feat(auth): authentication module with multi-tenant isolation (RFC-001)

Introduce an always-on auth layer with auto-created admin on first boot,
multi-tenant isolation for threads/stores, and a full setup/login flow.

Backend
- JWT access tokens with `ver` field for stale-token rejection; bump on
  password/email change
- Password hashing, HttpOnly+Secure cookies (Secure derived from request
  scheme at runtime)
- CSRF middleware covering both REST and LangGraph routes
- IP-based login rate limiting (5 attempts / 5-min lockout) with bounded
  dict growth and X-Forwarded-For bypass fix
- Multi-worker-safe admin auto-creation (single DB write, WAL once)
- needs_setup + token_version on User model; SQLite schema migration
- Thread/store isolation by owner; orphan thread migration on first admin
  registration
- thread_id validated as UUID to prevent log injection
- CLI tool to reset admin password
- Decorator-based authz module extracted from auth core

Frontend
- Login and setup pages with SSR guard for needs_setup flow
- Account settings page (change password / email)
- AuthProvider + route guards; skips redirect when no users registered
- i18n (en-US / zh-CN) for auth surfaces
- Typed auth API client; parseAuthError unwraps FastAPI detail envelope

Infra & tooling
- Unified `serve.sh` with gateway mode + auto dep install
- Public PyPI uv.toml pin for CI compatibility
- Regenerated uv.lock with public index

Tests
- HTTP vs HTTPS cookie security tests
- Auth middleware, rate limiter, CSRF, setup flow coverage

README.md

@@ -46,6 +46,7 @@ DeerFlow has newly integrated the intelligent search and crawling toolset indepe
 
 - [🦌 DeerFlow - 2.0](#-deerflow---20)
   - [Official Website](#official-website)
+  - [Coding Plan from ByteDance Volcengine](#coding-plan-from-bytedance-volcengine)
   - [InfoQuest](#infoquest)
   - [Table of Contents](#table-of-contents)
   - [One-Line Agent Setup](#one-line-agent-setup)
@@ -59,6 +60,8 @@ DeerFlow has newly integrated the intelligent search and crawling toolset indepe
       - [MCP Server](#mcp-server)
       - [IM Channels](#im-channels)
       - [LangSmith Tracing](#langsmith-tracing)
+      - [Langfuse Tracing](#langfuse-tracing)
+      - [Using Both Providers](#using-both-providers)
   - [From Deep Research to Super Agent Harness](#from-deep-research-to-super-agent-harness)
   - [Core Features](#core-features)
     - [Skills \& Tools](#skills--tools)
@@ -71,6 +74,8 @@ DeerFlow has newly integrated the intelligent search and crawling toolset indepe
   - [Embedded Python Client](#embedded-python-client)
   - [Documentation](#documentation)
   - [⚠️ Security Notice](#️-security-notice)
+    - [Improper Deployment May Introduce Security Risks](#improper-deployment-may-introduce-security-risks)
+    - [Security Recommendations](#security-recommendations)
   - [Contributing](#contributing)
   - [License](#license)
   - [Acknowledgments](#acknowledgments)
@@ -275,6 +280,60 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
 
 6. **Access**: http://localhost:2026
 
+#### Startup Modes
+
+DeerFlow supports multiple startup modes across two dimensions:
+
+- **Dev / Prod** — dev enables hot-reload; prod uses pre-built frontend
+- **Standard / Gateway** — standard uses a separate LangGraph server (4 processes); Gateway mode (experimental) embeds the agent runtime in the Gateway API (3 processes)
+
+| | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
+|---|---|---|---|---|
+| **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
+| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
+| **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
+| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
+
+| Action | Local | Docker Dev | Docker Prod |
+|---|---|---|---|
+| **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
+| **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
+
+> **Gateway mode** eliminates the LangGraph server process — the Gateway API handles agent execution directly via async tasks, managing its own concurrency.
+
+#### Why Gateway Mode?
+
+In standard mode, DeerFlow runs a dedicated [LangGraph Platform](https://langchain-ai.github.io/langgraph/) server alongside the Gateway API. This architecture works well but has trade-offs:
+
+| | Standard Mode | Gateway Mode |
+|---|---|---|
+| **Architecture** | Gateway (REST API) + LangGraph (agent runtime) | Gateway embeds agent runtime |
+| **Concurrency** | `--n-jobs-per-worker` per worker (requires license) | `--workers` × async tasks (no per-worker cap) |
+| **Containers / Processes** | 4 (frontend, gateway, langgraph, nginx) | 3 (frontend, gateway, nginx) |
+| **Resource usage** | Higher (two Python runtimes) | Lower (single Python runtime) |
+| **LangGraph Platform license** | Required for production images | Not required |
+| **Cold start** | Slower (two services to initialize) | Faster |
+
+Both modes are functionally equivalent — the same agents, tools, and skills work in either mode.
+
+#### Docker Production Deployment
+
+`deploy.sh` supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
+
+```bash
+# One-step (build + start)
+deploy.sh                    # standard mode (default)
+deploy.sh --gateway          # gateway mode
+
+# Two-step (build once, start with any mode)
+deploy.sh build              # build all images
+deploy.sh start              # start in standard mode
+deploy.sh start --gateway    # start in gateway mode
+
+# Stop
+deploy.sh down
+```
+
 ### Advanced
 #### Sandbox Mode
 
@@ -302,6 +361,7 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when
 | Telegram | Bot API (long-polling) | Easy |
 | Slack | Socket Mode | Moderate |
 | Feishu / Lark | WebSocket | Moderate |
+| WeCom | WebSocket | Moderate |
 
 **Configuration in `config.yaml`:**
 
@@ -329,6 +389,11 @@ channels:
     # domain: https://open.feishu.cn       # China (default)
     # domain: https://open.larksuite.com   # International
 
+  wecom:
+    enabled: true
+    bot_id: $WECOM_BOT_ID
+    bot_secret: $WECOM_BOT_SECRET
+
   slack:
     enabled: true
     bot_token: $SLACK_BOT_TOKEN     # xoxb-...
@@ -372,6 +437,10 @@ SLACK_APP_TOKEN=xapp-...
 # Feishu / Lark
 FEISHU_APP_ID=cli_xxxx
 FEISHU_APP_SECRET=your_app_secret
+
+# WeCom
+WECOM_BOT_ID=your_bot_id
+WECOM_BOT_SECRET=your_bot_secret
 ```
 
 **Telegram Setup**
@@ -394,6 +463,14 @@ FEISHU_APP_SECRET=your_app_secret
 3. Under **Events**, subscribe to `im.message.receive_v1` and select **Long Connection** mode.
 4. Copy the App ID and App Secret. Set `FEISHU_APP_ID` and `FEISHU_APP_SECRET` in `.env` and enable the channel in `config.yaml`.
 
+**WeCom Setup**
+
+1. Create a bot on the WeCom AI Bot platform and obtain the `bot_id` and `bot_secret`.
+2. Enable `channels.wecom` in `config.yaml` and fill in `bot_id` / `bot_secret`.
+3. Set `WECOM_BOT_ID` and `WECOM_BOT_SECRET` in `.env`.
+4. Make sure backend dependencies include `wecom-aibot-python-sdk`. The channel uses a WebSocket long connection and does not require a public callback URL.
+5. The current integration supports inbound text, image, and file messages. Final images/files generated by the agent are also sent back to the WeCom conversation.
+
 When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://langgraph:2024` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
 
 **Commands**