "Skills not loading"
 - Check that `deer-flow/skills/` directory exists
 - Verify skills have valid `SKILL.md` files
-- Check `skills.path` configuration if using custom path
+- Check `skills.path` or `DEER_FLOW_SKILLS_PATH` if using a custom path
 
 ### "Docker sandbox fails to start"
 - Ensure Docker is running

backend/docs/FILE_UPLOAD.md

@@ -2,12 +2,12 @@
 
 ## 概述
 
-DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并自动将 Office 文档和 PDF 转换为 Markdown 格式。
+DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并可选地将 Office 文档和 PDF 转换为 Markdown 格式。
 
 ## 功能特性
 
 - ✅ 支持多文件同时上传
-- ✅ 自动转换文档为 Markdown（PDF、PPT、Excel、Word）
+- ✅ 可选地转换文档为 Markdown（PDF、PPT、Excel、Word）
 - ✅ 文件存储在线程隔离的目录中
 - ✅ Agent 自动感知已上传的文件
 - ✅ 支持文件列表查询和删除
@@ -22,6 +22,8 @@ POST /api/threads/{thread_id}/uploads
 **请求体：** `multipart/form-data`
 - `files`: 一个或多个文件
 
+网关会在应用层限制上传规模，默认最多 10 个文件、单文件 50 MiB、单次请求总计 100 MiB。可通过 `config.yaml` 的 `uploads.max_files`、`uploads.max_file_size`、`uploads.max_total_size` 调整；前端会读取同一组限制并在选择文件时提示，超过限制时后端返回 `413 Payload Too Large`。
+
 **响应：**
 ```json
 {
@@ -48,7 +50,23 @@ POST /api/threads/{thread_id}/uploads
 - `virtual_path`: Agent 在沙箱中使用的虚拟路径
 - `artifact_url`: 前端通过 HTTP 访问文件的 URL
 
-### 2. 列出已上传文件
+### 2. 查询上传限制
+```
+GET /api/threads/{thread_id}/uploads/limits
+```
+
+返回网关当前生效的上传限制，供前端在用户选择文件前提示和拦截。
+
+**响应：**
+```json
+{
+  "max_files": 10,
+  "max_file_size": 52428800,
+  "max_total_size": 104857600
+}
+```
+
+### 3. 列出已上传文件
 ```
 GET /api/threads/{thread_id}/uploads/list
 ```
@@ -71,7 +89,7 @@ GET /api/threads/{thread_id}/uploads/list
 }
 ```
 
-### 3. 删除文件
+### 4. 删除文件
 ```
 DELETE /api/threads/{thread_id}/uploads/{filename}
 ```
@@ -86,7 +104,7 @@ DELETE /api/threads/{thread_id}/uploads/{filename}
 
 ## 支持的文档格式
 
-以下格式会自动转换为 Markdown：
+以下格式在显式启用 `uploads.auto_convert_documents: true` 时会自动转换为 Markdown：
 - PDF (`.pdf`)
 - PowerPoint (`.ppt`, `.pptx`)
 - Excel (`.xls`, `.xlsx`)
@@ -94,6 +112,8 @@ DELETE /api/threads/{thread_id}/uploads/{filename}
 
 转换后的 Markdown 文件会保存在同一目录下，文件名为原文件名 + `.md` 扩展名。
 
+默认情况下，自动转换是关闭的，以避免在网关主机上对不受信任的 Office/PDF 上传执行解析。只有在受信任部署中明确接受此风险时，才应将 `uploads.auto_convert_documents` 设置为 `true`。
+
 ## Agent 集成
 
 ### 自动文件列举
@@ -207,6 +227,7 @@ backend/.deer-flow/threads/
 - 最大文件大小：100MB（可在 nginx.conf 中配置 `client_max_body_size`）
 - 文件名安全性：系统会自动验证文件路径，防止目录遍历攻击
 - 线程隔离：每个线程的上传文件相互隔离，无法跨线程访问
+- 自动文档转换默认关闭；如需启用，需在 `config.yaml` 中显式设置 `uploads.auto_convert_documents: true`
 
 ## 技术实现
 

backend/docs/GUARDRAILS.md

@@ -296,7 +296,7 @@ These are the tool names your provider will see in `request.tool_name`:
 | `web_search` | Web search query |
 | `web_fetch` | Fetch URL content |
 | `image_search` | Image search |
-| `present_file` | Present file to user |
+| `present_files` | Present file to user |
 | `view_image` | Display image |
 | `ask_clarification` | Ask user a question |
 | `task` | Delegate to subagent |

backend/docs/HARNESS_APP_SPLIT.md

@@ -1,343 +0,0 @@
-# DeerFlow 后端拆分设计文档：Harness + App
-
-> 状态：Draft
-> 作者：DeerFlow Team
-> 日期：2026-03-13
-
-## 1. 背景与动机
-
-DeerFlow 后端当前是一个单一 Python 包（`src.*`），包含了从底层 agent 编排到上层用户产品的所有代码。随着项目发展，这种结构带来了几个问题：
-
-- **复用困难**：其他产品（CLI 工具、Slack bot、第三方集成）想用 agent 能力，必须依赖整个后端，包括 FastAPI、IM SDK 等不需要的依赖
-- **职责模糊**：agent 编排逻辑和用户产品逻辑混在同一个 `src/` 下，边界不清晰
-- **依赖膨胀**：LangGraph Server 运行时不需要 FastAPI/uvicorn/Slack SDK，但当前必须安装全部依赖
-
-本文档提出将后端拆分为两部分：**deerflow-harness**（可发布的 agent 框架包）和 **app**（不打包的用户产品代码）。
-
-## 2. 核心概念
-
-### 2.1 Harness（线束/框架层）
-
-Harness 是 agent 的构建与编排框架，回答 **"如何构建和运行 agent"** 的问题：
-
-- Agent 工厂与生命周期管理
-- Middleware pipeline
-- 工具系统（内置工具 + MCP + 社区工具）
-- 沙箱执行环境
-- 子 agent 委派
-- 记忆系统
-- 技能加载与注入
-- 模型工厂
-- 配置系统
-
-**Harness 是一个可发布的 Python 包**（`deerflow-harness`），可以独立安装和使用。
-
-**Harness 的设计原则**：对上层应用完全无感知。它不知道也不关心谁在调用它——可以是 Web App、CLI、Slack Bot、或者一个单元测试。
-
-### 2.2 App（应用层）
-
-App 是面向用户的产品代码，回答 **"如何将 agent 呈现给用户"** 的问题：
-
-- Gateway API（FastAPI REST 接口）
-- IM Channels（飞书、Slack、Telegram 集成）
-- Custom Agent 的 CRUD 管理
-- 文件上传/下载的 HTTP 接口
-
-**App 不打包、不发布**，它是 DeerFlow 项目内部的应用代码，直接运行。
-
-**App 依赖 Harness，但 Harness 不依赖 App。**
-
-### 2.3 边界划分
-
-| 模块 | 归属 | 说明 |
-|------|------|------|
-| `config/` | Harness | 配置系统是基础设施 |
-| `reflection/` | Harness | 动态模块加载工具 |
-| `utils/` | Harness | 通用工具函数 |
-| `agents/` | Harness | Agent 工厂、middleware、state、memory |
-| `subagents/` | Harness | 子 agent 委派系统 |
-| `sandbox/` | Harness | 沙箱执行环境 |
-| `tools/` | Harness | 工具注册与发现 |
-| `mcp/` | Harness | MCP 协议集成 |
-| `skills/` | Harness | 技能加载、解析、定义 schema |
-| `models/` | Harness | LLM 模型工厂 |
-| `community/` | Harness | 社区工具（tavily、jina 等） |
-| `client.py` | Harness | 嵌入式 Python 客户端 |
-| `gateway/` | App | FastAPI REST API |
-| `channels/` | App | IM 平台集成 |
-
-**关于 Custom Agents**：agent 定义格式（`config.yaml` + `SOUL.md` schema）由 Harness 层的 `config/agents_config.py` 定义，但文件的存储、CRUD、发现机制由 App 层的 `gateway/routers/agents.py` 负责。
-
-## 3. 目标架构
-
-### 3.1 目录结构
-
-```
-backend/
-├── packages/
-│   └── harness/
-│       ├── pyproject.toml          # deerflow-harness 包定义
-│       └── deerflow/               # Python 包根（import 前缀: deerflow.*）
-│           ├── __init__.py
-│           ├── config/
-│           ├── reflection/
-│           ├── utils/
-│           ├── agents/
-│           │   ├── lead_agent/
-│           │   ├── middlewares/
-│           │   ├── memory/
-│           │   ├── checkpointer/
-│           │   └── thread_state.py
-│           ├── subagents/
-│           ├── sandbox/
-│           ├── tools/
-│           ├── mcp/
-│           ├── skills/
-│           ├── models/
-│           ├── community/
-│           └── client.py
-├── app/                            # 不打包（import 前缀: app.*）
-│   ├── __init__.py
-│   ├── gateway/
-│   │   ├── __init__.py
-│   │   ├── app.py
-│   │   ├── config.py
-│   │   ├── path_utils.py
-│   │   └── routers/
-│   └── channels/
-│       ├── __init__.py
-│       ├── base.py
-│       ├── manager.py
-│       ├── service.py
-│       ├── store.py
-│       ├── message_bus.py
-│       ├── feishu.py
-│       ├── slack.py
-│       └── telegram.py
-├── pyproject.toml                  # uv workspace root
-├── langgraph.json
-├── tests/
-├── docs/
-└── Makefile
-```
-
-### 3.2 Import 规则
-
-两个层使用不同的 import 前缀，职责边界一目了然：
-
-```python
-# ---------------------------------------------------------------
-# Harness 内部互相引用（deerflow.* 前缀）
-# ---------------------------------------------------------------
-from deerflow.agents import make_lead_agent
-from deerflow.models import create_chat_model
-from deerflow.config import get_app_config
-from deerflow.tools import get_available_tools
-
-# ---------------------------------------------------------------
-# App 内部互相引用（app.* 前缀）
-# ---------------------------------------------------------------
-from app.gateway.app import app
-from app.gateway.routers.uploads import upload_files
-from app.channels.service import start_channel_service
-
-# ---------------------------------------------------------------
-# App 调用 Harness（单向依赖，Harness 永远不 import app）
-# ---------------------------------------------------------------
-from deerflow.agents import make_lead_agent
-from deerflow.models import create_chat_model
-from deerflow.skills import load_skills
-from deerflow.config.extensions_config import get_extensions_config
-```
-
-**App 调用 Harness 示例 — Gateway 中启动 agent**：
-
-```python
-# app/gateway/routers/chat.py
-from deerflow.agents.lead_agent.agent import make_lead_agent
-from deerflow.models import create_chat_model
-from deerflow.config import get_app_config
-
-async def create_chat_session(thread_id: str, model_name: str):
-    config = get_app_config()
-    model = create_chat_model(name=model_name)
-    agent = make_lead_agent(config=...)
-    # ... 使用 agent 处理用户消息
-```
-
-**App 调用 Harness 示例 — Channel 中查询 skills**：
-
-```python
-# app/channels/manager.py
-from deerflow.skills import load_skills
-from deerflow.agents.memory.updater import get_memory_data
-
-def handle_status_command():
-    skills = load_skills(enabled_only=True)
-    memory = get_memory_data()
-    return f"Skills: {len(skills)}, Memory facts: {len(memory.get('facts', []))}"
-```
-
-**禁止方向**：Harness 代码中绝不能出现 `from app.` 或 `import app.`。
-
-### 3.3 为什么 App 不打包
-
-| 方面 | 打包（放 packages/ 下） | 不打包（放 backend/app/） |
-|------|------------------------|--------------------------|
-| 命名空间 | 需要 pkgutil `extend_path` 合并，或独立前缀 | 天然独立，`app.*` vs `deerflow.*` |
-| 发布需求 | 没有——App 是项目内部代码 | 不需要 pyproject.toml |
-| 复杂度 | 需要管理两个包的构建、版本、依赖声明 | 直接运行，零额外配置 |
-| 运行方式 | `pip install deerflow-app` | `PYTHONPATH=. uvicorn app.gateway.app:app` |
-
-App 的唯一消费者是 DeerFlow 项目自身，没有独立发布的需求。放在 `backend/app/` 下作为普通 Python 包，通过 `PYTHONPATH` 或 editable install 让 Python 找到即可。
-
-### 3.4 依赖关系
-
-```
-┌─────────────────────────────────────┐
-│  app/  (不打包，直接运行)             │
-│  ├── fastapi, uvicorn               │
-│  ├── slack-sdk, lark-oapi, ...      │
-│  └── import deerflow.*              │
-└──────────────┬──────────────────────┘
-               │
-               ▼
-┌─────────────────────────────────────┐
-│  deerflow-harness  (可发布的包)       │
-│  ├── langgraph, langchain           │
-│  ├── markitdown, pydantic, ...      │
-│  └── 零 app 依赖                     │
-└─────────────────────────────────────┘
-```
-
-**依赖分类**：
-
-| 分类 | 依赖包 |
-|------|--------|
-| Harness only | agent-sandbox, langchain*, langgraph*, markdownify, markitdown, pydantic, pyyaml, readabilipy, tavily-python, firecrawl-py, tiktoken, ddgs, duckdb, httpx, kubernetes, dotenv |
-| App only | fastapi, uvicorn, sse-starlette, python-multipart, lark-oapi, slack-sdk, python-telegram-bot, markdown-to-mrkdwn |
-| Shared | langgraph-sdk（channels 用 HTTP client）, pydantic, httpx |
-
-### 3.5 Workspace 配置
-
-`backend/pyproject.toml`（workspace root）：
-
-```toml
-[project]
-name = "deer-flow"
-version = "0.1.0"
-requires-python = ">=3.12"
-dependencies = ["deerflow-harness"]
-
-[dependency-groups]
-dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
-# App 的额外依赖（fastapi 等）也声明在 workspace root，因为 app 不打包
-app = ["fastapi", "uvicorn", "sse-starlette", "python-multipart"]
-channels = ["lark-oapi", "slack-sdk", "python-telegram-bot"]
-
-[tool.uv.workspace]
-members = ["packages/harness"]
-
-[tool.uv.sources]
-deerflow-harness = { workspace = true }
-```
-
-## 4. 当前的跨层依赖问题
-
-在拆分之前，需要先解决 `client.py` 中两处从 harness 到 app 的反向依赖：
-
-### 4.1 `_validate_skill_frontmatter`
-
-```python
-# client.py — harness 导入了 app 层代码
-from src.gateway.routers.skills import _validate_skill_frontmatter
-```
-
-**解决方案**：将该函数提取到 `deerflow/skills/validation.py`。这是一个纯逻辑函数（解析 YAML frontmatter、校验字段），与 FastAPI 无关。
-
-### 4.2 `CONVERTIBLE_EXTENSIONS` + `convert_file_to_markdown`
-
-```python
-# client.py — harness 导入了 app 层代码
-from src.gateway.routers.uploads import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
-```
-
-**解决方案**：将它们提取到 `deerflow/utils/file_conversion.py`。仅依赖 `markitdown` + `pathlib`，是通用工具函数。
-
-## 5. 基础设施变更
-
-### 5.1 LangGraph Server
-
-LangGraph Server 只需要 harness 包。`langgraph.json` 更新：
-
-```json
-{
-  "dependencies": ["./packages/harness"],
-  "graphs": {
-    "lead_agent": "deerflow.agents:make_lead_agent"
-  },
-  "checkpointer": {
-    "path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
-  }
-}
-```
-
-### 5.2 Gateway API
-
-```bash
-# serve.sh / Makefile
-# PYTHONPATH 包含 backend/ 根目录，使 app.* 和 deerflow.* 都能被找到
-PYTHONPATH=. uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
-```
-
-### 5.3 Nginx
-
-无需变更（只做 URL 路由，不涉及 Python 模块路径）。
-
-### 5.4 Docker
-
-Dockerfile 中的 module 引用从 `src.` 改为 `deerflow.` / `app.`，`COPY` 命令需覆盖 `packages/` 和 `app/` 目录。
-
-## 6. 实施计划
-
-分 3 个 PR 递进执行：
-
-### PR 1：提取共享工具函数（Low Risk）
-
-1. 创建 `src/skills/validation.py`，从 `gateway/routers/skills.py` 提取 `_validate_skill_frontmatter`
-2. 创建 `src/utils/file_conversion.py`，从 `gateway/routers/uploads.py` 提取文件转换逻辑
-3. 更新 `client.py`、`gateway/routers/skills.py`、`gateway/routers/uploads.py` 的 import
-4. 运行全部测试确认无回归
-
-### PR 2：Rename + 物理拆分（High Risk，原子操作）
-
-1. 创建 `packages/harness/` 目录，创建 `pyproject.toml`
-2. `git mv` 将 harness 相关模块从 `src/` 移入 `packages/harness/deerflow/`
-3. `git mv` 将 app 相关模块从 `src/` 移入 `app/`
-4. 全局替换 import：
-   - harness 模块：`src.*` → `deerflow.*`（所有 `.py` 文件、`langgraph.json`、测试、文档）
-   - app 模块：`src.gateway.*` → `app.gateway.*`、`src.channels.*` → `app.channels.*`
-5. 更新 workspace root `pyproject.toml`
-6. 更新 `langgraph.json`、`Makefile`、`Dockerfile`
-7. `uv sync` + 全部测试 + 手动验证服务启动
-
-### PR 3：边界检查 + 文档（Low Risk）
-
-1. 添加 lint 规则：检查 harness 不 import app 模块
-2. 更新 `CLAUDE.md`、`README.md`
-
-## 7. 风险与缓解
-
-| 风险 | 影响 | 缓解措施 |
-|------|------|----------|
-| 全局 rename 误伤 | 字符串中的 `src` 被错误替换 | 正则精确匹配 `\bsrc\.`，review diff |
-| LangGraph Server 找不到模块 | 服务启动失败 | `langgraph.json` 的 `dependencies` 指向正确的 harness 包路径 |
-| App 的 `PYTHONPATH` 缺失 | Gateway/Channel 启动 import 报错 | Makefile/Docker 统一设置 `PYTHONPATH=.` |
-| `config.yaml` 中的 `use` 字段引用旧路径 | 运行时模块解析失败 | `config.yaml` 中的 `use` 字段同步更新为 `deerflow.*` |
-| 测试中 `sys.path` 混乱 | 测试失败 | 用 editable install（`uv sync`）确保 deerflow 可导入，`conftest.py` 中添加 `app/` 到 `sys.path` |
-
-## 8. 未来演进
-
-- **独立发布**：harness 可以发布到内部 PyPI，让其他项目直接 `pip install deerflow-harness`
-- **插件化 App**：不同的 app（web、CLI、bot）可以各自独立，都依赖同一个 harness
-- **更细粒度拆分**：如果 harness 内部模块继续增长，可以进一步拆分（如 `deerflow-sandbox`、`deerflow-mcp`）

