update the code with review comments

* fix(auth): persist auto-generated JWT secret to survive restarts

  When AUTH_JWT_SECRET is not set, the auto-generated secret is now
  written to .deer-flow/.jwt_secret (mode 0600) and reused on subsequent
  starts. This prevents session invalidation on every restart while still
  allowing explicit AUTH_JWT_SECRET in .env to take precedence.

* Apply suggestions from code review

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

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* fix the lint errors of backend

---------

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

.env.example

@@ -1,3 +1,6 @@
+# Serper API Key (Google Search) - https://serper.dev
+SERPER_API_KEY=your-serper-api-key
+
 # TAVILY API Key
 TAVILY_API_KEY=your-tavily-api-key
 
@@ -6,8 +9,9 @@ JINA_API_KEY=your-jina-api-key
 
 # InfoQuest API Key
 INFOQUEST_API_KEY=your-infoquest-api-key
-# CORS Origins (comma-separated) - e.g., http://localhost:3000,http://localhost:3001
-# CORS_ORIGINS=http://localhost:3000
+# Browser CORS allowlist for split-origin or port-forwarded deployments (comma-separated exact origins).
+# Leave unset when using the unified nginx endpoint, e.g. http://localhost:2026.
+# GATEWAY_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
 
 # Optional:
 # FIRECRAWL_API_KEY=your-firecrawl-api-key
@@ -40,3 +44,19 @@ INFOQUEST_API_KEY=your-infoquest-api-key
 #
 # WECOM_BOT_ID=your-wecom-bot-id
 # WECOM_BOT_SECRET=your-wecom-bot-secret
+# DINGTALK_CLIENT_ID=your-dingtalk-client-id
+# DINGTALK_CLIENT_SECRET=your-dingtalk-client-secret
+
+# Set to "false" to disable Swagger UI, ReDoc, and OpenAPI schema in production
+# GATEWAY_ENABLE_DOCS=false
+
+# ── Frontend SSR → Gateway wiring ─────────────────────────────────────────────
+# The Next.js server uses these to reach the Gateway during SSR (auth checks,
+# /api/* rewrites). They default to localhost values that match `make dev` and
+# `make start`, so most local users do not need to set them.
+#
+# Override only when the Gateway is not on localhost:8001 (e.g. when the
+# frontend and gateway run on different hosts, in containers with a service
+# alias, or behind a different port). docker-compose already sets these.
+# DEER_FLOW_INTERNAL_GATEWAY_BASE_URL=http://localhost:8001
+# DEER_FLOW_TRUSTED_ORIGINS=http://localhost:3000,http://localhost:2026

.github/workflows/container.yaml

@@ -0,0 +1,101 @@
+name: Publish Containers
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+
+  backend-container:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}-backend
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+      - name: Log in to the Container registry
+        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=ref,event=branch
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
+        with:
+          context: .
+          file: backend/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true
+
+  frontend-container:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}-frontend
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+      - name: Log in to the Container registry
+        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=ref,event=branch
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
+        with:
+          context: .
+          file: frontend/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true

CONTRIBUTING.md

@@ -46,12 +46,12 @@ Docker provides a consistent, isolated environment with all dependencies pre-con
    All services will start with hot-reload enabled:
    - Frontend changes are automatically reloaded
    - Backend changes trigger automatic restart
-   - LangGraph server supports hot-reload
+   - Gateway-hosted LangGraph-compatible runtime supports hot-reload
 
 4. **Access the application**:
    - Web Interface: http://localhost:2026
    - API Gateway: http://localhost:2026/api/*
-   - LangGraph: http://localhost:2026/api/langgraph/*
+   - LangGraph-compatible API: http://localhost:2026/api/langgraph/*
 
 #### Docker Commands
 
@@ -94,7 +94,7 @@ Use these as practical starting points for development and review environments:
 If `make docker-init`, `make docker-start`, or `make docker-stop` fails on Linux with an error like below, your current user likely does not have permission to access the Docker daemon socket:
 
 ```text
-unable to get image 'deer-flow-dev-langgraph': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
+unable to get image 'deer-flow-gateway': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
 ```
 
 Recommended fix: add your current user to the `docker` group so Docker commands work without `sudo`.
@@ -131,9 +131,8 @@ Host Machine
 Docker Compose (deer-flow-dev)
   ├→ nginx (port 2026) ← Reverse proxy
   ├→ web (port 3000) ← Frontend with hot-reload
-  ├→ api (port 8001) ← Gateway API with hot-reload
-   ├→ langgraph (port 2024) ← LangGraph server with hot-reload
-   └→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
+  ├→ gateway (port 8001) ← Gateway API + LangGraph-compatible runtime with hot-reload
+  └→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
 ```
 
 **Benefits of Docker Development**:
@@ -184,17 +183,13 @@ Required tools:
 
 If you need to start services individually:
 
-1. **Start backend services**:
+1. **Start backend service**:
    ```bash
-   # Terminal 1: Start LangGraph Server (port 2024)
+   # Terminal 1: Start Gateway API + embedded agent runtime (port 8001)
    cd backend
    make dev
 
-   # Terminal 2: Start Gateway API (port 8001)
-   cd backend
-   make gateway
-
-   # Terminal 3: Start Frontend (port 3000)
+   # Terminal 2: Start Frontend (port 3000)
    cd frontend
    pnpm dev
    ```
@@ -212,10 +207,10 @@ If you need to start services individually:
 
 The nginx configuration provides:
 - Unified entry point on port 2026
-- Routes `/api/langgraph/*` to LangGraph Server (2024)
+- Rewrites `/api/langgraph/*` to Gateway's LangGraph-compatible API (8001)
 - Routes other `/api/*` endpoints to Gateway API (8001)
 - Routes non-API requests to Frontend (3000)
-- Centralized CORS handling
+- Same-origin API routing; split-origin or port-forwarded browser clients should use the Gateway `GATEWAY_CORS_ORIGINS` allowlist
 - SSE/streaming support for real-time agent responses
 - Optimized timeouts for long-running operations
 
@@ -235,8 +230,8 @@ deer-flow/
 │       └── nginx.local.conf # Nginx config for local dev
 ├── backend/                 # Backend application
 │   ├── src/
-│   │   ├── gateway/        # Gateway API (port 8001)
-│   │   ├── agents/         # LangGraph agents (port 2024)
+│   │   ├── gateway/        # Gateway API and LangGraph-compatible runtime (port 8001)
+│   │   ├── agents/         # LangGraph agent runtime used by Gateway
 │   │   ├── mcp/            # Model Context Protocol integration
 │   │   ├── skills/         # Skills system
 │   │   └── sandbox/        # Sandbox execution
@@ -256,8 +251,7 @@ Browser
   ↓
 Nginx (port 2026) ← Unified entry point
   ├→ Frontend (port 3000) ← / (non-API requests)
-  ├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
-  └→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
+  └→ Gateway API (port 8001) ← /api/* and /api/langgraph/* (LangGraph-compatible agent interactions)
 ```
 
 ## Development Workflow

README.md

@@ -245,13 +245,15 @@ make down   # Stop and remove containers
 
 Access: http://localhost:2026
 
+The unified nginx endpoint is same-origin by default and does not emit browser CORS headers. If you run a split-origin or port-forwarded browser client, set `GATEWAY_CORS_ORIGINS` to comma-separated exact origins such as `http://localhost:3000`; the Gateway then applies the CORS allowlist and matching CSRF origin checks.
+
 See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
 
 #### Option 2: Local Development
 
 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**:
@@ -345,6 +347,7 @@ 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`:**
 
@@ -414,6 +417,13 @@ 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:
@@ -442,6 +452,10 @@ 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**
@@ -480,6 +494,14 @@ 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.
 
+**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**
@@ -606,7 +628,7 @@ See [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerfl
 
 Complex tasks rarely fit in a single pass. DeerFlow decomposes them.
 
-The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
+The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output. When token usage tracking is enabled, completed sub-agent usage is attributed back to the dispatching step.
 
 This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.
 

README_fr.md

@@ -228,7 +228,7 @@ make down   # Stop and remove containers
 ```
 
 > [!NOTE]
-> Le serveur d'agents LangGraph fonctionne actuellement via `langgraph dev` (le serveur CLI open source).
+> Le runtime d'agent s'exécute actuellement dans la Gateway. nginx réécrit `/api/langgraph/*` vers l'API compatible LangGraph servie par la Gateway.
 
 Accès : http://localhost:2026
 
@@ -290,13 +290,14 @@ DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les ca
 | Telegram | Bot API (long-polling) | Facile |
 | Slack | Socket Mode | Modérée |
 | Feishu / Lark | WebSocket | Modérée |
+| DingTalk | Stream Push (WebSocket) | Modérée |
 
 **Configuration dans `config.yaml` :**
 
 ```yaml
 channels:
-  # LangGraph Server URL (default: http://localhost:2024)
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
+  langgraph_url: http://localhost:8001/api
   # Gateway API URL (default: http://localhost:8001)
   gateway_url: http://localhost:8001
 
@@ -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

@@ -181,7 +181,7 @@ make down   # コンテナを停止して削除
 ```
 
 > [!NOTE]
-> LangGraphエージェントサーバーは現在`langgraph dev`（オープンソースCLIサーバー）経由で実行されます。
+> Agentランタイムは現在Gateway内で実行されます。`/api/langgraph/*`はnginxによってGatewayのLangGraph-compatible APIへ書き換えられます。
 
 アクセス: http://localhost:2026
 
@@ -243,13 +243,14 @@ DeerFlowはメッセージングアプリからのタスク受信をサポート
 | Telegram | Bot API（ロングポーリング） | 簡単 |
 | Slack | Socket Mode | 中程度 |
 | Feishu / Lark | WebSocket | 中程度 |
+| DingTalk | Stream Push（WebSocket） | 中程度 |
 
 **`config.yaml`での設定：**
 
 ```yaml
 channels:
-  # LangGraphサーバーURL（デフォルト: http://localhost:2024）
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL（デフォルト: http://localhost:8001/api）
+  langgraph_url: http://localhost:8001/api
   # Gateway API URL（デフォルト: http://localhost:8001）
   gateway_url: http://localhost:8001
 
@@ -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

@@ -184,7 +184,7 @@ make down   # 停止并移除容器
 ```
 
 > [!NOTE]
-> 当前 LangGraph agent server 通过开源 CLI 服务 `langgraph dev` 运行。
+> 当前 Agent 运行时嵌入在 Gateway 中运行，`/api/langgraph/*` 会由 nginx 重写到 Gateway 的 LangGraph-compatible API。
 
 访问地址：http://localhost:2026
 
@@ -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**：
@@ -248,13 +248,14 @@ DeerFlow 支持从即时通讯应用接收任务。只要配置完成，对应
 | Slack | Socket Mode | 中等 |
 | Feishu / Lark | WebSocket | 中等 |
 | 企业微信智能机器人 | WebSocket | 中等 |
+| 钉钉 | Stream Push（WebSocket） | 中等 |
 
 **`config.yaml` 中的配置示例：**
 
 ```yaml
 channels:
-  # LangGraph Server URL（默认：http://localhost:2024）
-  langgraph_url: http://localhost:2024
+  # LangGraph-compatible Gateway API base URL（默认：http://localhost:8001/api）
+  langgraph_url: http://localhost:8001/api
   # Gateway API URL（默认：http://localhost:8001）
   gateway_url: http://localhost:8001
 
@@ -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

@@ -112,7 +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.
 
@@ -165,7 +165,7 @@ Lead-agent middlewares are assembled in strict append order across `packages/har
 8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
 9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
 10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional)
+11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
 12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
 13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
 14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
@@ -205,7 +205,9 @@ 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).
+
+CORS is same-origin by default when requests enter through nginx on port 2026. Split-origin or port-forwarded browser clients must opt in with `GATEWAY_CORS_ORIGINS` (comma-separated exact origins); Gateway `CORSMiddleware` and `CSRFMiddleware` both read that variable so browser CORS and auth-origin checks stay aligned.
 
 **Routers**:
 
@@ -223,7 +225,7 @@ FastAPI application on port 8001 with health check at `GET /health`.
 | **Feedback** (`/api/threads/{id}/runs/{rid}/feedback`) | `PUT /` - upsert feedback; `DELETE /` - delete user feedback; `POST /` - create feedback; `GET /` - list feedback; `GET /stats` - aggregate stats; `DELETE /{fid}` - delete specific |
 | **Runs** (`/api/runs`) | `POST /stream` - stateless run + SSE; `POST /wait` - stateless run + block; `GET /{rid}/messages` - paginated messages by run_id `{data, has_more}` (cursor: `after_seq`/`before_seq`); `GET /{rid}/feedback` - list feedback by run_id |
 
-Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
+Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runtime, all other `/api/*` → Gateway REST APIs.
 
 ### Sandbox System (`packages/harness/deerflow/sandbox/`)
 
@@ -243,7 +245,7 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 - `bash` - Execute commands with path translation and error handling
 - `ls` - Directory listing (tree format, max 2 levels)
 - `read_file` - Read file contents with optional line range
-- `write_file` - Write/append to files, creates directories
+- `write_file` - Write/append to files, creates directories; overwrites by default and exposes the `append` argument in the model-facing schema for end-of-file writes
 - `str_replace` - Substring replacement (single or all occurrences); same-path serialization is scoped to `(sandbox.id, path)` so isolated sandboxes do not contend on identical virtual paths inside one process
 
 ### Subagent System (`packages/harness/deerflow/subagents/`)
@@ -263,8 +265,10 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
    - `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
    - `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
    - `view_image` - Read image as base64 (added only if model supports vision)
+   - `setup_agent` - Bootstrap-only: persist a brand-new custom agent's `SOUL.md` and `config.yaml`. Bound only when `is_bootstrap=True`.
+   - `update_agent` - Custom-agent-only: persist self-updates to the current agent's `SOUL.md` / `config.yaml` from inside a normal chat (partial update + atomic write). Bound when `agent_name` is set and `is_bootstrap=False`.
 4. **Subagent tool** (if enabled):
-   - `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
+   - `task` - Delegate to subagent (description, prompt, subagent_type)
 
 **Community tools** (`packages/harness/deerflow/community/`):
 - `tavily/` - Web search (5 results default) and web fetch (4KB limit)
@@ -312,7 +316,8 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
 
 ### IM Channels System (`app/channels/`)
 
-Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via Gateway's LangGraph-compatible API.
+Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via the LangGraph Server.
+
 
 **Architecture**: Channels communicate with Gateway through the `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side. The internal SDK client injects process-local internal auth plus a matching CSRF cookie/header pair so Gateway accepts state-changing thread/run requests from channel workers without relying on browser session cookies.
 
@@ -322,7 +327,7 @@ 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()`
@@ -331,14 +336,16 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
 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
-8. Outbound → channel callbacks → platform reply
+7. DingTalk AI Card mode (when `card_template_id` configured): `runs.stream()` → create card with initial text → stream updates via `PUT /v1.0/card/streaming` → finalize on `is_final=True`. Falls back to `sampleMarkdown` if card creation or streaming fails
+8. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
+9. Outbound → channel callbacks → platform reply
 
 **Configuration** (`config.yaml` -> `channels`):
 - `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://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/`)
 
@@ -351,10 +358,11 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
 **Per-User Isolation**:
 - Memory is stored per-user at `{base_dir}/users/{user_id}/memory.json`
 - Per-agent per-user memory at `{base_dir}/users/{user_id}/agents/{agent_name}/memory.json`
+- Custom agent definitions (`SOUL.md` + `config.yaml`) are also per-user at `{base_dir}/users/{user_id}/agents/{agent_name}/`. The legacy shared layout `{base_dir}/agents/{agent_name}/` remains read-only fallback for unmigrated installations
 - `user_id` is resolved via `get_effective_user_id()` from `deerflow.runtime.user_context`
 - In no-auth mode, `user_id` defaults to `"default"` (constant `DEFAULT_USER_ID`)
 - Absolute `storage_path` in config opts out of per-user isolation
-- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json` and `threads/` into per-user layout; supports `--dry-run`
+- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json`, `threads/`, and `agents/` into per-user layout. Supports `--dry-run` (preview changes) and `--user-id USER_ID` (assign unowned legacy data to a user, defaults to `default`).
 
 **Data Structure** (stored in `{base_dir}/users/{user_id}/memory.json`):
 - **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
@@ -514,6 +522,7 @@ Multi-file upload with automatic document conversion:
 - Rejects directory inputs before copying so uploads stay all-or-nothing
 - Reuses one conversion worker per request when called from an active event loop
 - Files stored in thread-isolated directories
+- Duplicate filenames in a single upload request are auto-renamed with `_N` suffixes so later files do not truncate earlier files
 - Agent receives uploaded file list via `UploadsMiddleware`
 
 See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.

backend/CONTRIBUTING.md

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

backend/Dockerfile

@@ -50,6 +50,12 @@ COPY backend ./backend
 RUN --mount=type=cache,target=/root/.cache/uv \
     sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync ${UV_EXTRAS:+--extra $UV_EXTRAS}"
 
+# UTF-8 locale prevents UnicodeEncodeError on Chinese/emoji content in minimal
+# containers where locale configuration may be missing and the default encoding is not UTF-8.
+ENV LANG=C.UTF-8
+ENV LC_ALL=C.UTF-8
+ENV PYTHONIOENCODING=utf-8
+
 # ── Stage 2: Dev ──────────────────────────────────────────────────────────────
 # Retains compiler toolchain from builder so startup-time `uv sync` can build
 # source distributions in development containers.
@@ -66,6 +72,10 @@ CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app
 # Clean image without build-essential — reduces size (~200 MB) and attack surface.
 FROM python:3.12-slim-bookworm
 
+ENV LANG=C.UTF-8
+ENV LC_ALL=C.UTF-8
+ENV PYTHONIOENCODING=utf-8
+
 # Copy Node.js runtime from builder (provides npx for MCP servers)
 COPY --from=builder /usr/bin/node /usr/bin/node
 COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules

backend/README.md

@@ -11,31 +11,26 @@ DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent
                         │          Nginx (Port 2026)           │
                         │      Unified reverse proxy           │
                         └───────┬──────────────────┬───────────┘
-                                │                  │
-              /api/langgraph/*  │                  │  /api/* (other)
-                                ▼                  ▼
-               ┌────────────────────┐  ┌────────────────────────┐
-               │ LangGraph Server   │  │   Gateway API (8001)   │
-               │    (Port 2024)     │  │   FastAPI REST         │
-               │                    │  │                        │
-               │ ┌────────────────┐ │  │ Models, MCP, Skills,   │
-               │ │  Lead Agent    │ │  │ Memory, Uploads,       │
-               │ │  ┌──────────┐  │ │  │ Artifacts              │
-               │ │  │Middleware│  │ │  └────────────────────────┘
-               │ │  │  Chain   │  │ │
-               │ │  └──────────┘  │ │
-               │ │  ┌──────────┐  │ │
-               │ │  │  Tools   │  │ │
-               │ │  └──────────┘  │ │
-               │ │  ┌──────────┐  │ │
-               │ │  │Subagents │  │ │
-               │ │  └──────────┘  │ │
-               │ └────────────────┘ │
-               └────────────────────┘
+                                │
+            /api/langgraph/*    │    /api/* (other)
+            rewritten to /api/* │
+                                ▼
+               ┌────────────────────────────────────────┐
+               │        Gateway API (8001)              │
+               │        FastAPI REST + agent runtime    │
+               │                                        │
+               │ Models, MCP, Skills, Memory, Uploads,  │
+               │ Artifacts, Threads, Runs, Streaming    │
+               │                                        │
+               │ ┌────────────────────────────────────┐ │
+               │ │ Lead Agent                         │ │
+               │ │ Middleware Chain, Tools, Subagents │ │
+               │ └────────────────────────────────────┘ │
+               └────────────────────────────────────────┘
 ```
 
 **Request Routing** (via Nginx):
-- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
+- `/api/langgraph/*` → Gateway LangGraph-compatible API - agent interactions, threads, streaming
 - `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup
 - `/` (non-API) → Frontend - Next.js web interface
 
@@ -79,7 +74,7 @@ Per-thread isolated execution with virtual path translation:
 - **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory
 - **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths
 - **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match
-- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
+- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`write_file` overwrites by default and exposes `append` for end-of-file writes; `bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
 
 ### Subagent System
 
@@ -124,7 +119,7 @@ FastAPI application providing REST endpoints for frontend integration:
 | `POST /api/memory/reload` | Force memory reload |
 | `GET /api/memory/config` | Memory configuration |
 | `GET /api/memory/status` | Combined config + data |
-| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
+| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths, auto-renames duplicate filenames in one request) |
 | `GET /api/threads/{id}/uploads/list` | List uploaded files |
 | `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
 | `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
@@ -193,7 +188,7 @@ export OPENAI_API_KEY="your-api-key-here"
 **Full Application** (from project root):
 
 ```bash
-make dev  # Starts LangGraph + Gateway + Frontend + Nginx
+make dev  # Starts Gateway + Frontend + Nginx
 ```
 
 Access at: http://localhost:2026
@@ -201,14 +196,11 @@ Access at: http://localhost:2026
 **Backend Only** (from backend directory):
 
 ```bash
-# Terminal 1: LangGraph server
+# Gateway API + embedded agent runtime
 make dev
-
-# Terminal 2: Gateway API
-make gateway
 ```
 
-Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
+Direct access: Gateway at http://localhost:8001
 
 ---
 
@@ -244,12 +236,16 @@ backend/
 │   └── utils/                  # Utilities
 ├── docs/                       # Documentation
 ├── tests/                      # Test suite
-├── langgraph.json              # LangGraph server configuration
+├── langgraph.json              # LangGraph graph registry for tooling/Studio compatibility
 ├── pyproject.toml              # Python dependencies
 ├── Makefile                    # Development commands
 └── Dockerfile                  # Container build
 ```
 
+`langgraph.json` is not the default service entrypoint.  The scripts and Docker
+deployments run the Gateway embedded runtime; the file is kept for LangGraph
+tooling, Studio, or direct LangGraph Server compatibility.
+
 ---
 
 ## Configuration
@@ -362,8 +358,8 @@ If a provider is explicitly enabled but required credentials are missing, or the
 
 ```bash
 make install    # Install dependencies
-make dev        # Run LangGraph server (port 2024)
-make gateway    # Run Gateway API (port 8001)
+make dev        # Run Gateway API + embedded agent runtime (port 8001)
+make gateway    # Run Gateway API without reload (port 8001)
 make lint       # Run linter (ruff)
 make format     # Format code (ruff)
 ```

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/discord.py

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

backend/app/channels/feishu.py

@@ -63,6 +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

backend/app/channels/manager.py

@@ -38,6 +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},
@@ -48,6 +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] = {}
 
@@ -138,6 +146,13 @@ def _normalize_custom_agent_name(raw_value: str) -> str:
     return normalized
 
 
+def _strip_loop_warning_text(text: str) -> str:
+    """Remove middleware-authored loop warning lines from display text."""
+    if "[LOOP DETECTED]" not in text:
+        return text
+    return "\n".join(line for line in text.splitlines() if "[LOOP DETECTED]" not in line).strip()
+
+
 def _extract_response_text(result: dict | list) -> str:
     """Extract the last AI message text from a LangGraph runs.wait result.
 
@@ -147,7 +162,7 @@ def _extract_response_text(result: dict | list) -> str:
     Handles special cases:
     - Regular AI text responses
     - Clarification interrupts (``ask_clarification`` tool messages)
-    - AI messages with tool_calls but no text content
+    - Strips loop-detection warnings attached to tool-call AI messages
     """
     if isinstance(result, list):
         messages = result
@@ -177,7 +192,12 @@ def _extract_response_text(result: dict | list) -> str:
         # Regular AI message with text content
         if msg_type == "ai":
             content = msg.get("content", "")
+            has_tool_calls = bool(msg.get("tool_calls"))
             if isinstance(content, str) and content:
+                if has_tool_calls:
+                    content = _strip_loop_warning_text(content)
+                    if not content:
+                        continue
                 return content
             # content can be a list of content blocks
             if isinstance(content, list):
@@ -188,6 +208,8 @@ def _extract_response_text(result: dict | list) -> str:
                     elif isinstance(block, str):
                         parts.append(block)
                 text = "".join(parts)
+                if has_tool_calls:
+                    text = _strip_loop_warning_text(text)
                 if text:
                     return text
     return ""
@@ -412,7 +434,13 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
     if not msg.files:
         return []
 
-    from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
+    from deerflow.uploads.manager import (
+        UnsafeUploadPathError,
+        claim_unique_filename,
+        ensure_uploads_dir,
+        normalize_filename,
+        write_upload_file_no_symlink,
+    )
 
     uploads_dir = ensure_uploads_dir(thread_id)
     seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
@@ -463,7 +491,10 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
 
             dest = uploads_dir / safe_name
             try:
-                dest.write_bytes(data)
+                dest = write_upload_file_no_symlink(uploads_dir, safe_name, data)
+            except UnsafeUploadPathError:
+                logger.warning("[Manager] skipping inbound file with unsafe destination: %s", safe_name)
+                continue
             except Exception:
                 logger.exception("[Manager] failed to write inbound file: %s", dest)
                 continue
@@ -543,6 +574,13 @@ class ChannelManager:
 
     @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]]:
@@ -565,6 +603,17 @@ class ChannelManager:
             user_layer.get("config"),
         )
 
+        configurable = run_config.get("configurable")
+        if isinstance(configurable, Mapping):
+            configurable = dict(configurable)
+        else:
+            configurable = {}
+        run_config["configurable"] = configurable
+        # Pin channel-triggered runs to the root graph namespace so follow-up
+        # turns continue from the same conversation checkpoint.
+        configurable["checkpoint_ns"] = ""
+        configurable["thread_id"] = thread_id
+
         run_context = _merge_dicts(
             DEFAULT_RUN_CONTEXT,
             self._default_session.get("context"),
@@ -738,13 +787,22 @@ class ChannelManager:
             return
 
         logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
-        result = await client.runs.wait(
-            thread_id,
-            assistant_id,
-            input={"messages": [{"role": "human", "content": msg.text}]},
-            config=run_config,
-            context=run_context,
-        )
+        try:
+            result = await client.runs.wait(
+                thread_id,
+                assistant_id,
+                input={"messages": [{"role": "human", "content": msg.text}]},
+                config=run_config,
+                context=run_context,
+                multitask_strategy="reject",
+            )
+        except Exception as exc:
+            if _is_thread_busy_error(exc):
+                logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
+                await self._send_error(msg, THREAD_BUSY_MESSAGE)
+                return
+            else:
+                raise
 
         response_text = _extract_response_text(result)
         artifacts = _extract_artifacts(result)
@@ -772,6 +830,7 @@ 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)
@@ -833,6 +892,7 @@ class ChannelManager:
                         text=latest_text,
                         is_final=False,
                         thread_ts=msg.thread_ts,
+                        metadata=_slim_metadata(msg.metadata),
                     )
                 )
                 last_published_text = latest_text
@@ -877,6 +937,7 @@ class ChannelManager:
                     attachments=attachments,
                     is_final=True,
                     thread_ts=msg.thread_ts,
+                    metadata=_slim_metadata(msg.metadata),
                 )
             )
 
@@ -935,6 +996,7 @@ 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)
 
@@ -944,7 +1006,11 @@ class ChannelManager:
 
         try:
             async with httpx.AsyncClient() as http:
-                resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
+                resp = await http.get(
+                    f"{self._gateway_url}{path}",
+                    timeout=10,
+                    headers=create_internal_auth_headers(),
+                )
                 resp.raise_for_status()
                 data = resp.json()
         except Exception:
@@ -968,5 +1034,6 @@ 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",
@@ -25,6 +29,7 @@ _CHANNEL_REGISTRY: dict[str, str] = {
 
 # Keys that indicate a user has configured credentials for a channel.
 _CHANNEL_CREDENTIAL_KEYS: dict[str, list[str]] = {
+    "dingtalk": ["client_id", "client_secret"],
     "discord": ["bot_token"],
     "feishu": ["app_id", "app_secret"],
     "slack": ["bot_token", "app_token"],
@@ -75,14 +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)
@@ -161,12 +167,19 @@ class ChannelService:
             return False
 
         try:
+            config = dict(config)
+            config["channel_store"] = self.store
             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
 
@@ -201,12 +214,12 @@ 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/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

@@ -1,6 +1,5 @@
 import asyncio
 import logging
-import os
 from collections.abc import AsyncGenerator
 from contextlib import asynccontextmanager
 
@@ -9,7 +8,7 @@ from fastapi.middleware.cors import CORSMiddleware
 
 from app.gateway.auth_middleware import AuthMiddleware
 from app.gateway.config import get_gateway_config
-from app.gateway.csrf_middleware import CSRFMiddleware
+from app.gateway.csrf_middleware import CSRFMiddleware, get_configured_cors_origins
 from app.gateway.deps import langgraph_runtime
 from app.gateway.routers import (
     agents,
@@ -28,9 +27,13 @@ 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",
@@ -59,7 +62,7 @@ async def _ensure_admin_user(app: FastAPI) -> None:
 
     Subsequent boots (admin already exists):
       - Runs the one-time "no-auth → with-auth" orphan thread migration for
-        existing LangGraph thread metadata that has no owner_id.
+        existing LangGraph thread metadata that has no user_id.
 