backend/docs/MCP_SERVER.md

@@ -45,6 +45,41 @@ Example:
 }
 ```
 
+## Custom Tool Interceptors
+
+You can register custom interceptors that run before every MCP tool call. This is useful for injecting per-request headers (e.g., user auth tokens from the LangGraph execution context), logging, or metrics.
+
+Declare interceptors in `extensions_config.json` using the `mcpInterceptors` field:
+
+```json
+{
+  "mcpInterceptors": [
+    "my_package.mcp.auth:build_auth_interceptor"
+  ],
+  "mcpServers": { ... }
+}
+```
+
+Each entry is a Python import path in `module:variable` format (resolved via `resolve_variable`). The variable must be a **no-arg builder function** that returns an async interceptor compatible with `MultiServerMCPClient`’s `tool_interceptors` interface, or `None` to skip.
+
+Example interceptor that injects auth headers from LangGraph metadata:
+
+```python
+def build_auth_interceptor():
+    async def interceptor(request, handler):
+        from langgraph.config import get_config
+        metadata = get_config().get("metadata", {})
+        headers = dict(request.headers or {})
+        if token := metadata.get("auth_token"):
+            headers["X-Auth-Token"] = token
+        return await handler(request.override(headers=headers))
+    return interceptor
+```
+
+- A single string value is accepted and normalized to a one-element list.
+- Invalid paths or builder failures are logged as warnings without blocking other interceptors.
+- The builder return value must be `callable`; non-callable values are skipped with a warning.
+
 ## How It Works
 
 MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.

backend/docs/SETUP.md

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

backend/docs/TITLE_GENERATION_IMPLEMENTATION.md

@@ -124,7 +124,7 @@ title:
 # checkpointer.py
 from langgraph.checkpoint.sqlite import SqliteSaver
 
-checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
+checkpointer = SqliteSaver.from_conn_string("deerflow.db")
 ```
 
 ```json

backend/docs/TODO.md

@@ -24,7 +24,7 @@
 - [ ] Optimize async concurrency in agent hot path (IM channels multi-task scenario)
 - [ ] Replace `subprocess.run()` with `asyncio.create_subprocess_shell()` in `packages/harness/deerflow/sandbox/local/local_sandbox.py`
   - Replace sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
-  - Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
+  - [x] Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
   - Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
   - For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
 

backend/docs/summarization.md

@@ -41,6 +41,13 @@ summarization:
 
   # Custom summary prompt (optional)
   summary_prompt: null
+
+  # Tool names treated as skill file reads for skill rescue
+  skill_file_read_tool_names:
+    - read_file
+    - read
+    - view
+    - cat
 ```
 
 ### Configuration Options
@@ -125,6 +132,26 @@ keep:
 - **Default**: `null` (uses LangChain's default prompt)
 - **Description**: Custom prompt template for generating summaries. The prompt should guide the model to extract the most important context.
 
+#### `preserve_recent_skill_count`
+- **Type**: Integer (≥ 0)
+- **Default**: `5`
+- **Description**: Number of most-recently-loaded skill files (tool results whose tool name is in `skill_file_read_tool_names` and whose target path is under `skills.container_path`, e.g. `/mnt/skills/...`) that are rescued from summarization. Prevents the agent from losing skill instructions after compression. Set to `0` to disable skill rescue entirely.
+
+#### `preserve_recent_skill_tokens`
+- **Type**: Integer (≥ 0)
+- **Default**: `25000`
+- **Description**: Total token budget reserved for rescued skill reads. Once this budget is exhausted, older skill bundles are allowed to be summarized.
+
+#### `preserve_recent_skill_tokens_per_skill`
+- **Type**: Integer (≥ 0)
+- **Default**: `5000`
+- **Description**: Per-skill token cap. Any individual skill read whose tool result exceeds this size is not rescued (it falls through to the summarizer like ordinary content).
+
+#### `skill_file_read_tool_names`
+- **Type**: List of strings
+- **Default**: `["read_file", "read", "view", "cat"]`
+- **Description**: Tool names treated as skill file reads during summarization rescue. A tool call is only eligible for skill rescue when its name appears in this list and its target path is under `skills.container_path`.
+
 **Default Prompt Behavior:**
 The default LangChain prompt instructs the model to:
 - Extract highest quality/most relevant context
@@ -147,6 +174,7 @@ The default LangChain prompt instructs the model to:
    - A single summary message is added
    - Recent messages are preserved
 6. **AI/Tool Pair Protection**: The system ensures AI messages and their corresponding tool messages stay together
+7. **Skill Rescue**: Before the summary is generated, the most recently loaded skill files (tool results whose tool name is in `skill_file_read_tool_names` and whose target path is under `skills.container_path`) are lifted out of the summarization set and prepended to the preserved tail. Selection walks newest-first under three budgets: `preserve_recent_skill_count`, `preserve_recent_skill_tokens`, and `preserve_recent_skill_tokens_per_skill`. The triggering AIMessage and all of its paired ToolMessages move together so tool_call ↔ tool_result pairing stays intact.
 
 ### Token Counting
 

backend/langgraph.json

@@ -8,7 +8,10 @@
   "graphs": {
     "lead_agent": "deerflow.agents:make_lead_agent"
   },
+  "auth": {
+    "path": "./app/gateway/langgraph_auth.py:auth"
+  },
   "checkpointer": {
-    "path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
+    "path": "./packages/harness/deerflow/runtime/checkpointer/async_provider.py:make_checkpointer"
   }
 }

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

@@ -1,4 +1,3 @@
-from .checkpointer import get_checkpointer, make_checkpointer, reset_checkpointer
 from .factory import create_deerflow_agent
 from .features import Next, Prev, RuntimeFeatures
 from .lead_agent import make_lead_agent
@@ -18,7 +17,4 @@ __all__ = [
     "make_lead_agent",
     "SandboxState",
     "ThreadState",
-    "get_checkpointer",
-    "reset_checkpointer",
-    "make_checkpointer",
 ]

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

@@ -254,9 +254,11 @@ def _assemble_from_features(
             from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 
             chain.append(ViewImageMiddleware())
-        from deerflow.tools.builtins import view_image_tool
 
-        extra_tools.append(view_image_tool)
+        if feat.sandbox is not False:
+            from deerflow.tools.builtins import view_image_tool
+
+            extra_tools.append(view_image_tool)
 
     # --- [11] Subagent ---
     if feat.subagent is not False:

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

@@ -1,31 +1,43 @@
 import logging
 
 from langchain.agents import create_agent
-from langchain.agents.middleware import AgentMiddleware, SummarizationMiddleware
+from langchain.agents.middleware import AgentMiddleware
 from langchain_core.runnables import RunnableConfig
 
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
+from deerflow.agents.memory.summarization_hook import memory_flush_hook
 from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
 from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
 from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
 from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
+from deerflow.agents.middlewares.summarization_middleware import BeforeSummarizationHook, DeerFlowSummarizationMiddleware
 from deerflow.agents.middlewares.title_middleware import TitleMiddleware
 from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
 from deerflow.agents.middlewares.token_usage_middleware import TokenUsageMiddleware
 from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares
 from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 from deerflow.agents.thread_state import ThreadState
-from deerflow.config.agents_config import load_agent_config
+from deerflow.config.agents_config import load_agent_config, validate_agent_name
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig, get_app_config
+from deerflow.config.memory_config import get_memory_config
 from deerflow.config.summarization_config import get_summarization_config
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
 
 
-def _resolve_model_name(requested_model_name: str | None = None) -> str:
+def _get_runtime_config(config: RunnableConfig) -> dict:
+    """Merge legacy configurable options with LangGraph runtime context."""
+    cfg = dict(config.get("configurable", {}) or {})
+    context = config.get("context", {}) or {}
+    if isinstance(context, dict):
+        cfg.update(context)
+    return cfg
+
+
+def _resolve_model_name(requested_model_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
     """Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
-    app_config = get_app_config()
+    app_config = app_config or get_app_config()
     default_model_name = app_config.models[0].name if app_config.models else None
     if default_model_name is None:
         raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
@@ -38,7 +50,7 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
     return default_model_name
 
 
-def _create_summarization_middleware() -> SummarizationMiddleware | None:
+def _create_summarization_middleware(*, app_config: AppConfig | None = None) -> DeerFlowSummarizationMiddleware | None:
     """Create and configure the summarization middleware from config."""
     config = get_summarization_config()
 
@@ -56,13 +68,15 @@ def _create_summarization_middleware() -> SummarizationMiddleware | None:
     # Prepare keep parameter
     keep = config.keep.to_tuple()
 
-    # Prepare model parameter
+    # Prepare model parameter.
+    # Bind "middleware:summarize" tag so RunJournal identifies these LLM calls
+    # as middleware rather than lead_agent (SummarizationMiddleware is a
+    # LangChain built-in, so we tag the model at creation time).
     if config.model_name:
-        model = create_chat_model(name=config.model_name, thinking_enabled=False)
+        model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
     else:
-        # Use a lightweight model for summarization to save costs
+        model = create_chat_model(thinking_enabled=False, app_config=app_config)
-        # Falls back to default model if not explicitly specified
+    model = model.with_config(tags=["middleware:summarize"])
-        model = create_chat_model(thinking_enabled=False)
 
     # Prepare kwargs
     kwargs = {
@@ -77,7 +91,29 @@ def _create_summarization_middleware() -> SummarizationMiddleware | None:
     if config.summary_prompt is not None:
         kwargs["summary_prompt"] = config.summary_prompt
 
-    return SummarizationMiddleware(**kwargs)
+    hooks: list[BeforeSummarizationHook] = []
+    if get_memory_config().enabled:
+        hooks.append(memory_flush_hook)
+
+    # The logic below relies on two assumptions holding true: this factory is
+    # the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
+    # config is not expected to change after startup.
+    try:
+        resolved_app_config = app_config or get_app_config()
+        skills_container_path = resolved_app_config.skills.container_path or "/mnt/skills"
+    except Exception:
+        logger.exception("Failed to resolve skills container path; falling back to default")
+        skills_container_path = "/mnt/skills"
+
+    return DeerFlowSummarizationMiddleware(
+        **kwargs,
+        skills_container_path=skills_container_path,
+        skill_file_read_tool_names=config.skill_file_read_tool_names,
+        before_summarization=hooks,
+        preserve_recent_skill_count=config.preserve_recent_skill_count,
+        preserve_recent_skill_tokens=config.preserve_recent_skill_tokens,
+        preserve_recent_skill_tokens_per_skill=config.preserve_recent_skill_tokens_per_skill,
+    )
 
 
 def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
@@ -205,7 +241,14 @@ Being proactive with task management demonstrates thoroughness and ensures all r
 # ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
 # ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
 # ClarificationMiddleware should be last to intercept clarification requests after model calls
-def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = None):
+def _build_middlewares(
+    config: RunnableConfig,
+    model_name: str | None,
+    agent_name: str | None = None,
+    custom_middlewares: list[AgentMiddleware] | None = None,
+    *,
+    app_config: AppConfig | None = None,
+):
     """Build middleware chain based on runtime configuration.
 
     Args:
@@ -216,21 +259,23 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
     Returns:
         List of middleware instances.
     """
-    middlewares = build_lead_runtime_middlewares(lazy_init=True)
+    resolved_app_config = app_config or get_app_config()
+    middlewares = build_lead_runtime_middlewares(app_config=resolved_app_config, lazy_init=True)
 
     # Add summarization middleware if enabled
-    summarization_middleware = _create_summarization_middleware()
+    summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
     if summarization_middleware is not None:
         middlewares.append(summarization_middleware)
 
     # Add TodoList middleware if plan mode is enabled
-    is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
+    cfg = _get_runtime_config(config)
+    is_plan_mode = cfg.get("is_plan_mode", False)
     todo_list_middleware = _create_todo_list_middleware(is_plan_mode)
     if todo_list_middleware is not None:
         middlewares.append(todo_list_middleware)
 
     # Add TokenUsageMiddleware when token_usage tracking is enabled
-    if get_app_config().token_usage.enabled:
+    if resolved_app_config.token_usage.enabled:
         middlewares.append(TokenUsageMiddleware())
 
     # Add TitleMiddleware
@@ -241,21 +286,20 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
 
     # Add ViewImageMiddleware only if the current model supports vision.
     # Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
-    app_config = get_app_config()
+    model_config = resolved_app_config.get_model_config(model_name) if model_name else None
-    model_config = app_config.get_model_config(model_name) if model_name else None
     if model_config is not None and model_config.supports_vision:
         middlewares.append(ViewImageMiddleware())
 
     # Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
-    if app_config.tool_search.enabled:
+    if resolved_app_config.tool_search.enabled:
         from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
 
         middlewares.append(DeferredToolFilterMiddleware())
 
     # Add SubagentLimitMiddleware to truncate excess parallel task calls
-    subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
+    subagent_enabled = cfg.get("subagent_enabled", False)
     if subagent_enabled:
-        max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
+        max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
         middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
 
     # LoopDetectionMiddleware — detect and break repetitive tool call loops
@@ -271,11 +315,17 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
 
 
 def make_lead_agent(config: RunnableConfig):
+    """LangGraph graph factory; keep the signature compatible with LangGraph Server."""
+    return _make_lead_agent(config, app_config=get_app_config())
+
+
+def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
     from deerflow.tools.builtins import setup_agent
 
-    cfg = config.get("configurable", {})
+    cfg = _get_runtime_config(config)
+    resolved_app_config = app_config
 
     thinking_enabled = cfg.get("thinking_enabled", True)
     reasoning_effort = cfg.get("reasoning_effort", None)
@@ -284,17 +334,16 @@ def make_lead_agent(config: RunnableConfig):
     subagent_enabled = cfg.get("subagent_enabled", False)
     max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
     is_bootstrap = cfg.get("is_bootstrap", False)
-    agent_name = cfg.get("agent_name")
+    agent_name = validate_agent_name(cfg.get("agent_name"))
 
     agent_config = load_agent_config(agent_name) if not is_bootstrap else None
     # Custom agent model from agent config (if any), or None to let _resolve_model_name pick the default
     agent_model_name = agent_config.model if agent_config and agent_config.model else None
 
     # Final model name resolution: request → agent config → global default, with fallback for unknown names
-    model_name = _resolve_model_name(requested_model_name or agent_model_name)
+    model_name = _resolve_model_name(requested_model_name or agent_model_name, app_config=resolved_app_config)
 
-    app_config = get_app_config()
+    model_config = resolved_app_config.get_model_config(model_name)
-    model_config = app_config.get_model_config(model_name)
 
     if model_config is None:
         raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
@@ -325,26 +374,42 @@ def make_lead_agent(config: RunnableConfig):
             "reasoning_effort": reasoning_effort,
             "is_plan_mode": is_plan_mode,
             "subagent_enabled": subagent_enabled,
+            "tool_groups": agent_config.tool_groups if agent_config else None,
+            "available_skills": ["bootstrap"] if is_bootstrap else (agent_config.skills if agent_config and agent_config.skills is not None else None),
         }
     )
 
     if is_bootstrap:
         # Special bootstrap agent with minimal prompt for initial custom agent creation flow
         return create_agent(
-            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
+            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) + [setup_agent],
+            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent],
-            middleware=_build_middlewares(config, model_name=model_name),
+            middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
-            system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
+            system_prompt=apply_prompt_template(
+                subagent_enabled=subagent_enabled,
+                max_concurrent_subagents=max_concurrent_subagents,
+                available_skills=set(["bootstrap"]),
+                app_config=resolved_app_config,
+            ),
             state_schema=ThreadState,
         )
 
     # Default lead agent (unchanged behavior)
     return create_agent(
-        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
+        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),
+        tools=get_available_tools(
-        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
+            model_name=model_name,
+            groups=agent_config.tool_groups if agent_config else None,
+            subagent_enabled=subagent_enabled,
+            app_config=resolved_app_config,
+        ),
+        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, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
+            subagent_enabled=subagent_enabled,
+            max_concurrent_subagents=max_concurrent_subagents,
+            agent_name=agent_name,
+            available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None,
+            app_config=resolved_app_config,
         ),
         state_schema=ThreadState,
     )

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

@@ -1,14 +1,20 @@
+from __future__ import annotations
+
 import asyncio
 import logging
 import threading
 from datetime import datetime
 from functools import lru_cache
+from typing import TYPE_CHECKING
 
 from deerflow.config.agents_config import load_agent_soul
-from deerflow.skills import load_skills
+from deerflow.skills.storage import get_or_new_skill_storage
-from deerflow.skills.types import Skill
+from deerflow.skills.types import Skill, SkillCategory
 from deerflow.subagents import get_available_subagent_names
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
@@ -20,7 +26,7 @@ _enabled_skills_refresh_event = threading.Event()
 
 
 def _load_enabled_skills_sync() -> list[Skill]:
-    return list(load_skills(enabled_only=True))
+    return list(get_or_new_skill_storage().load_skills(enabled_only=True))
 
 
 def _start_enabled_skills_refresh_thread() -> None:
@@ -111,8 +117,21 @@ def _get_enabled_skills():
     return []
 
 
-def _skill_mutability_label(category: str) -> str:
+def _get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
-    return "[custom, editable]" if category == "custom" else "[built-in]"
+    """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.
+    """
+    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))
+
+
+def _skill_mutability_label(category: SkillCategory | str) -> str:
+    return "[custom, editable]" if category == SkillCategory.CUSTOM else "[built-in]"
 
 
 def clear_skills_system_prompt_cache() -> None:
@@ -123,31 +142,6 @@ async def refresh_skills_system_prompt_cache_async() -> None:
     await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
 
 
-def _reset_skills_system_prompt_cache_state() -> None:
-    global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
-
-    _get_cached_skills_prompt_section.cache_clear()
-    with _enabled_skills_lock:
-        _enabled_skills_cache = None
-        _enabled_skills_refresh_active = False
-        _enabled_skills_refresh_version = 0
-        _enabled_skills_refresh_event.clear()
-
-
-def _refresh_enabled_skills_cache() -> None:
-    """Backward-compatible test helper for direct synchronous reload."""
-    try:
-        skills = _load_enabled_skills_sync()
-    except Exception:
-        logger.exception("Failed to load enabled skills for prompt injection")
-        skills = []
-
-    with _enabled_skills_lock:
-        _enabled_skills_cache = skills
-        _enabled_skills_refresh_active = False
-        _enabled_skills_refresh_event.set()
-
-
 def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
     if not skill_evolution_enabled:
         return ""
@@ -164,6 +158,36 @@ Skip simple one-off tasks.
 """
 
 
+def _build_available_subagents_description(available_names: list[str], bash_available: bool) -> str:
+    """Dynamically build subagent type descriptions from registry.
+
+    Mirrors Codex's pattern where agent_type_description is dynamically generated
+    from all registered roles, so the LLM knows about every available type.
+    """
+    # Built-in descriptions (kept for backward compatibility with existing prompt quality)
+    builtin_descriptions = {
+        "general-purpose": "For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.",
+        "bash": (
+            "For command execution (git, build, test, deploy operations)" if bash_available else "Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
+        ),
+    }
+
+    # Lazy import moved outside loop to avoid repeated import overhead
+    from deerflow.subagents.registry import get_subagent_config
+
+    lines = []
+    for name in available_names:
+        if name in builtin_descriptions:
+            lines.append(f"- **{name}**: {builtin_descriptions[name]}")
+        else:
+            config = get_subagent_config(name)
+            if config is not None:
+                desc = config.description.split("\n")[0].strip()  # First line only for brevity
+                lines.append(f"- **{name}**: {desc}")
+
+    return "\n".join(lines)
+
+
 def _build_subagent_section(max_concurrent: int) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 
@@ -174,13 +198,12 @@ def _build_subagent_section(max_concurrent: int) -> str:
         Formatted subagent section string.
     """
     n = max_concurrent
-    bash_available = "bash" in get_available_subagent_names()
+    available_names = get_available_subagent_names()
-    available_subagents = (
+    bash_available = "bash" in available_names
-        "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n- **bash**: For command execution (git, build, test, deploy operations)"
+
-        if bash_available
+    # Dynamically build subagent type descriptions from registry (aligned with Codex's
-        else "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n"
+    # agent_type_description pattern where all registered roles are listed in the tool spec).
-        "- **bash**: Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
+    available_subagents = _build_available_subagents_description(available_names, bash_available)
-    )
     direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
     direct_execution_example = (
         '# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test")  # Direct execution, not task()'
@@ -420,7 +443,7 @@ You: "Deploying to staging..." [proceed]
 - Treat `/mnt/user-data/workspace` as your default current working directory for coding and file-editing tasks
 - When writing scripts or commands that create/read files from the workspace, prefer relative paths such as `hello.txt`, `../uploads/data.csv`, and `../outputs/report.md`
 - Avoid hardcoding `/mnt/user-data/...` inside generated scripts when a relative path from the workspace is enough
-- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_file` tool
+- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_files` tool
 {acp_section}
 </working_directory>
 
@@ -519,12 +542,13 @@ def _get_memory_context(agent_name: str | None = None) -> str:
     try:
         from deerflow.agents.memory import format_memory_for_injection, get_memory_data
         from deerflow.config.memory_config import get_memory_config
+        from deerflow.runtime.user_context import get_effective_user_id
 
         config = get_memory_config()
         if not config.enabled or not config.injection_enabled:
             return ""
 
-        memory_data = get_memory_data(agent_name)
+        memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
         memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
 
         if not memory_content.strip():
@@ -571,14 +595,14 @@ You have access to skills that provide optimized workflows for specific tasks. E
 </skill_system>"""
 
 
-def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
+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()
+    skills = _get_enabled_skills_for_config(app_config)
 
     try:
         from deerflow.config import get_app_config
 
-        config = get_app_config()
+        config = app_config or get_app_config()
         container_base_path = config.skills.container_path
         skill_evolution_enabled = config.skill_evolution.enabled
     except Exception:
@@ -607,7 +631,7 @@ def get_agent_soul(agent_name: str | None) -> str:
     return ""
 
 
-def get_deferred_tools_prompt_section() -> str:
+def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
     """Generate <available-deferred-tools> block for the system prompt.
 
     Lists only deferred tool names so the agent knows what exists
@@ -619,7 +643,8 @@ def get_deferred_tools_prompt_section() -> str:
     try:
         from deerflow.config import get_app_config
 
-        if not get_app_config().tool_search.enabled:
+        config = app_config or get_app_config()
+        if not config.tool_search.enabled:
             return ""
     except Exception:
         return ""
@@ -648,16 +673,17 @@ def _build_acp_section() -> str:
         "- ACP agents (e.g. codex, claude_code) run in their own independent workspace — NOT in `/mnt/user-data/`\n"
         "- When writing prompts for ACP agents, describe the task only — do NOT reference `/mnt/user-data` paths\n"
         "- ACP agent results are accessible at `/mnt/acp-workspace/` (read-only) — use `ls`, `read_file`, or `bash cp` to retrieve output files\n"
-        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_file`"
+        "- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_files`"
     )
 
 
-def _build_custom_mounts_section() -> str:
+def _build_custom_mounts_section(*, app_config: AppConfig | None = None) -> str:
     """Build a prompt section for explicitly configured sandbox mounts."""
     try:
         from deerflow.config import get_app_config
 
-        mounts = get_app_config().sandbox.mounts or []
+        config = app_config or get_app_config()
+        mounts = config.sandbox.mounts or []
     except Exception:
         logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
         return ""
@@ -674,7 +700,14 @@ def _build_custom_mounts_section() -> str:
     return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"
 
 
-def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
+def apply_prompt_template(
+    subagent_enabled: bool = False,
+    max_concurrent_subagents: int = 3,
+    *,
+    agent_name: str | None = None,
+    available_skills: set[str] | None = None,
+    app_config: AppConfig | None = None,
+) -> str:
     # Get memory context
     memory_context = _get_memory_context(agent_name)
 
@@ -701,14 +734,14 @@ def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagen
     )
 
     # Get skills section
-    skills_section = get_skills_prompt_section(available_skills)
+    skills_section = get_skills_prompt_section(available_skills, app_config=app_config)
 
     # Get deferred tools section (tool_search)
-    deferred_tools_section = get_deferred_tools_prompt_section()
+    deferred_tools_section = get_deferred_tools_prompt_section(app_config=app_config)
 
     # Build ACP agent section only if ACP agents are configured
     acp_section = _build_acp_section()
-    custom_mounts_section = _build_custom_mounts_section()
+    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

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

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

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

@@ -20,6 +20,7 @@ class ConversationContext:
     messages: list[Any]
     timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
     agent_name: str | None = None
+    user_id: str | None = None
     correction_detected: bool = False
     reinforcement_detected: bool = False
 
@@ -44,6 +45,7 @@ class MemoryUpdateQueue:
         thread_id: str,
         messages: list[Any],
         agent_name: str | None = None,
+        user_id: str | None = None,
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
     ) -> None:
@@ -53,6 +55,9 @@ class MemoryUpdateQueue:
             thread_id: The thread ID.
             messages: The conversation messages.
             agent_name: If provided, memory is stored per-agent. If None, uses global memory.
+            user_id: The user ID captured at enqueue time. Stored in ConversationContext so it
+                survives the threading.Timer boundary (ContextVar does not propagate across
+                raw threads).
             correction_detected: Whether recent turns include an explicit correction signal.
             reinforcement_detected: Whether recent turns include a positive reinforcement signal.
         """
@@ -61,48 +66,93 @@ class MemoryUpdateQueue:
             return
 
         with self._lock:
-            existing_context = next(
+            self._enqueue_locked(
-                (context for context in self._queue if context.thread_id == thread_id),
-                None,
-            )
-            merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
-            merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
-            context = ConversationContext(
                 thread_id=thread_id,
                 messages=messages,
                 agent_name=agent_name,
-                correction_detected=merged_correction_detected,
+                user_id=user_id,
-                reinforcement_detected=merged_reinforcement_detected,
+                correction_detected=correction_detected,
+                reinforcement_detected=reinforcement_detected,
             )
-
-            # Check if this thread already has a pending update
-            # If so, replace it with the newer one
-            self._queue = [c for c in self._queue if c.thread_id != thread_id]
-            self._queue.append(context)
-
-            # Reset or start the debounce timer
             self._reset_timer()
 
         logger.info("Memory update queued for thread %s, queue size: %d", thread_id, len(self._queue))
 