     No SQL persistence migration is needed: the four user_id columns
     (threads_meta, runs, run_events, feedback) only come into existence
@@ -160,7 +163,8 @@ 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}"
@@ -173,7 +177,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     async with langgraph_runtime(app):
         logger.info("LangGraph runtime initialised")
 
-        # Ensure admin user exists (auto-create on first boot)
+        # Check admin bootstrap state and migrate orphan threads after admin exists.
         # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
         await _ensure_admin_user(app)
 
@@ -181,7 +185,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         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")
@@ -213,6 +217,10 @@ def create_app() -> FastAPI:
     Returns:
         Configured FastAPI application instance.
     """
+    config = get_gateway_config()
+    docs_url = "/docs" if config.enable_docs else None
+    redoc_url = "/redoc" if config.enable_docs else None
+    openapi_url = "/openapi.json" if config.enable_docs else None
 
     app = FastAPI(
         title="DeerFlow API Gateway",
@@ -232,14 +240,14 @@ API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execu
 
 ### Architecture
 
-LangGraph requests are handled by nginx reverse proxy.
-This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
+LangGraph-compatible requests are routed through nginx to this gateway.
+This gateway provides runtime endpoints for agent runs plus custom endpoints for models, MCP configuration, skills, and artifacts.
         """,
         version="0.1.0",
         lifespan=lifespan,
-        docs_url="/docs",
-        redoc_url="/redoc",
-        openapi_url="/openapi.json",
+        docs_url=docs_url,
+        redoc_url=redoc_url,
+        openapi_url=openapi_url,
         openapi_tags=[
             {
                 "name": "models",
@@ -302,25 +310,18 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
     # CSRF: Double Submit Cookie pattern for state-changing requests
     app.add_middleware(CSRFMiddleware)
 
-    # CORS: when GATEWAY_CORS_ORIGINS is set (dev without nginx), add CORS middleware.
-    # In production, nginx handles CORS and no middleware is needed.
-    cors_origins_env = os.environ.get("GATEWAY_CORS_ORIGINS", "")
-    if cors_origins_env:
-        cors_origins = [o.strip() for o in cors_origins_env.split(",") if o.strip()]
-        # Validate: wildcard origin with credentials is a security misconfiguration
-        for origin in cors_origins:
-            if origin == "*":
-                logger.error("GATEWAY_CORS_ORIGINS contains wildcard '*' with allow_credentials=True. This is a security misconfiguration — browsers will reject the response. Use explicit scheme://host:port origins instead.")
-                cors_origins = [o for o in cors_origins if o != "*"]
-                break
-        if cors_origins:
-            app.add_middleware(
-                CORSMiddleware,
-                allow_origins=cors_origins,
-                allow_credentials=True,
-                allow_methods=["*"],
-                allow_headers=["*"],
-            )
+    # CORS: the unified nginx endpoint is same-origin by default. Split-origin
+    # browser clients must opt in with this explicit Gateway allowlist so CORS
+    # and CSRF origin checks share the same source of truth.
+    cors_origins = sorted(get_configured_cors_origins())
+    if cors_origins:
+        app.add_middleware(
+            CORSMiddleware,
+            allow_origins=cors_origins,
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
 
     # Include routers
     # Models API is mounted at /api/models
@@ -369,7 +370,7 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
     app.include_router(runs.router)
 
     @app.get("/health", tags=["health"])
-    async def health_check() -> dict:
+    async def health_check() -> dict[str, str]:
         """Health check endpoint.
 
         Returns:

backend/app/gateway/auth/config.py

@@ -4,13 +4,12 @@ import logging
 import os
 import secrets
 
-from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 
-load_dotenv()
-
 logger = logging.getLogger(__name__)
 
+_SECRET_FILE = ".jwt_secret"
+
 
 class AuthConfig(BaseModel):
     """JWT and auth-related configuration. Parsed once at startup.
@@ -33,17 +32,46 @@ class AuthConfig(BaseModel):
 _auth_config: AuthConfig | None = None
 
 
+def _load_or_create_secret() -> str:
+    """Load persisted JWT secret from ``{base_dir}/.jwt_secret``, or generate and persist a new one."""
+    from deerflow.config.paths import get_paths
+
+    paths = get_paths()
+    secret_file = paths.base_dir / _SECRET_FILE
+
+    try:
+        if secret_file.exists():
+            secret = secret_file.read_text(encoding="utf-8").strip()
+            if secret:
+                return secret
+    except OSError as exc:
+        raise RuntimeError(f"Failed to read JWT secret from {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can read its persisted auth secret.") from exc
+
+    secret = secrets.token_urlsafe(32)
+    try:
+        secret_file.parent.mkdir(parents=True, exist_ok=True)
+        fd = os.open(secret_file, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
+        with os.fdopen(fd, "w", encoding="utf-8") as fh:
+            fh.write(secret)
+    except OSError as exc:
+        raise RuntimeError(f"Failed to persist JWT secret to {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can store a stable auth secret.") from exc
+    return secret
+
+
 def get_auth_config() -> AuthConfig:
     """Get the global AuthConfig instance. Parses from env on first call."""
     global _auth_config
     if _auth_config is None:
+        from dotenv import load_dotenv
+
+        load_dotenv()
         jwt_secret = os.environ.get("AUTH_JWT_SECRET")
         if not jwt_secret:
-            jwt_secret = secrets.token_urlsafe(32)
+            jwt_secret = _load_or_create_secret()
             os.environ["AUTH_JWT_SECRET"] = jwt_secret
             logger.warning(
-                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated ephemeral secret. "
-                "Sessions will be invalidated on restart. "
+                "⚠ AUTH_JWT_SECRET is not set — using an auto-generated secret "
+                "persisted to .jwt_secret. Sessions will survive restarts. "
                 "For production, add AUTH_JWT_SECRET to your .env file: "
                 'python -c "import secrets; print(secrets.token_urlsafe(32))"'
             )

backend/app/gateway/auth/local_provider.py

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

backend/app/gateway/auth/models.py

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

backend/app/gateway/auth/password.py

@@ -1,18 +1,66 @@
-"""Password hashing utilities using bcrypt directly."""
+"""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 using bcrypt."""
-    return bcrypt.hashpw(password.encode("utf-8"), bcrypt.gensalt()).decode("utf-8")
+    """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 against its hash."""
-    return bcrypt.checkpw(plain_password.encode("utf-8"), hashed_password.encode("utf-8"))
+    """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:

backend/app/gateway/authz.py

@@ -145,7 +145,11 @@ async def _authenticate(request: Request) -> AuthContext:
 
 
 def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
-    """Decorator that authenticates the request and sets AuthContext.
+    """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).
 
@@ -158,7 +162,8 @@ def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
             ...
 
     Raises:
-        ValueError: If 'request' parameter is missing
+        HTTPException: 401 if the request is unauthenticated.
+        ValueError: If 'request' parameter is missing.
     """
 
     @functools.wraps(func)
@@ -181,6 +186,9 @@ def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
         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

backend/app/gateway/config.py

@@ -8,7 +8,7 @@ class GatewayConfig(BaseModel):
 
     host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
     port: int = Field(default=8001, description="Port to bind the gateway server")
-    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
@@ -18,10 +18,9 @@ def get_gateway_config() -> GatewayConfig:
     """Get gateway config, loading from environment if available."""
     global _gateway_config
     if _gateway_config is None:
-        cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
         _gateway_config = GatewayConfig(
             host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
             port=int(os.getenv("GATEWAY_PORT", "8001")),
-            cors_origins=cors_origins_str.split(","),
+            enable_docs=os.getenv("GATEWAY_ENABLE_DOCS", "true").lower() == "true",
         )
     return _gateway_config

backend/app/gateway/csrf_middleware.py

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

backend/app/gateway/deps.py

@@ -15,6 +15,7 @@ from typing import TYPE_CHECKING, TypeVar, cast
 from fastapi import FastAPI, HTTPException, Request
 from langgraph.types import Checkpointer
 
+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
@@ -29,6 +30,14 @@ if TYPE_CHECKING:
 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
 async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
     """Bootstrap and tear down all LangGraph runtime singletons.
@@ -38,22 +47,24 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
         async with langgraph_runtime(app):
             yield
     """
-    from deerflow.config import get_app_config
     from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
     from deerflow.runtime import make_store, make_stream_bridge
     from deerflow.runtime.checkpointer.async_provider import make_checkpointer
     from deerflow.runtime.events.store import make_run_event_store
 
     async with AsyncExitStack() as stack:
-        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
+        config = getattr(app.state, "config", None)
+        if config is None:
+            raise RuntimeError("langgraph_runtime() requires app.state.config to be initialized")
+
+        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).
-        config = get_app_config()
         await init_engine_from_config(config.database)
 
-        app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
-        app.state.store = await stack.enter_async_context(make_store())
+        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()
@@ -130,14 +141,14 @@ def get_run_context(request: Request) -> RunContext:
 
     Returns a *base* context with infrastructure dependencies.
     """
-    from deerflow.config import get_app_config
-
+    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(get_app_config(), "run_events", None),
+        run_events_config=getattr(config, "run_events", None),
         thread_store=get_thread_store(request),
+        app_config=config,
     )
 
 

backend/app/gateway/langgraph_auth.py

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

backend/app/gateway/routers/agents.py

@@ -11,6 +11,7 @@ from pydantic import BaseModel, Field
 from deerflow.config.agents_api_config import get_agents_api_config
 from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
 from deerflow.config.paths import get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["agents"])
@@ -86,11 +87,11 @@ def _require_agents_api_enabled() -> None:
         )
 
 
-def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
+def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False, *, user_id: str | None = None) -> AgentResponse:
     """Convert AgentConfig to AgentResponse."""
     soul: str | None = None
     if include_soul:
-        soul = load_agent_soul(agent_cfg.name) or ""
+        soul = load_agent_soul(agent_cfg.name, user_id=user_id) or ""
 
     return AgentResponse(
         name=agent_cfg.name,
@@ -116,9 +117,10 @@ async def list_agents() -> AgentsListResponse:
     """
     _require_agents_api_enabled()
 
+    user_id = get_effective_user_id()
     try:
-        agents = list_custom_agents()
-        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
+        agents = list_custom_agents(user_id=user_id)
+        return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True, user_id=user_id) for a in agents])
     except Exception as e:
         logger.error(f"Failed to list agents: {e}", exc_info=True)
         raise HTTPException(status_code=500, detail=f"Failed to list agents: {str(e)}")
@@ -144,7 +146,12 @@ async def check_agent_name(name: str) -> dict:
     _require_agents_api_enabled()
     _validate_agent_name(name)
     normalized = _normalize_agent_name(name)
-    available = not get_paths().agent_dir(normalized).exists()
+    user_id = get_effective_user_id()
+    paths = get_paths()
+    # Treat the name as taken if either the per-user path or the legacy shared
+    # path holds an agent — picking a name that collides with an unmigrated
+    # legacy agent would shadow the legacy entry once migration runs.
+    available = not paths.user_agent_dir(user_id, normalized).exists() and not paths.agent_dir(normalized).exists()
     return {"available": available, "name": normalized}
 
 
@@ -169,10 +176,11 @@ async def get_agent(name: str) -> AgentResponse:
     _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
+    user_id = get_effective_user_id()
 
     try:
-        agent_cfg = load_agent_config(name)
-        return _agent_config_to_response(agent_cfg, include_soul=True)
+        agent_cfg = load_agent_config(name, user_id=user_id)
+        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
     except FileNotFoundError:
         raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
     except Exception as e:
@@ -202,10 +210,13 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     _require_agents_api_enabled()
     _validate_agent_name(request.name)
     normalized_name = _normalize_agent_name(request.name)
+    user_id = get_effective_user_id()
+    paths = get_paths()
 
-    agent_dir = get_paths().agent_dir(normalized_name)
+    agent_dir = paths.user_agent_dir(user_id, normalized_name)
+    legacy_dir = paths.agent_dir(normalized_name)
 
-    if agent_dir.exists():
+    if agent_dir.exists() or legacy_dir.exists():
         raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
 
     try:
@@ -232,8 +243,8 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
 
         logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
 
-        agent_cfg = load_agent_config(normalized_name)
-        return _agent_config_to_response(agent_cfg, include_soul=True)
+        agent_cfg = load_agent_config(normalized_name, user_id=user_id)
+        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
 
     except HTTPException:
         raise
@@ -267,13 +278,20 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
     _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
+    user_id = get_effective_user_id()
 
     try:
-        agent_cfg = load_agent_config(name)
+        agent_cfg = load_agent_config(name, user_id=user_id)
     except FileNotFoundError:
         raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
 
-    agent_dir = get_paths().agent_dir(name)
+    paths = get_paths()
+    agent_dir = paths.user_agent_dir(user_id, name)
+    if not agent_dir.exists() and paths.agent_dir(name).exists():
+        raise HTTPException(
+            status_code=409,
+            detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating."),
+        )
 
     try:
         # Update config if any config fields changed
@@ -314,8 +332,8 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
 
         logger.info(f"Updated agent '{name}'")
 
-        refreshed_cfg = load_agent_config(name)
-        return _agent_config_to_response(refreshed_cfg, include_soul=True)
+        refreshed_cfg = load_agent_config(name, user_id=user_id)
+        return _agent_config_to_response(refreshed_cfg, include_soul=True, user_id=user_id)
 
     except HTTPException:
         raise
@@ -402,15 +420,22 @@ async def delete_agent(name: str) -> None:
         name: The agent name.
 
     Raises:
-        HTTPException: 404 if agent not found.
+        HTTPException: 404 if no per-user copy exists; 409 if only a legacy
+            shared copy exists (suggesting the migration script).
     """
     _require_agents_api_enabled()
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
-
-    agent_dir = get_paths().agent_dir(name)
+    user_id = get_effective_user_id()
+    paths = get_paths()
+    agent_dir = paths.user_agent_dir(user_id, name)
 
     if not agent_dir.exists():
+        if paths.agent_dir(name).exists():
+            raise HTTPException(
+                status_code=409,
+                detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
+            )
         raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
 
     try:

backend/app/gateway/routers/artifacts.py

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

backend/app/gateway/routers/auth.py

@@ -146,7 +146,13 @@ def _set_session_cookie(response: Response, token: str, request: Request) -> Non
 
 
 # ── Rate Limiting ────────────────────────────────────────────────────────
-# In-process dict — not shared across workers. Sufficient for single-worker deployments.
+# 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
@@ -299,7 +305,7 @@ async def login_local(
 async def register(request: Request, response: Response, body: RegisterRequest):
     """Register a new user account (always 'user' role).
 
-    Admin is auto-created on first boot. This endpoint creates regular users.
+    The first admin is created explicitly through /initialize. This endpoint creates regular users.
     Auto-login by setting the session cookie.
     """
     try:
@@ -376,9 +382,37 @@ async def get_me(request: Request):
     return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role, needs_setup=user.needs_setup)
 
 
+_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():
+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}
 

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"])
 
@@ -36,7 +37,7 @@ 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,
@@ -72,7 +73,6 @@ async def list_models() -> ModelsListResponse:
         }
         ```
     """
-    config = get_app_config()
     models = [
         ModelResponse(
             name=model.name,
@@ -96,7 +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:
@@ -118,7 +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/skills.py

@@ -1,30 +1,20 @@
-import errno
 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.installer import SkillAlreadyExistsError, install_skill_from_archive
-from deerflow.skills.manager import (
-    append_history,
-    atomic_write,
-    custom_skill_exists,
-    ensure_custom_skill_is_editable,
-    get_custom_skill_dir,
-    get_custom_skill_file,
-    get_skill_history_file,
-    read_custom_skill_content,
-    read_history,
-    validate_skill_markdown_content,
-)
+from deerflow.skills import Skill
+from deerflow.skills.installer import SkillAlreadyExistsError
 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__)
 
@@ -37,7 +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")
 
 
@@ -101,9 +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)
@@ -116,10 +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:
@@ -136,9 +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)
@@ -146,13 +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 = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
+        skill = next((s for s in skills if s.name == skill_name and s.category == SkillCategory.CUSTOM), None)
         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:
@@ -161,30 +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)
-        validate_skill_markdown_content(skill_name, request.content)
-        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        storage = get_or_new_skill_storage(app_config=config)
+        storage.ensure_custom_skill_is_editable(skill_name)
+        storage.validate_skill_markdown_content(skill_name, request.content)
+        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
         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 = skill_file.read_text(encoding="utf-8")
-        atomic_write(skill_file, request.content)
-        append_history(
+        prev_content = storage.read_custom_skill(skill_name)
+        storage.write_custom_skill(skill_name, SKILL_MD_FILE, request.content)
+        storage.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:
@@ -197,29 +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_dir = get_custom_skill_dir(skill_name)
-        prev_content = read_custom_skill_content(skill_name)
-        try:
-            append_history(
-                skill_name,
-                {
-                    "action": "human_delete",
-                    "author": "human",
-                    "thread_id": None,
-                    "file_path": "SKILL.md",
-                    "prev_content": prev_content,
-                    "new_content": None,
-                    "scanner": {"decision": "allow", "reason": "Deletion requested."},
-                },
-            )
-        except OSError as e:
-            if not isinstance(e, PermissionError) and e.errno not in {errno.EACCES, errno.EPERM, errno.EROFS}:
-                raise
-            logger.warning("Skipping delete history write for custom skill %s due to readonly/permission failure; continuing with skill directory removal: %s", skill_name, e)
-        shutil.rmtree(skill_dir)
+        skill_name = skill_name.replace("\r\n", "").replace("\n", "")
+        storage = get_or_new_skill_storage(app_config=config)
+        storage.delete_custom_skill(
+            skill_name,
+            history_meta={
+                "action": "human_delete",
+                "author": "human",
+                "thread_id": None,
+                "file_path": SKILL_MD_FILE,
+                "prev_content": None,
+                "new_content": None,
+                "scanner": {"decision": "allow", "reason": "Deletion requested."},
+            },
+        )
         await refresh_skills_system_prompt_cache_async()
         return {"success": True}
     except FileNotFoundError as e:
@@ -232,11 +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:
@@ -245,38 +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)
-        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
-        skill_file = get_custom_skill_file(skill_name)
+        storage.validate_skill_markdown_content(skill_name, target_content)
+        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
+        skill_file = storage.get_custom_skill_file(skill_name)
         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)
-        append_history(skill_name, history_entry)
+        storage.write_custom_skill(skill_name, SKILL_MD_FILE, target_content)
+        storage.append_history(skill_name, history_entry)
         await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name)
+        return await get_custom_skill(skill_name, config)
     except HTTPException:
         raise
     except IndexError:
@@ -296,9 +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:
@@ -318,9 +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:
@@ -346,7 +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

@@ -1,11 +1,13 @@
 import json
 import logging
 
-from fastapi import APIRouter, Request
+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__)
@@ -100,7 +102,12 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
     description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
 )
 @require_permission("threads", "read", owner_check=True)
-async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request) -> SuggestionsResponse:
+async def generate_suggestions(
+    thread_id: str,
+    body: SuggestionsRequest,
+    request: Request,
+    config: AppConfig = Depends(get_config),
+) -> SuggestionsResponse:
     if not body.messages:
         return SuggestionsResponse(suggestions=[])
 
@@ -122,7 +129,7 @@ async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request
     user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
 
     try:
-        model = create_chat_model(name=body.model_name, thinking_enabled=False)
+        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)], config={"run_name": "suggest_agent"})
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []

backend/app/gateway/routers/thread_runs.py

@@ -68,6 +68,27 @@ class RunResponse(BaseModel):
     updated_at: str = ""
 
 
+class ThreadTokenUsageModelBreakdown(BaseModel):
+    tokens: int = 0
+    runs: int = 0
+
+
+class ThreadTokenUsageCallerBreakdown(BaseModel):
+    lead_agent: int = 0
+    subagent: int = 0
+    middleware: int = 0
+
+
+class ThreadTokenUsageResponse(BaseModel):
+    thread_id: str
+    total_tokens: int = 0
+    total_input_tokens: int = 0
+    total_output_tokens: int = 0
+    total_runs: int = 0
+    by_model: dict[str, ThreadTokenUsageModelBreakdown] = Field(default_factory=dict)
+    by_caller: ThreadTokenUsageCallerBreakdown = Field(default_factory=ThreadTokenUsageCallerBreakdown)
+
+
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
@@ -368,10 +389,10 @@ async def list_run_events(
     return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)
 
 
-@router.get("/{thread_id}/token-usage")
+@router.get("/{thread_id}/token-usage", response_model=ThreadTokenUsageResponse)
 @require_permission("threads", "read", owner_check=True)
-async def thread_token_usage(thread_id: str, request: Request) -> dict:
+async def thread_token_usage(thread_id: str, request: Request) -> ThreadTokenUsageResponse:
     """Thread-level token usage aggregation."""
     run_store = get_run_store(request)
     agg = await run_store.aggregate_tokens_by_thread(thread_id)
-    return {"thread_id": thread_id, **agg}
+    return ThreadTokenUsageResponse(thread_id=thread_id, **agg)

backend/app/gateway/routers/threads.py

@@ -13,11 +13,11 @@ matching the LangGraph Platform wire format expected by the
 from __future__ import annotations
 
 import logging
-import time
 import uuid
 from typing import Any
 
 from fastapi import APIRouter, HTTPException, Request
+from langgraph.checkpoint.base import empty_checkpoint
 from pydantic import BaseModel, Field, field_validator
 
 from app.gateway.authz import require_permission
@@ -26,6 +26,7 @@ from app.gateway.utils import sanitize_log_param
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime import serialize_channel_values
 from deerflow.runtime.user_context import get_effective_user_id
+from deerflow.utils.time import coerce_iso, now_iso
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api/threads", tags=["threads"])
@@ -89,6 +90,28 @@ class ThreadSearchRequest(BaseModel):
     offset: int = Field(default=0, ge=0, description="Pagination offset")
     status: str | None = Field(default=None, description="Filter by thread status")
 
+    @field_validator("metadata")
+    @classmethod
+    def _validate_metadata_filters(cls, v: dict[str, Any]) -> dict[str, Any]:
+        """Reject filter entries the SQL backend cannot compile.
+
+        Enforces consistent behaviour across SQL and memory backends.
+        See ``deerflow.persistence.json_compat`` for the shared validators.
+        """
+        if not v:
+            return v
+        from deerflow.persistence.json_compat import validate_metadata_filter_key, validate_metadata_filter_value
+
+        bad_entries: list[str] = []
+        for key, value in v.items():
+            if not validate_metadata_filter_key(key):
+                bad_entries.append(f"{key!r} (unsafe key)")
+            elif not validate_metadata_filter_value(value):
+                bad_entries.append(f"{key!r} (unsupported value type {type(value).__name__})")
+        if bad_entries:
+            raise ValueError(f"Invalid metadata filter entries: {', '.join(bad_entries)}")
+        return v
+
 
 class ThreadStateResponse(BaseModel):
     """Response model for thread state."""
@@ -233,7 +256,7 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     checkpointer = get_checkpointer(request)
     thread_store = get_thread_store(request)
     thread_id = body.thread_id or str(uuid.uuid4())
-    now = time.time()
+    now = now_iso()
     # ``body.metadata`` is already stripped of server-reserved keys by
     # ``ThreadCreateRequest._strip_reserved`` — see the model definition.
 
@@ -243,8 +266,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
         return ThreadResponse(
             thread_id=thread_id,
             status=existing_record.get("status", "idle"),
-            created_at=str(existing_record.get("created_at", "")),
-            updated_at=str(existing_record.get("updated_at", "")),
+            created_at=coerce_iso(existing_record.get("created_at", "")),
+            updated_at=coerce_iso(existing_record.get("updated_at", "")),
             metadata=existing_record.get("metadata", {}),
         )
 
@@ -262,8 +285,6 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     # Write an empty checkpoint so state endpoints work immediately
     config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
     try:
-        from langgraph.checkpoint.base import empty_checkpoint
-
         ckpt_metadata = {
             "step": -1,
             "source": "input",
@@ -281,8 +302,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
     return ThreadResponse(
         thread_id=thread_id,
         status="idle",
-        created_at=str(now),
-        updated_at=str(now),
+        created_at=now,
+        updated_at=now,
         metadata=body.metadata,
     )
 
@@ -295,20 +316,27 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
     (SQL-backed for sqlite/postgres, Store-backed for memory mode).
     """
     from app.gateway.deps import get_thread_store
+    from deerflow.persistence.thread_meta import InvalidMetadataFilterError
 
     repo = get_thread_store(request)
-    rows = await repo.search(
-        metadata=body.metadata or None,
-        status=body.status,
-        limit=body.limit,
-        offset=body.offset,
-    )
+    try:
+        rows = await repo.search(
+            metadata=body.metadata or None,
+            status=body.status,
+            limit=body.limit,
+            offset=body.offset,
+        )
+    except InvalidMetadataFilterError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
     return [
         ThreadResponse(
             thread_id=r["thread_id"],
             status=r.get("status", "idle"),
-            created_at=r.get("created_at", ""),
-            updated_at=r.get("updated_at", ""),
+            # ``coerce_iso`` heals legacy unix-second values that
+            # ``MemoryThreadMetaStore`` historically wrote with ``time.time()``;
+            # SQL-backed rows already arrive as ISO strings and pass through.
+            created_at=coerce_iso(r.get("created_at", "")),
+            updated_at=coerce_iso(r.get("updated_at", "")),
             metadata=r.get("metadata", {}),
             values={"title": r["display_name"]} if r.get("display_name") else {},
             interrupts={},
@@ -340,8 +368,8 @@ async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Reques
     return ThreadResponse(
         thread_id=thread_id,
         status=record.get("status", "idle"),
-        created_at=str(record.get("created_at", "")),
-        updated_at=str(record.get("updated_at", "")),
+        created_at=coerce_iso(record.get("created_at", "")),
+        updated_at=coerce_iso(record.get("updated_at", "")),
         metadata=record.get("metadata", {}),
     )
 
@@ -381,8 +409,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
         record = {
             "thread_id": thread_id,
             "status": "idle",
-            "created_at": ckpt_meta.get("created_at", ""),
-            "updated_at": ckpt_meta.get("updated_at", ckpt_meta.get("created_at", "")),
+            "created_at": coerce_iso(ckpt_meta.get("created_at", "")),
+            "updated_at": coerce_iso(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
             "metadata": {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")},
         }
 
@@ -396,8 +424,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
     return ThreadResponse(
         thread_id=thread_id,
         status=status,
-        created_at=str(record.get("created_at", "")),
-        updated_at=str(record.get("updated_at", "")),
+        created_at=coerce_iso(record.get("created_at", "")),
+        updated_at=coerce_iso(record.get("updated_at", "")),
         metadata=record.get("metadata", {}),
         values=serialize_channel_values(channel_values),
     )
@@ -448,10 +476,10 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
         values=values,
         next=next_tasks,
         metadata=metadata,
-        checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
+        checkpoint={"id": checkpoint_id, "ts": coerce_iso(metadata.get("created_at", ""))},
         checkpoint_id=checkpoint_id,
         parent_checkpoint_id=parent_checkpoint_id,
-        created_at=str(metadata.get("created_at", "")),
+        created_at=coerce_iso(metadata.get("created_at", "")),
         tasks=tasks,
     )
 
@@ -501,7 +529,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
         channel_values.update(body.values)
 
     checkpoint["channel_values"] = channel_values
-    metadata["updated_at"] = time.time()
+    metadata["updated_at"] = now_iso()
 
     if body.as_node:
         metadata["source"] = "update"
@@ -542,7 +570,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
         next=[],
         metadata=metadata,
         checkpoint_id=new_checkpoint_id,
-        created_at=str(metadata.get("created_at", "")),
+        created_at=coerce_iso(metadata.get("created_at", "")),
     )
 
 
@@ -609,7 +637,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
                     parent_checkpoint_id=parent_id,
                     metadata=user_meta,
                     values=values,
-                    created_at=str(metadata.get("created_at", "")),
+                    created_at=coerce_iso(metadata.get("created_at", "")),
                     next=next_tasks,
                 )
             )

backend/app/gateway/routers/uploads.py

@@ -4,22 +4,26 @@ import logging
 import os
 import stat
 
-from fastapi import APIRouter, File, HTTPException, Request, UploadFile
-from pydantic import BaseModel
+from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
+from pydantic import BaseModel, Field
 
 from app.gateway.authz import require_permission
-from deerflow.config.app_config import get_app_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
 from deerflow.uploads.manager import (
     PathTraversalError,
+    UnsafeUploadPathError,
+    claim_unique_filename,
     delete_file_safe,
     enrich_file_listing,
     ensure_uploads_dir,
     get_uploads_dir,
     list_files_in_dir,
     normalize_filename,
+    open_upload_file_no_symlink,
     upload_artifact_url,
     upload_virtual_path,
 )
@@ -29,6 +33,11 @@ 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."""
@@ -36,6 +45,15 @@ class UploadResponse(BaseModel):
     success: bool
     files: list[dict[str, str]]
     message: str
+    skipped_files: list[str] = Field(default_factory=list)
+
+
+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:
@@ -60,23 +78,88 @@ def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
     return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
 
 
-def _get_uploads_config_value(key: str, default: object) -> object:
+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."""
-    cfg = get_app_config()
-    uploads_cfg = getattr(cfg, "uploads", None)
+    uploads_cfg = getattr(app_config, "uploads", None)
     if isinstance(uploads_cfg, dict):
         return uploads_cfg.get(key, default)
     return getattr(uploads_cfg, key, default)
 
 
-def _auto_convert_documents_enabled() -> bool:
+def _get_upload_limit(app_config: AppConfig, key: str, default: int, *, legacy_key: str | None = None) -> int:
+    try:
+        value = _get_uploads_config_value(app_config, key, None)
+        if value is None and legacy_key is not None:
+            value = _get_uploads_config_value(app_config, legacy_key, None)
+        if value is None:
+            value = default
+        limit = int(value)
+        if limit <= 0:
+            raise ValueError
+        return limit
+    except Exception:
+        logger.warning("Invalid uploads.%s value; falling back to %d", key, default)
+        return default
+
+
+def _get_upload_limits(app_config: AppConfig) -> UploadLimits:
+    return UploadLimits(
+        max_files=_get_upload_limit(app_config, "max_files", DEFAULT_MAX_FILES, legacy_key="max_file_count"),
+        max_file_size=_get_upload_limit(app_config, "max_file_size", DEFAULT_MAX_FILE_SIZE, legacy_key="max_single_file_size"),
+        max_total_size=_get_upload_limit(app_config, "max_total_size", DEFAULT_MAX_TOTAL_SIZE),
+    )
+
+
+def _cleanup_uploaded_paths(paths: list[os.PathLike[str] | str]) -> None:
+    for path in reversed(paths):
+        try:
+            os.unlink(path)
+        except FileNotFoundError:
+            pass
+        except Exception:
+            logger.warning("Failed to clean up upload path after rejected request: %s", path, exc_info=True)
+
+
+async def _write_upload_file_with_limits(
+    file: UploadFile,
+    *,
+    uploads_dir: os.PathLike[str] | str,
+    display_filename: str,
+    max_single_file_size: int,
+    max_total_size: int,
+    total_size: int,
+) -> tuple[os.PathLike[str] | str, int, int]:
+    file_size = 0
+    file_path, fh = open_upload_file_no_symlink(uploads_dir, display_filename)
+    try:
+        while chunk := await file.read(UPLOAD_CHUNK_SIZE):
+            file_size += len(chunk)
+            total_size += len(chunk)
+            if file_size > max_single_file_size:
+                raise HTTPException(status_code=413, detail=f"File too large: {display_filename}")
+            if total_size > max_total_size:
+                raise HTTPException(status_code=413, detail="Total upload size too large")
+            fh.write(chunk)
+    except Exception:
+        fh.close()
+        try:
+            os.unlink(file_path)
+        except FileNotFoundError:
+            pass
+        raise
+    else:
+        fh.close()
+    return file_path, file_size, total_size
+
+
+def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
     """Return whether automatic host-side document conversion is enabled.
 
     The secure default is disabled unless an operator explicitly opts in via
     uploads.auto_convert_documents in config.yaml.
     """
     try:
-        raw = _get_uploads_config_value("auto_convert_documents", False)
+        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)
@@ -90,17 +173,30 @@ 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, user_id=get_effective_user_id())
     uploaded_files = []
+    written_paths = []
+    sandbox_sync_targets = []
+    skipped_files = []
+    total_size = 0
+    # Track filenames within this request so duplicate form parts do not
+    # silently truncate each other. Existing uploads keep the historical
+    # overwrite behavior for a single replacement upload.
+    seen_filenames: set[str] = set()
 
     sandbox_provider = get_sandbox_provider()
     sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
@@ -108,48 +204,58 @@ async def upload_files(
     if sync_to_sandbox:
         sandbox_id = sandbox_provider.acquire(thread_id)
         sandbox = sandbox_provider.get(sandbox_id)
-    auto_convert_documents = _auto_convert_documents_enabled()
+        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:
             continue
 
         try:
-            safe_filename = normalize_filename(file.filename)
+            original_filename = normalize_filename(file.filename)
+            safe_filename = claim_unique_filename(original_filename, seen_filenames)
         except ValueError:
             logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
             continue
 
         try:
-            content = await file.read()
-            file_path = uploads_dir / safe_filename
-            file_path.write_bytes(content)
+            file_path, file_size, total_size = await _write_upload_file_with_limits(
+                file,
+                uploads_dir=uploads_dir,
+                display_filename=safe_filename,
+                max_single_file_size=limits.max_file_size,
+                max_total_size=limits.max_total_size,
+                total_size=total_size,
+            )
+            written_paths.append(file_path)
 
             virtual_path = upload_virtual_path(safe_filename)
 
-            if sync_to_sandbox and sandbox is not None:
-                _make_file_sandbox_writable(file_path)
-                sandbox.update_file(virtual_path, content)
+            if sync_to_sandbox:
+                sandbox_sync_targets.append((file_path, virtual_path))
 
             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),
             }
+            if safe_filename != original_filename:
+                file_info["original_filename"] = original_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 auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
                 md_path = await convert_file_to_markdown(file_path)
                 if md_path:
+                    written_paths.append(md_path)
                     md_virtual_path = upload_virtual_path(md_path.name)
 
-                    if sync_to_sandbox and sandbox is not None:
-                        _make_file_sandbox_writable(md_path)
-                        sandbox.update_file(md_virtual_path, md_path.read_bytes())
+                    if sync_to_sandbox:
+                        sandbox_sync_targets.append((md_path, md_virtual_path))
 
                     file_info["markdown_file"] = md_path.name
                     file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
@@ -158,17 +264,46 @@ async def upload_files(
 
             uploaded_files.append(file_info)
 
+        except HTTPException as e:
+            _cleanup_uploaded_paths(written_paths)
+            raise e
+        except UnsafeUploadPathError as e:
+            logger.warning("Skipping upload with unsafe destination %s: %s", file.filename, e)
+            skipped_files.append(safe_filename)
+            continue
         except Exception as e:
             logger.error(f"Failed to upload {file.filename}: {e}")
+            _cleanup_uploaded_paths(written_paths)
             raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
 
+    if sync_to_sandbox:
+        for file_path, virtual_path in sandbox_sync_targets:
+            _make_file_sandbox_writable(file_path)
+            sandbox.update_file(virtual_path, file_path.read_bytes())
+
+    message = f"Successfully uploaded {len(uploaded_files)} file(s)"
+    if skipped_files:
+        message += f"; skipped {len(skipped_files)} unsafe file(s)"
+
     return UploadResponse(
-        success=True,
+        success=not skipped_files,
         files=uploaded_files,
-        message=f"Successfully uploaded {len(uploaded_files)} file(s)",
+        message=message,
+        skipped_files=skipped_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)
 @require_permission("threads", "read", owner_check=True)
 async def list_uploaded_files(thread_id: str, request: Request) -> dict:

backend/app/gateway/services.py

@@ -19,6 +19,7 @@ from langchain_core.messages import HumanMessage
 
 from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
 from app.gateway.utils import sanitize_log_param
+from deerflow.config.app_config import get_app_config
 from deerflow.runtime import (
     END_SENTINEL,
     HEARTBEAT_SENTINEL,
@@ -98,6 +99,62 @@ 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 inject_authenticated_user_context(config: dict[str, Any], request: Request) -> None:
+    """Stamp the authenticated user into the run context for background tools.
+
+    Tool execution may happen after the request handler has returned, so tools
+    that persist user-scoped files should not rely only on ambient ContextVars.
+    The value comes from server-side auth state, never from client context.
+    """
+
+    user = getattr(request.state, "user", None)
+    user_id = getattr(user, "id", None)
+    if user_id is None:
+        return
+
+    runtime_context = config.setdefault("context", {})
+    if isinstance(runtime_context, dict):
+        runtime_context["user_id"] = str(user_id)
+
+
 def resolve_agent_factory(assistant_id: str | None):
     """Resolve the agent factory callable from config.
 
@@ -211,6 +268,23 @@ async def start_run(
 
     disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
 
+    body_context = getattr(body, "context", None) or {}
+    model_name = body_context.get("model_name")
+
+    # Coerce non-string model_name values to str before truncation.
+    if model_name is not None and not isinstance(model_name, str):
+        model_name = str(model_name)
+
+    # Validate model against the allowlist when a model_name is provided.
+    if model_name:
+        app_config = get_app_config()
+        resolved = app_config.get_model_config(model_name)
+        if resolved is None:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Model {model_name!r} is not in the configured model allowlist",
+            )
+
     try:
         record = await run_mgr.create_or_reject(
             thread_id,
@@ -219,6 +293,7 @@ async def start_run(
             metadata=body.metadata or {},
             kwargs={"input": body.input, "config": body.config},
             multitask_strategy=body.multitask_strategy,
+            model_name=model_name,
         )
     except ConflictError as exc:
         raise HTTPException(status_code=409, detail=str(exc)) from exc
@@ -245,27 +320,12 @@ async def start_run(
     graph_input = normalize_input(body.input)
     config = build_run_config(thread_id, body.config, body.metadata, assistant_id=body.assistant_id)
 
-    # Merge DeerFlow-specific context overrides into 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)
-    if context:
-        _CONTEXT_CONFIGURABLE_KEYS = {
-            "model_name",
-            "mode",
-            "thinking_enabled",
-            "reasoning_effort",
-            "is_plan_mode",
-            "subagent_enabled",
-            "max_concurrent_subagents",
-            "agent_name",
-            "is_bootstrap",
-        }
-        configurable = config.setdefault("configurable", {})
-        for key in _CONTEXT_CONFIGURABLE_KEYS:
-            if key in context:
-                configurable.setdefault(key, context[key])
+    merge_run_context_overrides(config, getattr(body, "context", None))
+    inject_authenticated_user_context(config, request)
 
     stream_modes = normalize_stream_modes(body.stream_mode)
 

backend/debug.py

@@ -34,50 +34,42 @@ _LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 _LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
 
 
-def _logging_level_from_config(name: str) -> int:
-    """Map ``config.yaml`` ``log_level`` string to a ``logging`` level constant."""
-    mapping = logging.getLevelNamesMapping()
-    return mapping.get((name or "info").strip().upper(), logging.INFO)
+def _setup_logging(log_level: 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``.
 
-def _setup_logging(log_level: str) -> None:
-    """Send application logs to ``debug.log`` at *log_level*; do not print them on the console.
-
-    Idempotent: any pre-existing handlers on the root logger (e.g. installed by
-    ``logging.basicConfig`` in transitively imported modules) are removed so the
-    debug session output only lands in ``debug.log``.
+    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.
     """
-    level = _logging_level_from_config(log_level)
     root = logging.root
     for h in list(root.handlers):
         root.removeHandler(h)
         h.close()
-    root.setLevel(level)
+    root.setLevel(log_level)
 
     file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
-    file_handler.setLevel(level)
+    file_handler.setLevel(log_level)
     file_handler.setFormatter(logging.Formatter(_LOG_FMT, datefmt=_LOG_DATEFMT))
     root.addHandler(file_handler)
 
 
-def _update_logging_level(log_level: str) -> None:
-    """Update the root logger and existing handlers to *log_level*."""
-    level = _logging_level_from_config(log_level)
-    root = logging.root
-    root.setLevel(level)
-    for handler in root.handlers:
-        handler.setLevel(level)
-
-
 async def main():
     # Install file logging first so warnings emitted while loading config do not
     # leak onto the interactive terminal via Python's lastResort handler.
-    _setup_logging("info")
+    _setup_logging()
 
     from deerflow.config import get_app_config
+    from deerflow.config.app_config import apply_logging_level
 
     app_config = get_app_config()
-    _update_logging_level(app_config.log_level)
+    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
@@ -87,7 +79,9 @@ async def main():
     from langgraph.runtime import Runtime
 
     from deerflow.agents import make_lead_agent
+    from deerflow.config.paths import get_paths
     from deerflow.mcp import initialize_mcp_tools
+    from deerflow.runtime.user_context import get_effective_user_id
 
     # Initialize MCP tools at startup
     try:
@@ -121,6 +115,8 @@ async def main():
         print("Tip: `uv sync --group dev` to enable arrow-key & history support")
     print("=" * 50)
 
+    seen_artifacts: set[str] = set()
+
     while True:
         try:
             if session:
@@ -142,6 +138,22 @@ async def main():
                 last_message = result["messages"][-1]
                 print(f"\nAgent: {last_message.content}")
 
+            # Show files presented to the user this turn (new artifacts only)
+            artifacts = result.get("artifacts") or []
+            new_artifacts = [p for p in artifacts if p not in seen_artifacts]
+            if new_artifacts:
+                thread_id = config["configurable"]["thread_id"]
+                user_id = get_effective_user_id()
+                paths = get_paths()
+                print("\n[Presented files]")
+                for virtual in new_artifacts:
+                    try:
+                        physical = paths.resolve_virtual_path(thread_id, virtual, user_id=user_id)
+                        print(f"  - {virtual}\n    → {physical}")
+                    except ValueError as exc:
+                        print(f"  - {virtual}    (failed to resolve physical path: {exc})")
+                seen_artifacts.update(new_artifacts)
+
         except (KeyboardInterrupt, EOFError):
             print("\nGoodbye!")
             break

backend/docs/API.md

@@ -6,16 +6,16 @@ This document provides a complete reference for the DeerFlow backend APIs.
 
 DeerFlow backend exposes two sets of APIs:
 
-1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
+1. **LangGraph-compatible API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
 2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)
 
 All APIs are accessed through the Nginx reverse proxy at port 2026.
 
-## LangGraph API
+## LangGraph-compatible API
 
 Base URL: `/api/langgraph`
 
-The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
+The public LangGraph-compatible API follows LangGraph SDK conventions. In the unified nginx deployment, Gateway owns `/api/langgraph/*` and translates those paths to its native `/api/*` run, thread, and streaming routers.
 
 ### Threads
 
@@ -104,17 +104,11 @@ Content-Type: application/json
 **Recursion Limit:**
 
 `config.recursion_limit` caps the number of graph steps LangGraph will execute
-in a single run. The `/api/langgraph/*` endpoints go straight to the LangGraph
-server and therefore inherit LangGraph's native default of **25**, which is
-too low for plan-mode or subagent-heavy runs — the agent typically errors out
-with `GraphRecursionError` after the first round of subagent results comes
-back, before the lead agent can synthesize the final answer.
-
-DeerFlow's own Gateway and IM-channel paths mitigate this by defaulting to
-`100` in `build_run_config` (see `backend/app/gateway/services.py`), but
-clients calling the LangGraph API directly must set `recursion_limit`
-explicitly in the request body. `100` matches the Gateway default and is a
-safe starting point; increase it if you run deeply nested subagent graphs.
+in a single run. The unified Gateway path defaults to `100` in
+`build_run_config` (see `backend/app/gateway/services.py`), which is a safer
+starting point for plan-mode or subagent-heavy runs. Clients can still set
+`recursion_limit` explicitly in the request body; increase it if you run deeply
+nested subagent graphs.
 
 **Configurable Options:**
 - `model_name` (string): Override the default model
@@ -541,14 +535,28 @@ All APIs return errors in a consistent format:
 
 ## Authentication
 
-Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
+DeerFlow enforces authentication for all non-public HTTP routes. Public routes are limited to health/docs metadata and these public auth endpoints:
 
-Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
+- `POST /api/v1/auth/initialize` creates the first admin account when no admin exists.
+- `POST /api/v1/auth/login/local` logs in with email/password and sets an HttpOnly `access_token` cookie.
+- `POST /api/v1/auth/register` creates a regular `user` account and sets the session cookie.
+- `POST /api/v1/auth/logout` clears the session cookie.
+- `GET /api/v1/auth/setup-status` reports whether the first admin still needs to be created.
 
-For production deployments, it is recommended to:
-1. Use Nginx for basic auth or OAuth integration
-2. Deploy behind a VPN or private network
-3. Implement custom authentication middleware
+The authenticated auth endpoints are:
+
+- `GET /api/v1/auth/me` returns the current user.
+- `POST /api/v1/auth/change-password` changes password, optionally changes email during setup, increments `token_version`, and reissues the cookie.
+
+Protected state-changing requests also require the CSRF double-submit token: send the `csrf_token` cookie value as the `X-CSRF-Token` header. Login/register/initialize/logout are bootstrap auth endpoints: they are exempt from the double-submit token but still reject hostile browser `Origin` headers.
+
+User isolation is enforced from the authenticated user context:
+
+- Thread metadata is scoped by `threads_meta.user_id`; search/read/write/delete APIs only expose the current user's threads.
+- Thread files live under `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/` and are exposed inside the sandbox as `/mnt/user-data/`.
+- Memory and custom agents are stored under `{base_dir}/users/{user_id}/...`.
+
+Note: MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers; that is separate from DeerFlow API authentication.
 
 ---
 
@@ -567,12 +575,13 @@ location /api/ {
 
 ---
 
-## WebSocket Support
+## Streaming Support
 
-The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
+Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE):
 
-```
-ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
+```http
+POST /api/langgraph/threads/{thread_id}/runs/stream
+Accept: text/event-stream
 ```
 
 ---
@@ -608,13 +617,21 @@ const response = await fetch('/api/models');
 const data = await response.json();
 console.log(data.models);
 
-// Using EventSource for streaming
-const eventSource = new EventSource(
-  `/api/langgraph/threads/${threadId}/runs/stream`
-);
-eventSource.onmessage = (event) => {
-  console.log(JSON.parse(event.data));
-};
+// Create a run and stream SSE events
+const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
+  method: "POST",
+  headers: {
+    "Content-Type": "application/json",
+    Accept: "text/event-stream",
+  },
+  body: JSON.stringify({
+    input: { messages: [{ role: "user", content: "Hello" }] },
+    stream_mode: ["values", "messages-tuple", "custom"],
+  }),
+});
+
+const reader = streamResponse.body?.getReader();
+// Decode and parse SSE frames from reader in your client code.
 ```
 
 ### cURL Examples
@@ -649,7 +666,7 @@ curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
   }'
 ```
 
-> The `/api/langgraph/*` endpoints bypass DeerFlow's Gateway and inherit
-> LangGraph's native `recursion_limit` default of 25, which is too low for
-> plan-mode or subagent runs. Set `config.recursion_limit` explicitly — see
-> the [Create Run](#create-run) section for details.
+> The unified Gateway path defaults `config.recursion_limit` to 100 for
+> plan-mode and subagent-heavy runs. Clients may still set
+> `config.recursion_limit` explicitly — see the [Create Run](#create-run)
+> section for details.

backend/docs/ARCHITECTURE.md

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

backend/docs/AUTH_DESIGN.md

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

backend/docs/AUTH_TEST_DOCKER_GAP.md

@@ -24,11 +24,11 @@ All other test plan sections were executed against either:
 
 | Case | Title | What it covers | Why not run |
 |---|---|---|---|
-| TC-DOCKER-01 | `users.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
+| TC-DOCKER-01 | `deerflow.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
 | TC-DOCKER-02 | Session persistence across container restart | `AUTH_JWT_SECRET` env var keeps cookies valid after `docker compose down && up` | needs `docker compose down/up` |
 | TC-DOCKER-03 | Per-worker rate limiter divergence | Confirms in-process `_login_attempts` dict doesn't share state across `gunicorn` workers (4 by default in the compose file); known limitation, documented | needs multi-worker container |
-| TC-DOCKER-04 | IM channels skip AuthMiddleware | Verify Feishu/Slack/Telegram dispatchers run in-container against `http://langgraph:2024` without going through nginx | needs `docker logs` |
-| TC-DOCKER-05 | Admin credentials surfacing | **Updated post-simplify** — was "log scrape", now "0600 credential file in `DEER_FLOW_HOME`". The file-based behavior is already validated by TC-1.1 + TC-UPG-13 on sg_dev (non-Docker), so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
+| TC-DOCKER-04 | IM channels use internal Gateway auth | Verify Feishu/Slack/Telegram dispatchers attach the process-local internal auth header plus CSRF cookie/header when calling Gateway-compatible LangGraph APIs | needs `docker logs` |
+| TC-DOCKER-05 | Reset credentials surfacing | `reset_admin` writes a 0600 credential file in `DEER_FLOW_HOME` instead of logging plaintext. The file-based behavior is validated by non-Docker reset tests, so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
 | TC-DOCKER-06 | Gateway-mode Docker deploy | `./scripts/deploy.sh --gateway` produces a 3-container topology (no `langgraph` container); same auth flow as standard mode | needs `docker compose --profile gateway` |
 
 ## Coverage already provided by non-Docker tests
@@ -41,8 +41,8 @@ the test cases that ran on sg_dev or local:
 | TC-DOCKER-01 (volume persistence) | TC-REENT-01 on sg_dev (admin row survives gateway restart) — same SQLite file, just no container layer between |
 | TC-DOCKER-02 (session persistence) | TC-API-02/03/06 (cookie roundtrip), plus TC-REENT-04 (multi-cookie) — JWT verification is process-state-free, container restart is equivalent to `pkill uvicorn && uv run uvicorn` |
 | TC-DOCKER-03 (per-worker rate limit) | TC-GW-04 + TC-REENT-09 (single-worker rate limit + 5min expiry). The cross-worker divergence is an architectural property of the in-memory dict; no auth code path differs |
-| TC-DOCKER-04 (IM channels skip auth) | Code-level only: `app/channels/manager.py` uses `langgraph_sdk` directly with no cookie handling. The langgraph_auth handler is bypassed by going through SDK, not HTTP |
-| TC-DOCKER-05 (credential surfacing) | TC-1.1 on sg_dev (file at `~/deer-flow/backend/.deer-flow/admin_initial_credentials.txt`, mode 0600, password 22 chars) — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
+| TC-DOCKER-04 (IM channels use internal auth) | Code-level: `app/channels/manager.py` creates the `langgraph_sdk` client with `create_internal_auth_headers()` plus CSRF cookie/header, so channel workers do not rely on browser cookies |
+| TC-DOCKER-05 (credential surfacing) | `reset_admin` writes `.deer-flow/admin_initial_credentials.txt` with mode 0600 and logs only the path — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
 | TC-DOCKER-06 (gateway-mode container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (gateway-mode auth flow on sg_dev) — same Gateway code, container is just a packaging change |
 
 ## Reproduction steps when Docker becomes available
@@ -72,6 +72,6 @@ Then run TC-DOCKER-01..06 from the test plan as written.
   about *container packaging* details (bind mounts, multi-worker, log
   collection), not about whether the auth code paths work.
 - **TC-DOCKER-05 was updated in place** in `AUTH_TEST_PLAN.md` to reflect
-  the post-simplify reality (credentials file → 0600 file, no log leak).
+  the current reset flow (`reset_admin` → 0600 credentials file, no log leak).
   The old "grep 'Password:' in docker logs" expectation would have failed
   silently and given a false sense of coverage.

backend/docs/AUTH_TEST_PLAN.md

@@ -19,7 +19,7 @@
 
 ```bash
 # 清除已有数据
-rm -f backend/.deer-flow/users.db
+rm -f backend/.deer-flow/data/deerflow.db
 
 # 选择模式启动
 make dev          # 标准模式
@@ -28,10 +28,11 @@ make dev-pro      # Gateway 模式
 ```
 
 **验证点：**
-- [ ] 控制台输出 admin 邮箱和随机密码
-- [ ] 密码格式为 `secrets.token_urlsafe(16)` 的 22 字符字符串
-- [ ] 邮箱为 `admin@deerflow.dev`
-- [ ] 提示 `Change it after login: Settings -> Account`
+- [ ] 控制台不输出 admin 邮箱或明文密码
+- [ ] 控制台提示 `First boot detected — no admin account exists.`
+- [ ] 控制台提示访问 `/setup` 完成 admin 创建
+- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": true}`
+- [ ] 前端访问 `/login` 会跳转 `/setup`
 
 ### 1.2 非首次启动
 
@@ -42,7 +43,8 @@ make dev
 
 **验证点：**
 - [ ] 控制台不输出密码
-- [ ] 如果 admin 仍 `needs_setup=True`，控制台有 warning 提示
+- [ ] `GET /api/v1/auth/setup-status` 返回 `{"needs_setup": false}`
+- [ ] 已登录用户如果 `needs_setup=True`，访问 workspace 会被引导到 `/setup` 完成改邮箱 / 改密码流程
 
 ### 1.3 环境变量配置
 
@@ -76,19 +78,22 @@ make dev
 curl -s $BASE/api/v1/auth/setup-status | jq .
 ```
 
-**预期：** 返回 `{"needs_setup": false}`（admin 在启动时已自动创建，`count_users() > 0`）。仅在启动完成前的极短窗口内可能返回 `true`。
+**预期：**
+- 干净数据库且尚未初始化 admin：返回 `{"needs_setup": true}`
+- 已存在 admin：返回 `{"needs_setup": false}`
 
-#### TC-API-02: Admin 首次登录
+#### TC-API-02: 首次初始化 Admin
 
 ```bash
-curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<控制台密码>" \
+curl -s -X POST $BASE/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
   -c cookies.txt | jq .
 ```
 
 **预期：**
-- 状态码 200
-- Body: `{"expires_in": 604800, "needs_setup": true}`
+- 状态码 201
+- Body: `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`
 - `cookies.txt` 包含 `access_token`（HttpOnly）和 `csrf_token`（非 HttpOnly）
 
 #### TC-API-03: 获取当前用户
@@ -97,9 +102,9 @@ curl -s -X POST $BASE/api/v1/auth/login/local \
 curl -s $BASE/api/v1/auth/me -b cookies.txt | jq .
 ```
 
-**预期：** `{"id": "...", "email": "admin@deerflow.dev", "system_role": "admin", "needs_setup": true}`
+**预期：** `{"id": "...", "email": "admin@example.com", "system_role": "admin", "needs_setup": false}`
 
-#### TC-API-04: Setup 流程（改邮箱 + 改密码）
+#### TC-API-04: 改密码流程
 
 ```bash
 CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
@@ -107,13 +112,36 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b cookies.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<控制台密码>","new_password":"NewPass123!","new_email":"admin@example.com"}' | jq .
+  -d '{"current_password":"AdminPass1!","new_password":"NewPass123!"}' | jq .
 ```
 
 **预期：**
 - 状态码 200
 - `{"message": "Password changed successfully"}`
-- 再调 `/auth/me` 邮箱变为 `admin@example.com`，`needs_setup` 变为 `false`
+- 再调 `/auth/me` 仍为 `admin@example.com`，`needs_setup` 仍为 `false`
+
+#### TC-API-04a: reset_admin 后的 Setup 流程（改邮箱 + 改密码）
+
+```bash
+cd backend
+python -m app.gateway.auth.reset_admin --email admin@example.com
+# 从 .deer-flow/admin_initial_credentials.txt 读取 reset 后密码
+
+curl -s -X POST $BASE/api/v1/auth/login/local \
+  -d "username=admin@example.com&password=<凭据文件密码>" \
+  -c cookies.txt | jq .
+
+CSRF=$(grep csrf_token cookies.txt | awk '{print $NF}')
+curl -s -X POST $BASE/api/v1/auth/change-password \
+  -b cookies.txt \
+  -H "Content-Type: application/json" \
+  -H "X-CSRF-Token: $CSRF" \
+  -d '{"current_password":"<凭据文件密码>","new_password":"AdminPass2!","new_email":"admin2@example.com"}' | jq .
+```
+
+**预期：**
+- 登录返回 `{"expires_in": 604800, "needs_setup": true}`
+- `change-password` 后 `/auth/me` 邮箱变为 `admin2@example.com`，`needs_setup` 变为 `false`
 
 #### TC-API-05: 普通用户注册
 
@@ -493,7 +521,7 @@ curl -s -X POST $BASE/api/v1/auth/register \
 
 ```bash
 # 检查数据库
-sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMIT 3;"
+sqlite3 backend/.deer-flow/data/deerflow.db "SELECT email, password_hash FROM users LIMIT 3;"
 ```
 
 **预期：** `password_hash` 以 `$2b$` 开头（bcrypt 格式）
@@ -506,24 +534,25 @@ sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMI
 
 ### 4.1 首次登录流程
 
-#### TC-UI-01: 访问首页跳转登录
+#### TC-UI-01: 无 admin 时访问 workspace 跳转 setup
 
 1. 打开 `http://localhost:2026/workspace`
-2. **预期：** 自动跳转到 `/login`
+2. **预期：** 自动跳转到 `/setup`
 
-#### TC-UI-02: Login 页面
+#### TC-UI-02: Setup 页面创建 admin
 
-1. 输入 admin 邮箱和控制台密码
-2. 点击 Login
-3. **预期：** 跳转到 `/setup`（因为 `needs_setup=true`）
-
-#### TC-UI-03: Setup 页面
-
-1. 输入新邮箱、控制台密码（current）、新密码、确认密码
-2. 点击 Complete Setup
+1. 输入 admin 邮箱、密码、确认密码
+2. 点击 Create Admin Account
 3. **预期：** 跳转到 `/workspace`
 4. 刷新页面不跳回 `/setup`
 
+#### TC-UI-03: 已初始化后 Login 页面
+
+1. 退出登录后访问 `/login`
+2. 输入 admin 邮箱和密码
+3. 点击 Login
+4. **预期：** 跳转到 `/workspace`
+
 #### TC-UI-04: Setup 密码不匹配
 
 1. 新密码和确认密码不一致
@@ -602,7 +631,7 @@ sqlite3 backend/.deer-flow/users.db "SELECT email, password_hash FROM users LIMI
 #### TC-UI-15: reset_admin 后重新登录
 
 1. 执行 `cd backend && python -m app.gateway.auth.reset_admin`
-2. 使用新密码登录
+2. 从 `.deer-flow/admin_initial_credentials.txt` 读取新密码并登录
 3. **预期：** 跳转到 `/setup` 页面（`needs_setup` 被重置为 true）
 4. 旧 session 已失效
 
@@ -645,18 +674,28 @@ make install
 make dev
 ```
 
-#### TC-UPG-01: 首次启动创建 admin
+#### TC-UPG-01: 首次启动等待 admin 初始化
 
 **预期：**
-- [ ] 控制台输出 admin 邮箱（`admin@deerflow.dev`）和随机密码
+- [ ] 控制台不输出 admin 邮箱或随机密码
+- [ ] 访问 `/setup` 可创建第一个 admin
 - [ ] 无报错，正常启动
 
 #### TC-UPG-02: 旧 Thread 迁移到 admin
 
 ```bash
+# 创建第一个 admin
+curl -s -X POST http://localhost:2026/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
+  -c cookies.txt
+
+# 重启一次：启动迁移只在已有 admin 的启动路径执行
+make stop && make dev
+
 # 登录 admin
 curl -s -X POST http://localhost:2026/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<控制台密码>" \
+  -d "username=admin@example.com&password=AdminPass1!" \
   -c cookies.txt
 
 # 查看 thread 列表
@@ -670,8 +709,8 @@ curl -s -X POST http://localhost:2026/api/threads/search \
 
 **预期：**
 - [ ] 返回的 thread 数量 ≥ 旧版创建的数量
-- [ ] 控制台日志有 `Migrated N orphaned thread(s) to admin`
-- [ ] 每个 thread 的 `metadata.owner_id` 都已被设为 admin 的 ID
+- [ ] 控制台日志有 `Migrated N orphan LangGraph thread(s) to admin`
+- [ ] 旧 thread 只对 admin 可见
 
 #### TC-UPG-03: 旧 Thread 内容完整
 
@@ -683,7 +722,7 @@ curl -s http://localhost:2026/api/threads/<old-thread-id> \
 
 **预期：**
 - [ ] `metadata.title` 保留原值（如 `old-thread-1`）
-- [ ] `metadata.owner_id` 已填充
+- [ ] 响应不回显服务端保留的 `user_id` / `owner_id`
 
 #### TC-UPG-04: 新用户看不到旧 Thread
 
@@ -706,18 +745,19 @@ curl -s -X POST http://localhost:2026/api/threads/search \
 
 ### 5.3 数据库 Schema 兼容
 
-#### TC-UPG-05: 无 users.db 时自动创建
+#### TC-UPG-05: 无 deerflow.db 时创建 schema 但不创建默认用户
 
 ```bash
-ls -la backend/.deer-flow/users.db
+ls -la backend/.deer-flow/data/deerflow.db
+sqlite3 backend/.deer-flow/data/deerflow.db "SELECT COUNT(*) FROM users;"
 ```
 
-**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列
+**预期：** 文件存在，`sqlite3` 可查到 `users` 表含 `needs_setup`、`token_version` 列；未调用 `/initialize` 前用户数为 0
 
-#### TC-UPG-06: users.db WAL 模式
+#### TC-UPG-06: deerflow.db WAL 模式
 
 ```bash
-sqlite3 backend/.deer-flow/users.db "PRAGMA journal_mode;"
+sqlite3 backend/.deer-flow/data/deerflow.db "PRAGMA journal_mode;"
 ```
 
 **预期：** 返回 `wal`
@@ -768,9 +808,9 @@ make dev
 ```
 