+    def add_nowait(
+        self,
+        thread_id: str,
+        messages: list[Any],
+        agent_name: str | None = None,
+        user_id: str | None = None,
+        correction_detected: bool = False,
+        reinforcement_detected: bool = False,
+    ) -> None:
+        """Add a conversation and start processing immediately in the background."""
+        config = get_memory_config()
+        if not config.enabled:
+            return
+
+        with self._lock:
+            self._enqueue_locked(
+                thread_id=thread_id,
+                messages=messages,
+                agent_name=agent_name,
+                user_id=user_id,
+                correction_detected=correction_detected,
+                reinforcement_detected=reinforcement_detected,
+            )
+            self._schedule_timer(0)
+
+        logger.info("Memory update queued for immediate processing on thread %s, queue size: %d", thread_id, len(self._queue))
+
+    def _enqueue_locked(
+        self,
+        *,
+        thread_id: str,
+        messages: list[Any],
+        agent_name: str | None,
+        user_id: str | None,
+        correction_detected: bool,
+        reinforcement_detected: bool,
+    ) -> None:
+        existing_context = next(
+            (context for context in self._queue if context.thread_id == thread_id),
+            None,
+        )
+        merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
+        merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
+        context = ConversationContext(
+            thread_id=thread_id,
+            messages=messages,
+            agent_name=agent_name,
+            user_id=user_id,
+            correction_detected=merged_correction_detected,
+            reinforcement_detected=merged_reinforcement_detected,
+        )
+
+        self._queue = [c for c in self._queue if c.thread_id != thread_id]
+        self._queue.append(context)
+
     def _reset_timer(self) -> None:
         """Reset the debounce timer."""
         config = get_memory_config()
+        self._schedule_timer(config.debounce_seconds)
 
+        logger.debug("Memory update timer set for %ss", config.debounce_seconds)
+
+    def _schedule_timer(self, delay_seconds: float) -> None:
+        """Schedule queue processing after the provided delay."""
         # Cancel existing timer if any
         if self._timer is not None:
             self._timer.cancel()
 
-        # Start new timer
         self._timer = threading.Timer(
-            config.debounce_seconds,
+            delay_seconds,
             self._process_queue,
         )
         self._timer.daemon = True
         self._timer.start()
 
-        logger.debug("Memory update timer set for %ss", config.debounce_seconds)
-
     def _process_queue(self) -> None:
         """Process all queued conversation contexts."""
         # Import here to avoid circular dependency
@@ -110,8 +160,8 @@ class MemoryUpdateQueue:
 
         with self._lock:
             if self._processing:
-                # Already processing, reschedule
+                # Preserve immediate flush semantics even if another worker is active.
-                self._reset_timer()
+                self._schedule_timer(0)
                 return
 
             if not self._queue:
@@ -136,6 +186,7 @@ class MemoryUpdateQueue:
                         agent_name=context.agent_name,
                         correction_detected=context.correction_detected,
                         reinforcement_detected=context.reinforcement_detected,
+                        user_id=context.user_id,
                     )
                     if success:
                         logger.info("Memory updated successfully for thread %s", context.thread_id)
@@ -164,6 +215,13 @@ class MemoryUpdateQueue:
 
         self._process_queue()
 
+    def flush_nowait(self) -> None:
+        """Start queue processing immediately in a background thread."""
+        with self._lock:
+            # Daemon thread: queued messages may be lost if the process exits
+            # before _process_queue completes. Acceptable for best-effort memory updates.
+            self._schedule_timer(0)
+
     def clear(self) -> None:
         """Clear the queue without processing.
 

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

@@ -4,6 +4,7 @@ import abc
 import json
 import logging
 import threading
+import uuid
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
@@ -43,17 +44,17 @@ class MemoryStorage(abc.ABC):
     """Abstract base class for memory storage providers."""
 
     @abc.abstractmethod
-    def load(self, agent_name: str | None = None) -> dict[str, Any]:
+    def load(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Load memory data for the given agent."""
         pass
 
     @abc.abstractmethod
-    def reload(self, agent_name: str | None = None) -> dict[str, Any]:
+    def reload(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Force reload memory data for the given agent."""
         pass
 
     @abc.abstractmethod
-    def save(self, memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
+    def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
         """Save memory data for the given agent."""
         pass
 
@@ -63,9 +64,11 @@ class FileMemoryStorage(MemoryStorage):
 
     def __init__(self):
         """Initialize the file memory storage."""
-        # Per-agent memory cache: keyed by agent_name (None = global)
+        # Per-user/agent memory cache: keyed by (user_id, agent_name) tuple (None = global)
         # Value: (memory_data, file_mtime)
-        self._memory_cache: dict[str | None, tuple[dict[str, Any], float | None]] = {}
+        self._memory_cache: dict[tuple[str | None, str | None], tuple[dict[str, Any], float | None]] = {}
+        # Guards all reads and writes to _memory_cache across concurrent callers.
+        self._cache_lock = threading.Lock()
 
     def _validate_agent_name(self, agent_name: str) -> None:
         """Validate that the agent name is safe to use in filesystem paths.
@@ -78,21 +81,29 @@ class FileMemoryStorage(MemoryStorage):
         if not AGENT_NAME_PATTERN.match(agent_name):
             raise ValueError(f"Invalid agent name {agent_name!r}: names must match {AGENT_NAME_PATTERN.pattern}")
 
-    def _get_memory_file_path(self, agent_name: str | None = None) -> Path:
+    def _get_memory_file_path(self, agent_name: str | None = None, *, user_id: str | None = None) -> Path:
         """Get the path to the memory file."""
+        if user_id is not None:
+            if agent_name is not None:
+                self._validate_agent_name(agent_name)
+                return get_paths().user_agent_memory_file(user_id, agent_name)
+            config = get_memory_config()
+            if config.storage_path and Path(config.storage_path).is_absolute():
+                return Path(config.storage_path)
+            return get_paths().user_memory_file(user_id)
+        # Legacy: no user_id
         if agent_name is not None:
             self._validate_agent_name(agent_name)
             return get_paths().agent_memory_file(agent_name)
-
         config = get_memory_config()
         if config.storage_path:
             p = Path(config.storage_path)
             return p if p.is_absolute() else get_paths().base_dir / p
         return get_paths().memory_file
 
-    def _load_memory_from_file(self, agent_name: str | None = None) -> dict[str, Any]:
+    def _load_memory_from_file(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Load memory data from file."""
-        file_path = self._get_memory_file_path(agent_name)
+        file_path = self._get_memory_file_path(agent_name, user_id=user_id)
 
         if not file_path.exists():
             return create_empty_memory()
@@ -105,46 +116,60 @@ class FileMemoryStorage(MemoryStorage):
             logger.warning("Failed to load memory file: %s", e)
             return create_empty_memory()
 
-    def load(self, agent_name: str | None = None) -> dict[str, Any]:
+    @staticmethod
+    def _cache_key(agent_name: str | None = None, *, user_id: str | None = None) -> tuple[str | None, str | None]:
+        return (user_id, agent_name)
+
+    def load(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Load memory data (cached with file modification time check)."""
-        file_path = self._get_memory_file_path(agent_name)
+        file_path = self._get_memory_file_path(agent_name, user_id=user_id)
+        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             current_mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             current_mtime = None
 
-        cached = self._memory_cache.get(agent_name)
+        with self._cache_lock:
+            cached = self._memory_cache.get(cache_key)
+            if cached is not None and cached[1] == current_mtime:
+                return cached[0]
 
-        if cached is None or cached[1] != current_mtime:
+        memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
-            memory_data = self._load_memory_from_file(agent_name)
-            self._memory_cache[agent_name] = (memory_data, current_mtime)
-            return memory_data
 
-        return cached[0]
+        with self._cache_lock:
+            self._memory_cache[cache_key] = (memory_data, current_mtime)
 
-    def reload(self, agent_name: str | None = None) -> dict[str, Any]:
+        return memory_data
+
+    def reload(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Reload memory data from file, forcing cache invalidation."""
-        file_path = self._get_memory_file_path(agent_name)
+        file_path = self._get_memory_file_path(agent_name, user_id=user_id)
-        memory_data = self._load_memory_from_file(agent_name)
+        memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
+        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             mtime = None
 
-        self._memory_cache[agent_name] = (memory_data, mtime)
+        with self._cache_lock:
+            self._memory_cache[cache_key] = (memory_data, mtime)
         return memory_data
 
-    def save(self, memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
+    def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
         """Save memory data to file and update cache."""
-        file_path = self._get_memory_file_path(agent_name)
+        file_path = self._get_memory_file_path(agent_name, user_id=user_id)
+        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             file_path.parent.mkdir(parents=True, exist_ok=True)
-            memory_data["lastUpdated"] = utc_now_iso_z()
+            # Shallow-copy before adding lastUpdated so the caller's dict is not
+            # mutated as a side-effect, and the cache reference is not silently
+            # updated before the file write succeeds.
+            memory_data = {**memory_data, "lastUpdated": utc_now_iso_z()}
 
-            temp_path = file_path.with_suffix(".tmp")
+            temp_path = file_path.with_suffix(f".{uuid.uuid4().hex}.tmp")
             with open(temp_path, "w", encoding="utf-8") as f:
                 json.dump(memory_data, f, indent=2, ensure_ascii=False)
 
@@ -155,7 +180,8 @@ class FileMemoryStorage(MemoryStorage):
             except OSError:
                 mtime = None
 
-            self._memory_cache[agent_name] = (memory_data, mtime)
+            with self._cache_lock:
+                self._memory_cache[cache_key] = (memory_data, mtime)
             logger.info("Memory saved to %s", file_path)
             return True
         except OSError as e:

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

@@ -0,0 +1,31 @@
+"""Hooks fired before summarization removes messages from state."""
+
+from __future__ import annotations
+
+from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
+from deerflow.agents.memory.queue import get_memory_queue
+from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
+from deerflow.config.memory_config import get_memory_config
+
+
+def memory_flush_hook(event: SummarizationEvent) -> None:
+    """Flush messages about to be summarized into the memory queue."""
+    if not get_memory_config().enabled or not event.thread_id:
+        return
+
+    filtered_messages = filter_messages_for_memory(list(event.messages_to_summarize))
+    user_messages = [message for message in filtered_messages if getattr(message, "type", None) == "human"]
+    assistant_messages = [message for message in filtered_messages if getattr(message, "type", None) == "ai"]
+    if not user_messages or not assistant_messages:
+        return
+
+    correction_detected = detect_correction(filtered_messages)
+    reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
+    queue = get_memory_queue()
+    queue.add_nowait(
+        thread_id=event.thread_id,
+        messages=filtered_messages,
+        agent_name=event.agent_name,
+        correction_detected=correction_detected,
+        reinforcement_detected=reinforcement_detected,
+    )

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

@@ -1,5 +1,9 @@
 """Memory updater for reading, writing, and updating memory data."""
 
+import asyncio
+import atexit
+import concurrent.futures
+import copy
 import json
 import logging
 import math
@@ -22,32 +26,45 @@ from deerflow.models import create_chat_model
 logger = logging.getLogger(__name__)
 
 
+# Thread pool for offloading sync memory updates when called from an async
+# context.  Unlike the previous asyncio.run() approach, this runs *sync*
+# model.invoke() calls — no event loop is created, so the langchain async
+# httpx client pool (globally cached via @lru_cache) is never touched and
+# cross-loop connection reuse is impossible.
+_SYNC_MEMORY_UPDATER_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
+    max_workers=4,
+    thread_name_prefix="memory-updater-sync",
+)
+atexit.register(lambda: _SYNC_MEMORY_UPDATER_EXECUTOR.shutdown(wait=False))
+
+
 def _create_empty_memory() -> dict[str, Any]:
     """Backward-compatible wrapper around the storage-layer empty-memory factory."""
     return create_empty_memory()
 
 
-def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
+def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
     """Backward-compatible wrapper around the configured memory storage save path."""
-    return get_memory_storage().save(memory_data, agent_name)
+    return get_memory_storage().save(memory_data, agent_name, user_id=user_id)
 
 
-def get_memory_data(agent_name: str | None = None) -> dict[str, Any]:
+def get_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Get the current memory data via storage provider."""
-    return get_memory_storage().load(agent_name)
+    return get_memory_storage().load(agent_name, user_id=user_id)
 
 
-def reload_memory_data(agent_name: str | None = None) -> dict[str, Any]:
+def reload_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Reload memory data via storage provider."""
-    return get_memory_storage().reload(agent_name)
+    return get_memory_storage().reload(agent_name, user_id=user_id)
 
 
-def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None) -> dict[str, Any]:
+def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Persist imported memory data via storage provider.
 
     Args:
         memory_data: Full memory payload to persist.
         agent_name: If provided, imports into per-agent memory.
+        user_id: If provided, scopes memory to a specific user.
 