 **预期：**
-- [ ] 服务正常启动（忽略 `users.db`，无 auth 相关代码不报错）
+- [ ] 服务正常启动（忽略 `deerflow.db`，无 auth 相关代码不报错）
 - [ ] 旧对话数据仍然可访问
-- [ ] `users.db` 文件残留但不影响运行
+- [ ] `deerflow.db` 文件残留但不影响运行
 
 #### TC-UPG-12: 再次升级到 auth 分支
 
@@ -781,51 +821,47 @@ make dev
 ```
 
 **预期：**
-- [ ] 识别已有 `users.db`，不重新创建 admin
-- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `users.db`）
+- [ ] 识别已有 `deerflow.db`，不重新创建 admin
+- [ ] 旧的 admin 账号仍可登录（如果回退期间未删 `deerflow.db`）
 
-### 5.7 休眠 Admin（初始密码未使用/未更改）
+### 5.7 Admin 初始化与 reset_admin
 
-> 首次启动生成 admin + 随机密码，但运维未登录、未改密码。
-> 密码只在首次启动的控制台闪过一次，后续启动不再显示。
+> 首次启动不生成默认 admin，也不在日志输出密码。忘记密码时走 `reset_admin`，新密码写入 0600 凭据文件。
 
-#### TC-UPG-13: 重启后自动重置密码并打印
+#### TC-UPG-13: 未初始化 admin 时重启不创建默认账号
 
 ```bash
-# 首次启动，记录密码
-rm -f backend/.deer-flow/users.db
+rm -f backend/.deer-flow/data/deerflow.db
 make dev
-# 控制台输出密码 P0，不登录
 make stop
 
-# 隔了几天，再次启动
 make dev
-# 控制台输出新密码 P1
+curl -s $BASE/api/v1/auth/setup-status | jq .
 ```
 
 **预期：**
-- [ ] 控制台输出 `Admin account setup incomplete — password reset`
-- [ ] 输出新密码 P1（P0 已失效）
-- [ ] 用 P1 可以登录，P0 不可以
-- [ ] 登录后 `needs_setup=true`，跳转 `/setup`
-- [ ] `token_version` 递增（旧 session 如有也失效）
+- [ ] 控制台不输出密码
+- [ ] `setup-status` 仍为 `{"needs_setup": true}`
+- [ ] 访问 `/setup` 仍可创建第一个 admin
 
-#### TC-UPG-14: 密码丢失 — 无需 CLI，重启即可
+#### TC-UPG-14: 密码丢失 — reset_admin 写入凭据文件
 
 ```bash
-# 忘记了控制台密码 → 直接重启服务
-make stop && make dev
-# 控制台自动输出新密码
+python -m app.gateway.auth.reset_admin --email admin@example.com
+ls -la backend/.deer-flow/admin_initial_credentials.txt
+cat backend/.deer-flow/admin_initial_credentials.txt
 ```
 
 **预期：**
-- [ ] 无需 `reset_admin`，重启服务即可拿到新密码
-- [ ] `reset_admin` CLI 仍然可用作手动备选方案
+- [ ] 命令行只输出凭据文件路径，不输出明文密码
+- [ ] 凭据文件权限为 `0600`
+- [ ] 凭据文件包含 email + password 行
+- [ ] 该用户下次登录返回 `needs_setup=true`
 
-#### TC-UPG-15: 休眠 admin 期间普通用户注册
+#### TC-UPG-15: 未初始化 admin 期间普通用户注册策略边界
 
 ```bash
-# admin 存在但从未登录，普通用户先注册
+# admin 尚不存在，普通用户尝试注册
 curl -s -X POST $BASE/api/v1/auth/register \
   -H "Content-Type: application/json" \
   -d '{"email":"earlybird@example.com","password":"EarlyPass1!"}' \
@@ -833,11 +869,11 @@ curl -s -X POST $BASE/api/v1/auth/register \
 ```
 
 **预期：**
-- [ ] 注册成功（201），角色为 `user`
-- [ ] 无法提权为 admin
-- [ ] 普通用户的数据与 admin 隔离
+- [ ] 当前代码允许注册普通用户并自动登录（201，角色为 `user`）
+- [ ] 但 `setup-status` 仍为 `{"needs_setup": true}`，因为 admin 仍不存在
+- [ ] 这是一个产品策略边界：若要求“必须先有 admin”，需要在 `/register` 增加 admin-exists gate
 
-#### TC-UPG-16: 休眠 admin 不影响后续操作
+#### TC-UPG-16: 普通用户数据与后续 admin 隔离
 
 ```bash
 # 普通用户正常创建 thread、发消息
@@ -849,14 +885,13 @@ curl -s -X POST $BASE/api/threads \
   -d '{"metadata":{}}' | jq .thread_id
 ```
 
-**预期：** 正常创建，不受休眠 admin 影响
+**预期：** 普通用户正常创建 thread；后续 admin 创建后，搜索不到该普通用户 thread
 
-#### TC-UPG-17: 休眠 admin 最终完成 Setup
+#### TC-UPG-17: reset_admin 后完成 Setup
 
 ```bash
-# 运维终于登录
 curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<P0或P1>" \
+  -d "username=admin@example.com&password=<凭据文件密码>" \
   -c admin.txt | jq .needs_setup
 # 预期: true
 
@@ -866,7 +901,7 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b admin.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"current_password":"<密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
+  -d '{"current_password":"<凭据文件密码>","new_password":"AdminFinal1!","new_email":"admin@real.com"}' \
   -c admin.txt
 
 # 验证
@@ -876,7 +911,7 @@ curl -s $BASE/api/v1/auth/me -b admin.txt | jq '{email, needs_setup}'
 **预期：**
 - [ ] `email` 变为 `admin@real.com`
 - [ ] `needs_setup` 变为 `false`
-- [ ] 后续重启控制台不再有 warning
+- [ ] 后续登录使用新密码
 
 #### TC-UPG-18: 长期未用后 JWT 密钥轮换
 
@@ -890,8 +925,8 @@ make stop && make dev
 
 **预期：**
 - [ ] 服务正常启动
-- [ ] 旧密码仍可登录（密码存在 DB，与 JWT 密钥无关）
-- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）— 但因为从未登录过也没有旧 token
+- [ ] 账号密码仍可登录（密码存在 DB，与 JWT 密钥无关）
+- [ ] 旧的 JWT token 失效（密钥变了签名不匹配）
 
 ---
 
@@ -910,7 +945,7 @@ for i in 1 2 3; do
 done
 
 # 检查 admin 数量
-sqlite3 backend/.deer-flow/users.db \
+sqlite3 backend/.deer-flow/data/deerflow.db \
   "SELECT COUNT(*) FROM users WHERE system_role='admin';"
 ```
 
@@ -1055,7 +1090,7 @@ curl -s -X POST $BASE/api/v1/auth/register \
 wait
 
 # 检查用户数
-sqlite3 backend/.deer-flow/users.db \
+sqlite3 backend/.deer-flow/data/deerflow.db \
   "SELECT COUNT(*) FROM users WHERE email='race@example.com';"
 ```
 
@@ -1165,13 +1200,16 @@ curl -s -w "%{http_code}" -X DELETE "$BASE/api/threads/$TID" \
 ```bash
 cd backend
 python -m app.gateway.auth.reset_admin
-# 记录密码 P1
+cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p1.txt
+P1=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p1.txt)
 
 python -m app.gateway.auth.reset_admin
-# 记录密码 P2
+cp .deer-flow/admin_initial_credentials.txt /tmp/deerflow-reset-p2.txt
+P2=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p2.txt)
 ```
 
 **预期：**
+- [ ] `.deer-flow/admin_initial_credentials.txt` 每次都会被重写，文件权限为 `0600`
 - [ ] P1 ≠ P2（每次生成新随机密码）
 - [ ] P1 不可用，只有 P2 有效
 - [ ] `token_version` 递增了 2
@@ -1324,7 +1362,8 @@ done
 ```bash
 GW=http://localhost:8001
 
-for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local /api/v1/auth/register; do
+for path in /health /api/v1/auth/setup-status /api/v1/auth/login/local \
+            /api/v1/auth/register /api/v1/auth/initialize /api/v1/auth/logout; do
   echo "$path: $(curl -s -w '%{http_code}' -o /dev/null $GW$path)"
 done
 # 预期: 200 或 405/422（方法不对但不是 401）
@@ -1399,9 +1438,9 @@ done
 >
 > 前置条件：
 > - `.env` 中设置 `AUTH_JWT_SECRET`（否则每次容器重启 session 全部失效）
-> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `users.db`）
+> - `DEER_FLOW_HOME` 挂载到宿主机目录（持久化 `deerflow.db`）
 
-#### TC-DOCKER-01: users.db 通过 volume 持久化
+#### TC-DOCKER-01: deerflow.db 通过 volume 持久化
 
 ```bash
 # 启动容器
@@ -1416,13 +1455,13 @@ curl -s -X POST $BASE/api/v1/auth/register \
   -H "Content-Type: application/json" \
   -d '{"email":"docker-test@example.com","password":"DockerTest1!"}' -w "\nHTTP %{http_code}"
 
-# 检查宿主机上的 users.db
-ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db
-sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/users.db \
+# 检查宿主机上的 deerflow.db
+ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db
+sqlite3 ${DEER_FLOW_HOME:-backend/.deer-flow}/data/deerflow.db \
   "SELECT email FROM users WHERE email='docker-test@example.com';"
 ```
 
-**预期：** users.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。
+**预期：** deerflow.db 在宿主机 `DEER_FLOW_HOME` 目录中，查询可见刚注册的用户。
 
 #### TC-DOCKER-02: 重启容器后 session 保持
 
@@ -1466,22 +1505,24 @@ done
 
 **已知限制：** In-process rate limiter 不跨 worker 共享。生产环境如需精确限速，需要 Redis 等外部存储。
 
-#### TC-DOCKER-04: IM 渠道不经过 auth
+#### TC-DOCKER-04: IM 渠道使用内部认证
 
 ```bash
-# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 通信
-# 不走 nginx，不经过 AuthMiddleware
+# IM 渠道（Feishu/Slack/Telegram）在 gateway 容器内部通过 LangGraph SDK 调 Gateway
+# 请求携带 process-local internal auth header，并带匹配的 CSRF cookie/header
 
 # 验证方式：检查 gateway 日志中 channel manager 的请求不包含 auth 错误
 docker logs deer-flow-gateway 2>&1 | grep -E "ChannelManager|channel" | head -10
 ```
 
-**预期：** 无 auth 相关错误。渠道通过 `langgraph-sdk` 直连 LangGraph Server（`http://langgraph:2024`），不走 auth 层。
+**预期：** 无 auth 相关错误。渠道不依赖浏览器 cookie；服务端通过内部认证头把请求归入 `default` 用户桶。
 
-#### TC-DOCKER-05: admin 密码写入 0600 凭证文件（不再走日志）
+#### TC-DOCKER-05: reset_admin 密码写入 0600 凭证文件（不再走日志）
 
 ```bash
-# 凭证文件写在挂载到宿主机的 DEER_FLOW_HOME 下
+# 首次启动不会自动生成 admin 密码。先重置已有 admin，凭据文件写在挂载到宿主机的 DEER_FLOW_HOME 下。
+docker exec deer-flow-gateway python -m app.gateway.auth.reset_admin --email docker-test@example.com
+
 ls -la ${DEER_FLOW_HOME:-backend/.deer-flow}/admin_initial_credentials.txt
 # 预期文件权限: -rw------- (0600)
 
@@ -1512,14 +1553,15 @@ sleep 15
 docker ps --filter name=deer-flow-langgraph --format '{{.Names}}' | wc -l
 # 预期: 0
 
-# auth 流程正常
+# auth 流程正常：未登录受保护接口返回 401
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models
 # 预期: 401
 
-curl -s -X POST $BASE/api/v1/auth/login/local \
-  -d "username=admin@deerflow.dev&password=<日志密码>" \
+curl -s -X POST $BASE/api/v1/auth/initialize \
+  -H "Content-Type: application/json" \
+  -d '{"email":"admin@example.com","password":"AdminPass1!"}' \
   -c cookies.txt -w "\nHTTP %{http_code}"
-# 预期: 200
+# 预期: 201
 ```
 
 ### 7.4 补充边界用例
@@ -1587,13 +1629,15 @@ curl -s -D - -X POST $BASE/api/v1/auth/login/local \
 #### TC-EDGE-05: HTTP 无 max_age / HTTPS 有 max_age
 
 ```bash
+GW=http://localhost:8001
+
 # HTTP
-curl -s -D - -X POST $BASE/api/v1/auth/login/local \
+curl -s -D - -X POST $GW/api/v1/auth/login/local \
   -d "username=admin@example.com&password=正确密码" 2>/dev/null \
   | grep "access_token=" | grep -oi "max-age=[0-9]*" || echo "NO max-age (HTTP session cookie)"
 
-# HTTPS
-curl -s -D - -X POST $BASE/api/v1/auth/login/local \
+# HTTPS：直连 Gateway 才能用 X-Forwarded-Proto 模拟 HTTPS；nginx 会覆盖该 header
+curl -s -D - -X POST $GW/api/v1/auth/login/local \
   -H "X-Forwarded-Proto: https" \
   -d "username=admin@example.com&password=正确密码" 2>/dev/null \
   | grep "access_token=" | grep -oi "max-age=[0-9]*"
@@ -1712,10 +1756,10 @@ curl -s -X POST $BASE/api/threads \
   -b cookies.txt \
   -H "Content-Type: application/json" \
   -H "X-CSRF-Token: $CSRF" \
-  -d '{"metadata":{"owner_id":"victim-user-id"}}' | jq .metadata.owner_id
+  -d '{"metadata":{"owner_id":"victim-user-id","user_id":"victim-user-id"}}' | jq .metadata
 ```
 
-**预期：** 返回的 `metadata.owner_id` 应为当前登录用户的 ID，不是请求中注入的 `victim-user-id`。服务端应覆盖客户端提供的 `user_id`。
+**预期：** 返回的 `metadata` 不包含 `owner_id` 或 `user_id`。真实所有权写入 `threads_meta.user_id`，不从客户端 metadata 接收，也不通过 metadata 回显。
 
 #### 7.5.6 HTTP Method 探测
 
@@ -1796,6 +1840,6 @@ cd backend && PYTHONPATH=. uv run pytest \
 # 核心接口冒烟
 curl -s $BASE/health                              # 200
 curl -s $BASE/api/models                          # 401 (无 cookie)
-curl -s -X POST $BASE/api/v1/auth/setup-status    # 200
+curl -s $BASE/api/v1/auth/setup-status            # 200
 curl -s $BASE/api/v1/auth/me -b cookies.txt       # 200 (有 cookie)
 ```

backend/docs/AUTH_UPGRADE.md

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

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)
-4. `config.yaml` in parent directory (project root: `deer-flow/`)
+3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or under the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
+4. Legacy backend/repository-root locations for monorepo compatibility
 