     Returns:
         The saved memory data after storage normalization.
@@ -56,15 +73,15 @@ def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = Non
         OSError: If persisting the imported memory fails.
     """
     storage = get_memory_storage()
-    if not storage.save(memory_data, agent_name):
+    if not storage.save(memory_data, agent_name, user_id=user_id):
         raise OSError("Failed to save imported memory data")
-    return storage.load(agent_name)
+    return storage.load(agent_name, user_id=user_id)
 
 
-def clear_memory_data(agent_name: str | None = None) -> dict[str, Any]:
+def clear_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Clear all stored memory data and persist an empty structure."""
     cleared_memory = create_empty_memory()
-    if not _save_memory_to_file(cleared_memory, agent_name):
+    if not _save_memory_to_file(cleared_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save cleared memory data")
     return cleared_memory
 
@@ -81,6 +98,8 @@ def create_memory_fact(
     category: str = "context",
     confidence: float = 0.5,
     agent_name: str | None = None,
+    *,
+    user_id: str | None = None,
 ) -> dict[str, Any]:
     """Create a new fact and persist the updated memory data."""
     normalized_content = content.strip()
@@ -90,7 +109,7 @@ def create_memory_fact(
     normalized_category = category.strip() or "context"
     validated_confidence = _validate_confidence(confidence)
     now = utc_now_iso_z()
-    memory_data = get_memory_data(agent_name)
+    memory_data = get_memory_data(agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     facts = list(memory_data.get("facts", []))
     facts.append(
@@ -105,15 +124,15 @@ def create_memory_fact(
     )
     updated_memory["facts"] = facts
 
-    if not _save_memory_to_file(updated_memory, agent_name):
+    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save memory data after creating fact")
 
     return updated_memory
 
 
-def delete_memory_fact(fact_id: str, agent_name: str | None = None) -> dict[str, Any]:
+def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Delete a fact by its id and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name)
+    memory_data = get_memory_data(agent_name, user_id=user_id)
     facts = memory_data.get("facts", [])
     updated_facts = [fact for fact in facts if fact.get("id") != fact_id]
     if len(updated_facts) == len(facts):
@@ -122,7 +141,7 @@ def delete_memory_fact(fact_id: str, agent_name: str | None = None) -> dict[str,
     updated_memory = dict(memory_data)
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name):
+    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after deleting fact '{fact_id}'")
 
     return updated_memory
@@ -134,9 +153,11 @@ def update_memory_fact(
     category: str | None = None,
     confidence: float | None = None,
     agent_name: str | None = None,
+    *,
+    user_id: str | None = None,
 ) -> dict[str, Any]:
     """Update an existing fact and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name)
+    memory_data = get_memory_data(agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     updated_facts: list[dict[str, Any]] = []
     found = False
@@ -163,7 +184,7 @@ def update_memory_fact(
 
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name):
+    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")
 
     return updated_memory
@@ -269,6 +290,154 @@ class MemoryUpdater:
         model_name = self._model_name or config.model_name
         return create_chat_model(name=model_name, thinking_enabled=False)
 
+    def _build_correction_hint(
+        self,
+        correction_detected: bool,
+        reinforcement_detected: bool,
+    ) -> str:
+        """Build optional prompt hints for correction and reinforcement signals."""
+        correction_hint = ""
+        if correction_detected:
+            correction_hint = (
+                "IMPORTANT: Explicit correction signals were detected in this conversation. "
+                "Pay special attention to what the agent got wrong, what the user corrected, "
+                "and record the correct approach as a fact with category "
+                '"correction" and confidence >= 0.95 when appropriate.'
+            )
+        if reinforcement_detected:
+            reinforcement_hint = (
+                "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
+                "The user explicitly confirmed the agent's approach was correct or helpful. "
+                "Record the confirmed approach, style, or preference as a fact with category "
+                '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
+            )
+            correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
+
+        return correction_hint
+
+    def _prepare_update_prompt(
+        self,
+        messages: list[Any],
+        agent_name: str | None,
+        correction_detected: bool,
+        reinforcement_detected: bool,
+        user_id: str | None = None,
+    ) -> tuple[dict[str, Any], str] | None:
+        """Load memory and build the update prompt for a conversation."""
+        config = get_memory_config()
+        if not config.enabled or not messages:
+            return None
+
+        current_memory = get_memory_data(agent_name, user_id=user_id)
+        conversation_text = format_conversation_for_update(messages)
+        if not conversation_text.strip():
+            return None
+
+        correction_hint = self._build_correction_hint(
+            correction_detected=correction_detected,
+            reinforcement_detected=reinforcement_detected,
+        )
+        prompt = MEMORY_UPDATE_PROMPT.format(
+            current_memory=json.dumps(current_memory, indent=2),
+            conversation=conversation_text,
+            correction_hint=correction_hint,
+        )
+        return current_memory, prompt
+
+    def _finalize_update(
+        self,
+        current_memory: dict[str, Any],
+        response_content: Any,
+        thread_id: str | None,
+        agent_name: str | None,
+        user_id: str | None = None,
+    ) -> bool:
+        """Parse the model response, apply updates, and persist memory."""
+        response_text = _extract_text(response_content).strip()
+
+        if response_text.startswith("```"):
+            lines = response_text.split("\n")
+            response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
+
+        update_data = json.loads(response_text)
+        # Deep-copy before in-place mutation so a subsequent save() failure
+        # cannot corrupt the still-cached original object reference.
+        updated_memory = self._apply_updates(copy.deepcopy(current_memory), update_data, thread_id)
+        updated_memory = _strip_upload_mentions_from_memory(updated_memory)
+        return get_memory_storage().save(updated_memory, agent_name, user_id=user_id)
+
+    async def aupdate_memory(
+        self,
+        messages: list[Any],
+        thread_id: str | None = None,
+        agent_name: str | None = None,
+        correction_detected: bool = False,
+        reinforcement_detected: bool = False,
+        user_id: str | None = None,
+    ) -> bool:
+        """Update memory asynchronously by delegating to the sync path.
+
+        Uses ``asyncio.to_thread`` to run the *sync* ``model.invoke()`` path
+        in a worker thread so no second event loop is created and the
+        langchain async httpx client pool (shared with the lead agent) is
+        never touched.  This eliminates the cross-loop connection-reuse bug
+        described in issue #2615.
+        """
+        return await asyncio.to_thread(
+            self._do_update_memory_sync,
+            messages=messages,
+            thread_id=thread_id,
+            agent_name=agent_name,
+            correction_detected=correction_detected,
+            reinforcement_detected=reinforcement_detected,
+            user_id=user_id,
+        )
+
+    def _do_update_memory_sync(
+        self,
+        messages: list[Any],
+        thread_id: str | None = None,
+        agent_name: str | None = None,
+        correction_detected: bool = False,
+        reinforcement_detected: bool = False,
+        user_id: str | None = None,
+    ) -> bool:
+        """Pure-sync memory update using ``model.invoke()``.
+
+        Uses the *sync* LLM call path so no event loop is created.  This
+        guarantees that the langchain provider's globally cached async
+        httpx ``AsyncClient`` / connection pool (the one shared with the
+        lead agent) is never touched — no cross-loop connection reuse is
+        possible.
+        """
+        try:
+            prepared = self._prepare_update_prompt(
+                messages=messages,
+                agent_name=agent_name,
+                correction_detected=correction_detected,
+                reinforcement_detected=reinforcement_detected,
+                user_id=user_id,
+            )
+            if prepared is None:
+                return False
+
+            current_memory, prompt = prepared
+            model = self._get_model()
+            response = model.invoke(prompt, config={"run_name": "memory_agent"})
+            return self._finalize_update(
+                current_memory=current_memory,
+                response_content=response.content,
+                thread_id=thread_id,
+                agent_name=agent_name,
+                user_id=user_id,
+            )
+        except json.JSONDecodeError as e:
+            logger.warning("Failed to parse LLM response for memory update: %s", e)
+            return False
+        except Exception as e:
+            logger.exception("Memory update failed: %s", e)
+            return False
+
     def update_memory(
         self,
         messages: list[Any],
@@ -276,8 +445,18 @@ class MemoryUpdater:
         agent_name: str | None = None,
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
+        user_id: str | None = None,
     ) -> bool:
-        """Update memory based on conversation messages.
+        """Synchronously update memory using the sync LLM path.
+
+        Uses ``model.invoke()`` (sync HTTP) which operates on a completely
+        separate connection pool from the async ``AsyncClient`` shared by
+        the lead agent.  This eliminates the cross-loop connection-reuse
+        bug described in issue #2615.
+
+        When called from within a running event loop (e.g. from a LangGraph
+        node), the blocking sync call is offloaded to a thread pool so the
+        caller's loop is not blocked.
 
         Args:
             messages: List of conversation messages.
@@ -285,82 +464,40 @@ class MemoryUpdater:
             agent_name: If provided, updates per-agent memory. If None, updates global memory.
             correction_detected: Whether recent turns include an explicit correction signal.
             reinforcement_detected: Whether recent turns include a positive reinforcement signal.
+            user_id: If provided, scopes memory to a specific user.
 
         Returns:
             True if update was successful, False otherwise.
         """
-        config = get_memory_config()
-        if not config.enabled:
-            return False
-
-        if not messages:
-            return False
-
         try:
-            # Get current memory
+            loop = asyncio.get_running_loop()
-            current_memory = get_memory_data(agent_name)
+        except RuntimeError:
+            loop = None
 
-            # Format conversation for prompt
+        if loop is not None and loop.is_running():
-            conversation_text = format_conversation_for_update(messages)
+            try:
-
+                future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(
-            if not conversation_text.strip():
+                    self._do_update_memory_sync,
+                    messages=messages,
+                    thread_id=thread_id,
+                    agent_name=agent_name,
+                    correction_detected=correction_detected,
+                    reinforcement_detected=reinforcement_detected,
+                    user_id=user_id,
+                )
+                return future.result()
+            except Exception:
+                logger.exception("Failed to offload memory update to executor")
                 return False
 
-            # Build prompt
+        return self._do_update_memory_sync(
-            correction_hint = ""
+            messages=messages,
-            if correction_detected:
+            thread_id=thread_id,
-                correction_hint = (
+            agent_name=agent_name,
-                    "IMPORTANT: Explicit correction signals were detected in this conversation. "
+            correction_detected=correction_detected,
-                    "Pay special attention to what the agent got wrong, what the user corrected, "
+            reinforcement_detected=reinforcement_detected,
-                    "and record the correct approach as a fact with category "
+            user_id=user_id,
-                    '"correction" and confidence >= 0.95 when appropriate.'
+        )
-                )
-            if reinforcement_detected:
-                reinforcement_hint = (
-                    "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
-                    "The user explicitly confirmed the agent's approach was correct or helpful. "
-                    "Record the confirmed approach, style, or preference as a fact with category "
-                    '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
-                )
-                correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
-
-            prompt = MEMORY_UPDATE_PROMPT.format(
-                current_memory=json.dumps(current_memory, indent=2),
-                conversation=conversation_text,
-                correction_hint=correction_hint,
-            )
-
-            # Call LLM
-            model = self._get_model()
-            response = model.invoke(prompt)
-            response_text = _extract_text(response.content).strip()
-
-            # Parse response
-            # Remove markdown code blocks if present
-            if response_text.startswith("```"):
-                lines = response_text.split("\n")
-                response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
-
-            update_data = json.loads(response_text)
-
-            # Apply updates
-            updated_memory = self._apply_updates(current_memory, update_data, thread_id)
-
-            # Strip file-upload mentions from all summaries before saving.
-            # Uploaded files are session-scoped and won't exist in future sessions,
-            # so recording upload events in long-term memory causes the agent to
-            # try (and fail) to locate those files in subsequent conversations.
-            updated_memory = _strip_upload_mentions_from_memory(updated_memory)
-
-            # Save
-            return get_memory_storage().save(updated_memory, agent_name)
-
-        except json.JSONDecodeError as e:
-            logger.warning("Failed to parse LLM response for memory update: %s", e)
-            return False
-        except Exception as e:
-            logger.exception("Memory update failed: %s", e)
-            return False
 
     def _apply_updates(
         self,
@@ -455,6 +592,7 @@ def update_memory_from_conversation(
     agent_name: str | None = None,
     correction_detected: bool = False,
     reinforcement_detected: bool = False,
+    user_id: str | None = None,
 ) -> bool:
     """Convenience function to update memory from a conversation.
 
@@ -464,9 +602,10 @@ def update_memory_from_conversation(
         agent_name: If provided, updates per-agent memory. If None, updates global memory.
         correction_detected: Whether recent turns include an explicit correction signal.
         reinforcement_detected: Whether recent turns include a positive reinforcement signal.
+        user_id: If provided, scopes memory to a specific user.
 
     Returns:
         True if successful, False otherwise.
     """
     updater = MemoryUpdater()
-    return updater.update_memory(messages, thread_id, agent_name, correction_detected, reinforcement_detected)
+    return updater.update_memory(messages, thread_id, agent_name, correction_detected, reinforcement_detected, user_id=user_id)

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

@@ -3,6 +3,7 @@
 import json
 import logging
 from collections.abc import Callable
+from hashlib import sha256
 from typing import override
 
 from langchain.agents import AgentState
@@ -36,6 +37,13 @@ class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
 
     state_schema = ClarificationMiddlewareState
 
+    def _stable_message_id(self, tool_call_id: str, formatted_message: str) -> str:
+        """Build a deterministic message ID so retried clarification calls replace, not append."""
+        if tool_call_id:
+            return f"clarification:{tool_call_id}"
+        digest = sha256(formatted_message.encode("utf-8")).hexdigest()[:16]
+        return f"clarification:{digest}"
+
     def _is_chinese(self, text: str) -> bool:
         """Check if text contains Chinese characters.
 
@@ -131,6 +139,7 @@ class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
         # Create a ToolMessage with the formatted question
         # This will be added to the message history
         tool_message = ToolMessage(
+            id=self._stable_message_id(tool_call_id, formatted_message),
             content=formatted_message,
             tool_call_id=tool_call_id,
             name="ask_clarification",

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

@@ -13,6 +13,7 @@ at the correct positions (immediately after each dangling AIMessage), not append
 to the end of the message list as before_model + add_messages reducer would do.
 """
 
+import json
 import logging
 from collections.abc import Awaitable, Callable
 from typing import override
@@ -33,6 +34,44 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
     offending AIMessage so the LLM receives a well-formed conversation.
     """
 
+    @staticmethod
+    def _message_tool_calls(msg) -> list[dict]:
+        """Return normalized tool calls from structured fields or raw provider payloads."""
+        tool_calls = getattr(msg, "tool_calls", None) or []
+        if tool_calls:
+            return list(tool_calls)
+
+        raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
+        normalized: list[dict] = []
+        for raw_tc in raw_tool_calls:
+            if not isinstance(raw_tc, dict):
+                continue
+
+            function = raw_tc.get("function")
+            name = raw_tc.get("name")
+            if not name and isinstance(function, dict):
+                name = function.get("name")
+
+            args = raw_tc.get("args", {})
+            if not args and isinstance(function, dict):
+                raw_args = function.get("arguments")
+                if isinstance(raw_args, str):
+                    try:
+                        parsed_args = json.loads(raw_args)
+                    except (TypeError, ValueError, json.JSONDecodeError):
+                        parsed_args = {}
+                    args = parsed_args if isinstance(parsed_args, dict) else {}
+
+            normalized.append(
+                {
+                    "id": raw_tc.get("id"),
+                    "name": name or "unknown",
+                    "args": args if isinstance(args, dict) else {},
+                }
+            )
+
+        return normalized
+
     def _build_patched_messages(self, messages: list) -> list | None:
         """Return a new message list with patches inserted at the correct positions.
 