 ## 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

@@ -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}
 ```

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/runtime/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/README.md

@@ -8,6 +8,7 @@ This directory contains detailed documentation for the DeerFlow backend.
 |----------|-------------|
 | [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
 | [API.md](API.md) | Complete API reference |
+| [AUTH_DESIGN.md](AUTH_DESIGN.md) | User authentication, CSRF, and per-user isolation design |
 | [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
 | [SETUP.md](SETUP.md) | Quick setup guide |
 
@@ -42,6 +43,7 @@ docs/
 ├── README.md                  # This file
 ├── ARCHITECTURE.md            # System architecture
 ├── API.md                     # API reference
+├── AUTH_DESIGN.md             # User authentication and isolation design
 ├── CONFIGURATION.md           # Configuration guide
 ├── SETUP.md                   # Setup instructions
 ├── FILE_UPLOAD.md             # File upload feature

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)
-2. `backend/config.yaml` (current directory when running from backend/)
-3. `deer-flow/config.yaml` (parent directory - **recommended location**)
+1. Explicit `config_path` argument from code
+2. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
+3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
+4. Legacy backend/repository-root locations for monorepo compatibility
 
 **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
-3. Check the file exists: `ls -la ../config.yaml`
+2. Verify you're in the project root, or set `DEER_FLOW_PROJECT_ROOT`
+3. Check the file exists: `ls -la config.yaml`
 
 ### 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/packages/harness/deerflow/agents/factory.py

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

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

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

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

@@ -18,10 +18,10 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
 from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import load_agent_config, validate_agent_name
-from deerflow.config.app_config import get_app_config
-from deerflow.config.memory_config import get_memory_config
-from deerflow.config.summarization_config import get_summarization_config
+from deerflow.config.app_config import AppConfig, get_app_config
 from deerflow.models import create_chat_model
+from deerflow.skills.tool_policy import filter_tools_by_skill_allowed_tools
+from deerflow.skills.types import Skill
 
 logger = logging.getLogger(__name__)
 
@@ -35,9 +35,9 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
     return cfg
 
 
-def _resolve_model_name(requested_model_name: str | None = None) -> str:
+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.")
@@ -50,9 +50,10 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
     return default_model_name
 
 
-def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
+def _create_summarization_middleware(*, app_config: AppConfig | None = None) -> DeerFlowSummarizationMiddleware | None:
     """Create and configure the summarization middleware from config."""
-    config = get_summarization_config()
+    resolved_app_config = app_config or get_app_config()
+    config = resolved_app_config.summarization
 
     if not config.enabled:
         return None
@@ -73,9 +74,9 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
     # 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=resolved_app_config)
     else:
-        model = create_chat_model(thinking_enabled=False)
+        model = create_chat_model(thinking_enabled=False, app_config=resolved_app_config)
     model = model.with_config(tags=["middleware:summarize"])
 
     # Prepare kwargs
@@ -92,17 +93,13 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
         kwargs["summary_prompt"] = config.summary_prompt
 
     hooks: list[BeforeSummarizationHook] = []
-    if get_memory_config().enabled:
+    if resolved_app_config.memory.enabled:
         hooks.append(memory_flush_hook)
 
     # The logic below relies on two assumptions holding true: this factory is
     # the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
     # config is not expected to change after startup.
-    try:
-        skills_container_path = get_app_config().skills.container_path or "/mnt/skills"
-    except Exception:
-        logger.exception("Failed to resolve skills container path; falling back to default")
-        skills_container_path = "/mnt/skills"
+    skills_container_path = resolved_app_config.skills.container_path or "/mnt/skills"
 
     return DeerFlowSummarizationMiddleware(
         **kwargs,
@@ -240,7 +237,14 @@ 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:
@@ -251,10 +255,17 @@ 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)
+
+    # Always inject current date (and optionally memory) as <system-reminder> into the
+    # first HumanMessage to keep the system prompt fully static for prefix-cache reuse.
+    from deerflow.agents.middlewares.dynamic_context_middleware import DynamicContextMiddleware
+
+    middlewares.append(DynamicContextMiddleware(agent_name=agent_name, app_config=resolved_app_config))
 
     # Add summarization middleware if enabled
-    summarization_middleware = _create_summarization_middleware()
+    summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
     if summarization_middleware is not None:
         middlewares.append(summarization_middleware)
 
@@ -266,24 +277,23 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
         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
-    middlewares.append(TitleMiddleware())
+    middlewares.append(TitleMiddleware(app_config=resolved_app_config))
 
     # Add MemoryMiddleware (after TitleMiddleware)
-    middlewares.append(MemoryMiddleware(agent_name=agent_name))
+    middlewares.append(MemoryMiddleware(agent_name=agent_name, memory_config=resolved_app_config.memory))
 
     # Add ViewImageMiddleware only if the current model supports vision.
     # Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
-    app_config = get_app_config()
-    model_config = app_config.get_model_config(model_name) if model_name else None
+    model_config = resolved_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())
@@ -295,7 +305,9 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
         middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
 
     # LoopDetectionMiddleware — detect and break repetitive tool call loops
-    middlewares.append(LoopDetectionMiddleware())
+    loop_detection_config = resolved_app_config.loop_detection
+    if loop_detection_config.enabled:
+        middlewares.append(LoopDetectionMiddleware.from_config(loop_detection_config))
 
     # Inject custom middlewares before ClarificationMiddleware
     if custom_middlewares:
@@ -306,12 +318,42 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
     return middlewares
 
 
+def _available_skill_names(agent_config, is_bootstrap: bool) -> set[str] | None:
+    if is_bootstrap:
+        return {"bootstrap"}
+    if agent_config and agent_config.skills is not None:
+        return set(agent_config.skills)
+    return None
+
+
+def _load_enabled_skills_for_tool_policy(available_skills: set[str] | None, *, app_config: AppConfig) -> list[Skill]:
+    try:
+        from deerflow.agents.lead_agent.prompt import get_enabled_skills_for_config
+
+        skills = get_enabled_skills_for_config(app_config)
+    except Exception:
+        logger.exception("Failed to load skills for allowed-tools policy")
+        raise
+
+    if available_skills is None:
+        return skills
+    return [skill for skill in skills if skill.name in available_skills]
+
+
 def make_lead_agent(config: RunnableConfig):
+    """LangGraph graph factory; keep the signature compatible with LangGraph Server."""
+    runtime_config = _get_runtime_config(config)
+    runtime_app_config = runtime_config.get("app_config")
+    return _make_lead_agent(config, app_config=runtime_app_config or get_app_config())
+
+
+def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
-    from deerflow.tools.builtins import setup_agent
+    from deerflow.tools.builtins import setup_agent, update_agent
 
     cfg = _get_runtime_config(config)
+    resolved_app_config = app_config
 
     thinking_enabled = cfg.get("thinking_enabled", True)
     reasoning_effort = cfg.get("reasoning_effort", None)
@@ -323,14 +365,14 @@ def make_lead_agent(config: RunnableConfig):
     agent_name = validate_agent_name(cfg.get("agent_name"))
 
     agent_config = load_agent_config(agent_name) if not is_bootstrap else None
+    available_skills = _available_skill_names(agent_config, is_bootstrap)
     # Custom agent model from agent config (if any), or None to let _resolve_model_name pick the default
     agent_model_name = agent_config.model if agent_config and agent_config.model else None
 
     # Final model name resolution: request → agent config → global default, with fallback for unknown names
-    model_name = _resolve_model_name(requested_model_name or agent_model_name)
+    model_name = _resolve_model_name(requested_model_name or agent_model_name, app_config=resolved_app_config)
 
-    app_config = get_app_config()
-    model_config = app_config.get_model_config(model_name)
+    model_config = resolved_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.")
@@ -362,27 +404,43 @@ def make_lead_agent(config: RunnableConfig):
             "is_plan_mode": is_plan_mode,
             "subagent_enabled": subagent_enabled,
             "tool_groups": agent_config.tool_groups if agent_config else None,
-            "available_skills": ["bootstrap"] if is_bootstrap else (agent_config.skills if agent_config and agent_config.skills is not None else None),
+            "available_skills": sorted(available_skills) if available_skills is not None else None,
         }
     )
 
+    skills_for_tool_policy = _load_enabled_skills_for_tool_policy(available_skills, app_config=resolved_app_config)
+
     if is_bootstrap:
         # Special bootstrap agent with minimal prompt for initial custom agent creation flow
+        tools = get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent]
         return create_agent(
-            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
-            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
-            middleware=_build_middlewares(config, model_name=model_name),
-            system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
+            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config),
+            tools=filter_tools_by_skill_allowed_tools(tools, skills_for_tool_policy),
+            middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
+            system_prompt=apply_prompt_template(
+                subagent_enabled=subagent_enabled,
+                max_concurrent_subagents=max_concurrent_subagents,
+                available_skills=set(["bootstrap"]),
+                app_config=resolved_app_config,
+            ),
             state_schema=ThreadState,
         )
 
+    # Custom agents can update their own SOUL.md / config via update_agent.
+    # The default agent (no agent_name) does not see this tool.
+    extra_tools = [update_agent] if agent_name else []
     # Default lead agent (unchanged behavior)
+    tools = get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=resolved_app_config)
     return create_agent(
-        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
-        tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
-        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
+        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config),
+        tools=filter_tools_by_skill_allowed_tools(tools + extra_tools, skills_for_tool_policy),
+        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config),
         system_prompt=apply_prompt_template(
-            subagent_enabled=subagent_enabled, 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

@@ -1,26 +1,32 @@
+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.types import Skill
+from deerflow.skills.storage import get_or_new_skill_storage
+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
 _enabled_skills_lock = threading.Lock()
 _enabled_skills_cache: list[Skill] | None = None
+_enabled_skills_by_config_cache: dict[int, tuple[object, list[Skill]]] = {}
 _enabled_skills_refresh_active = False
 _enabled_skills_refresh_version = 0
 _enabled_skills_refresh_event = threading.Event()
 
 
 def _load_enabled_skills_sync() -> list[Skill]:
-    return list(load_skills(enabled_only=True))
+    return list(get_or_new_skill_storage().load_skills(enabled_only=True))
 
 
 def _start_enabled_skills_refresh_thread() -> None:
@@ -78,6 +84,7 @@ def _invalidate_enabled_skills_cache() -> threading.Event:
     _get_cached_skills_prompt_section.cache_clear()
     with _enabled_skills_lock:
         _enabled_skills_cache = None
+        _enabled_skills_by_config_cache.clear()
         _enabled_skills_refresh_version += 1
         _enabled_skills_refresh_event.clear()
         if _enabled_skills_refresh_active:
@@ -101,6 +108,15 @@ def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_W
 
 
 def _get_enabled_skills():
+    return get_cached_enabled_skills()
+
+
+def get_cached_enabled_skills() -> list[Skill]:
+    """Return the cached enabled-skills list, kicking off a background refresh on miss.
+
+    Safe to call from request paths: never blocks on disk I/O. Returns an empty
+    list on cache miss; the next call will see the warmed result.
+    """
     with _enabled_skills_lock:
         cached = _enabled_skills_cache
 
@@ -111,8 +127,33 @@ def _get_enabled_skills():
     return []
 
 
-def _skill_mutability_label(category: str) -> str:
-    return "[custom, editable]" if category == "custom" else "[built-in]"
+def get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
+    """Return enabled skills using the caller's config source.
+
+    When a concrete ``app_config`` is supplied, cache the loaded skills by that
+    config object's identity so request-scoped config injection still resolves
+    skill paths from the matching config without rescanning storage on every
+    agent factory call.
+    """
+    if app_config is None:
+        return _get_enabled_skills()
+
+    cache_key = id(app_config)
+    with _enabled_skills_lock:
+        cached = _enabled_skills_by_config_cache.get(cache_key)
+        if cached is not None:
+            cached_config, cached_skills = cached
+            if cached_config is app_config:
+                return list(cached_skills)
+
+    skills = list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
+    with _enabled_skills_lock:
+        _enabled_skills_by_config_cache[cache_key] = (app_config, skills)
+    return list(skills)
+
+
+def _skill_mutability_label(category: SkillCategory | str) -> str:
+    return "[custom, editable]" if category == SkillCategory.CUSTOM else "[built-in]"
 
 
 def clear_skills_system_prompt_cache() -> None:
@@ -139,7 +180,7 @@ Skip simple one-off tasks.
 """
 
 
-def _build_available_subagents_description(available_names: list[str], bash_available: bool) -> str:
+def _build_available_subagents_description(available_names: list[str], bash_available: bool, *, app_config: AppConfig | None = None) -> str:
     """Dynamically build subagent type descriptions from registry.
 
     Mirrors Codex's pattern where agent_type_description is dynamically generated
@@ -161,7 +202,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
         if name in builtin_descriptions:
             lines.append(f"- **{name}**: {builtin_descriptions[name]}")
         else:
-            config = get_subagent_config(name)
+            config = get_subagent_config(name, app_config=app_config)
             if config is not None:
                 desc = config.description.split("\n")[0].strip()  # First line only for brevity
                 lines.append(f"- **{name}**: {desc}")
@@ -169,7 +210,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
     return "\n".join(lines)
 
 
-def _build_subagent_section(max_concurrent: int) -> str:
+def _build_subagent_section(max_concurrent: int, *, app_config: AppConfig | None = None) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 
     Args:
@@ -179,12 +220,12 @@ def _build_subagent_section(max_concurrent: int) -> str:
         Formatted subagent section string.
     """
     n = max_concurrent
-    available_names = get_available_subagent_names()
+    available_names = get_available_subagent_names(app_config=app_config) if app_config is not None else get_available_subagent_names()
     bash_available = "bash" in available_names
 
     # Dynamically build subagent type descriptions from registry (aligned with Codex's
     # agent_type_description pattern where all registered roles are listed in the tool spec).
-    available_subagents = _build_available_subagents_description(available_names, bash_available)
+    available_subagents = _build_available_subagents_description(available_names, bash_available, app_config=app_config)
     direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
     direct_execution_example = (
         '# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test")  # Direct execution, not task()'
@@ -325,8 +366,7 @@ You are {agent_name}, an open-source super agent.
 </role>
 
 {soul}
-{memory_context}
-
+{self_update_section}
 <thinking_style>
 - Think concisely and strategically about the user's request BEFORE taking action
 - Break down the task: What is clear? What is ambiguous? What is missing?
@@ -511,21 +551,28 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 """
 
 
-def _get_memory_context(agent_name: str | None = None) -> str:
+def _get_memory_context(agent_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
     """Get memory context for injection into system prompt.
 
     Args:
         agent_name: If provided, loads per-agent memory. If None, loads global memory.
+        app_config: Explicit application config. When provided, memory options
+            are read from this value instead of the global config singleton.
 
     Returns:
         Formatted memory context string wrapped in XML tags, or empty string if disabled.
     """
     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 app_config is None:
+            from deerflow.config.memory_config import get_memory_config
+
+            config = get_memory_config()
+        else:
+            config = app_config.memory
+
         if not config.enabled or not config.injection_enabled:
             return ""
 
@@ -539,8 +586,8 @@ def _get_memory_context(agent_name: str | None = None) -> str:
 {memory_content}
 </memory>
 """
-    except Exception as e:
-        logger.error("Failed to load memory context: %s", e)
+    except Exception:
+        logger.exception("Failed to load memory context")
         return ""
 
 
@@ -576,19 +623,24 @@ 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
+    if app_config is None:
+        try:
+            from deerflow.config import get_app_config
 
-        config = get_app_config()
+            config = get_app_config()
+            container_base_path = config.skills.container_path
+            skill_evolution_enabled = config.skill_evolution.enabled
+        except Exception:
+            container_base_path = "/mnt/skills"
+            skill_evolution_enabled = False
+    else:
+        config = app_config
         container_base_path = config.skills.container_path
         skill_evolution_enabled = config.skill_evolution.enabled
-    except Exception:
-        container_base_path = "/mnt/skills"
-        skill_evolution_enabled = False
 
     if not skills and not skill_evolution_enabled:
         return ""
@@ -612,7 +664,27 @@ def get_agent_soul(agent_name: str | None) -> str:
     return ""
 
 
-def get_deferred_tools_prompt_section() -> str:
+def _build_self_update_section(agent_name: str | None) -> str:
+    """Prompt block that teaches the custom agent to persist self-updates via update_agent."""
+    if not agent_name:
+        return ""
+    return f"""<self_update>
+You are running as the custom agent **{agent_name}** with a persisted SOUL.md and config.yaml.
+
+When the user asks you to update your own description, personality, behaviour, skill set, tool groups, or default model,
+you MUST persist the change with the `update_agent` tool. Do NOT use `bash`, `write_file`, or any sandbox tool to edit
+SOUL.md or config.yaml — those write into a temporary sandbox/tool workspace and the changes will be lost on the next turn.
+
+Rules:
+- Always pass the FULL replacement text for `soul` (no patch semantics). Start from your current SOUL above and apply the user's edits.
+- Only pass the fields that should change. Omit the others to preserve them.
+- Pass `skills=[]` to disable all skills, or omit `skills` to keep the existing whitelist.
+- After `update_agent` returns successfully, tell the user the change is persisted and will take effect on the next turn.
+</self_update>
+"""
+
+
+def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
     """Generate <available-deferred-tools> block for the system prompt.
 
     Lists only deferred tool names so the agent knows what exists
@@ -621,12 +693,17 @@ def get_deferred_tools_prompt_section() -> str:
     """
     from deerflow.tools.builtins.tool_search import get_deferred_registry
 
-    try:
-        from deerflow.config import get_app_config
+    if app_config is None:
+        try:
+            from deerflow.config import get_app_config
 
-        if not get_app_config().tool_search.enabled:
+            config = get_app_config()
+        except Exception:
             return ""
-    except Exception:
+    else:
+        config = app_config
+
+    if not config.tool_search.enabled:
         return ""
 
     registry = get_deferred_registry()
@@ -637,15 +714,19 @@ def get_deferred_tools_prompt_section() -> str:
     return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
 
 
-def _build_acp_section() -> str:
+def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
     """Build the ACP agent prompt section, only if ACP agents are configured."""
-    try:
-        from deerflow.config.acp_config import get_acp_agents
+    if app_config is None:
+        try:
+            from deerflow.config.acp_config import get_acp_agents
 
-        agents = get_acp_agents()
-        if not agents:
+            agents = get_acp_agents()
+        except Exception:
             return ""
-    except Exception:
+    else:
+        agents = getattr(app_config, "acp_agents", {}) or {}
+
+    if not agents:
         return ""
 
     return (
@@ -657,15 +738,20 @@ def _build_acp_section() -> str:
     )
 
 
-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
+    if app_config is None:
+        try:
+            from deerflow.config import get_app_config
 
-        mounts = get_app_config().sandbox.mounts or []
-    except Exception:
-        logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
-        return ""
+            config = get_app_config()
+        except Exception:
+            logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
+            return ""
+    else:
+        config = app_config
+
+    mounts = config.sandbox.mounts or []
 
     if not mounts:
         return ""
@@ -679,13 +765,17 @@ 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:
-    # Get memory context
-    memory_context = _get_memory_context(agent_name)
-
+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:
     # Include subagent section only if enabled (from runtime parameter)
     n = max_concurrent_subagents
-    subagent_section = _build_subagent_section(n) if subagent_enabled else ""
+    subagent_section = _build_subagent_section(n, app_config=app_config) if subagent_enabled else ""
 
     # Add subagent reminder to critical_reminders if enabled
     subagent_reminder = (
@@ -706,27 +796,28 @@ 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()
+    acp_section = _build_acp_section(app_config=app_config)
+    custom_mounts_section = _build_custom_mounts_section(app_config=app_config)
     acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
 
-    # Format the prompt with dynamic skills and memory
-    prompt = SYSTEM_PROMPT_TEMPLATE.format(
+    # Build and return the fully static system prompt.
+    # Memory and current date are injected per-turn via DynamicContextMiddleware
+    # as a <system-reminder> in the first HumanMessage, keeping this prompt
+    # identical across users and sessions for maximum prefix-cache reuse.
+    return SYSTEM_PROMPT_TEMPLATE.format(
         agent_name=agent_name or "DeerFlow 2.0",
         soul=get_agent_soul(agent_name),
+        self_update_section=_build_self_update_section(agent_name),
         skills_section=skills_section,
         deferred_tools_section=deferred_tools_section,
-        memory_context=memory_context,
         subagent_section=subagent_section,
         subagent_reminder=subagent_reminder,
         subagent_thinking=subagent_thinking,
         acp_section=acp_and_mounts_section,
     )
-
-    return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"

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

@@ -40,6 +40,15 @@ class MemoryUpdateQueue:
         self._timer: threading.Timer | None = None
         self._processing = False
 
+    @staticmethod
+    def _queue_key(
+        thread_id: str,
+        user_id: str | None,
+        agent_name: str | None,
+    ) -> tuple[str, str | None, str | None]:
+        """Return the debounce identity for a memory update target."""
+        return (thread_id, user_id, agent_name)
+
     def add(
         self,
         thread_id: str,
@@ -115,8 +124,9 @@ class MemoryUpdateQueue:
         correction_detected: bool,
         reinforcement_detected: bool,
     ) -> None:
+        queue_key = self._queue_key(thread_id, user_id, agent_name)
         existing_context = next(
-            (context for context in self._queue if context.thread_id == thread_id),
+            (context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) == queue_key),
             None,
         )
         merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
@@ -130,7 +140,7 @@ class MemoryUpdateQueue:
             reinforcement_detected=merged_reinforcement_detected,
         )
 
-        self._queue = [c for c in self._queue if c.thread_id != thread_id]
+        self._queue = [context for context in self._queue if self._queue_key(context.thread_id, context.user_id, context.agent_name) != queue_key]
         self._queue.append(context)
 
     def _reset_timer(self) -> None:

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

@@ -6,6 +6,7 @@ from deerflow.agents.memory.message_processing import detect_correction, detect_
 from deerflow.agents.memory.queue import get_memory_queue
 from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
 from deerflow.config.memory_config import get_memory_config
+from deerflow.runtime.user_context import resolve_runtime_user_id
 
 
 def memory_flush_hook(event: SummarizationEvent) -> None:
@@ -21,11 +22,13 @@ def memory_flush_hook(event: SummarizationEvent) -> None:
 
     correction_detected = detect_correction(filtered_messages)
     reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
+    user_id = resolve_runtime_user_id(event.runtime)
     queue = get_memory_queue()
     queue.add_nowait(
         thread_id=event.thread_id,
         messages=filtered_messages,
         agent_name=event.agent_name,
+        user_id=user_id,
         correction_detected=correction_detected,
         reinforcement_detected=reinforcement_detected,
     )

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

@@ -9,7 +9,6 @@ import logging
 import math
 import re
 import uuid
-from collections.abc import Awaitable
 from typing import Any
 
 from deerflow.agents.memory.prompt import (
@@ -26,6 +25,12 @@ 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",
@@ -222,39 +227,6 @@ def _extract_text(content: Any) -> str:
     return str(content)
 
 
-def _run_async_update_sync(coro: Awaitable[bool]) -> bool:
-    """Run an async memory update from sync code, including nested-loop contexts."""
-    handed_off = False
-
-    try:
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = None
-
-        if loop is not None and loop.is_running():
-            future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(asyncio.run, coro)
-            handed_off = True
-            return future.result()
-
-        handed_off = True
-        return asyncio.run(coro)
-    except Exception:
-        if not handed_off:
-            close = getattr(coro, "close", None)
-            if callable(close):
-                try:
-                    close()
-                except Exception:
-                    logger.debug(
-                        "Failed to close un-awaited memory update coroutine",
-                        exc_info=True,
-                    )
-
-        logger.exception("Failed to run async memory update from sync context")
-        return False
-
-
 # Matches sentences that describe a file-upload *event* rather than general
 # file-related work.  Deliberately narrow to avoid removing legitimate facts
 # such as "User works with CSV files" or "prefers PDF export".
@@ -349,13 +321,14 @@ class MemoryUpdater:
         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)
+        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
@@ -377,6 +350,7 @@ class MemoryUpdater:
         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()
@@ -390,7 +364,7 @@ class MemoryUpdater:
         # 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)
+        return get_memory_storage().save(updated_memory, agent_name, user_id=user_id)
 
     async def aupdate_memory(
         self,
@@ -399,28 +373,63 @@ class MemoryUpdater:
         agent_name: str | None = None,
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
+        user_id: str | None = None,
     ) -> bool:
-        """Update memory asynchronously based on conversation messages."""
+        """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 = await asyncio.to_thread(
-                self._prepare_update_prompt,
+            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 = await model.ainvoke(prompt, config={"run_name": "memory_agent"})
-            return await asyncio.to_thread(
-                self._finalize_update,
+            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)
@@ -438,7 +447,16 @@ class MemoryUpdater:
         reinforcement_detected: bool = False,
         user_id: str | None = None,
     ) -> bool:
-        """Synchronously update memory via the async updater path.
+        """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.
@@ -451,14 +469,34 @@ class MemoryUpdater:
         Returns:
             True if update was successful, False otherwise.
         """
-        return _run_async_update_sync(
-            self.aupdate_memory(
-                messages=messages,
-                thread_id=thread_id,
-                agent_name=agent_name,
-                correction_detected=correction_detected,
-                reinforcement_detected=reinforcement_detected,
-            )
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = None
+
+        if loop is not None and loop.is_running():
+            try:
+                future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(
+                    self._do_update_memory_sync,
+                    messages=messages,
+                    thread_id=thread_id,
+                    agent_name=agent_name,
+                    correction_detected=correction_detected,
+                    reinforcement_detected=reinforcement_detected,
+                    user_id=user_id,
+                )
+                return future.result()
+            except Exception:
+                logger.exception("Failed to offload memory update to executor")
+                return False
+
+        return self._do_update_memory_sync(
+            messages=messages,
+            thread_id=thread_id,
+            agent_name=agent_name,
+            correction_detected=correction_detected,
+            reinforcement_detected=reinforcement_detected,
+            user_id=user_id,
         )
 
     def _apply_updates(

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

@@ -36,94 +36,130 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
 
     @staticmethod
     def _message_tool_calls(msg) -> list[dict]:
-        """Return normalized tool calls from structured fields or raw provider payloads."""
+        """Return normalized tool calls from structured fields or raw provider payloads.
+
+        LangChain stores malformed provider function calls in ``invalid_tool_calls``.
+        They do not execute, but provider adapters may still serialize enough of
+        the call id/name back into the next request that strict OpenAI-compatible
+        validators expect a matching ToolMessage. Treat them as dangling calls so
+        the next model request stays well-formed and the model sees a recoverable
+        tool error instead of another provider 400.
+        """
+        normalized: list[dict] = []
+
         tool_calls = getattr(msg, "tool_calls", None) or []
-        if tool_calls:
-            return list(tool_calls)
+        normalized.extend(list(tool_calls))
 
         raw_tool_calls = (getattr(msg, "additional_kwargs", None) or {}).get("tool_calls") or []
-        normalized: list[dict] = []
-        for raw_tc in raw_tool_calls:
-            if not isinstance(raw_tc, dict):
+        if not tool_calls:
+            for raw_tc in raw_tool_calls:
+                if not isinstance(raw_tc, dict):
+                    continue
+
+                function = raw_tc.get("function")
+                name = raw_tc.get("name")
+                if not name and isinstance(function, dict):
+                    name = function.get("name")
+
+                args = raw_tc.get("args", {})
+                if not args and isinstance(function, dict):
+                    raw_args = function.get("arguments")
+                    if isinstance(raw_args, str):
+                        try:
+                            parsed_args = json.loads(raw_args)
+                        except (TypeError, ValueError, json.JSONDecodeError):
+                            parsed_args = {}
+                        args = parsed_args if isinstance(parsed_args, dict) else {}
+
+                normalized.append(
+                    {
+                        "id": raw_tc.get("id"),
+                        "name": name or "unknown",
+                        "args": args if isinstance(args, dict) else {},
+                    }
+                )
+
+        for invalid_tc in getattr(msg, "invalid_tool_calls", None) or []:
+            if not isinstance(invalid_tc, dict):
                 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 {},
+                    "id": invalid_tc.get("id"),
+                    "name": invalid_tc.get("name") or "unknown",
+                    "args": {},
+                    "invalid": True,
+                    "error": invalid_tc.get("error"),
                 }
             )
 
         return normalized
 
-    def _build_patched_messages(self, messages: list) -> list | None:
-        """Return a new message list with patches inserted at the correct positions.
+    @staticmethod
+    def _synthetic_tool_message_content(tool_call: dict) -> str:
+        if tool_call.get("invalid"):
+            error = tool_call.get("error")
+            if isinstance(error, str) and error:
+                return f"[Tool call could not be executed because its arguments were invalid: {error}]"
+            return "[Tool call could not be executed because its arguments were invalid.]"
+        return "[Tool call was interrupted and did not return a result.]"
 
-        For each AIMessage with dangling tool_calls (no corresponding ToolMessage),
-        a synthetic ToolMessage is inserted immediately after that AIMessage.
-        Returns None if no patches are needed.
+    def _build_patched_messages(self, messages: list) -> list | None:
+        """Return messages with tool results grouped after their tool-call AIMessage.
+
+        This normalizes model-bound causal order before provider serialization while
+        preserving already-valid transcripts unchanged.
         """
-        # Collect IDs of all existing ToolMessages
-        existing_tool_msg_ids: set[str] = set()
+        tool_messages_by_id: dict[str, ToolMessage] = {}
         for msg in messages:
             if isinstance(msg, ToolMessage):
-                existing_tool_msg_ids.add(msg.tool_call_id)
+                tool_messages_by_id.setdefault(msg.tool_call_id, msg)
 
-        # Check if any patching is needed
-        needs_patch = False
+        tool_call_ids: set[str] = set()
         for msg in messages:
             if getattr(msg, "type", None) != "ai":
                 continue
             for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
-                if tc_id and tc_id not in existing_tool_msg_ids:
-                    needs_patch = True
-                    break
-            if needs_patch:
-                break
+                if tc_id:
+                    tool_call_ids.add(tc_id)
 
-        if not needs_patch:
-            return None
-
-        # Build new list with patches inserted right after each dangling AIMessage
         patched: list = []
-        patched_ids: set[str] = set()
+        consumed_tool_msg_ids: set[str] = set()
         patch_count = 0
         for msg in messages:
+            if isinstance(msg, ToolMessage) and msg.tool_call_id in tool_call_ids:
+                continue
+
             patched.append(msg)
             if getattr(msg, "type", None) != "ai":
                 continue
+
             for tc in self._message_tool_calls(msg):
                 tc_id = tc.get("id")
-                if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
+                if not tc_id or tc_id in consumed_tool_msg_ids:
+                    continue
+
+                existing_tool_msg = tool_messages_by_id.get(tc_id)
+                if existing_tool_msg is not None:
+                    patched.append(existing_tool_msg)
+                    consumed_tool_msg_ids.add(tc_id)
+                else:
                     patched.append(
                         ToolMessage(
-                            content="[Tool call was interrupted and did not return a result.]",
+                            content=self._synthetic_tool_message_content(tc),
                             tool_call_id=tc_id,
                             name=tc.get("name", "unknown"),
                             status="error",
                         )
                     )
-                    patched_ids.add(tc_id)
+                    consumed_tool_msg_ids.add(tc_id)
                     patch_count += 1
 
-        logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
+        if patched == messages:
+            return None
+
+        if patch_count:
+            logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
         return patched
 
     @override

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

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

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

@@ -20,7 +20,7 @@ from langchain.agents.middleware.types import (
 from langchain_core.messages import AIMessage
 from langgraph.errors import GraphBubbleUp
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 logger = logging.getLogger(__name__)
 
@@ -70,20 +70,11 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
     retry_base_delay_ms: int = 1000
     retry_cap_delay_ms: int = 8000
 
-    circuit_failure_threshold: int = 5
-    circuit_recovery_timeout_sec: int = 60
-
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(self, *, app_config: AppConfig, **kwargs: Any) -> None:
         super().__init__(**kwargs)
 
-        # Load Circuit Breaker configs from app config if available, fall back to defaults
-        try:
-            app_config = get_app_config()
-            self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
-            self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