@@ -51,7 +90,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
         for msg in messages:
             if getattr(msg, "type", None) != "ai":
                 continue
-            for tc in getattr(msg, "tool_calls", None) or []:
+            for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
                 if tc_id and tc_id not in existing_tool_msg_ids:
                     needs_patch = True
@@ -70,7 +109,7 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
             patched.append(msg)
             if getattr(msg, "type", None) != "ai":
                 continue
-            for tc in getattr(msg, "tool_calls", None) or []:
+            for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
                 if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
                     patched.append(

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

@@ -16,6 +16,9 @@ from typing import override
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt.tool_node import ToolCallRequest
+from langgraph.types import Command
 
 logger = logging.getLogger(__name__)
 
@@ -35,7 +38,7 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
         if not registry:
             return request
 
-        deferred_names = {e.name for e in registry.entries}
+        deferred_names = registry.deferred_names
         active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
 
         if len(active_tools) < len(request.tools):
@@ -43,6 +46,28 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
 
         return request.override(tools=active_tools)
 
+    def _blocked_tool_message(self, request: ToolCallRequest) -> ToolMessage | None:
+        from deerflow.tools.builtins.tool_search import get_deferred_registry
+
+        registry = get_deferred_registry()
+        if not registry:
+            return None
+
+        tool_name = str(request.tool_call.get("name") or "")
+        if not tool_name:
+            return None
+
+        if not registry.contains(tool_name):
+            return None
+
+        tool_call_id = str(request.tool_call.get("id") or "missing_tool_call_id")
+        return ToolMessage(
+            content=(f"Error: Tool '{tool_name}' is deferred and has not been promoted yet. Call tool_search first to expose and promote this tool's schema, then retry."),
+            tool_call_id=tool_call_id,
+            name=tool_name,
+            status="error",
+        )
+
     @override
     def wrap_model_call(
         self,
@@ -51,6 +76,17 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
     ) -> ModelCallResult:
         return handler(self._filter_tools(request))
 
+    @override
+    def wrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        blocked = self._blocked_tool_message(request)
+        if blocked is not None:
+            return blocked
+        return handler(request)
+
     @override
     async def awrap_model_call(
         self,
@@ -58,3 +94,14 @@ class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
         handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
     ) -> ModelCallResult:
         return await handler(self._filter_tools(request))
+
+    @override
+    async def awrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
+    ) -> ToolMessage | Command:
+        blocked = self._blocked_tool_message(request)
+        if blocked is not None:
+            return blocked
+        return await handler(request)

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

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import asyncio
 import logging
+import threading
 import time
 from collections.abc import Awaitable, Callable
 from email.utils import parsedate_to_datetime
@@ -19,6 +20,8 @@ from langchain.agents.middleware.types import (
 from langchain_core.messages import AIMessage
 from langgraph.errors import GraphBubbleUp
 
+from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 _RETRIABLE_STATUS_CODES = {408, 409, 425, 429, 500, 502, 503, 504}
@@ -67,6 +70,71 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
     retry_base_delay_ms: int = 1000
     retry_cap_delay_ms: int = 8000
 
+    def __init__(self, *, app_config: AppConfig, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+
+        self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
+        self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
+
+        # Circuit Breaker state
+        self._circuit_lock = threading.Lock()
+        self._circuit_failure_count = 0
+        self._circuit_open_until = 0.0
+        self._circuit_state = "closed"
+        self._circuit_probe_in_flight = False
+
+    def _check_circuit(self) -> bool:
+        """Returns True if circuit is OPEN (fast fail), False otherwise."""
+        with self._circuit_lock:
+            now = time.time()
+
+            if self._circuit_state == "open":
+                if now < self._circuit_open_until:
+                    return True
+                self._circuit_state = "half_open"
+                self._circuit_probe_in_flight = False
+
+            if self._circuit_state == "half_open":
+                if self._circuit_probe_in_flight:
+                    return True
+                self._circuit_probe_in_flight = True
+                return False
+
+            return False
+
+    def _record_success(self) -> None:
+        with self._circuit_lock:
+            if self._circuit_state != "closed" or self._circuit_failure_count > 0:
+                logger.info("Circuit breaker reset (Closed). LLM service recovered.")
+            self._circuit_failure_count = 0
+            self._circuit_open_until = 0.0
+            self._circuit_state = "closed"
+            self._circuit_probe_in_flight = False
+
+    def _record_failure(self) -> None:
+        with self._circuit_lock:
+            if self._circuit_state == "half_open":
+                self._circuit_open_until = time.time() + self.circuit_recovery_timeout_sec
+                self._circuit_state = "open"
+                self._circuit_probe_in_flight = False
+                logger.error(
+                    "Circuit breaker probe failed (Open). Will probe again after %ds.",
+                    self.circuit_recovery_timeout_sec,
+                )
+                return
+
+            self._circuit_failure_count += 1
+            if self._circuit_failure_count >= self.circuit_failure_threshold:
+                self._circuit_open_until = time.time() + self.circuit_recovery_timeout_sec
+                if self._circuit_state != "open":
+                    self._circuit_state = "open"
+                    self._circuit_probe_in_flight = False
+                    logger.error(
+                        "Circuit breaker tripped (Open). Threshold reached (%d). Will probe after %ds.",
+                        self.circuit_failure_threshold,
+                        self.circuit_recovery_timeout_sec,
+                    )
+
     def _classify_error(self, exc: BaseException) -> tuple[bool, str]:
         detail = _extract_error_detail(exc)
         lowered = detail.lower()
@@ -83,6 +151,8 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
             "APITimeoutError",
             "APIConnectionError",
             "InternalServerError",
+            "ReadError",  # httpx.ReadError: connection dropped mid-stream
+            "RemoteProtocolError",  # httpx: server closed connection unexpectedly
         }:
             return True, "transient"
         if status_code in _RETRIABLE_STATUS_CODES:
@@ -104,6 +174,9 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         reason_text = "provider is busy" if reason == "busy" else "provider request failed temporarily"
         return f"LLM request retry {attempt}/{self.retry_max_attempts}: {reason_text}. Retrying in {seconds}s."
 
+    def _build_circuit_breaker_message(self) -> str:
+        return "The configured LLM provider is currently unavailable due to continuous failures. Circuit breaker is engaged to protect the system. Please wait a moment before trying again."
+
     def _build_user_message(self, exc: BaseException, reason: str) -> str:
         detail = _extract_error_detail(exc)
         if reason == "quota":
@@ -138,12 +211,20 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         request: ModelRequest,
         handler: Callable[[ModelRequest], ModelResponse],
     ) -> ModelCallResult:
+        if self._check_circuit():
+            return AIMessage(content=self._build_circuit_breaker_message())
+
         attempt = 1
         while True:
             try:
-                return handler(request)
+                response = handler(request)
+                self._record_success()
+                return response
             except GraphBubbleUp:
                 # Preserve LangGraph control-flow signals (interrupt/pause/resume).
+                with self._circuit_lock:
+                    if self._circuit_state == "half_open":
+                        self._circuit_probe_in_flight = False
                 raise
             except Exception as exc:
                 retriable, reason = self._classify_error(exc)
@@ -166,6 +247,8 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                     _extract_error_detail(exc),
                     exc_info=exc,
                 )
+                if retriable:
+                    self._record_failure()
                 return AIMessage(content=self._build_user_message(exc, reason))
 
     @override
@@ -174,12 +257,20 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         request: ModelRequest,
         handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
     ) -> ModelCallResult:
+        if self._check_circuit():
+            return AIMessage(content=self._build_circuit_breaker_message())
+
         attempt = 1
         while True:
             try:
-                return await handler(request)
+                response = await handler(request)
+                self._record_success()
+                return response
             except GraphBubbleUp:
                 # Preserve LangGraph control-flow signals (interrupt/pause/resume).
+                with self._circuit_lock:
+                    if self._circuit_state == "half_open":
+                        self._circuit_probe_in_flight = False
                 raise
             except Exception as exc:
                 retriable, reason = self._classify_error(exc)
@@ -202,6 +293,8 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                     _extract_error_detail(exc),
                     exc_info=exc,
                 )
+                if retriable:
+                    self._record_failure()
                 return AIMessage(content=self._build_user_message(exc, reason))
 
 

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

@@ -17,6 +17,7 @@ import json
 import logging
 import threading
 from collections import OrderedDict, defaultdict
+from copy import deepcopy
 from typing import override
 
 from langchain.agents import AgentState
@@ -323,6 +324,26 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
         # Fallback: coerce unexpected types to str to avoid TypeError
         return str(content) + f"\n\n{text}"
 
+    @staticmethod
+    def _build_hard_stop_update(last_msg, content: str | list) -> dict:
+        """Clear tool-call metadata so forced-stop messages serialize as plain assistant text."""
+        update = {
+            "tool_calls": [],
+            "content": content,
+        }
+
+        additional_kwargs = dict(getattr(last_msg, "additional_kwargs", {}) or {})
+        for key in ("tool_calls", "function_call"):
+            additional_kwargs.pop(key, None)
+        update["additional_kwargs"] = additional_kwargs
+
+        response_metadata = deepcopy(getattr(last_msg, "response_metadata", {}) or {})
+        if response_metadata.get("finish_reason") == "tool_calls":
+            response_metadata["finish_reason"] = "stop"
+        update["response_metadata"] = response_metadata
+
+        return update
+
     def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
         warning, hard_stop = self._track_and_check(state, runtime)
 
@@ -330,12 +351,8 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             # Strip tool_calls from the last AIMessage to force text output
             messages = state.get("messages", [])
             last_msg = messages[-1]
-            stripped_msg = last_msg.model_copy(
+            content = self._append_text(last_msg.content, warning or _HARD_STOP_MSG)
-                update={
+            stripped_msg = last_msg.model_copy(update=self._build_hard_stop_update(last_msg, content))
-                    "tool_calls": [],
-                    "content": self._append_text(last_msg.content, warning),
-                }
-            )
             return {"messages": [stripped_msg]}
 
         if warning:
@@ -345,7 +362,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             # the conversation; injecting one mid-conversation crashes
             # langchain_anthropic's _format_messages(). HumanMessage works
             # with all providers. See #1299.
-            return {"messages": [HumanMessage(content=warning)]}
+            return {"messages": [HumanMessage(content=warning, name="loop_warning")]}
 
         return None
 

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

@@ -1,50 +1,20 @@
 """Middleware for memory mechanism."""
 
 import logging
-import re
+from typing import override
-from typing import Any, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
+from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 from deerflow.agents.memory.queue import get_memory_queue
 from deerflow.config.memory_config import get_memory_config
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
-_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
-_CORRECTION_PATTERNS = (
-    re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
-    re.compile(r"\byou misunderstood\b", re.IGNORECASE),
-    re.compile(r"\btry again\b", re.IGNORECASE),
-    re.compile(r"\bredo\b", re.IGNORECASE),
-    re.compile(r"不对"),
-    re.compile(r"你理解错了"),
-    re.compile(r"你理解有误"),
-    re.compile(r"重试"),
-    re.compile(r"重新来"),
-    re.compile(r"换一种"),
-    re.compile(r"改用"),
-)
-
-_REINFORCEMENT_PATTERNS = (
-    re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
-    re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
-    re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
-    re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
-    re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
-    re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
-    re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
-    re.compile(r"完全正确(?:[。！？!?.]|$)"),
-    re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
-    re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
-    re.compile(r"继续保持(?:[。！？!?.]|$)"),
-)
-
 
 class MemoryMiddlewareState(AgentState):
     """Compatible with the `ThreadState` schema."""
@@ -52,125 +22,6 @@ class MemoryMiddlewareState(AgentState):
     pass
 
 
-def _extract_message_text(message: Any) -> str:
-    """Extract plain text from message content for filtering and signal detection."""
-    content = getattr(message, "content", "")
-    if isinstance(content, list):
-        text_parts: list[str] = []
-        for part in content:
-            if isinstance(part, str):
-                text_parts.append(part)
-            elif isinstance(part, dict):
-                text_val = part.get("text")
-                if isinstance(text_val, str):
-                    text_parts.append(text_val)
-        return " ".join(text_parts)
-    return str(content)
-
-
-def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
-    """Filter messages to keep only user inputs and final assistant responses.
-
-    This filters out:
-    - Tool messages (intermediate tool call results)
-    - AI messages with tool_calls (intermediate steps, not final responses)
-    - The <uploaded_files> block injected by UploadsMiddleware into human messages
-      (file paths are session-scoped and must not persist in long-term memory).
-      The user's actual question is preserved; only turns whose content is entirely
-      the upload block (nothing remains after stripping) are dropped along with
-      their paired assistant response.
-
-    Only keeps:
-    - Human messages (with the ephemeral upload block removed)
-    - AI messages without tool_calls (final assistant responses), unless the
-      paired human turn was upload-only and had no real user text.
-
-    Args:
-        messages: List of all conversation messages.
-
-    Returns:
-        Filtered list containing only user inputs and final assistant responses.
-    """
-    filtered = []
-    skip_next_ai = False
-    for msg in messages:
-        msg_type = getattr(msg, "type", None)
-
-        if msg_type == "human":
-            content_str = _extract_message_text(msg)
-            if "<uploaded_files>" in content_str:
-                # Strip the ephemeral upload block; keep the user's real question.
-                stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
-                if not stripped:
-                    # Nothing left — the entire turn was upload bookkeeping;
-                    # skip it and the paired assistant response.
-                    skip_next_ai = True
-                    continue
-                # Rebuild the message with cleaned content so the user's question
-                # is still available for memory summarisation.
-                from copy import copy
-
-                clean_msg = copy(msg)
-                clean_msg.content = stripped
-                filtered.append(clean_msg)
-                skip_next_ai = False
-            else:
-                filtered.append(msg)
-                skip_next_ai = False
-        elif msg_type == "ai":
-            tool_calls = getattr(msg, "tool_calls", None)
-            if not tool_calls:
-                if skip_next_ai:
-                    skip_next_ai = False
-                    continue
-                filtered.append(msg)
-        # Skip tool messages and AI messages with tool_calls
-
-    return filtered
-
-
-def detect_correction(messages: list[Any]) -> bool:
-    """Detect explicit user corrections in recent conversation turns.
-
-    The queue keeps only one pending context per thread, so callers pass the
-    latest filtered message list. Checking only recent user turns keeps signal
-    detection conservative while avoiding stale corrections from long histories.
-    """
-    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
-
-    for msg in recent_user_msgs:
-        content = _extract_message_text(msg).strip()
-        if not content:
-            continue
-        if any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
-            return True
-
-    return False
-
-
-def detect_reinforcement(messages: list[Any]) -> bool:
-    """Detect explicit positive reinforcement signals in recent conversation turns.
-
-    Complements detect_correction() by identifying when the user confirms the
-    agent's approach was correct. This allows the memory system to record what
-    worked well, not just what went wrong.
-
-    The queue keeps only one pending context per thread, so callers pass the
-    latest filtered message list. Checking only recent user turns keeps signal
-    detection conservative while avoiding stale signals from long histories.
-    """
-    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
-
-    for msg in recent_user_msgs:
-        content = _extract_message_text(msg).strip()
-        if not content:
-            continue
-        if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
-            return True
-
-    return False
-
-
 class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
     """Middleware that queues conversation for memory update after agent execution.
 