-        except (FileNotFoundError, RuntimeError):
-            # Gracefully fall back to class defaults in test environments
-            pass
+        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()

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

@@ -12,19 +12,23 @@ Detection strategy:
      response so the agent is forced to produce a final text answer.
 """
 
+from __future__ import annotations
+
 import hashlib
 import json
 import logging
 import threading
 from collections import OrderedDict, defaultdict
 from copy import deepcopy
-from typing import override
+from typing import TYPE_CHECKING, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
-from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime
 
+if TYPE_CHECKING:
+    from deerflow.config.loop_detection_config import LoopDetectionConfig
+
 logger = logging.getLogger(__name__)
 
 # Defaults — can be overridden via constructor
@@ -140,6 +144,9 @@ _TOOL_FREQ_HARD_STOP_MSG = "[FORCED STOP] Tool {tool_name} called {count} times
 class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
     """Detects and breaks repetitive tool call loops.
 
+    Threshold parameters are validated upstream by :class:`LoopDetectionConfig`;
+    construct via :meth:`from_config` to ensure values pass Pydantic validation.
+
     Args:
         warn_threshold: Number of identical tool call sets before injecting
             a warning message. Default: 3.
@@ -155,6 +162,14 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             Default: 30.
         tool_freq_hard_limit: Number of calls to the same tool type before
             forcing a stop. Default: 50.
+        tool_freq_overrides: Per-tool overrides for frequency thresholds,
+            keyed by tool name. Each value is a ``(warn, hard_limit)`` tuple
+            that replaces ``tool_freq_warn`` / ``tool_freq_hard_limit`` for
+            that specific tool. Tools not listed here fall back to the global
+            thresholds. Useful for raising limits on intentionally
+            high-frequency tools (e.g. ``bash`` in batch pipelines) without
+            weakening protection on all other tools. Default: ``None``
+            (no overrides).
     """
 
     def __init__(
@@ -165,6 +180,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
         max_tracked_threads: int = _DEFAULT_MAX_TRACKED_THREADS,
         tool_freq_warn: int = _DEFAULT_TOOL_FREQ_WARN,
         tool_freq_hard_limit: int = _DEFAULT_TOOL_FREQ_HARD_LIMIT,
+        tool_freq_overrides: dict[str, tuple[int, int]] | None = None,
     ):
         super().__init__()
         self.warn_threshold = warn_threshold
@@ -173,14 +189,26 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
         self.max_tracked_threads = max_tracked_threads
         self.tool_freq_warn = tool_freq_warn
         self.tool_freq_hard_limit = tool_freq_hard_limit
+        self._tool_freq_overrides: dict[str, tuple[int, int]] = tool_freq_overrides or {}
         self._lock = threading.Lock()
-        # Per-thread tracking using OrderedDict for LRU eviction
         self._history: OrderedDict[str, list[str]] = OrderedDict()
         self._warned: dict[str, set[str]] = defaultdict(set)
-        # Per-thread, per-tool-type cumulative call counts
         self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
         self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)
 
+    @classmethod
+    def from_config(cls, config: LoopDetectionConfig) -> LoopDetectionMiddleware:
+        """Construct from a Pydantic-validated config, trusting its validation."""
+        return cls(
+            warn_threshold=config.warn_threshold,
+            hard_limit=config.hard_limit,
+            window_size=config.window_size,
+            max_tracked_threads=config.max_tracked_threads,
+            tool_freq_warn=config.tool_freq_warn,
+            tool_freq_hard_limit=config.tool_freq_hard_limit,
+            tool_freq_overrides={name: (o.warn, o.hard_limit) for name, o in config.tool_freq_overrides.items()},
+        )
+
     def _get_thread_id(self, runtime: Runtime) -> str:
         """Extract thread_id from runtime context for per-thread tracking."""
         thread_id = runtime.context.get("thread_id") if runtime.context else None
@@ -280,7 +308,12 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
                 freq[name] += 1
                 tc_count = freq[name]
 
-                if tc_count >= self.tool_freq_hard_limit:
+                if name in self._tool_freq_overrides:
+                    eff_warn, eff_hard = self._tool_freq_overrides[name]
+                else:
+                    eff_warn, eff_hard = self.tool_freq_warn, self.tool_freq_hard_limit
+
+                if tc_count >= eff_hard:
                     logger.error(
                         "Tool frequency hard limit reached — forcing stop",
                         extra={
@@ -291,7 +324,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
                     )
                     return _TOOL_FREQ_HARD_STOP_MSG.format(tool_name=name, count=tc_count), True
 
-                if tc_count >= self.tool_freq_warn:
+                if tc_count >= eff_warn:
                     warned = self._tool_freq_warned[thread_id]
                     if name not in warned:
                         warned.add(name)
@@ -356,13 +389,30 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
             return {"messages": [stripped_msg]}
 
         if warning:
-            # Inject as HumanMessage instead of SystemMessage to avoid
-            # Anthropic's "multiple non-consecutive system messages" error.
-            # Anthropic models require system messages only at the start of
-            # the conversation; injecting one mid-conversation crashes
-            # langchain_anthropic's _format_messages(). HumanMessage works
-            # with all providers. See #1299.
-            return {"messages": [HumanMessage(content=warning, name="loop_warning")]}
+            # WORKAROUND for v2.0-m1 — see #2724.
+            #
+            # Append the warning to the AIMessage content instead of
+            # injecting a separate HumanMessage. Inserting any non-tool
+            # message between an AIMessage(tool_calls=...) and its
+            # ToolMessage responses breaks OpenAI/Moonshot strict pairing
+            # validation ("tool_call_ids did not have response messages")
+            # because the tools node has not run yet at after_model time.
+            # tool_calls are preserved so the tools node still executes.
+            #
+            # This is a temporary mitigation: mutating an existing
+            # AIMessage to carry framework-authored text leaks loop-warning
+            # text into downstream consumers (MemoryMiddleware fact
+            # extraction, TitleMiddleware, telemetry, model replay) as if
+            # the model said it. The proper fix is to defer warning
+            # injection from after_model to wrap_model_call so every prior
+            # ToolMessage is already in the request — see RFC #2517 (which
+            # lists "loop intervention does not leave invalid
+            # tool-call/tool-message state" as acceptance criteria) and
+            # the prototype on `fix/loop-detection-tool-call-pairing`.
+            messages = state.get("messages", [])
+            last_msg = messages[-1]
+            patched_msg = last_msg.model_copy(update={"content": self._append_text(last_msg.content, warning)})
+            return {"messages": [patched_msg]}
 
         return None
 

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

@@ -1,7 +1,7 @@
 """Middleware for memory mechanism."""
 
 import logging
-from typing import override
+from typing import TYPE_CHECKING, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
@@ -13,6 +13,9 @@ 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
 
+if TYPE_CHECKING:
+    from deerflow.config.memory_config import MemoryConfig
+
 logger = logging.getLogger(__name__)
 
 
@@ -34,14 +37,17 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
 
     state_schema = MemoryMiddlewareState
 
-    def __init__(self, agent_name: str | None = None):
+    def __init__(self, agent_name: str | None = None, *, memory_config: "MemoryConfig | None" = None):
         """Initialize the MemoryMiddleware.
 
         Args:
             agent_name: If provided, memory is stored per-agent. If None, uses global memory.
+            memory_config: Explicit memory config. When omitted, legacy global
+                config fallback is used.
         """
         super().__init__()
         self._agent_name = agent_name
+        self._memory_config = memory_config
 
     @override
     def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
@@ -54,7 +60,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         Returns:
             None (no state changes needed from this middleware).
         """
-        config = get_memory_config()
+        config = self._memory_config or get_memory_config()
         if not config.enabled:
             return None
 

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

@@ -7,6 +7,7 @@ from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langgraph.runtime import Runtime
 
+from deerflow.agents.middlewares.tool_call_metadata import clone_ai_message_with_tool_calls
 from deerflow.subagents.executor import MAX_CONCURRENT_SUBAGENTS
 
 logger = logging.getLogger(__name__)
@@ -63,7 +64,7 @@ class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
         logger.warning(f"Truncated {dropped_count} excess task tool call(s) from model response (limit: {self.max_concurrent})")
 
         # Replace the AIMessage with truncated tool_calls (same id triggers replacement)
-        updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
+        updated_msg = clone_ai_message_with_tool_calls(last_msg, truncated_tool_calls)
         return {"messages": [updated_msg]}
 
     @override

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

@@ -10,10 +10,14 @@ from typing import Any, Protocol, override, runtime_checkable
 from langchain.agents import AgentState
 from langchain.agents.middleware import SummarizationMiddleware
 from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage
+from langchain_core.messages.utils import get_buffer_string
 from langgraph.config import get_config
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
 from langgraph.runtime import Runtime
 
+from deerflow.agents.middlewares.dynamic_context_middleware import is_dynamic_context_reminder
+from deerflow.agents.middlewares.tool_call_metadata import clone_ai_message_with_tool_calls
+
 logger = logging.getLogger(__name__)
 
 
@@ -78,10 +82,7 @@ def _clone_ai_message(
     content: Any | None = None,
 ) -> AIMessage:
     """Clone an AIMessage while replacing its tool_calls list and optional content."""
-    update: dict[str, Any] = {"tool_calls": tool_calls}
-    if content is not None:
-        update["content"] = content
-    return message.model_copy(update=update)
+    return clone_ai_message_with_tool_calls(message, tool_calls, content=content)
 
 
 @dataclass
@@ -136,6 +137,7 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
             return None
 
         messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
+        messages_to_summarize, preserved_messages = self._preserve_dynamic_context_reminders(messages_to_summarize, preserved_messages)
         self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
         summary = self._create_summary(messages_to_summarize)
         new_messages = self._build_new_messages(summary)
@@ -161,6 +163,7 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
             return None
 
         messages_to_summarize, preserved_messages = self._partition_with_skill_rescue(messages, cutoff_index)
+        messages_to_summarize, preserved_messages = self._preserve_dynamic_context_reminders(messages_to_summarize, preserved_messages)
         self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
         summary = await self._acreate_summary(messages_to_summarize)
         new_messages = self._build_new_messages(summary)
@@ -173,12 +176,102 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
             ]
         }
 
+    @override
+    def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary without emitting streaming events to the client.
+
+        Suppresses callbacks to prevent the internal summarization LLM call from
+        producing visible AI message chunks in the frontend's ``messages-tuple``
+        stream (issue #2804).
+        """
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed:
+            return "Previous conversation was too long to summarize."
+
+        formatted = get_buffer_string(trimmed)
+
+        try:
+            response = self.model.with_config(callbacks=[]).invoke(
+                self.summary_prompt.format(messages=formatted).rstrip(),
+                config={
+                    "metadata": {"lc_source": "summarization"},
+                    "callbacks": [],
+                },
+            )
+            return self._extract_summary_text(response)
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    @override
+    async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary without emitting streaming events to the client.
+
+        Suppresses callbacks to prevent the internal summarization LLM call from
+        producing visible AI message chunks in the frontend's ``messages-tuple``
+        stream (issue #2804).
+        """
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed:
+            return "Previous conversation was too long to summarize."
+
+        formatted = get_buffer_string(trimmed)
+
+        try:
+            response = await self.model.with_config(callbacks=[]).ainvoke(
+                self.summary_prompt.format(messages=formatted).rstrip(),
+                config={
+                    "metadata": {"lc_source": "summarization"},
+                    "callbacks": [],
+                },
+            )
+            return self._extract_summary_text(response)
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    def _extract_summary_text(self, response: Any) -> str:
+        # Prefer .text which normalizes list content blocks (e.g. [{"type": "text", "text": "..."}]).
+        # Fall back to .content for non-LangChain responses.
+        summary_text = getattr(response, "text", None)
+        if summary_text is None:
+            summary_text = getattr(response, "content", "")
+        return summary_text.strip() if isinstance(summary_text, str) else str(summary_text).strip()
+
     @override
     def _build_new_messages(self, summary: str) -> list[HumanMessage]:
         """Override the base implementation to let the human message with the special name 'summary'.
         And this message will be ignored to display in the frontend, but still can be used as context for the model.
         """
-        return [HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}", name="summary")]
+        return [
+            HumanMessage(
+                content=f"Here is a summary of the conversation to date:\n\n{summary}",
+                name="summary",
+                additional_kwargs={"hide_from_ui": True},
+            )
+        ]
+
+    def _preserve_dynamic_context_reminders(
+        self,
+        messages_to_summarize: list[AnyMessage],
+        preserved_messages: list[AnyMessage],
+    ) -> tuple[list[AnyMessage], list[AnyMessage]]:
+        """Keep hidden dynamic-context reminders out of summary compression.
+
+        These reminders carry the current date and optional memory. If summarization
+        removes them, DynamicContextMiddleware can mistake the summary HumanMessage
+        for the first user message and inject the reminder in the wrong place.
+        """
+        reminders = [msg for msg in messages_to_summarize if is_dynamic_context_reminder(msg)]
+        if not reminders:
+            return messages_to_summarize, preserved_messages
+
+        remaining = [msg for msg in messages_to_summarize if not is_dynamic_context_reminder(msg)]
+        return remaining, reminders + preserved_messages
 
     def _partition_with_skill_rescue(
         self,

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

@@ -2,16 +2,21 @@
 
 import logging
 import re
-from typing import Any, NotRequired, override
+from typing import TYPE_CHECKING, 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.agents.middlewares.dynamic_context_middleware import is_dynamic_context_reminder
 from deerflow.config.title_config import get_title_config
 from deerflow.models import create_chat_model
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+    from deerflow.config.title_config import TitleConfig
+
 logger = logging.getLogger(__name__)
 
 
@@ -26,6 +31,18 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
 
     state_schema = TitleMiddlewareState
 
+    def __init__(self, *, app_config: "AppConfig | None" = None, title_config: "TitleConfig | None" = None):
+        super().__init__()
+        self._app_config = app_config
+        self._title_config = title_config
+
+    def _get_title_config(self):
+        if self._title_config is not None:
+            return self._title_config
+        if self._app_config is not None:
+            return self._app_config.title
+        return get_title_config()
+
     def _normalize_content(self, content: object) -> str:
         if isinstance(content, str):
             return content
@@ -45,9 +62,13 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
 
         return ""
 
+    @staticmethod
+    def _is_user_message_for_title(message: object) -> bool:
+        return getattr(message, "type", None) == "human" and not is_dynamic_context_reminder(message)
+
     def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
         """Check if we should generate a title for this thread."""
-        config = get_title_config()
+        config = self._get_title_config()
         if not config.enabled:
             return False
 
@@ -61,7 +82,7 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
             return False
 
         # Count user and assistant messages
-        user_messages = [m for m in messages if m.type == "human"]
+        user_messages = [m for m in messages if self._is_user_message_for_title(m)]
         assistant_messages = [m for m in messages if m.type == "ai"]
 
         # Generate title after first complete exchange
@@ -72,10 +93,10 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
 
         Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
         """
-        config = get_title_config()
+        config = self._get_title_config()
         messages = state.get("messages", [])
 
-        user_msg_content = next((m.content for m in messages if m.type == "human"), "")
+        user_msg_content = next((m.content for m in messages if self._is_user_message_for_title(m)), "")
         assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")
 
         user_msg = self._normalize_content(user_msg_content)
@@ -94,14 +115,14 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
 
     def _parse_title(self, content: object) -> str:
         """Normalize model output into a clean title string."""
-        config = get_title_config()
+        config = self._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
 
     def _fallback_title(self, user_msg: str) -> str:
-        config = get_title_config()
+        config = self._get_title_config()
         fallback_chars = min(config.max_chars, 50)
         if len(user_msg) > fallback_chars:
             return user_msg[:fallback_chars].rstrip() + "..."
@@ -135,14 +156,17 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         if not self._should_generate_title(state):
             return None
 
-        config = get_title_config()
+        config = self._get_title_config()
         prompt, user_msg = self._build_title_prompt(state)
 
         try:
+            model_kwargs = {"thinking_enabled": False}
+            if self._app_config is not None:
+                model_kwargs["app_config"] = self._app_config
             if config.model_name:
-                model = create_chat_model(name=config.model_name, thinking_enabled=False)
+                model = create_chat_model(name=config.model_name, **model_kwargs)
             else:
-                model = create_chat_model(thinking_enabled=False)
+                model = create_chat_model(**model_kwargs)
             response = await model.ainvoke(prompt, config=self._get_runnable_config())
             title = self._parse_title(response.content)
             if title:

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

@@ -7,17 +7,21 @@ reminder message so the model still knows about the outstanding todo list.
 
 Additionally, this middleware prevents the agent from exiting the loop while
 there are still incomplete todo items. When the model produces a final response
-(no tool calls) but todos are not yet complete, the middleware injects a reminder
-and jumps back to the model node to force continued engagement.
+(no tool calls) but todos are not yet complete, the middleware queues a reminder
+for the next model request and jumps back to the model node to force continued
+engagement. The completion reminder is injected via ``wrap_model_call`` instead
+of being persisted into graph state as a normal user-visible message.
 """
 
 from __future__ import annotations
 
+import threading
+from collections.abc import Awaitable, Callable
 from typing import Any, override
 
 from langchain.agents.middleware import TodoListMiddleware
 from langchain.agents.middleware.todo import PlanningState, Todo
-from langchain.agents.middleware.types import hook_config
+from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse, hook_config
 from langchain_core.messages import AIMessage, HumanMessage
 from langgraph.runtime import Runtime
 
@@ -55,6 +59,51 @@ def _format_todos(todos: list[Todo]) -> str:
     return "\n".join(lines)
 
 
+def _format_completion_reminder(todos: list[Todo]) -> str:
+    """Format a completion reminder for incomplete todo items."""
+    incomplete = [t for t in todos if t.get("status") != "completed"]
+    incomplete_text = "\n".join(f"- [{t.get('status', 'pending')}] {t.get('content', '')}" for t in incomplete)
+    return (
+        "<system_reminder>\n"
+        "You have incomplete todo items that must be finished before giving your final response:\n\n"
+        f"{incomplete_text}\n\n"
+        "Please continue working on these tasks. Call `write_todos` to mark items as completed "
+        "as you finish them, and only respond when all items are done.\n"
+        "</system_reminder>"
+    )
+
+
+_TOOL_CALL_FINISH_REASONS = {"tool_calls", "function_call"}
+
+
+def _has_tool_call_intent_or_error(message: AIMessage) -> bool:
+    """Return True when an AIMessage is not a clean final answer.
+
+    Todo completion reminders should only fire when the model has produced a
+    plain final response. Provider/tool parsing details have moved across
+    LangChain versions and integrations, so keep all tool-intent/error signals
+    behind this helper instead of checking one concrete field at the call site.
+    """
+    if message.tool_calls:
+        return True
+
+    if getattr(message, "invalid_tool_calls", None):
+        return True
+
+    # Backward/provider compatibility: some integrations preserve raw or legacy
+    # tool-call intent in additional_kwargs even when structured tool_calls is
+    # empty. If this helper changes, update the matching sentinel test
+    # `TestToolCallIntentOrError.test_langchain_ai_message_tool_fields_are_explicitly_handled`;
+    # if that test fails after a LangChain upgrade, review this helper so new
+    # tool-call/error fields are not silently treated as clean final answers.
+    additional_kwargs = getattr(message, "additional_kwargs", {}) or {}
+    if additional_kwargs.get("tool_calls") or additional_kwargs.get("function_call"):
+        return True
+
+    response_metadata = getattr(message, "response_metadata", {}) or {}
+    return response_metadata.get("finish_reason") in _TOOL_CALL_FINISH_REASONS
+
+
 class TodoMiddleware(TodoListMiddleware):
     """Extends TodoListMiddleware with `write_todos` context-loss detection.
 
@@ -89,6 +138,7 @@ class TodoMiddleware(TodoListMiddleware):
         formatted = _format_todos(todos)
         reminder = HumanMessage(
             name="todo_reminder",
+            additional_kwargs={"hide_from_ui": True},
             content=(
                 "<system_reminder>\n"
                 "Your todo list from earlier is no longer visible in the current context window, "
@@ -113,6 +163,100 @@ class TodoMiddleware(TodoListMiddleware):
     # Maximum number of completion reminders before allowing the agent to exit.
     # This prevents infinite loops when the agent cannot make further progress.
     _MAX_COMPLETION_REMINDERS = 2
+    # Hard cap for per-run reminder bookkeeping in long-lived middleware instances.
+    _MAX_COMPLETION_REMINDER_KEYS = 4096
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self._lock = threading.Lock()
+        self._pending_completion_reminders: dict[tuple[str, str], list[str]] = {}
+        self._completion_reminder_counts: dict[tuple[str, str], int] = {}
+        self._completion_reminder_touch_order: dict[tuple[str, str], int] = {}
+        self._completion_reminder_next_order = 0
+
+    @staticmethod
+    def _get_thread_id(runtime: Runtime) -> str:
+        context = getattr(runtime, "context", None)
+        thread_id = context.get("thread_id") if context else None
+        return str(thread_id) if thread_id else "default"
+
+    @staticmethod
+    def _get_run_id(runtime: Runtime) -> str:
+        context = getattr(runtime, "context", None)
+        run_id = context.get("run_id") if context else None
+        return str(run_id) if run_id else "default"
+
+    def _pending_key(self, runtime: Runtime) -> tuple[str, str]:
+        return self._get_thread_id(runtime), self._get_run_id(runtime)
+
+    def _touch_completion_reminder_key_locked(self, key: tuple[str, str]) -> None:
+        self._completion_reminder_next_order += 1
+        self._completion_reminder_touch_order[key] = self._completion_reminder_next_order
+
+    def _completion_reminder_keys_locked(self) -> set[tuple[str, str]]:
+        keys = set(self._pending_completion_reminders)
+        keys.update(self._completion_reminder_counts)
+        keys.update(self._completion_reminder_touch_order)
+        return keys
+
+    def _drop_completion_reminder_key_locked(self, key: tuple[str, str]) -> None:
+        self._pending_completion_reminders.pop(key, None)
+        self._completion_reminder_counts.pop(key, None)
+        self._completion_reminder_touch_order.pop(key, None)
+
+    def _prune_completion_reminder_state_locked(self, protected_key: tuple[str, str]) -> None:
+        keys = self._completion_reminder_keys_locked()
+        overflow = len(keys) - self._MAX_COMPLETION_REMINDER_KEYS
+        if overflow <= 0:
+            return
+
+        candidates = [key for key in keys if key != protected_key]
+        candidates.sort(key=lambda key: self._completion_reminder_touch_order.get(key, 0))
+        for key in candidates[:overflow]:
+            self._drop_completion_reminder_key_locked(key)
+
+    def _queue_completion_reminder(self, runtime: Runtime, reminder: str) -> None:
+        key = self._pending_key(runtime)
+        with self._lock:
+            self._pending_completion_reminders.setdefault(key, []).append(reminder)
+            self._completion_reminder_counts[key] = self._completion_reminder_counts.get(key, 0) + 1
+            self._touch_completion_reminder_key_locked(key)
+            self._prune_completion_reminder_state_locked(protected_key=key)
+
+    def _completion_reminder_count_for_runtime(self, runtime: Runtime) -> int:
+        key = self._pending_key(runtime)
+        with self._lock:
+            return self._completion_reminder_counts.get(key, 0)
+
+    def _drain_completion_reminders(self, runtime: Runtime) -> list[str]:
+        key = self._pending_key(runtime)
+        with self._lock:
+            reminders = self._pending_completion_reminders.pop(key, [])
+            if reminders or key in self._completion_reminder_counts:
+                self._touch_completion_reminder_key_locked(key)
+            return reminders
+
+    def _clear_other_run_completion_reminders(self, runtime: Runtime) -> None:
+        thread_id, current_run_id = self._pending_key(runtime)
+        with self._lock:
+            for key in self._completion_reminder_keys_locked():
+                if key[0] == thread_id and key[1] != current_run_id:
+                    self._drop_completion_reminder_key_locked(key)
+
+    def _clear_current_run_completion_reminders(self, runtime: Runtime) -> None:
+        key = self._pending_key(runtime)
+        with self._lock:
+            self._drop_completion_reminder_key_locked(key)
+
+    @override
+    def before_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_other_run_completion_reminders(runtime)
+        return None
+
+    @override
+    async def abefore_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_other_run_completion_reminders(runtime)
+        return None
 
     @hook_config(can_jump_to=["model"])
     @override
@@ -137,10 +281,12 @@ class TodoMiddleware(TodoListMiddleware):
         if base_result is not None:
             return base_result
 
-        # 2. Only intervene when the agent wants to exit (no tool calls).
+        # 2. Only intervene when the agent wants to exit cleanly. Tool-call
+        # intent or tool-call parse errors should be handled by the tool path
+        # instead of being masked by todo reminders.
         messages = state.get("messages") or []
         last_ai = next((m for m in reversed(messages) if isinstance(m, AIMessage)), None)
-        if not last_ai or last_ai.tool_calls:
+        if not last_ai or _has_tool_call_intent_or_error(last_ai):
             return None
 
         # 3. Allow exit when all todos are completed or there are no todos.
@@ -149,24 +295,14 @@ class TodoMiddleware(TodoListMiddleware):
             return None
 
         # 4. Enforce a reminder cap to prevent infinite re-engagement loops.
-        if _completion_reminder_count(messages) >= self._MAX_COMPLETION_REMINDERS:
+        if self._completion_reminder_count_for_runtime(runtime) >= self._MAX_COMPLETION_REMINDERS:
             return None
 
-        # 5. Inject a reminder and force the agent back to the model.
-        incomplete = [t for t in todos if t.get("status") != "completed"]
-        incomplete_text = "\n".join(f"- [{t.get('status', 'pending')}] {t.get('content', '')}" for t in incomplete)
-        reminder = HumanMessage(
-            name="todo_completion_reminder",
-            content=(
-                "<system_reminder>\n"
-                "You have incomplete todo items that must be finished before giving your final response:\n\n"
-                f"{incomplete_text}\n\n"
-                "Please continue working on these tasks. Call `write_todos` to mark items as completed "
-                "as you finish them, and only respond when all items are done.\n"
-                "</system_reminder>"
-            ),
-        )
-        return {"jump_to": "model", "messages": [reminder]}
+        # 5. Queue a reminder for the next model request and jump back. We must
+        # not persist this control prompt as a normal HumanMessage, otherwise it
+        # can leak into user-visible message streams and saved transcripts.
+        self._queue_completion_reminder(runtime, _format_completion_reminder(todos))
+        return {"jump_to": "model"}
 
     @override
     @hook_config(can_jump_to=["model"])
@@ -177,3 +313,47 @@ class TodoMiddleware(TodoListMiddleware):
     ) -> dict[str, Any] | None:
         """Async version of after_model."""
         return self.after_model(state, runtime)
+
+    @staticmethod
+    def _format_pending_completion_reminders(reminders: list[str]) -> str:
+        return "\n\n".join(dict.fromkeys(reminders))
+
+    def _augment_request(self, request: ModelRequest) -> ModelRequest:
+        reminders = self._drain_completion_reminders(request.runtime)
+        if not reminders:
+            return request
+        new_messages = [
+            *request.messages,
+            HumanMessage(
+                content=self._format_pending_completion_reminders(reminders),
+                name="todo_completion_reminder",
+                additional_kwargs={"hide_from_ui": True},
+            ),
+        ]
+        return request.override(messages=new_messages)
+
+    @override
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelCallResult:
+        return handler(self._augment_request(request))
+
+    @override
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelCallResult:
+        return await handler(self._augment_request(request))
+
+    @override
+    def after_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_current_run_completion_reminders(runtime)
+        return None
+
+    @override
+    async def aafter_agent(self, state: PlanningState, runtime: Runtime) -> dict[str, Any] | None:
+        self._clear_current_run_completion_reminders(runtime)
+        return None

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

@@ -1,37 +1,358 @@
-"""Middleware for logging LLM token usage."""
+"""Middleware for logging token usage and annotating step attribution."""
+
+from __future__ import annotations
 
 import logging
-from typing import override
+from collections import defaultdict
+from typing import Any, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware.todo import Todo
+from langchain_core.messages import AIMessage, ToolMessage
 from langgraph.runtime import Runtime
 
 logger = logging.getLogger(__name__)
 
+TOKEN_USAGE_ATTRIBUTION_KEY = "token_usage_attribution"
+
+
+def _string_arg(value: Any) -> str | None:
+    if isinstance(value, str):
+        normalized = value.strip()
+        return normalized or None
+    return None
+
+
+def _normalize_todos(value: Any) -> list[Todo]:
+    if not isinstance(value, list):
+        return []
+
+    normalized: list[Todo] = []
+    for item in value:
+        if not isinstance(item, dict):
+            continue
+
+        todo: Todo = {}
+        content = _string_arg(item.get("content"))
+        status = item.get("status")
+
+        if content is not None:
+            todo["content"] = content
+        if status in {"pending", "in_progress", "completed"}:
+            todo["status"] = status
+
+        normalized.append(todo)
+
+    return normalized
+
+
+def _todo_action_kind(previous: Todo | None, current: Todo) -> str:
+    status = current.get("status")
+    previous_content = previous.get("content") if previous else None
+    current_content = current.get("content")
+
+    if previous is None:
+        if status == "completed":
+            return "todo_complete"
+        if status == "in_progress":
+            return "todo_start"
+        return "todo_update"
+
+    if previous_content != current_content:
+        return "todo_update"
+
+    if status == "completed":
+        return "todo_complete"
+    if status == "in_progress":
+        return "todo_start"
+    return "todo_update"
+
+
+def _build_todo_actions(previous_todos: list[Todo], next_todos: list[Todo]) -> list[dict[str, Any]]:
+    # This is the single source of truth for precise write_todos token
+    # attribution. The frontend intentionally falls back to a generic
+    # "Update to-do list" label when this metadata is missing or malformed.
+    previous_by_content: dict[str, list[tuple[int, Todo]]] = defaultdict(list)
+    matched_previous_indices: set[int] = set()
+
+    for index, todo in enumerate(previous_todos):
+        content = todo.get("content")
+        if isinstance(content, str) and content:
+            previous_by_content[content].append((index, todo))
+
+    actions: list[dict[str, Any]] = []
+
+    for index, todo in enumerate(next_todos):
+        content = todo.get("content")
+        if not isinstance(content, str) or not content:
+            continue
+
+        previous_match: Todo | None = None
+        content_matches = previous_by_content.get(content)
+        if content_matches:
+            while content_matches and content_matches[0][0] in matched_previous_indices:
+                content_matches.pop(0)
+            if content_matches:
+                previous_index, previous_match = content_matches.pop(0)
+                matched_previous_indices.add(previous_index)
+
+        if previous_match is None and index < len(previous_todos) and index not in matched_previous_indices:
+            previous_match = previous_todos[index]
+            matched_previous_indices.add(index)
+
+        if previous_match is not None:
+            previous_content = previous_match.get("content")
+            previous_status = previous_match.get("status")
+            if previous_content == content and previous_status == todo.get("status"):
+                continue
+
+        actions.append(
+            {
+                "kind": _todo_action_kind(previous_match, todo),
+                "content": content,
+            }
+        )
+
+    for index, todo in enumerate(previous_todos):
+        if index in matched_previous_indices:
+            continue
+
+        content = todo.get("content")
+        if not isinstance(content, str) or not content:
+            continue
+
+        actions.append(
+            {
+                "kind": "todo_remove",
+                "content": content,
+            }
+        )
+
+    return actions
+
+
+def _describe_tool_call(tool_call: dict[str, Any], todos: list[Todo]) -> list[dict[str, Any]]:
+    name = _string_arg(tool_call.get("name")) or "unknown"
+    args = tool_call.get("args") if isinstance(tool_call.get("args"), dict) else {}
+    tool_call_id = _string_arg(tool_call.get("id"))
+
+    if name == "write_todos":
+        next_todos = _normalize_todos(args.get("todos"))
+        actions = _build_todo_actions(todos, next_todos)
+        if not actions:
+            return [
+                {
+                    "kind": "tool",
+                    "tool_name": name,
+                    "tool_call_id": tool_call_id,
+                }
+            ]
+        return [
+            {
+                **action,
+                "tool_call_id": tool_call_id,
+            }
+            for action in actions
+        ]
+
+    if name == "task":
+        return [
+            {
+                "kind": "subagent",
+                "description": _string_arg(args.get("description")),
+                "subagent_type": _string_arg(args.get("subagent_type")),
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name in {"web_search", "image_search"}:
+        query = _string_arg(args.get("query"))
+        return [
+            {
+                "kind": "search",
+                "tool_name": name,
+                "query": query,
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name == "present_files":
+        return [
+            {
+                "kind": "present_files",
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    if name == "ask_clarification":
+        return [
+            {
+                "kind": "clarification",
+                "tool_call_id": tool_call_id,
+            }
+        ]
+
+    return [
+        {
+            "kind": "tool",
+            "tool_name": name,
+            "description": _string_arg(args.get("description")),
+            "tool_call_id": tool_call_id,
+        }
+    ]
+
+
+def _infer_step_kind(message: AIMessage, actions: list[dict[str, Any]]) -> str:
+    if actions:
+        first_kind = actions[0].get("kind")
+        if len(actions) == 1 and first_kind in {"todo_start", "todo_complete", "todo_update", "todo_remove"}:
+            return "todo_update"
+        if len(actions) == 1 and first_kind == "subagent":
+            return "subagent_dispatch"
+        return "tool_batch"
+
+    if message.content:
+        return "final_answer"
+    return "thinking"
+
+
+def _has_tool_call(message: AIMessage, tool_call_id: str) -> bool:
+    """Return True if the AIMessage contains a tool_call with the given id."""
+    for tc in message.tool_calls or []:
+        if isinstance(tc, dict):
+            if tc.get("id") == tool_call_id:
+                return True
+        elif hasattr(tc, "id") and tc.id == tool_call_id:
+            return True
+    return False
+
+
+def _build_attribution(message: AIMessage, todos: list[Todo]) -> dict[str, Any]:
+    tool_calls = getattr(message, "tool_calls", None) or []
+    actions: list[dict[str, Any]] = []
+    current_todos = list(todos)
+
+    for raw_tool_call in tool_calls:
+        if not isinstance(raw_tool_call, dict):
+            continue
+
+        described_actions = _describe_tool_call(raw_tool_call, current_todos)
+        actions.extend(described_actions)
+
+        if raw_tool_call.get("name") == "write_todos":
+            args = raw_tool_call.get("args") if isinstance(raw_tool_call.get("args"), dict) else {}
+            current_todos = _normalize_todos(args.get("todos"))
+
+    tool_call_ids: list[str] = []
+    for tool_call in tool_calls:
+        if not isinstance(tool_call, dict):
+            continue
+
+        tool_call_id = _string_arg(tool_call.get("id"))
+        if tool_call_id is not None:
+            tool_call_ids.append(tool_call_id)
+
+    return {
+        # Schema changes should remain additive where possible so older
+        # frontends can ignore unknown fields and fall back safely.
+        "version": 1,
+        "kind": _infer_step_kind(message, actions),
+        "shared_attribution": len(actions) > 1,
+        "tool_call_ids": tool_call_ids,
+        "actions": actions,
+    }
+
 
 class TokenUsageMiddleware(AgentMiddleware):
-    """Logs token usage from model response usage_metadata."""
+    """Logs token usage from model responses and annotates the AI step."""
 
-    @override
-    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return self._log_usage(state)
-
-    @override
-    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
-        return self._log_usage(state)
-
-    def _log_usage(self, state: AgentState) -> None:
+    def _apply(self, state: AgentState) -> dict | None:
         messages = state.get("messages", [])
         if not messages:
             return None
+
+        # Annotate subagent token usage onto the AIMessage that dispatched it.
+        # When a task tool completes, its usage is cached by tool_call_id.  Detect
+        # the ToolMessage → search backward for the corresponding AIMessage → merge.
+        # Walk backward through consecutive ToolMessages before the new AIMessage
+        # so that multiple concurrent task tool calls all get their subagent tokens
+        # written back to the same dispatch message (merging into one update).
+        state_updates: dict[int, AIMessage] = {}
+        if len(messages) >= 2:
+            from deerflow.tools.builtins.task_tool import pop_cached_subagent_usage
+
+            idx = len(messages) - 2
+            while idx >= 0:
+                tool_msg = messages[idx]
+                if not isinstance(tool_msg, ToolMessage) or not tool_msg.tool_call_id:
+                    break
+
+                subagent_usage = pop_cached_subagent_usage(tool_msg.tool_call_id)
+                if subagent_usage:
+                    # Search backward from the ToolMessage to find the AIMessage
+                    # that dispatched it.  A single model response can dispatch
+                    # multiple task tool calls, so we can't assume a fixed offset.
+                    dispatch_idx = idx - 1
+                    while dispatch_idx >= 0:
+                        candidate = messages[dispatch_idx]
+                        if isinstance(candidate, AIMessage) and _has_tool_call(candidate, tool_msg.tool_call_id):
+                            # Accumulate into an existing update for the same
+                            # AIMessage (multiple task calls in one response),
+                            # or merge fresh from the original message.
+                            existing_update = state_updates.get(dispatch_idx)
+                            prev = existing_update.usage_metadata if existing_update else (getattr(candidate, "usage_metadata", None) or {})
+                            merged = {
+                                **prev,
+                                "input_tokens": prev.get("input_tokens", 0) + subagent_usage["input_tokens"],
+                                "output_tokens": prev.get("output_tokens", 0) + subagent_usage["output_tokens"],
+                                "total_tokens": prev.get("total_tokens", 0) + subagent_usage["total_tokens"],
+                            }
+                            state_updates[dispatch_idx] = candidate.model_copy(update={"usage_metadata": merged})
+                            break
+                        dispatch_idx -= 1
+                idx -= 1
+
         last = messages[-1]
+        if not isinstance(last, AIMessage):
+            if state_updates:
+                return {"messages": [state_updates[idx] for idx in sorted(state_updates)]}
+            return None
+
         usage = getattr(last, "usage_metadata", None)
         if usage:
+            input_token_details = usage.get("input_token_details") or {}
+            output_token_details = usage.get("output_token_details") or {}
+            detail_parts = []
+            if input_token_details:
+                detail_parts.append(f"input_token_details={input_token_details}")
+            if output_token_details:
+                detail_parts.append(f"output_token_details={output_token_details}")
+            detail_suffix = f" {' '.join(detail_parts)}" if detail_parts else ""
             logger.info(
-                "LLM token usage: input=%s output=%s total=%s",
+                "LLM token usage: input=%s output=%s total=%s%s",
                 usage.get("input_tokens", "?"),
                 usage.get("output_tokens", "?"),
                 usage.get("total_tokens", "?"),
+                detail_suffix,
             )
-        return None
+
+        todos = state.get("todos") or []
+        attribution = _build_attribution(last, todos if isinstance(todos, list) else [])
+        additional_kwargs = dict(getattr(last, "additional_kwargs", {}) or {})
+
+        if additional_kwargs.get(TOKEN_USAGE_ATTRIBUTION_KEY) == attribution:
+            return {"messages": [state_updates[idx] for idx in sorted(state_updates)]} if state_updates else None
+
+        additional_kwargs[TOKEN_USAGE_ATTRIBUTION_KEY] = attribution
+        updated_msg = last.model_copy(update={"additional_kwargs": additional_kwargs})
+        state_updates[len(messages) - 1] = updated_msg
+        return {"messages": [state_updates[idx] for idx in sorted(state_updates)]}
+
+    @override
+    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._apply(state)
+
+    @override
+    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+        return self._apply(state)

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

@@ -0,0 +1,50 @@
+"""Helpers for keeping AIMessage tool-call metadata consistent."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.messages import AIMessage
+
+
+def _raw_tool_call_id(raw_tool_call: Any) -> str | None:
+    if not isinstance(raw_tool_call, dict):
+        return None
+
+    raw_id = raw_tool_call.get("id")
+    return raw_id if isinstance(raw_id, str) and raw_id else None
+
+
+def clone_ai_message_with_tool_calls(
+    message: AIMessage,
+    tool_calls: list[dict[str, Any]],
+    *,
+    content: Any | None = None,
+) -> AIMessage:
+    """Clone an AIMessage while keeping raw provider tool-call metadata in sync."""
+    kept_ids = {tc["id"] for tc in tool_calls if isinstance(tc.get("id"), str) and tc["id"]}
+
+    update: dict[str, Any] = {"tool_calls": tool_calls}
+    if content is not None:
+        update["content"] = content
+
+    additional_kwargs = dict(getattr(message, "additional_kwargs", {}) or {})
+    raw_tool_calls = additional_kwargs.get("tool_calls")
+    if isinstance(raw_tool_calls, list):
+        synced_raw_tool_calls = [raw_tc for raw_tc in raw_tool_calls if _raw_tool_call_id(raw_tc) in kept_ids]
+        if synced_raw_tool_calls:
+            additional_kwargs["tool_calls"] = synced_raw_tool_calls
+        else:
+            additional_kwargs.pop("tool_calls", None)
+
+    if not tool_calls:
+        additional_kwargs.pop("function_call", None)
+
+    update["additional_kwargs"] = additional_kwargs
+
+    response_metadata = dict(getattr(message, "response_metadata", {}) or {})
+    if not tool_calls and response_metadata.get("finish_reason") == "tool_calls":
+        response_metadata["finish_reason"] = "stop"
+    update["response_metadata"] = response_metadata
+
+    return message.model_copy(update=update)

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 = get_guardrails_config()
+    guardrails_config = app_config.guardrails
     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/client.py

@@ -41,7 +41,7 @@ from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig
 from deerflow.config.paths import get_paths
 from deerflow.models import create_chat_model
 from deerflow.runtime.user_context import get_effective_user_id
-from deerflow.skills.installer import install_skill_from_archive
+from deerflow.skills.storage import get_or_new_skill_storage
 from deerflow.uploads.manager import (
     claim_unique_filename,
     delete_file_safe,
@@ -264,25 +264,35 @@ class DeerFlowClient:
         return [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in tool_calls]
 
     @staticmethod
-    def _ai_text_event(msg_id: str | None, text: str, usage: dict | None) -> "StreamEvent":
-        """Build a ``messages-tuple`` AI text event, attaching usage when present."""
+    def _serialize_additional_kwargs(msg) -> dict[str, Any] | None:
+        """Copy message additional_kwargs when present."""
+        additional_kwargs = getattr(msg, "additional_kwargs", None)
+        if isinstance(additional_kwargs, dict) and additional_kwargs:
+            return dict(additional_kwargs)
+        return None
+
+    @staticmethod
+    def _ai_text_event(msg_id: str | None, text: str, usage: dict | None, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
+        """Build a ``messages-tuple`` AI text event."""
         data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
         if usage:
             data["usage_metadata"] = usage
+        if additional_kwargs:
+            data["additional_kwargs"] = additional_kwargs
         return StreamEvent(type="messages-tuple", data=data)
 
     @staticmethod
-    def _ai_tool_calls_event(msg_id: str | None, tool_calls) -> "StreamEvent":
+    def _ai_tool_calls_event(msg_id: str | None, tool_calls, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
         """Build a ``messages-tuple`` AI tool-calls event."""
-        return StreamEvent(
-            type="messages-tuple",
-            data={
-                "type": "ai",
-                "content": "",
-                "id": msg_id,
-                "tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
-            },
-        )
+        data: dict[str, Any] = {
+            "type": "ai",
+            "content": "",
+            "id": msg_id,
+            "tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
+        }
+        if additional_kwargs:
+            data["additional_kwargs"] = additional_kwargs
+        return StreamEvent(type="messages-tuple", data=data)
 
     @staticmethod
     def _tool_message_event(msg: ToolMessage) -> "StreamEvent":
@@ -307,19 +317,30 @@ class DeerFlowClient:
                 d["tool_calls"] = DeerFlowClient._serialize_tool_calls(msg.tool_calls)
             if getattr(msg, "usage_metadata", None):
                 d["usage_metadata"] = msg.usage_metadata
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
             return d
         if isinstance(msg, ToolMessage):
-            return {
+            d = {
                 "type": "tool",
                 "content": DeerFlowClient._extract_text(msg.content),
                 "name": getattr(msg, "name", None),
                 "tool_call_id": getattr(msg, "tool_call_id", None),
                 "id": getattr(msg, "id", None),
             }
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
         if isinstance(msg, HumanMessage):
-            return {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
+            d = {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
         if isinstance(msg, SystemMessage):
-            return {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
+            d = {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
+            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
+                d["additional_kwargs"] = additional_kwargs
+            return d
         return {"type": "unknown", "content": str(msg), "id": getattr(msg, "id", None)}
 
     @staticmethod
@@ -542,6 +563,7 @@ class DeerFlowClient:
             - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str}
             - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str, "usage_metadata": {...}}
             - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
+            - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "additional_kwargs": {...}}
             - type="messages-tuple"  data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
             - type="end"             data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
         """
@@ -564,6 +586,7 @@ class DeerFlowClient:
         # in both the final ``messages`` chunk and the values snapshot —
         # count it only on whichever arrives first.
         counted_usage_ids: set[str] = set()
+        sent_additional_kwargs_by_id: dict[str, dict[str, Any]] = {}
         cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
 
         def _account_usage(msg_id: str | None, usage: Any) -> dict | None:
@@ -593,6 +616,20 @@ class DeerFlowClient:
                 "total_tokens": total_tokens,
             }
 
+        def _unsent_additional_kwargs(msg_id: str | None, additional_kwargs: dict[str, Any] | None) -> dict[str, Any] | None:
+            if not additional_kwargs:
+                return None
+            if not msg_id:
+                return additional_kwargs
+
+            sent = sent_additional_kwargs_by_id.setdefault(msg_id, {})
+            delta = {key: value for key, value in additional_kwargs.items() if sent.get(key) != value}
+            if not delta:
+                return None
+
+            sent.update(delta)
+            return delta
+
         for item in self._agent.stream(
             state,
             config=config,
@@ -620,17 +657,31 @@ class DeerFlowClient:
 
                 if isinstance(msg_chunk, AIMessage):
                     text = self._extract_text(msg_chunk.content)
+                    additional_kwargs = self._serialize_additional_kwargs(msg_chunk)
                     counted_usage = _account_usage(msg_id, msg_chunk.usage_metadata)
+                    sent_additional_kwargs = False
 
                     if text:
                         if msg_id:
                             streamed_ids.add(msg_id)
-                        yield self._ai_text_event(msg_id, text, counted_usage)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_text_event(
+                            msg_id,
+                            text,
+                            counted_usage,
+                            additional_kwargs_delta,
+                        )
+                        sent_additional_kwargs = bool(additional_kwargs_delta)
 
                     if msg_chunk.tool_calls:
                         if msg_id:
                             streamed_ids.add(msg_id)
-                        yield self._ai_tool_calls_event(msg_id, msg_chunk.tool_calls)
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_tool_calls_event(
+                            msg_id,
+                            msg_chunk.tool_calls,
+                            additional_kwargs_delta,
+                        )
 
                 elif isinstance(msg_chunk, ToolMessage):
                     if msg_id:
@@ -653,17 +704,45 @@ class DeerFlowClient:
                 if msg_id and msg_id in streamed_ids:
                     if isinstance(msg, AIMessage):
                         _account_usage(msg_id, getattr(msg, "usage_metadata", None))
+                        additional_kwargs = self._serialize_additional_kwargs(msg)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        if additional_kwargs_delta:
+                            # Metadata-only follow-up: ``messages-tuple`` has no
+                            # dedicated attribution event, so clients should
+                            # merge this empty-content AI event by message id
+                            # and ignore it for text rendering.
+                            yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
                     continue
 
                 if isinstance(msg, AIMessage):
                     counted_usage = _account_usage(msg_id, msg.usage_metadata)
+                    additional_kwargs = self._serialize_additional_kwargs(msg)
+                    sent_additional_kwargs = False
 
                     if msg.tool_calls:
-                        yield self._ai_tool_calls_event(msg_id, msg.tool_calls)
+                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_tool_calls_event(
+                            msg_id,
+                            msg.tool_calls,
+                            additional_kwargs_delta,
+                        )
+                        sent_additional_kwargs = bool(additional_kwargs_delta)
 
                     text = self._extract_text(msg.content)
                     if text:
-                        yield self._ai_text_event(msg_id, text, counted_usage)
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        yield self._ai_text_event(
+                            msg_id,
+                            text,
+                            counted_usage,
+                            additional_kwargs_delta,
+                        )
+                    elif msg_id:
+                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
+                        if not additional_kwargs_delta:
+                            continue
+                        # See the metadata-only follow-up convention above.
+                        yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
 
                 elif isinstance(msg, ToolMessage):
                     yield self._tool_message_event(msg)
@@ -752,8 +831,6 @@ 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": [
                 {
@@ -763,7 +840,7 @@ 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)
             ]
         }
 
@@ -872,9 +949,9 @@ 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 {
@@ -899,9 +976,9 @@ 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")
@@ -924,7 +1001,7 @@ 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 {
@@ -948,7 +1025,7 @@ 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

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/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/aio_sandbox/remote_backend.py

@@ -84,8 +84,52 @@ class RemoteSandboxBackend(SandboxBackend):
         """
         return self._provisioner_discover(sandbox_id)
 
+    def list_running(self) -> list[SandboxInfo]:
+        """Return all sandboxes currently managed by the provisioner.
+
+        Calls ``GET /api/sandboxes`` so that ``AioSandboxProvider._reconcile_orphans()``
+        can adopt pods that were created by a previous process and were never
+        explicitly destroyed.
+        Without this, a process restart silently orphans all existing k8s Pods —
+        they stay running forever because the idle checker only
+        tracks in-process state.
+        """
+        return self._provisioner_list()
+
     # ── Provisioner API calls ─────────────────────────────────────────────
 
+    def _provisioner_list(self) -> list[SandboxInfo]:
+        """GET /api/sandboxes → list all running sandboxes."""
+        try:
+            resp = requests.get(f"{self._provisioner_url}/api/sandboxes", timeout=10)
+            resp.raise_for_status()
+            data = resp.json()
+            if not isinstance(data, dict):
+                logger.warning("Provisioner list_running returned non-dict payload: %r", type(data))
+                return []
+
+            sandboxes = data.get("sandboxes", [])
+            if not isinstance(sandboxes, list):
+                logger.warning("Provisioner list_running returned non-list sandboxes: %r", type(sandboxes))
+                return []
+
+            infos: list[SandboxInfo] = []
+            for sandbox in sandboxes:
+                if not isinstance(sandbox, dict):
+                    logger.warning("Provisioner list_running entry is not a dict: %r", type(sandbox))
+                    continue
+
+                sandbox_id = sandbox.get("sandbox_id")
+                sandbox_url = sandbox.get("sandbox_url")
+                if isinstance(sandbox_id, str) and sandbox_id and isinstance(sandbox_url, str) and sandbox_url:
+                    infos.append(SandboxInfo(sandbox_id=sandbox_id, sandbox_url=sandbox_url))
+
+            logger.info("Provisioner list_running: %d sandbox(es) found", len(infos))
+            return infos
+        except requests.RequestException as exc:
+            logger.warning("Provisioner list_running failed: %s", exc)
+            return []
+
     def _provisioner_create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
         """POST /api/sandboxes → create Pod + Service."""
         try:

backend/packages/harness/deerflow/community/serper/__init__.py

@@ -0,0 +1,3 @@
+from .tools import web_search_tool
+
+__all__ = ["web_search_tool"]

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

@@ -0,0 +1,95 @@
+"""
+Web Search Tool - Search the web using Serper (Google Search API).
+
+Serper provides real-time Google Search results via a JSON API.
+An API key is required. Sign up at https://serper.dev to get one.
+"""
+
+import json
+import logging
+import os
+
+import httpx
+from langchain.tools import tool
+
+from deerflow.config import get_app_config
+
+logger = logging.getLogger(__name__)
+
+_SERPER_ENDPOINT = "https://google.serper.dev/search"
+_api_key_warned = False
+
+
+def _get_api_key() -> str | None:
+    config = get_app_config().get_tool_config("web_search")
+    if config is not None:
+        api_key = config.model_extra.get("api_key")
+        if isinstance(api_key, str) and api_key.strip():
+            return api_key
+    return os.getenv("SERPER_API_KEY")
+
+
+@tool("web_search", parse_docstring=True)
+def web_search_tool(query: str, max_results: int = 5) -> str:
+    """Search the web for information using Google Search via Serper.
+
+    Args:
+        query: Search keywords describing what you want to find. Be specific for better results.
+        max_results: Maximum number of search results to return. Default is 5.
+    """
+    global _api_key_warned
+
+    config = get_app_config().get_tool_config("web_search")
+    if config is not None and "max_results" in config.model_extra:
+        max_results = config.model_extra.get("max_results", max_results)
+
+    api_key = _get_api_key()
+    if not api_key:
+        if not _api_key_warned:
+            _api_key_warned = True
+            logger.warning("Serper API key is not set. Set SERPER_API_KEY in your environment or provide api_key in config.yaml. Sign up at https://serper.dev")
+        return json.dumps(
+            {"error": "SERPER_API_KEY is not configured", "query": query},
+            ensure_ascii=False,
+        )
+
+    headers = {
+        "X-API-KEY": api_key,
+        "Content-Type": "application/json",
+    }
+    payload = {"q": query, "num": max_results}
+
+    try:
+        with httpx.Client(timeout=30) as client:
+            response = client.post(_SERPER_ENDPOINT, headers=headers, json=payload)
+        response.raise_for_status()
+        data = response.json()
+    except httpx.HTTPStatusError as e:
+        logger.error(f"Serper API returned HTTP {e.response.status_code}: {e.response.text}")
+        return json.dumps(
+            {"error": f"Serper API error: HTTP {e.response.status_code}", "query": query},
+            ensure_ascii=False,
+        )
+    except Exception as e:
+        logger.error(f"Serper search failed: {type(e).__name__}: {e}")
+        return json.dumps({"error": str(e), "query": query}, ensure_ascii=False)
+
+    organic = data.get("organic", [])
+    if not organic:
+        return json.dumps({"error": "No results found", "query": query}, ensure_ascii=False)
+
+    normalized_results = [
+        {
+            "title": r.get("title", ""),
+            "url": r.get("link", ""),
+            "content": r.get("snippet", ""),
+        }
+        for r in organic[:max_results]
+    ]
+
+    output = {
+        "query": query,
+        "total_results": len(normalized_results),
+        "results": normalized_results,
+    }
+    return json.dumps(output, indent=2, ensure_ascii=False)

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

@@ -1,5 +1,6 @@
 from .app_config import get_app_config
 from .extensions_config import ExtensionsConfig, get_extensions_config
+from .loop_detection_config import LoopDetectionConfig
 from .memory_config import MemoryConfig, get_memory_config
 from .paths import Paths, get_paths
 from .skill_evolution_config import SkillEvolutionConfig
@@ -20,6 +21,7 @@ __all__ = [
     "SkillsConfig",
     "ExtensionsConfig",
     "get_extensions_config",
+    "LoopDetectionConfig",
     "MemoryConfig",
     "get_memory_config",
     "get_tracing_config",

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

@@ -1,13 +1,22 @@
-"""Configuration and loaders for custom agents."""
+"""Configuration and loaders for custom agents.
+
+Custom agents are stored per-user under ``{base_dir}/users/{user_id}/agents/{name}/``.
+A legacy shared layout at ``{base_dir}/agents/{name}/`` is still readable so that
+installations that pre-date user isolation continue to work until they run the
+``scripts/migrate_user_isolation.py`` migration. New writes always target the
+per-user layout.
+"""
 
 import logging
 import re
+from pathlib import Path
 from typing import Any
 
 import yaml
 from pydantic import BaseModel
 
 from deerflow.config.paths import get_paths
+from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
 
@@ -40,14 +49,47 @@ class AgentConfig(BaseModel):
     skills: list[str] | None = None
 
 
-def load_agent_config(name: str | None) -> AgentConfig | None:
+def resolve_agent_dir(name: str, *, user_id: str | None = None) -> Path:
+    """Return the on-disk directory for an agent, preferring the per-user layout.
+
+    Resolution order:
+    1. ``{base_dir}/users/{user_id}/agents/{name}/`` (per-user, current layout).
+    2. ``{base_dir}/agents/{name}/`` (legacy shared layout — read-only fallback).
+
+    If neither exists, the per-user path is returned so callers that intend to
+    create the agent write into the new layout.
+
+    Args:
+        name: Validated agent name.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            request context (or ``"default"`` in no-auth mode).
+    """
+    paths = get_paths()
+    effective_user = user_id or get_effective_user_id()
+    user_path = paths.user_agent_dir(effective_user, name)
+    if user_path.exists():
+        return user_path
+
+    legacy_path = paths.agent_dir(name)
+    if legacy_path.exists():
+        return legacy_path
+
+    return user_path
+
+
+def load_agent_config(name: str | None, *, user_id: str | None = None) -> AgentConfig | None:
     """Load the custom or default agent's config from its directory.
 
+    Reads from the per-user layout first; falls back to the legacy shared layout
+    for installations that have not yet been migrated.
+
     Args:
         name: The agent name.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            current request context.
 
     Returns:
-        AgentConfig instance.
+        AgentConfig instance, or ``None`` if ``name`` is ``None``.
 
     Raises:
         FileNotFoundError: If the agent directory or config.yaml does not exist.
@@ -58,7 +100,7 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
         return None
 
     name = validate_agent_name(name)
-    agent_dir = get_paths().agent_dir(name)
+    agent_dir = resolve_agent_dir(name, user_id=user_id)
     config_file = agent_dir / "config.yaml"
 
     if not agent_dir.exists():
@@ -84,7 +126,7 @@ def load_agent_config(name: str | None) -> AgentConfig | None:
     return AgentConfig(**data)
 
 
-def load_agent_soul(agent_name: str | None) -> str | None:
+def load_agent_soul(agent_name: str | None, *, user_id: str | None = None) -> str | None:
     """Read the SOUL.md file for a custom agent, if it exists.
 
     SOUL.md defines the agent's personality, values, and behavioral guardrails.
@@ -92,11 +134,16 @@ def load_agent_soul(agent_name: str | None) -> str | None:
 
     Args:
         agent_name: The name of the agent or None for the default agent.
+        user_id: Owner of the agent. Defaults to the effective user from the
+            current request context.
 
     Returns:
         The SOUL.md content as a string, or None if the file does not exist.
     """
-    agent_dir = get_paths().agent_dir(agent_name) if agent_name else get_paths().base_dir
+    if agent_name:
+        agent_dir = resolve_agent_dir(agent_name, user_id=user_id)
+    else:
+        agent_dir = get_paths().base_dir
     soul_path = agent_dir / SOUL_FILENAME
     if not soul_path.exists():
         return None
@@ -104,32 +151,50 @@ def load_agent_soul(agent_name: str | None) -> str | None:
     return content or None
 
 
-def list_custom_agents() -> list[AgentConfig]:
+def list_custom_agents(*, user_id: str | None = None) -> list[AgentConfig]:
     """Scan the agents directory and return all valid custom agents.
 
+    Returns the union of agents in the per-user layout and the legacy shared
+    layout, so that pre-migration installations remain visible until they are
+    migrated. Per-user entries shadow legacy entries with the same name.
+
+    Args:
+        user_id: Owner whose agents to list. Defaults to the effective user
+            from the current request context.
+
     Returns:
         List of AgentConfig for each valid agent directory found.
     """
-    agents_dir = get_paths().agents_dir
-
-    if not agents_dir.exists():
-        return []
+    paths = get_paths()
+    effective_user = user_id or get_effective_user_id()
 
+    seen: set[str] = set()
     agents: list[AgentConfig] = []
 
-    for entry in sorted(agents_dir.iterdir()):
-        if not entry.is_dir():
+    user_root = paths.user_agents_dir(effective_user)
+    legacy_root = paths.agents_dir
+
+    for root in (user_root, legacy_root):
+        if not root.exists():
             continue
+        for entry in sorted(root.iterdir()):
+            if not entry.is_dir():
+                continue
+            if entry.name in seen:
+                continue
+            config_file = entry / "config.yaml"
+            if not config_file.exists():
+                logger.debug(f"Skipping {entry.name}: no config.yaml")
+                continue
 
-        config_file = entry / "config.yaml"
-        if not config_file.exists():
-            logger.debug(f"Skipping {entry.name}: no config.yaml")
-            continue
-
-        try:
-            agent_cfg = load_agent_config(entry.name)
-            agents.append(agent_cfg)
-        except Exception as e:
-            logger.warning(f"Skipping agent '{entry.name}': {e}")
+            try:
+                agent_cfg = load_agent_config(entry.name, user_id=effective_user)
+                if agent_cfg is None:
+                    continue
+                agents.append(agent_cfg)
+                seen.add(entry.name)
+            except Exception as e:
+                logger.warning(f"Skipping agent '{entry.name}': {e}")
 
+    agents.sort(key=lambda a: a.name)
     return agents

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

@@ -1,5 +1,6 @@
 import logging
 import os
+from collections.abc import Mapping
 from contextvars import ContextVar
 from pathlib import Path
 from typing import Any, Self
@@ -8,15 +9,17 @@ import yaml
 from dotenv import load_dotenv
 from pydantic import BaseModel, ConfigDict, Field
 
-from deerflow.config.acp_config import load_acp_config_from_dict
+from deerflow.config.acp_config import ACPAgentConfig, load_acp_config_from_dict
 from deerflow.config.agents_api_config import AgentsApiConfig, load_agents_api_config_from_dict
 from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
 from deerflow.config.database_config import DatabaseConfig
 from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
+from deerflow.config.loop_detection_config import LoopDetectionConfig
 from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.run_events_config import RunEventsConfig
+from deerflow.config.runtime_paths import existing_project_file
 from deerflow.config.sandbox_config import SandboxConfig
 from deerflow.config.skill_evolution_config import SkillEvolutionConfig
 from deerflow.config.skills_config import SkillsConfig
@@ -46,17 +49,41 @@ class CircuitBreakerConfig(BaseModel):
     recovery_timeout_sec: int = Field(default=60, description="Time in seconds before attempting to recover the circuit")
 
 
-def _default_config_candidates() -> tuple[Path, ...]:
-    """Return deterministic config.yaml locations without relying on cwd."""
+def _legacy_config_candidates() -> tuple[Path, ...]:
+    """Return source-tree config.yaml locations for monorepo compatibility."""
     backend_dir = Path(__file__).resolve().parents[4]
     repo_root = backend_dir.parent
     return (backend_dir / "config.yaml", repo_root / "config.yaml")
 
 
+def logging_level_from_config(name: str | None) -> int:
+    """Map ``config.yaml`` ``log_level`` string to a :mod:`logging` level constant."""
+    mapping = logging.getLevelNamesMapping()
+    return mapping.get((name or "info").strip().upper(), logging.INFO)
+
+
+def apply_logging_level(name: str | None) -> None:
+    """Resolve *name* to a logging level and apply it to the ``deerflow``/``app`` logger hierarchies.
+
+    Only the ``deerflow`` and ``app`` logger levels are changed so that
+    third-party library verbosity (e.g. uvicorn, sqlalchemy) is not
+    affected. Root handler levels are lowered (never raised) so that
+    messages from the configured loggers can propagate through without
+    being filtered, while preserving handler thresholds that may be
+    intentionally restrictive for third-party log output.
+    """
+    level = logging_level_from_config(name)
+    for logger_name in ("deerflow", "app"):
+        logging.getLogger(logger_name).setLevel(level)
+    for handler in logging.root.handlers:
+        if level < handler.level:
+            handler.setLevel(level)
+
+
 class AppConfig(BaseModel):
     """Config for the DeerFlow application"""
 
-    log_level: str = Field(default="info", description="Logging level for deerflow modules (debug/info/warning/error)")
+    log_level: str = Field(default="info", description="Logging level for deerflow and app modules (debug/info/warning/error); third-party libraries are not affected")
     token_usage: TokenUsageConfig = Field(default_factory=TokenUsageConfig, description="Token usage tracking configuration")
     models: list[ModelConfig] = Field(default_factory=list, description="Available models")
     sandbox: SandboxConfig = Field(description="Sandbox configuration")
@@ -70,10 +97,12 @@ class AppConfig(BaseModel):
     summarization: SummarizationConfig = Field(default_factory=SummarizationConfig, description="Conversation summarization configuration")
     memory: MemoryConfig = Field(default_factory=MemoryConfig, description="Memory subsystem configuration")
     agents_api: AgentsApiConfig = Field(default_factory=AgentsApiConfig, description="Custom-agent management API configuration")
+    acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP-compatible agent configuration")
     subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
     guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
     circuit_breaker: CircuitBreakerConfig = Field(default_factory=CircuitBreakerConfig, description="LLM circuit breaker configuration")
-    model_config = ConfigDict(extra="allow", frozen=False)
+    loop_detection: LoopDetectionConfig = Field(default_factory=LoopDetectionConfig, description="Loop detection middleware configuration")
+    model_config = ConfigDict(extra="allow")
     database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
     run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
     checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
@@ -86,7 +115,8 @@ class AppConfig(BaseModel):
         Priority:
         1. If provided `config_path` argument, use it.
         2. If provided `DEER_FLOW_CONFIG_PATH` environment variable, use it.
-        3. Otherwise, search deterministic backend/repository-root defaults from `_default_config_candidates()`.
+        3. Otherwise, search the caller project root.
+        4. Finally, search legacy backend/repository-root defaults for monorepo compatibility.
         """
         if config_path:
             path = Path(config_path)
@@ -99,10 +129,14 @@ class AppConfig(BaseModel):
                 raise FileNotFoundError(f"Config file specified by environment variable `DEER_FLOW_CONFIG_PATH` not found at {path}")
             return path
         else:
-            for path in _default_config_candidates():
+            project_config = existing_project_file(("config.yaml",))
+            if project_config is not None:
+                return project_config
+
+            for path in _legacy_config_candidates():
                 if path.exists():
                     return path
-            raise FileNotFoundError("`config.yaml` file not found at the default backend or repository root locations")
+            raise FileNotFoundError("`config.yaml` file not found in the project root or legacy backend/repository root locations")
 
     @classmethod
     def from_file(cls, config_path: str | None = None) -> Self:
@@ -126,56 +160,54 @@ class AppConfig(BaseModel):
         config_data = cls.resolve_env_variables(config_data)
         cls._apply_database_defaults(config_data)
 
-        # Load title config if present
-        if "title" in config_data:
-            load_title_config_from_dict(config_data["title"])
-
-        # Load summarization config if present
-        if "summarization" in config_data:
-            load_summarization_config_from_dict(config_data["summarization"])
-
-        # Load memory config if present
-        if "memory" in config_data:
-            load_memory_config_from_dict(config_data["memory"])
-
-        # Always refresh agents API config so removed config sections reset
-        # singleton-backed state to its default/disabled values on reload.
-        load_agents_api_config_from_dict(config_data.get("agents_api") or {})
-
-        # Load subagents config if present
-        if "subagents" in config_data:
-            load_subagents_config_from_dict(config_data["subagents"])
-
-        # Load tool_search config if present
-        if "tool_search" in config_data:
-            load_tool_search_config_from_dict(config_data["tool_search"])
-
-        # Load guardrails config if present
-        if "guardrails" in config_data:
-            load_guardrails_config_from_dict(config_data["guardrails"])
-
         # Load circuit_breaker config if present
         if "circuit_breaker" in config_data:
             config_data["circuit_breaker"] = config_data["circuit_breaker"]
 
-        # Load checkpointer config if present
-        if "checkpointer" in config_data:
-            load_checkpointer_config_from_dict(config_data["checkpointer"])
-
-        # Load stream bridge config if present
-        if "stream_bridge" in config_data:
-            load_stream_bridge_config_from_dict(config_data["stream_bridge"])
-
-        # Always refresh ACP agent config so removed entries do not linger across reloads.
-        load_acp_config_from_dict(config_data.get("acp_agents", {}))
-
         # Load extensions config separately (it's in a different file)
         extensions_config = ExtensionsConfig.from_file()
         config_data["extensions"] = extensions_config.model_dump()
 
         result = cls.model_validate(config_data)