@@ -223,7 +74,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
             return None
 
         # Filter to only keep user inputs and final assistant responses
-        filtered_messages = _filter_messages_for_memory(messages)
+        filtered_messages = filter_messages_for_memory(messages)
 
         # Only queue if there's meaningful conversation
         # At minimum need one user message and one assistant response
@@ -236,11 +87,16 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         # Queue the filtered conversation for memory update
         correction_detected = detect_correction(filtered_messages)
         reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
+        # Capture user_id at enqueue time while the request context is still alive.
+        # threading.Timer fires on a different thread where ContextVar values are not
+        # propagated, so we must store user_id explicitly in ConversationContext.
+        user_id = get_effective_user_id()
         queue = get_memory_queue()
         queue.add(
             thread_id=thread_id,
             messages=filtered_messages,
             agent_name=self._agent_name,
+            user_id=user_id,
             correction_detected=correction_detected,
             reinforcement_detected=reinforcement_detected,
         )

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

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

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

@@ -1,13 +1,16 @@
 import logging
+from datetime import UTC, datetime
 from typing import NotRequired, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langchain_core.messages import HumanMessage
 from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
 from deerflow.agents.thread_state import ThreadDataState
 from deerflow.config.paths import Paths, get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
@@ -46,32 +49,34 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
         self._paths = Paths(base_dir) if base_dir else get_paths()
         self._lazy_init = lazy_init
 
-    def _get_thread_paths(self, thread_id: str) -> dict[str, str]:
+    def _get_thread_paths(self, thread_id: str, user_id: str | None = None) -> dict[str, str]:
         """Get the paths for a thread's data directories.
 
         Args:
             thread_id: The thread ID.
+            user_id: Optional user ID for per-user path isolation.
 
         Returns:
             Dictionary with workspace_path, uploads_path, and outputs_path.
         """
         return {
-            "workspace_path": str(self._paths.sandbox_work_dir(thread_id)),
+            "workspace_path": str(self._paths.sandbox_work_dir(thread_id, user_id=user_id)),
-            "uploads_path": str(self._paths.sandbox_uploads_dir(thread_id)),
+            "uploads_path": str(self._paths.sandbox_uploads_dir(thread_id, user_id=user_id)),
-            "outputs_path": str(self._paths.sandbox_outputs_dir(thread_id)),
+            "outputs_path": str(self._paths.sandbox_outputs_dir(thread_id, user_id=user_id)),
         }
 
-    def _create_thread_directories(self, thread_id: str) -> dict[str, str]:
+    def _create_thread_directories(self, thread_id: str, user_id: str | None = None) -> dict[str, str]:
         """Create the thread data directories.
 
         Args:
             thread_id: The thread ID.
+            user_id: Optional user ID for per-user path isolation.
 
         Returns:
             Dictionary with the created directory paths.
         """
-        self._paths.ensure_thread_dirs(thread_id)
+        self._paths.ensure_thread_dirs(thread_id, user_id=user_id)
-        return self._get_thread_paths(thread_id)
+        return self._get_thread_paths(thread_id, user_id=user_id)
 
     @override
     def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
@@ -84,16 +89,30 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
         if thread_id is None:
             raise ValueError("Thread ID is required in runtime context or config.configurable")
 
+        user_id = get_effective_user_id()
+
         if self._lazy_init:
             # Lazy initialization: only compute paths, don't create directories
-            paths = self._get_thread_paths(thread_id)
+            paths = self._get_thread_paths(thread_id, user_id=user_id)
         else:
             # Eager initialization: create directories immediately
-            paths = self._create_thread_directories(thread_id)
+            paths = self._create_thread_directories(thread_id, user_id=user_id)
             logger.debug("Created thread data directories for thread %s", thread_id)
 
+        messages = list(state.get("messages", []))
+        last_message = messages[-1] if messages else None
+
+        if last_message and isinstance(last_message, HumanMessage):
+            messages[-1] = HumanMessage(
+                content=last_message.content,
+                id=last_message.id,
+                name=last_message.name or "user-input",
+                additional_kwargs={**last_message.additional_kwargs, "run_id": runtime.context.get("run_id"), "timestamp": datetime.now(UTC).isoformat()},
+            )
+
         return {
             "thread_data": {
                 **paths,
-            }
+            },
+            "messages": messages,
         }

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

@@ -1,10 +1,12 @@
 """Middleware for automatic thread title generation."""
 
 import logging
-from typing import NotRequired, override
+import re
+from typing import Any, NotRequired, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
 from deerflow.config.title_config import get_title_config
@@ -77,7 +79,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")
 
         user_msg = self._normalize_content(user_msg_content)
-        assistant_msg = self._normalize_content(assistant_msg_content)
+        assistant_msg = self._strip_think_tags(self._normalize_content(assistant_msg_content))
 
         prompt = config.prompt_template.format(
             max_words=config.max_words,
@@ -86,10 +88,15 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         )
         return prompt, user_msg
 
+    def _strip_think_tags(self, text: str) -> str:
+        """Remove <think>...</think> blocks emitted by reasoning models (e.g. minimax, DeepSeek-R1)."""
+        return re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.IGNORECASE).strip()
+
     def _parse_title(self, content: object) -> str:
         """Normalize model output into a clean title string."""
         config = get_title_config()
         title_content = self._normalize_content(content)
+        title_content = self._strip_think_tags(title_content)
         title = title_content.strip().strip('"').strip("'")
         return title[: config.max_chars] if len(title) > config.max_chars else title
 
@@ -100,6 +107,21 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
             return user_msg[:fallback_chars].rstrip() + "..."
         return user_msg if user_msg else "New Conversation"
 
+    def _get_runnable_config(self) -> dict[str, Any]:
+        """Inherit the parent RunnableConfig and add middleware tag.
+
+        This ensures RunJournal identifies LLM calls from this middleware
+        as ``middleware:title`` instead of ``lead_agent``.
+        """
+        try:
+            parent = get_config()
+        except Exception:
+            parent = {}
+        config = {**parent}
+        config["run_name"] = "title_agent"
+        config["tags"] = [*(config.get("tags") or []), "middleware:title"]
+        return config
+
     def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
         """Generate a local fallback title without blocking on an LLM call."""
         if not self._should_generate_title(state):
@@ -121,7 +143,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
                 model = create_chat_model(name=config.model_name, thinking_enabled=False)
             else:
                 model = create_chat_model(thinking_enabled=False)
-            response = await model.ainvoke(prompt)
+            response = await model.ainvoke(prompt, config=self._get_runnable_config())
             title = self._parse_title(response.content)
             if title:
                 return {"title": title}

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

@@ -1,9 +1,14 @@
-"""Middleware that extends TodoListMiddleware with context-loss detection.
+"""Middleware that extends TodoListMiddleware with context-loss detection and premature-exit prevention.
 
 When the message history is truncated (e.g., by SummarizationMiddleware), the
 original `write_todos` tool call and its ToolMessage can be scrolled out of the
 active context window. This middleware detects that situation and injects a
 reminder message so the model still knows about the outstanding todo list.
+
+Additionally, this middleware prevents the agent from exiting the loop while
+there are still incomplete todo items. When the model produces a final response
+(no tool calls) but todos are not yet complete, the middleware injects a reminder
+and jumps back to the model node to force continued engagement.
 """
 
 from __future__ import annotations
@@ -12,6 +17,7 @@ from typing import Any, override
 
 from langchain.agents.middleware import TodoListMiddleware
 from langchain.agents.middleware.todo import PlanningState, Todo
+from langchain.agents.middleware.types import hook_config
 from langchain_core.messages import AIMessage, HumanMessage
 from langgraph.runtime import Runtime
 
@@ -34,6 +40,11 @@ def _reminder_in_messages(messages: list[Any]) -> bool:
     return False
 
 
+def _completion_reminder_count(messages: list[Any]) -> int:
+    """Return the number of todo_completion_reminder HumanMessages in *messages*."""
+    return sum(1 for msg in messages if isinstance(msg, HumanMessage) and getattr(msg, "name", None) == "todo_completion_reminder")
+
+
 def _format_todos(todos: list[Todo]) -> str:
     """Format a list of Todo items into a human-readable string."""
     lines: list[str] = []
@@ -57,7 +68,7 @@ class TodoMiddleware(TodoListMiddleware):
     def before_model(
         self,
         state: PlanningState,
-        runtime: Runtime,  # noqa: ARG002
+        runtime: Runtime,
     ) -> dict[str, Any] | None:
         """Inject a todo-list reminder when write_todos has left the context window."""
         todos: list[Todo] = state.get("todos") or []  # type: ignore[assignment]
@@ -98,3 +109,71 @@ class TodoMiddleware(TodoListMiddleware):
     ) -> dict[str, Any] | None:
         """Async version of before_model."""
         return self.before_model(state, runtime)
+
+    # Maximum number of completion reminders before allowing the agent to exit.
+    # This prevents infinite loops when the agent cannot make further progress.
+    _MAX_COMPLETION_REMINDERS = 2
+
+    @hook_config(can_jump_to=["model"])
+    @override
+    def after_model(
+        self,
+        state: PlanningState,
+        runtime: Runtime,
+    ) -> dict[str, Any] | None:
+        """Prevent premature agent exit when todo items are still incomplete.
+
+        In addition to the base class check for parallel ``write_todos`` calls,
+        this override intercepts model responses that have no tool calls while
+        there are still incomplete todo items. It injects a reminder
+        ``HumanMessage`` and jumps back to the model node so the agent
+        continues working through the todo list.
+
+        A retry cap of ``_MAX_COMPLETION_REMINDERS`` (default 2) prevents
+        infinite loops when the agent cannot make further progress.
+        """
+        # 1. Preserve base class logic (parallel write_todos detection).
+        base_result = super().after_model(state, runtime)
+        if base_result is not None:
+            return base_result
+
+        # 2. Only intervene when the agent wants to exit (no tool calls).
+        messages = state.get("messages") or []
+        last_ai = next((m for m in reversed(messages) if isinstance(m, AIMessage)), None)
+        if not last_ai or last_ai.tool_calls:
+            return None
+
+        # 3. Allow exit when all todos are completed or there are no todos.
+        todos: list[Todo] = state.get("todos") or []  # type: ignore[assignment]
+        if not todos or all(t.get("status") == "completed" for t in todos):
+            return None
+
+        # 4. Enforce a reminder cap to prevent infinite re-engagement loops.
+        if _completion_reminder_count(messages) >= self._MAX_COMPLETION_REMINDERS:
+            return None
+
+        # 5. Inject a reminder and force the agent back to the model.
+        incomplete = [t for t in todos if t.get("status") != "completed"]
+        incomplete_text = "\n".join(f"- [{t.get('status', 'pending')}] {t.get('content', '')}" for t in incomplete)
+        reminder = HumanMessage(
+            name="todo_completion_reminder",
+            content=(
+                "<system_reminder>\n"
+                "You have incomplete todo items that must be finished before giving your final response:\n\n"
+                f"{incomplete_text}\n\n"
+                "Please continue working on these tasks. Call `write_todos` to mark items as completed "
+                "as you finish them, and only respond when all items are done.\n"
+                "</system_reminder>"
+            ),
+        )
+        return {"jump_to": "model", "messages": [reminder]}
+
+    @override
+    @hook_config(can_jump_to=["model"])
+    async def aafter_model(
+        self,
+        state: PlanningState,
+        runtime: Runtime,
+    ) -> dict[str, Any] | None:
+        """Async version of after_model."""
+        return self.after_model(state, runtime)

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

@@ -11,6 +11,8 @@ from langgraph.errors import GraphBubbleUp
 from langgraph.prebuilt.tool_node import ToolCallRequest
 from langgraph.types import Command
 
+from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 _MISSING_TOOL_CALL_ID = "missing_tool_call_id"
@@ -67,6 +69,7 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
 
 def _build_runtime_middlewares(
     *,
+    app_config: AppConfig,
     include_uploads: bool,
     include_dangling_tool_call_patch: bool,
     lazy_init: bool = True,
@@ -91,12 +94,10 @@ def _build_runtime_middlewares(
 
         middlewares.append(DanglingToolCallMiddleware())
 
-    middlewares.append(LLMErrorHandlingMiddleware())
+    middlewares.append(LLMErrorHandlingMiddleware(app_config=app_config))
 
     # Guardrail middleware (if configured)
-    from deerflow.config.guardrails_config import get_guardrails_config
+    guardrails_config = app_config.guardrails
-
-    guardrails_config = get_guardrails_config()
     if guardrails_config.enabled and guardrails_config.provider:
         import inspect
 
@@ -125,19 +126,42 @@ def _build_runtime_middlewares(
     return middlewares
 
 
-def build_lead_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
+def build_lead_runtime_middlewares(*, app_config: AppConfig, lazy_init: bool = True) -> list[AgentMiddleware]:
     """Middlewares shared by lead agent runtime before lead-only middlewares."""
     return _build_runtime_middlewares(
+        app_config=app_config,
         include_uploads=True,
         include_dangling_tool_call_patch=True,
         lazy_init=lazy_init,
     )
 
 
-def build_subagent_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
+def build_subagent_runtime_middlewares(
+    *,
+    app_config: AppConfig | None = None,
+    model_name: str | None = None,
+    lazy_init: bool = True,
+) -> list[AgentMiddleware]:
     """Middlewares shared by subagent runtime before subagent-only middlewares."""
-    return _build_runtime_middlewares(
+    if app_config is None:
+        from deerflow.config import get_app_config
+
+        app_config = get_app_config()
+
+    middlewares = _build_runtime_middlewares(
+        app_config=app_config,
         include_uploads=False,
         include_dangling_tool_call_patch=True,
         lazy_init=lazy_init,
     )
+
+    if model_name is None and app_config.models:
+        model_name = app_config.models[0].name
+
+    model_config = app_config.get_model_config(model_name) if model_name else None
+    if model_config is not None and model_config.supports_vision:
+        from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
+
+        middlewares.append(ViewImageMiddleware())
+
+    return middlewares

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

@@ -10,6 +10,7 @@ from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime
 
 from deerflow.config.paths import Paths, get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.utils.file_conversion import extract_outline
 
 logger = logging.getLogger(__name__)
@@ -221,7 +222,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
                 thread_id = get_config().get("configurable", {}).get("thread_id")
             except RuntimeError:
                 pass  # get_config() raises outside a runnable context (e.g. unit tests)
-        uploads_dir = self._paths.sandbox_uploads_dir(thread_id) if thread_id else None
+        uploads_dir = self._paths.sandbox_uploads_dir(thread_id, user_id=get_effective_user_id()) if thread_id else None
 
         # Get newly uploaded files from the current message's additional_kwargs.files
         new_files = self._files_from_kwargs(last_message, uploads_dir) or []
@@ -282,6 +283,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
         updated_message = HumanMessage(
             content=updated_content,
             id=last_message.id,
+            name=last_message.name,
             additional_kwargs=last_message.additional_kwargs,
         )
 

backend/packages/harness/deerflow/client.py

@@ -40,7 +40,8 @@ from deerflow.config.app_config import get_app_config, reload_app_config
 from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
 from deerflow.config.paths import get_paths
 from deerflow.models import create_chat_model
-from deerflow.skills.installer import install_skill_from_archive
+from deerflow.runtime.user_context import get_effective_user_id
+from deerflow.skills.storage import get_or_new_skill_storage
 from deerflow.uploads.manager import (
     claim_unique_filename,
     delete_file_safe,
@@ -240,7 +241,7 @@ class DeerFlowClient:
         }
         checkpointer = self._checkpointer
         if checkpointer is None:
-            from deerflow.agents.checkpointer import get_checkpointer
+            from deerflow.runtime.checkpointer import get_checkpointer
 
             checkpointer = get_checkpointer()
         if checkpointer is not None:
@@ -374,7 +375,7 @@ class DeerFlowClient:
         """
         checkpointer = self._checkpointer
         if checkpointer is None:
-            from deerflow.agents.checkpointer.provider import get_checkpointer
+            from deerflow.runtime.checkpointer.provider import get_checkpointer
 
             checkpointer = get_checkpointer()
 