+        acp_agents = cls._validate_acp_agents(config_data.get("acp_agents", {}))
+        cls._apply_singleton_configs(result, acp_agents)
         return result
 
+    @classmethod
+    def _validate_acp_agents(
+        cls,
+        config_data: Mapping[str, Mapping[str, object]] | None,
+    ) -> dict[str, ACPAgentConfig]:
+        if config_data is None:
+            config_data = {}
+        return {name: ACPAgentConfig(**cfg) for name, cfg in config_data.items()}
+
+    @classmethod
+    def _apply_singleton_configs(cls, config: Self, acp_agents: dict[str, ACPAgentConfig]) -> None:
+        from deerflow.config.checkpointer_config import get_checkpointer_config
+
+        previous_checkpointer_config = get_checkpointer_config()
+
+        load_title_config_from_dict(config.title.model_dump())
+        load_summarization_config_from_dict(config.summarization.model_dump())
+        load_memory_config_from_dict(config.memory.model_dump())
+        load_agents_api_config_from_dict(config.agents_api.model_dump())
+        load_subagents_config_from_dict(config.subagents.model_dump())
+        load_tool_search_config_from_dict(config.tool_search.model_dump())
+        load_guardrails_config_from_dict(config.guardrails.model_dump())
+        load_checkpointer_config_from_dict(config.checkpointer.model_dump() if config.checkpointer is not None else None)
+        load_stream_bridge_config_from_dict(config.stream_bridge.model_dump() if config.stream_bridge is not None else None)
+        load_acp_config_from_dict({name: agent.model_dump() for name, agent in acp_agents.items()})
+
+        if previous_checkpointer_config != config.checkpointer:
+            # These runtime singletons derive their backend from checkpointer config.
+            # Keep imports local to avoid cycles: both providers import get_app_config.
+            from deerflow.runtime.checkpointer import reset_checkpointer
+            from deerflow.runtime.store import reset_store
+
+            reset_checkpointer()
+            reset_store()
+
     @classmethod
     def _apply_database_defaults(cls, config_data: dict[str, Any]) -> None:
         """Apply config.yaml defaults for persistence when the section is absent."""
@@ -292,6 +324,9 @@ class AppConfig(BaseModel):
         return next((group for group in self.tool_groups if group.name == name), None)
 
 
+# Compatibility singleton layer for code paths that have not yet been
+# migrated to explicit ``AppConfig`` threading. New composition roots should
+# prefer constructing ``AppConfig`` once and passing it down directly.
 _app_config: AppConfig | None = None
 _app_config_path: Path | None = None
 _app_config_mtime: float | None = None

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

@@ -14,12 +14,13 @@ class CheckpointerConfig(BaseModel):
         description="Checkpointer backend type. "
         "'memory' is in-process only (lost on restart). "
         "'sqlite' persists to a local file (requires langgraph-checkpoint-sqlite). "
-        "'postgres' persists to PostgreSQL (requires langgraph-checkpoint-postgres)."
+        "'postgres' persists to PostgreSQL (install with deerflow-harness[postgres])."
     )
     connection_string: str | None = Field(
         default=None,
         description="Connection string for sqlite (file path) or postgres (DSN). "
-        "Required for sqlite and postgres types. "
+        "Optional for sqlite and defaults to 'store.db' when omitted. "
+        "Required for postgres. "
         "For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
         "For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
     )
@@ -40,7 +41,10 @@ def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
     _checkpointer_config = config
 
 
-def load_checkpointer_config_from_dict(config_dict: dict) -> None:
+def load_checkpointer_config_from_dict(config_dict: dict | None) -> None:
     """Load checkpointer configuration from a dictionary."""
     global _checkpointer_config
+    if config_dict is None:
+        _checkpointer_config = None
+        return
     _checkpointer_config = CheckpointerConfig(**config_dict)

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

@@ -7,6 +7,8 @@ from typing import Any, Literal
 
 from pydantic import BaseModel, ConfigDict, Field
 
+from deerflow.config.runtime_paths import existing_project_file
+
 
 class McpOAuthConfig(BaseModel):
     """OAuth configuration for an MCP server (HTTP/SSE transports)."""
@@ -73,8 +75,8 @@ class ExtensionsConfig(BaseModel):
         Priority:
         1. If provided `config_path` argument, use it.
         2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
-        3. Otherwise, check for `extensions_config.json` in the current directory, then in the parent directory.
-        4. For backward compatibility, also check for `mcp_config.json` if `extensions_config.json` is not found.
+        3. Otherwise, search the caller project root for `extensions_config.json`, then `mcp_config.json`.
+        4. For backward compatibility, also search legacy backend/repository-root defaults.
         5. If not found, return None (extensions are optional).
 
         Args:
@@ -83,8 +85,9 @@ class ExtensionsConfig(BaseModel):
         Resolution order:
             1. If provided `config_path` argument, use it.
             2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
-            3. Otherwise, search backend/repository-root defaults for
+            3. Otherwise, search the caller project root for
                `extensions_config.json`, then legacy `mcp_config.json`.
+            4. Finally, search backend/repository-root defaults for monorepo compatibility.
 
         Returns:
             Path to the extensions config file if found, otherwise None.
@@ -100,6 +103,10 @@ class ExtensionsConfig(BaseModel):
                 raise FileNotFoundError(f"Extensions config file specified by environment variable `DEER_FLOW_EXTENSIONS_CONFIG_PATH` not found at {path}")
             return path
         else:
+            project_config = existing_project_file(("extensions_config.json", "mcp_config.json"))
+            if project_config is not None:
+                return project_config
+
             backend_dir = Path(__file__).resolve().parents[4]
             repo_root = backend_dir.parent
             for path in (

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

@@ -0,0 +1,73 @@
+"""Configuration for loop detection middleware."""
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class ToolFreqOverride(BaseModel):
+    """Per-tool frequency threshold override.
+
+    Can be higher or lower than the global defaults. Commonly used to raise
+    thresholds for high-frequency tools like bash in batch workflows (e.g.
+    RNA-seq pipelines) without weakening protection on every other tool.
+    """
+
+    warn: int = Field(ge=1)
+    hard_limit: int = Field(ge=1)
+
+    @model_validator(mode="after")
+    def _validate(self) -> "ToolFreqOverride":
+        if self.hard_limit < self.warn:
+            raise ValueError("hard_limit must be >= warn")
+        return self
+
+
+class LoopDetectionConfig(BaseModel):
+    """Configuration for repetitive tool-call loop detection."""
+
+    enabled: bool = Field(
+        default=True,
+        description="Whether to enable repetitive tool-call loop detection",
+    )
+    warn_threshold: int = Field(
+        default=3,
+        ge=1,
+        description="Number of identical tool-call sets before injecting a warning",
+    )
+    hard_limit: int = Field(
+        default=5,
+        ge=1,
+        description="Number of identical tool-call sets before forcing a stop",
+    )
+    window_size: int = Field(
+        default=20,
+        ge=1,
+        description="Number of recent tool-call sets to track per thread",
+    )
+    max_tracked_threads: int = Field(
+        default=100,
+        ge=1,
+        description="Maximum number of thread histories to keep in memory",
+    )
+    tool_freq_warn: int = Field(
+        default=30,
+        ge=1,
+        description="Number of calls to the same tool type before injecting a frequency warning",
+    )
+    tool_freq_hard_limit: int = Field(
+        default=50,
+        ge=1,
+        description="Number of calls to the same tool type before forcing a stop",
+    )
+    tool_freq_overrides: dict[str, ToolFreqOverride] = Field(
+        default_factory=dict,
+        description=("Per-tool overrides for tool_freq_warn / tool_freq_hard_limit, keyed by tool name. Values can be higher or lower than the global defaults. Commonly used to raise thresholds for high-frequency tools like bash."),
+    )
+
+    @model_validator(mode="after")
+    def validate_thresholds(self) -> "LoopDetectionConfig":
+        """Ensure hard stop cannot happen before the warning threshold."""
+        if self.hard_limit < self.warn_threshold:
+            raise ValueError("hard_limit must be greater than or equal to warn_threshold")
+        if self.tool_freq_hard_limit < self.tool_freq_warn:
+            raise ValueError("tool_freq_hard_limit must be greater than or equal to tool_freq_warn")
+        return self

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

@@ -3,6 +3,8 @@ import re
 import shutil
 from pathlib import Path, PureWindowsPath
 
+from deerflow.config.runtime_paths import runtime_home
+
 # Virtual path prefix seen by agents inside the sandbox
 VIRTUAL_PATH_PREFIX = "/mnt/user-data"
 
@@ -11,9 +13,8 @@ _SAFE_USER_ID_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
 
 
 def _default_local_base_dir() -> Path:
-    """Return the repo-local DeerFlow state directory without relying on cwd."""
-    backend_dir = Path(__file__).resolve().parents[4]
-    return backend_dir / ".deer-flow"
+    """Return the caller project's writable DeerFlow state directory."""
+    return runtime_home()
 
 
 def _validate_thread_id(thread_id: str) -> str:
@@ -81,7 +82,7 @@ class Paths:
     BaseDir resolution (in priority order):
         1. Constructor argument `base_dir`
         2. DEER_FLOW_HOME environment variable
-        3. Repo-local fallback derived from this module path: `{backend_dir}/.deer-flow`
+        3. Caller project fallback: `{project_root}/.deer-flow`
     """
 
     def __init__(self, base_dir: str | Path | None = None) -> None:
@@ -131,15 +132,20 @@ class Paths:
 
     @property
     def agents_dir(self) -> Path:
-        """Root directory for all custom agents: `{base_dir}/agents/`."""
+        """Legacy root for shared (pre user-isolation) custom agents: `{base_dir}/agents/`.
+
+        New code should use :meth:`user_agents_dir` instead. This property remains
+        only as a read-side fallback for installations that have not yet run the
+        ``migrate_user_isolation.py`` script.
+        """
         return self.base_dir / "agents"
 
     def agent_dir(self, name: str) -> Path:
-        """Directory for a specific agent: `{base_dir}/agents/{name}/`."""
+        """Legacy per-agent directory (no user isolation): `{base_dir}/agents/{name}/`."""
         return self.agents_dir / name.lower()
 
     def agent_memory_file(self, name: str) -> Path:
-        """Per-agent memory file: `{base_dir}/agents/{name}/memory.json`."""
+        """Legacy per-agent memory file: `{base_dir}/agents/{name}/memory.json`."""
         return self.agent_dir(name) / "memory.json"
 
     def user_dir(self, user_id: str) -> Path:
@@ -150,9 +156,17 @@ class Paths:
         """Per-user memory file: `{base_dir}/users/{user_id}/memory.json`."""
         return self.user_dir(user_id) / "memory.json"
 
+    def user_agents_dir(self, user_id: str) -> Path:
+        """Per-user root for that user's custom agents: `{base_dir}/users/{user_id}/agents/`."""
+        return self.user_dir(user_id) / "agents"
+
+    def user_agent_dir(self, user_id: str, agent_name: str) -> Path:
+        """Per-user per-agent directory: `{base_dir}/users/{user_id}/agents/{name}/`."""
+        return self.user_agents_dir(user_id) / agent_name.lower()
+
     def user_agent_memory_file(self, user_id: str, agent_name: str) -> Path:
         """Per-user per-agent memory: `{base_dir}/users/{user_id}/agents/{name}/memory.json`."""
-        return self.user_dir(user_id) / "agents" / agent_name.lower() / "memory.json"
+        return self.user_agent_dir(user_id, agent_name) / "memory.json"
 
     def thread_dir(self, thread_id: str, *, user_id: str | None = None) -> Path:
         """

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

@@ -0,0 +1,41 @@
+"""Runtime path resolution for standalone harness usage."""
+
+import os
+from pathlib import Path
+
+
+def project_root() -> Path:
+    """Return the caller project root for runtime-owned files."""
+    if env_root := os.getenv("DEER_FLOW_PROJECT_ROOT"):
+        root = Path(env_root).resolve()
+        if not root.exists():
+            raise ValueError(f"DEER_FLOW_PROJECT_ROOT is set to '{env_root}', but the resolved path '{root}' does not exist.")
+        if not root.is_dir():
+            raise ValueError(f"DEER_FLOW_PROJECT_ROOT is set to '{env_root}', but the resolved path '{root}' is not a directory.")
+        return root
+    return Path.cwd().resolve()
+
+
+def runtime_home() -> Path:
+    """Return the writable DeerFlow state directory."""
+    if env_home := os.getenv("DEER_FLOW_HOME"):
+        return Path(env_home).resolve()
+    return project_root() / ".deer-flow"
+
+
+def resolve_path(value: str | os.PathLike[str], *, base: Path | None = None) -> Path:
+    """Resolve absolute paths as-is and relative paths against the project root."""
+    path = Path(value)
+    if not path.is_absolute():
+        path = (base or project_root()) / path
+    return path.resolve()
+
+
+def existing_project_file(names: tuple[str, ...]) -> Path | None:
+    """Return the first existing named file under the project root."""
+    root = project_root()
+    for name in names:
+        candidate = root / name
+        if candidate.is_file():
+            return candidate
+    return None

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

@@ -1,19 +1,28 @@
+import os
 from pathlib import Path
 
 from pydantic import BaseModel, Field
 
+from deerflow.config.runtime_paths import project_root, resolve_path
 
-def _default_repo_root() -> Path:
-    """Resolve the repo root without relying on the current working directory."""
-    return Path(__file__).resolve().parents[5]
+
+def _legacy_skills_candidates() -> tuple[Path, ...]:
+    """Return source-tree skills locations for monorepo compatibility."""
+    backend_dir = Path(__file__).resolve().parents[4]
+    repo_root = backend_dir.parent
+    return (repo_root / "skills",)
 
 
 class SkillsConfig(BaseModel):
     """Configuration for skills system"""
 
+    use: str = Field(
+        default="deerflow.skills.storage.local_skill_storage:LocalSkillStorage",
+        description="Class path of the SkillStorage implementation.",
+    )
     path: str | None = Field(
         default=None,
-        description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
+        description=("Path to skills directory. If not specified, defaults to `skills` under the caller project root, falling back to the legacy repo-root location for monorepo compatibility."),
     )
     container_path: str = Field(
         default="/mnt/skills",
@@ -24,21 +33,30 @@ class SkillsConfig(BaseModel):
         """
         Get the resolved skills directory path.
 
-        Returns:
-            Path to the skills directory
+        Resolution order:
+            1. Explicit ``path`` field
+            2. ``DEER_FLOW_SKILLS_PATH`` environment variable
+            3. ``skills`` under the caller project root (``project_root()``)
+            4. Legacy repo-root candidates for monorepo compatibility (``_legacy_skills_candidates``)
+
+        When none of (3) or (4) exist on disk, the project-root default is returned so callers
+        can still surface a stable "no skills" location without raising.
         """
         if self.path:
-            # Use configured path (can be absolute or relative)
-            path = Path(self.path)
-            if not path.is_absolute():
-                # If relative, resolve from the repo root for deterministic behavior.
-                path = _default_repo_root() / path
-            return path.resolve()
-        else:
-            # Default: ../skills relative to backend directory
-            from deerflow.skills.loader import get_skills_root_path
+            # Use configured path (can be absolute or relative to project root)
+            return resolve_path(self.path)
+        if env_path := os.getenv("DEER_FLOW_SKILLS_PATH"):
+            return resolve_path(env_path)
 
-            return get_skills_root_path()
+        project_default = project_root() / "skills"
+        if project_default.is_dir():
+            return project_default
+
+        for candidate in _legacy_skills_candidates():
+            if candidate.is_dir():
+                return candidate
+
+        return project_default
 
     def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
         """

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

@@ -40,7 +40,10 @@ def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
     _stream_bridge_config = config
 
 
-def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
+def load_stream_bridge_config_from_dict(config_dict: dict | None) -> None:
     """Load stream bridge configuration from a dictionary."""
     global _stream_bridge_config
+    if config_dict is None:
+        _stream_bridge_config = None
+        return
     _stream_bridge_config = StreamBridgeConfig(**config_dict)

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

@@ -179,9 +179,3 @@ def load_subagents_config_from_dict(config_dict: dict) -> None:
             overrides_summary or "none",
             custom_agents_names or "none",
         )
-    else:
-        logger.info(
-            "Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
-            _subagents_config.timeout_seconds,
-            _subagents_config.max_turns,
-        )

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

@@ -4,4 +4,4 @@ from pydantic import BaseModel, Field
 class TokenUsageConfig(BaseModel):
     """Configuration for token usage tracking."""
 
-    enabled: bool = Field(default=False, description="Enable token usage tracking middleware")
+    enabled: bool = Field(default=True, description="Enable token usage tracking middleware")

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

@@ -1,11 +1,6 @@
 """Load MCP tools using langchain-mcp-adapters."""
 
-import asyncio
-import atexit
-import concurrent.futures
 import logging
-from collections.abc import Callable
-from typing import Any
 
 from langchain_core.tools import BaseTool
 
@@ -13,46 +8,10 @@ from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.mcp.client import build_servers_config
 from deerflow.mcp.oauth import build_oauth_tool_interceptor, get_initial_oauth_headers
 from deerflow.reflection import resolve_variable
+from deerflow.tools.sync import make_sync_tool_wrapper
 
 logger = logging.getLogger(__name__)
 
-# Global thread pool for sync tool invocation in async environments
-_SYNC_TOOL_EXECUTOR = concurrent.futures.ThreadPoolExecutor(max_workers=10, thread_name_prefix="mcp-sync-tool")
-
-# Register shutdown hook for the global executor
-atexit.register(lambda: _SYNC_TOOL_EXECUTOR.shutdown(wait=False))
-
-
-def _make_sync_tool_wrapper(coro: Callable[..., Any], tool_name: str) -> Callable[..., Any]:
-    """Build a synchronous wrapper for an asynchronous tool coroutine.
-
-    Args:
-        coro: The tool's asynchronous coroutine.
-        tool_name: Name of the tool (for logging).
-
-    Returns:
-        A synchronous function that correctly handles nested event loops.
-    """
-
-    def sync_wrapper(*args: Any, **kwargs: Any) -> Any:
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = None
-
-        try:
-            if loop is not None and loop.is_running():
-                # Use global executor to avoid nested loop issues and improve performance
-                future = _SYNC_TOOL_EXECUTOR.submit(asyncio.run, coro(*args, **kwargs))
-                return future.result()
-            else:
-                return asyncio.run(coro(*args, **kwargs))
-        except Exception as e:
-            logger.error(f"Error invoking MCP tool '{tool_name}' via sync wrapper: {e}", exc_info=True)
-            raise
-
-    return sync_wrapper
-
 
 async def get_mcp_tools() -> list[BaseTool]:
     """Get all tools from enabled MCP servers.
@@ -126,7 +85,7 @@ async def get_mcp_tools() -> list[BaseTool]:
         # Patch tools to support sync invocation, as deerflow client streams synchronously
         for tool in tools:
             if getattr(tool, "func", None) is None and getattr(tool, "coroutine", None) is not None:
-                tool.func = _make_sync_tool_wrapper(tool.coroutine, tool.name)
+                tool.func = make_sync_tool_wrapper(tool.coroutine, tool.name)
 
         return tools
 

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

@@ -196,6 +196,10 @@ class ClaudeChatModel(ChatAnthropic):
         enforced by both the Anthropic API and AWS Bedrock.  Breakpoints are
         placed on the *last* eligible blocks because later breakpoints cover a
         larger prefix and yield better cache hit rates.
+
+        The system prompt is expected to be fully static (no per-user memory or
+        current date).  Dynamic context is injected per-turn via
+        DynamicContextMiddleware as a <system-reminder> in the first HumanMessage.
         """
         MAX_CACHE_BREAKPOINTS = 4
 

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

@@ -3,6 +3,7 @@ import logging
 from langchain.chat_models import BaseChatModel
 
 from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.reflection import resolve_class
 from deerflow.tracing import build_tracing_callbacks
 
@@ -46,7 +47,7 @@ def _enable_stream_usage_by_default(model_use_path: str, model_settings_from_con
         model_settings_from_config["stream_usage"] = True
 
 
-def create_chat_model(name: str | None = None, thinking_enabled: bool = False, **kwargs) -> BaseChatModel:
+def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *, app_config: AppConfig | None = None, **kwargs) -> BaseChatModel:
     """Create a chat model instance from the config.
 
     Args:
@@ -55,7 +56,7 @@ def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *
     Returns:
         A chat model instance.
     """
-    config = get_app_config()
+    config = app_config or get_app_config()
     if name is None:
         name = config.models[0].name
     model_config = config.get_model_config(name)

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

@@ -1,4 +1,5 @@
 import ast
+import html
 import json
 import re
 import uuid
@@ -36,8 +37,8 @@ def _fix_messages(messages: list) -> list:
         if isinstance(msg, AIMessage) and getattr(msg, "tool_calls", []):
             xml_parts = []
             for tool in msg.tool_calls:
-                args_xml = " ".join(f"<parameter={k}>{json.dumps(v, ensure_ascii=False)}</parameter>" for k, v in tool.get("args", {}).items())
-                xml_parts.append(f"<tool_call> <function={tool['name']}> {args_xml} </function> </tool_call>")
+                args_xml = " ".join(f"<parameter={html.escape(str(k), quote=False)}>{html.escape(v if isinstance(v, str) else json.dumps(v, ensure_ascii=False), quote=False)}</parameter>" for k, v in tool.get("args", {}).items())
+                xml_parts.append(f"<tool_call> <function={html.escape(str(tool['name']), quote=False)}> {args_xml} </function> </tool_call>")
             full_text = f"{text}\n" + "\n".join(xml_parts) if text else "\n".join(xml_parts)
             fixed.append(AIMessage(content=full_text.strip() or " "))
             continue
@@ -80,13 +81,24 @@ def _parse_xml_tool_call_to_dict(content: str) -> tuple[str, list[dict]]:
         func_match = re.search(r"<function=([^>]+)>", inner_content)
         if not func_match:
             continue
-        function_name = func_match.group(1).strip()
+        function_name = html.unescape(func_match.group(1).strip())
+
+        # Ignore nested tool blocks when extracting parameters for this call.
+        # Nested `<tool_call>` sections represent separate invocations and
+        # their `<parameter>` tags must not leak into the current call args.
+        param_source_parts: list[str] = []
+        nested_cursor = 0
+        for nested_start, nested_end, _ in _iter_tool_call_blocks(inner_content):
+            param_source_parts.append(inner_content[nested_cursor:nested_start])
+            nested_cursor = nested_end
+        param_source_parts.append(inner_content[nested_cursor:])
+        param_source = "".join(param_source_parts)
 
         args = {}
         param_pattern = re.compile(r"<parameter=([^>]+)>(.*?)</parameter>", re.DOTALL)
-        for param_match in param_pattern.finditer(inner_content):
-            key = param_match.group(1).strip()
-            raw_value = param_match.group(2).strip()
+        for param_match in param_pattern.finditer(param_source):
+            key = html.unescape(param_match.group(1).strip())
+            raw_value = html.unescape(param_match.group(2).strip())
 
             # Attempt to deserialize string values into native Python types
             # to satisfy downstream Pydantic validation.

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

@@ -27,6 +27,34 @@ from deerflow.models.credential_loader import CodexCliCredential, load_codex_cli
 logger = logging.getLogger(__name__)
 
 CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
+
+
+def _build_usage_metadata(oai_usage: dict) -> dict:
+    """Convert Codex/Responses API usage dict to LangChain usage_metadata format.
+
+    Maps OpenAI Responses API token usage fields to the dict structure that
+    LangChain AIMessage.usage_metadata expects. This avoids depending on
+    langchain_openai private helpers like ``_create_usage_metadata_responses``.
+    """
+    input_tokens = oai_usage.get("input_tokens", 0)
+    output_tokens = oai_usage.get("output_tokens", 0)
+    total_tokens = oai_usage.get("total_tokens", input_tokens + output_tokens)
+    metadata: dict = {
+        "input_tokens": input_tokens,
+        "output_tokens": output_tokens,
+        "total_tokens": total_tokens,
+    }
+    input_details = oai_usage.get("input_tokens_details") or {}
+    output_details = oai_usage.get("output_tokens_details") or {}
+    cache_read = input_details.get("cached_tokens")
+    if cache_read is not None:
+        metadata["input_token_details"] = {"cache_read": cache_read}
+    reasoning = output_details.get("reasoning_tokens")
+    if reasoning is not None:
+        metadata["output_token_details"] = {"reasoning": reasoning}
+    return metadata
+
+
 MAX_RETRIES = 3
 
 
@@ -346,6 +374,7 @@ class CodexChatModel(BaseChatModel):
                 )
 
         usage = response.get("usage", {})
+        usage_metadata = _build_usage_metadata(usage) if usage else None
         additional_kwargs = {}
         if reasoning_content:
             additional_kwargs["reasoning_content"] = reasoning_content
@@ -355,6 +384,7 @@ class CodexChatModel(BaseChatModel):
             tool_calls=tool_calls if tool_calls else [],
             invalid_tool_calls=invalid_tool_calls,
             additional_kwargs=additional_kwargs,
+            usage_metadata=usage_metadata,
             response_metadata={
                 "model": response.get("model", self.model),
                 "usage": usage,

backend/packages/harness/deerflow/persistence/engine.py

@@ -81,7 +81,16 @@ async def init_engine(
         try:
             import asyncpg  # noqa: F401
         except ImportError:
-            raise ImportError("database.backend is set to 'postgres' but asyncpg is not installed.\nInstall it with:\n    uv sync --extra postgres\nOr switch to backend: sqlite in config.yaml for single-node deployment.") from None
+            raise ImportError(
+                "database.backend is set to 'postgres' but asyncpg is not installed.\n"
+                "Install it with:\n"
+                "    cd backend && uv sync --all-packages --extra postgres\n"
+                "On the next `make dev` the postgres extra is auto-detected from\n"
+                "config.yaml (database.backend: postgres) and reinstalled, so it\n"
+                "will not be wiped again. Set UV_EXTRAS=postgres in .env to opt in\n"
+                "explicitly. Or switch to backend: sqlite in config.yaml for\n"
+                "single-node deployment."
+            ) from None
 
     if backend == "sqlite":
         import os

backend/packages/harness/deerflow/persistence/json_compat.py

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

backend/packages/harness/deerflow/persistence/run/sql.py

@@ -23,6 +23,18 @@ class RunRepository(RunStore):
     def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
         self._sf = session_factory
 
+    @staticmethod
+    def _normalize_model_name(model_name: str | None) -> str | None:
+        """Normalize model_name for storage: strip whitespace, truncate to 128 chars."""
+        if model_name is None:
+            return None
+        if not isinstance(model_name, str):
+            model_name = str(model_name)
+        normalized = model_name.strip()
+        if len(normalized) > 128:
+            normalized = normalized[:128]
+        return normalized
+
     @staticmethod
     def _safe_json(obj: Any) -> Any:
         """Ensure obj is JSON-serializable. Falls back to model_dump() or str()."""
@@ -70,6 +82,7 @@ class RunRepository(RunStore):
         thread_id,
         assistant_id=None,
         user_id: str | None | _AutoSentinel = AUTO,
+        model_name: str | None = None,
         status="pending",
         multitask_strategy="reject",
         metadata=None,
@@ -85,6 +98,7 @@ class RunRepository(RunStore):
             thread_id=thread_id,
             assistant_id=assistant_id,
             user_id=resolved_user_id,
+            model_name=self._normalize_model_name(model_name),
             status=status,
             multitask_strategy=multitask_strategy,
             metadata_json=self._safe_json(metadata) or {},
@@ -209,10 +223,11 @@ class RunRepository(RunStore):
         """Aggregate token usage via a single SQL GROUP BY query."""
         _completed = RunRow.status.in_(("success", "error"))
         _thread = RunRow.thread_id == thread_id
+        model_name = func.coalesce(RunRow.model_name, "unknown")
 
         stmt = (
             select(
-                func.coalesce(RunRow.model_name, "unknown").label("model"),
+                model_name.label("model"),
                 func.count().label("runs"),
                 func.coalesce(func.sum(RunRow.total_tokens), 0).label("total_tokens"),
                 func.coalesce(func.sum(RunRow.total_input_tokens), 0).label("total_input_tokens"),
@@ -222,7 +237,7 @@ class RunRepository(RunStore):
                 func.coalesce(func.sum(RunRow.middleware_tokens), 0).label("middleware"),
             )
             .where(_thread, _completed)
-            .group_by(func.coalesce(RunRow.model_name, "unknown"))
+            .group_by(model_name)
         )
 
         async with self._sf() as session:

backend/packages/harness/deerflow/persistence/thread_meta/__init__.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from deerflow.persistence.thread_meta.base import ThreadMetaStore
+from deerflow.persistence.thread_meta.base import InvalidMetadataFilterError, ThreadMetaStore
 from deerflow.persistence.thread_meta.memory import MemoryThreadMetaStore
 from deerflow.persistence.thread_meta.model import ThreadMetaRow
 from deerflow.persistence.thread_meta.sql import ThreadMetaRepository
@@ -14,6 +14,7 @@ if TYPE_CHECKING:
     from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
 
 __all__ = [
+    "InvalidMetadataFilterError",
     "MemoryThreadMetaStore",
     "ThreadMetaRepository",
     "ThreadMetaRow",

backend/packages/harness/deerflow/persistence/thread_meta/base.py

@@ -15,10 +15,15 @@ three-state semantics (see :mod:`deerflow.runtime.user_context`):
 from __future__ import annotations
 
 import abc
+from typing import Any
 
 from deerflow.runtime.user_context import AUTO, _AutoSentinel
 
 
+class InvalidMetadataFilterError(ValueError):
+    """Raised when all client-supplied metadata filter keys are rejected."""
+
+
 class ThreadMetaStore(abc.ABC):
     @abc.abstractmethod
     async def create(
@@ -40,12 +45,12 @@ class ThreadMetaStore(abc.ABC):
     async def search(
         self,
         *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
         status: str | None = None,
         limit: int = 100,
         offset: int = 0,
         user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
         pass
 
     @abc.abstractmethod

backend/packages/harness/deerflow/persistence/thread_meta/memory.py

@@ -7,13 +7,13 @@ router for thread records.
 
 from __future__ import annotations
 
-import time
 from typing import Any
 
 from langgraph.store.base import BaseStore
 
 from deerflow.persistence.thread_meta.base import ThreadMetaStore
 from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id
+from deerflow.utils.time import coerce_iso, now_iso
 
 THREADS_NS: tuple[str, ...] = ("threads",)
 
@@ -48,7 +48,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
         metadata: dict | None = None,
     ) -> dict:
         resolved_user_id = resolve_user_id(user_id, method_name="MemoryThreadMetaStore.create")
-        now = time.time()
+        now = now_iso()
         record: dict[str, Any] = {
             "thread_id": thread_id,
             "assistant_id": assistant_id,
@@ -69,12 +69,12 @@ class MemoryThreadMetaStore(ThreadMetaStore):
     async def search(
         self,
         *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
         status: str | None = None,
         limit: int = 100,
         offset: int = 0,
         user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
         resolved_user_id = resolve_user_id(user_id, method_name="MemoryThreadMetaStore.search")
         filter_dict: dict[str, Any] = {}
         if metadata:
@@ -106,7 +106,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
         if record is None:
             return
         record["display_name"] = display_name
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
         await self._store.aput(THREADS_NS, thread_id, record)
 
     async def update_status(self, thread_id: str, status: str, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -114,7 +114,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
         if record is None:
             return
         record["status"] = status
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
         await self._store.aput(THREADS_NS, thread_id, record)
 
     async def update_metadata(self, thread_id: str, metadata: dict, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -124,7 +124,7 @@ class MemoryThreadMetaStore(ThreadMetaStore):
         merged = dict(record.get("metadata") or {})
         merged.update(metadata)
         record["metadata"] = merged
-        record["updated_at"] = time.time()
+        record["updated_at"] = now_iso()
         await self._store.aput(THREADS_NS, thread_id, record)
 
     async def delete(self, thread_id: str, *, user_id: str | None | _AutoSentinel = AUTO) -> None:
@@ -144,6 +144,8 @@ class MemoryThreadMetaStore(ThreadMetaStore):
             "display_name": val.get("display_name"),
             "status": val.get("status", "idle"),
             "metadata": val.get("metadata", {}),
-            "created_at": str(val.get("created_at", "")),
-            "updated_at": str(val.get("updated_at", "")),
+            # ``coerce_iso`` heals legacy unix-second values written by
+            # earlier Gateway versions that called ``str(time.time())``.
+            "created_at": coerce_iso(val.get("created_at", "")),
+            "updated_at": coerce_iso(val.get("updated_at", "")),
         }

backend/packages/harness/deerflow/persistence/thread_meta/sql.py

@@ -2,16 +2,20 @@
 
 from __future__ import annotations
 
+import logging
 from datetime import UTC, datetime
 from typing import Any
 
 from sqlalchemy import select, update
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
 
-from deerflow.persistence.thread_meta.base import ThreadMetaStore
+from deerflow.persistence.json_compat import json_match
+from deerflow.persistence.thread_meta.base import InvalidMetadataFilterError, ThreadMetaStore
 from deerflow.persistence.thread_meta.model import ThreadMetaRow
 from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id
 
+logger = logging.getLogger(__name__)
+
 
 class ThreadMetaRepository(ThreadMetaStore):
     def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
@@ -20,7 +24,7 @@ class ThreadMetaRepository(ThreadMetaStore):
     @staticmethod
     def _row_to_dict(row: ThreadMetaRow) -> dict[str, Any]:
         d = row.to_dict()
-        d["metadata"] = d.pop("metadata_json", {})
+        d["metadata"] = d.pop("metadata_json", None) or {}
         for key in ("created_at", "updated_at"):
             val = d.get(key)
             if isinstance(val, datetime):
@@ -104,39 +108,43 @@ class ThreadMetaRepository(ThreadMetaStore):
     async def search(
         self,
         *,
-        metadata: dict | None = None,
+        metadata: dict[str, Any] | None = None,
         status: str | None = None,
         limit: int = 100,
         offset: int = 0,
         user_id: str | None | _AutoSentinel = AUTO,
-    ) -> list[dict]:
+    ) -> list[dict[str, Any]]:
         """Search threads with optional metadata and status filters.
 
         Owner filter is enforced by default: caller must be in a user
         context. Pass ``user_id=None`` to bypass (migration/CLI).
         """
         resolved_user_id = resolve_user_id(user_id, method_name="ThreadMetaRepository.search")
-        stmt = select(ThreadMetaRow).order_by(ThreadMetaRow.updated_at.desc())
+        stmt = select(ThreadMetaRow).order_by(ThreadMetaRow.updated_at.desc(), ThreadMetaRow.thread_id.desc())
         if resolved_user_id is not None:
             stmt = stmt.where(ThreadMetaRow.user_id == resolved_user_id)
         if status:
             stmt = stmt.where(ThreadMetaRow.status == status)
 
         if metadata:
-            # When metadata filter is active, fetch a larger window and filter
-            # in Python. TODO(Phase 2): use JSON DB operators (Postgres @>,
-            # SQLite json_extract) for server-side filtering.
-            stmt = stmt.limit(limit * 5 + offset)
-            async with self._sf() as session:
-                result = await session.execute(stmt)
-                rows = [self._row_to_dict(r) for r in result.scalars()]
-            rows = [r for r in rows if all(r.get("metadata", {}).get(k) == v for k, v in metadata.items())]
-            return rows[offset : offset + limit]
-        else:
-            stmt = stmt.limit(limit).offset(offset)
-            async with self._sf() as session:
-                result = await session.execute(stmt)
-                return [self._row_to_dict(r) for r in result.scalars()]
+            applied = 0
+            for key, value in metadata.items():
+                try:
+                    stmt = stmt.where(json_match(ThreadMetaRow.metadata_json, key, value))
+                    applied += 1
+                except (ValueError, TypeError) as exc:
+                    logger.warning("Skipping metadata filter key %s: %s", ascii(key), exc)
+            if applied == 0:
+                # Comma-separated plain string (no list repr / nested
+                # quoting) so the 400 detail surfaced by the Gateway is
+                # easy for clients to read. Sorted for determinism.
+                rejected_keys = ", ".join(sorted(str(k) for k in metadata))
+                raise InvalidMetadataFilterError(f"All metadata filter keys were rejected as unsafe: {rejected_keys}")
+
+        stmt = stmt.limit(limit).offset(offset)
+        async with self._sf() as session:
+            result = await session.execute(stmt)
+            return [self._row_to_dict(r) for r in result.scalars()]
 
     async def _check_ownership(self, session: AsyncSession, thread_id: str, resolved_user_id: str | None) -> bool:
         """Return True if the row exists and is owned (or filter bypassed)."""