@@ -429,7 +430,7 @@ class DeerFlowClient:
         """
         checkpointer = self._checkpointer
         if checkpointer is None:
-            from deerflow.agents.checkpointer.provider import get_checkpointer
+            from deerflow.runtime.checkpointer.provider import get_checkpointer
 
             checkpointer = get_checkpointer()
 
@@ -722,6 +723,10 @@ class DeerFlowClient:
             Dict with "models" key containing list of model info dicts,
             matching the Gateway API ``ModelsListResponse`` schema.
         """
+        token_usage_enabled = getattr(getattr(self._app_config, "token_usage", None), "enabled", False)
+        if not isinstance(token_usage_enabled, bool):
+            token_usage_enabled = False
+
         return {
             "models": [
                 {
@@ -733,7 +738,8 @@ class DeerFlowClient:
                     "supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
                 }
                 for model in self._app_config.models
-            ]
+            ],
+            "token_usage": {"enabled": token_usage_enabled},
         }
 
     def list_skills(self, enabled_only: bool = False) -> dict:
@@ -746,8 +752,6 @@ class DeerFlowClient:
             Dict with "skills" key containing list of skill info dicts,
             matching the Gateway API ``SkillsListResponse`` schema.
         """
-        from deerflow.skills.loader import load_skills
-
         return {
             "skills": [
                 {
@@ -757,7 +761,7 @@ class DeerFlowClient:
                     "category": s.category,
                     "enabled": s.enabled,
                 }
-                for s in load_skills(enabled_only=enabled_only)
+                for s in get_or_new_skill_storage().load_skills(enabled_only=enabled_only)
             ]
         }
 
@@ -769,19 +773,19 @@ class DeerFlowClient:
         """
         from deerflow.agents.memory.updater import get_memory_data
 
-        return get_memory_data()
+        return get_memory_data(user_id=get_effective_user_id())
 
     def export_memory(self) -> dict:
         """Export current memory data for backup or transfer."""
         from deerflow.agents.memory.updater import get_memory_data
 
-        return get_memory_data()
+        return get_memory_data(user_id=get_effective_user_id())
 
     def import_memory(self, memory_data: dict) -> dict:
         """Import and persist full memory data."""
         from deerflow.agents.memory.updater import import_memory_data
 
-        return import_memory_data(memory_data)
+        return import_memory_data(memory_data, user_id=get_effective_user_id())
 
     def get_model(self, name: str) -> dict | None:
         """Get a specific model's configuration by name.
@@ -866,9 +870,9 @@ class DeerFlowClient:
         Returns:
             Skill info dict, or None if not found.
         """
-        from deerflow.skills.loader import load_skills
+        from deerflow.skills.storage import get_or_new_skill_storage
 
-        skill = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
+        skill = next((s for s in get_or_new_skill_storage().load_skills(enabled_only=False) if s.name == name), None)
         if skill is None:
             return None
         return {
@@ -893,9 +897,9 @@ class DeerFlowClient:
             ValueError: If the skill is not found.
             OSError: If the config file cannot be written.
         """
-        from deerflow.skills.loader import load_skills
+        from deerflow.skills.storage import get_or_new_skill_storage
 
-        skills = load_skills(enabled_only=False)
+        skills = get_or_new_skill_storage().load_skills(enabled_only=False)
         skill = next((s for s in skills if s.name == name), None)
         if skill is None:
             raise ValueError(f"Skill '{name}' not found")
@@ -918,7 +922,7 @@ class DeerFlowClient:
         self._agent_config_key = None
         reload_extensions_config()
 
-        updated = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
+        updated = next((s for s in get_or_new_skill_storage().load_skills(enabled_only=False) if s.name == name), None)
         if updated is None:
             raise RuntimeError(f"Skill '{name}' disappeared after update")
         return {
@@ -942,7 +946,7 @@ class DeerFlowClient:
             FileNotFoundError: If the file does not exist.
             ValueError: If the file is invalid.
         """
-        return install_skill_from_archive(skill_path)
+        return get_or_new_skill_storage().install_skill_from_archive(skill_path)
 
     # ------------------------------------------------------------------
     # Public API — memory management
@@ -956,13 +960,13 @@ class DeerFlowClient:
         """
         from deerflow.agents.memory.updater import reload_memory_data
 
-        return reload_memory_data()
+        return reload_memory_data(user_id=get_effective_user_id())
 
     def clear_memory(self) -> dict:
         """Clear all persisted memory data."""
         from deerflow.agents.memory.updater import clear_memory_data
 
-        return clear_memory_data()
+        return clear_memory_data(user_id=get_effective_user_id())
 
     def create_memory_fact(self, content: str, category: str = "context", confidence: float = 0.5) -> dict:
         """Create a single fact manually."""
@@ -1179,7 +1183,7 @@ class DeerFlowClient:
             ValueError: If the path is invalid.
         """
         try:
-            actual = get_paths().resolve_virtual_path(thread_id, path)
+            actual = get_paths().resolve_virtual_path(thread_id, path, user_id=get_effective_user_id())
         except ValueError as exc:
             if "traversal" in str(exc):
                 from deerflow.uploads.manager import PathTraversalError

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

@@ -48,6 +48,12 @@ class AioSandbox(Sandbox):
             self._home_dir = context.home_dir
         return self._home_dir
 
+    # Default no_change_timeout for exec_command (seconds).  Matches the
+    # client-level timeout so that long-running commands which produce no
+    # output are not prematurely terminated by the sandbox's built-in 120 s
+    # default.
+    _DEFAULT_NO_CHANGE_TIMEOUT = 600
+
     def execute_command(self, command: str) -> str:
         """Execute a shell command in the sandbox.
 
@@ -66,13 +72,13 @@ class AioSandbox(Sandbox):
         """
         with self._lock:
             try:
-                result = self._client.shell.exec_command(command=command)
+                result = self._client.shell.exec_command(command=command, no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
                 output = result.data.output if result.data else ""
 
                 if output and _ERROR_OBSERVATION_SIGNATURE in output:
                     logger.warning("ErrorObservation detected in sandbox output, retrying with a fresh session")
                     fresh_id = str(uuid.uuid4())
-                    result = self._client.shell.exec_command(command=command, id=fresh_id)
+                    result = self._client.shell.exec_command(command=command, id=fresh_id, no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
                     output = result.data.output if result.data else ""
 
                 return output if output else "(no output)"
@@ -108,7 +114,7 @@ class AioSandbox(Sandbox):
         """
         with self._lock:
             try:
-                result = self._client.shell.exec_command(command=f"find {shlex.quote(path)} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500")
+                result = self._client.shell.exec_command(command=f"find {shlex.quote(path)} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500", no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
                 output = result.data.output if result.data else ""
                 if output:
                     return [line.strip() for line in output.strip().split("\n") if line.strip()]

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

@@ -27,6 +27,7 @@ except ImportError:  # pragma: no cover - Windows fallback
 
 from deerflow.config import get_app_config
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox import Sandbox
 from deerflow.sandbox.sandbox_provider import SandboxProvider
 
@@ -119,6 +120,16 @@ class AioSandboxProvider(SandboxProvider):
         if self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT) > 0:
             self._start_idle_checker()
 
+    @property
+    def uses_thread_data_mounts(self) -> bool:
+        """Whether thread workspace/uploads/outputs are visible via mounts.
+
+        Local container backends bind-mount the thread data directories, so files
+        written by the gateway are already visible when the sandbox starts.
+        Remote backends may require explicit file sync.
+        """
+        return isinstance(self._backend, LocalContainerBackend)
+
     # ── Factory methods ──────────────────────────────────────────────────
 
     def _create_backend(self) -> SandboxBackend:
@@ -260,15 +271,16 @@ class AioSandboxProvider(SandboxProvider):
         mounted Docker socket (DooD), the host Docker daemon can resolve the paths.
         """
         paths = get_paths()
-        paths.ensure_thread_dirs(thread_id)
+        user_id = get_effective_user_id()
+        paths.ensure_thread_dirs(thread_id, user_id=user_id)
 
         return [
-            (paths.host_sandbox_work_dir(thread_id), f"{VIRTUAL_PATH_PREFIX}/workspace", False),
+            (paths.host_sandbox_work_dir(thread_id, user_id=user_id), f"{VIRTUAL_PATH_PREFIX}/workspace", False),
-            (paths.host_sandbox_uploads_dir(thread_id), f"{VIRTUAL_PATH_PREFIX}/uploads", False),
+            (paths.host_sandbox_uploads_dir(thread_id, user_id=user_id), f"{VIRTUAL_PATH_PREFIX}/uploads", False),
-            (paths.host_sandbox_outputs_dir(thread_id), f"{VIRTUAL_PATH_PREFIX}/outputs", False),
+            (paths.host_sandbox_outputs_dir(thread_id, user_id=user_id), f"{VIRTUAL_PATH_PREFIX}/outputs", False),
             # ACP workspace: read-only inside the sandbox (lead agent reads results;
             # the ACP subprocess writes from the host side, not from within the container).
-            (paths.host_acp_workspace_dir(thread_id), "/mnt/acp-workspace", True),
+            (paths.host_acp_workspace_dir(thread_id, user_id=user_id), "/mnt/acp-workspace", True),
         ]
 
     @staticmethod
@@ -480,8 +492,9 @@ class AioSandboxProvider(SandboxProvider):
         across multiple processes, preventing container-name conflicts.
         """
         paths = get_paths()
-        paths.ensure_thread_dirs(thread_id)
+        user_id = get_effective_user_id()
-        lock_path = paths.thread_dir(thread_id) / f"{sandbox_id}.lock"
+        paths.ensure_thread_dirs(thread_id, user_id=user_id)
+        lock_path = paths.thread_dir(thread_id, user_id=user_id) / f"{sandbox_id}.lock"
 
         with open(lock_path, "a", encoding="utf-8") as lock_file:
             locked = False

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

@@ -9,6 +9,7 @@ from __future__ import annotations
 import json
 import logging
 import os
+import shlex
 import subprocess
 from datetime import datetime
 
@@ -86,6 +87,88 @@ def _format_container_mount(runtime: str, host_path: str, container_path: str, r
     return ["-v", mount_spec]
 
 
+def _redact_container_command_for_log(cmd: list[str]) -> list[str]:
+    """Return a Docker/Container command with environment values redacted."""
+    redacted: list[str] = []
+    redact_next_env = False
+
+    for arg in cmd:
+        if redact_next_env:
+            if "=" in arg:
+                key = arg.split("=", 1)[0]
+                redacted.append(f"{key}=<redacted>" if key else "<redacted>")
+            else:
+                redacted.append(arg)
+            redact_next_env = False
+            continue
+
+        if arg in {"-e", "--env"}:
+            redacted.append(arg)
+            redact_next_env = True
+            continue
+
+        if arg.startswith("--env="):
+            value = arg.removeprefix("--env=")
+            if "=" in value:
+                key = value.split("=", 1)[0]
+                redacted.append(f"--env={key}=<redacted>" if key else "--env=<redacted>")
+            else:
+                redacted.append(arg)
+            continue
+
+        redacted.append(arg)
+
+    return redacted
+
+
+def _format_container_command_for_log(cmd: list[str]) -> str:
+    if os.name == "nt":
+        return subprocess.list2cmdline(cmd)
+    return shlex.join(cmd)
+
+
+def _normalize_sandbox_host(host: str) -> str:
+    return host.strip().lower()
+
+
+def _is_ipv6_loopback_sandbox_host(host: str) -> bool:
+    return _normalize_sandbox_host(host) in {"::1", "[::1]"}
+
+
+def _is_loopback_sandbox_host(host: str) -> bool:
+    return _normalize_sandbox_host(host) in {"", "localhost", "127.0.0.1", "::1", "[::1]"}
+
+
+def _resolve_docker_bind_host(sandbox_host: str | None = None, bind_host: str | None = None) -> str:
+    """Choose the host interface for legacy Docker ``-p`` sandbox publishing.
+
+    Bare-metal/local runs talk to sandboxes through localhost and should not
+    expose the sandbox HTTP API on every host interface.  Docker-outside-of-
+    Docker deployments commonly use ``host.docker.internal`` from another
+    container; keep their legacy broad bind unless operators opt into a
+    narrower bind with ``DEER_FLOW_SANDBOX_BIND_HOST``.  When operators choose
+    an IPv6 loopback sandbox host, bind Docker to IPv6 loopback as well so the
+    advertised sandbox URL and published socket use the same address family.
+    """
+    explicit_bind = bind_host if bind_host is not None else os.environ.get("DEER_FLOW_SANDBOX_BIND_HOST")
+    if explicit_bind is not None:
+        explicit_bind = explicit_bind.strip()
+        if explicit_bind:
+            logger.debug("Docker sandbox bind: %s (explicit bind host override)", explicit_bind)
+            return explicit_bind
+
+    host = sandbox_host if sandbox_host is not None else os.environ.get("DEER_FLOW_SANDBOX_HOST", "localhost")
+    if _is_ipv6_loopback_sandbox_host(host):
+        logger.debug("Docker sandbox bind: [::1] (IPv6 loopback sandbox host)")
+        return "[::1]"
+    if _is_loopback_sandbox_host(host):
+        logger.debug("Docker sandbox bind: 127.0.0.1 (loopback default)")
+        return "127.0.0.1"
+
+    logger.debug("Docker sandbox bind: 0.0.0.0 (non-loopback sandbox host compatibility)")
+    return "0.0.0.0"
+
+
 class LocalContainerBackend(SandboxBackend):
     """Backend that manages sandbox containers locally using Docker or Apple Container.
 
@@ -424,12 +507,17 @@ class LocalContainerBackend(SandboxBackend):
         if self._runtime == "docker":
             cmd.extend(["--security-opt", "seccomp=unconfined"])
 
+        if self._runtime == "docker":
+            port_mapping = f"{_resolve_docker_bind_host()}:{port}:8080"
+        else:
+            port_mapping = f"{port}:8080"
+
         cmd.extend(
             [
                 "--rm",
                 "-d",
                 "-p",
-                f"{port}:8080",
+                port_mapping,
                 "--name",
                 container_name,
             ]
@@ -464,7 +552,8 @@ class LocalContainerBackend(SandboxBackend):
 
         cmd.append(self._image)
 
-        logger.info(f"Starting container using {self._runtime}: {' '.join(cmd)}")
+        log_cmd = _format_container_command_for_log(_redact_container_command_for_log(cmd))
+        logger.info(f"Starting container using {self._runtime}: {log_cmd}")
 
         try:
             result = subprocess.run(cmd, capture_output=True, text=True, check=True)

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

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