fix: update version to 4.9.5 and langbot-plugin to 0.3.6 in project files

* feat(wecom): add user feedback support for WeChat Work AI Bot

This commit implements user feedback functionality (like/dislike) for
WeChat Work AI Bot conversations, including:

Backend changes:
- Add feedback_id and stream_id fields to WecomBotEvent
- Implement feedback event handling in WecomBotClient (api.py)
- Add StreamSessionManager._feedback_index for feedback_id lookup
- Add on_feedback decorator for custom feedback handlers
- Create MonitoringFeedback entity for database persistence
- Add dbm025 migration for monitoring_feedback table
- Implement FeedbackMonitor helper class
- Update all platform adapters with ap parameter support
- Update botmgr to pass bot_info for monitoring context

Frontend changes:
- Add FeedbackCard and FeedbackList components
- Add useFeedbackData hook for feedback data fetching
- Add feedback tab to monitoring page
- Add feedback types and interfaces
- Add i18n translations (zh-Hans, en-US)

Other changes:
- Update Dockerfile with Chinese mirror for faster builds
- Update docker-compose.yaml with network configuration
- Update .gitignore for docker data and backup files

Note: Known issues that need future improvement:
- feedback_type=3 (cancel) is recorded but not properly handled
- Duplicate feedback records are not deduplicated

* chore: remove unnecessary migration for new table will be created automatically

* chore: ruff format

* chore: prettier

* feat: add feedback handling support across multiple platform adapters

* fix(web): remove unused imports and variables in monitoring module

---------

Co-authored-by: 6mvp6 <13727783693@163.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: 漏洞反馈
-description: 【供中文用户】报错或漏洞请使用这个模板创建，不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决，请勿提 issue。容器间网络连接问题，参考文档 https://docs.langbot.app/zh/workshop/network-details.html  
+description: 【供中文用户】报错或漏洞请使用这个模板创建，不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决，请勿提 issue。容器间网络连接问题，参考文档 https://link.langbot.app/zh/docs/network  
 title: "[Bug]: "
 labels: ["bug?"]
 body:

.github/ISSUE_TEMPLATE/bug-report_en.yml

@@ -1,5 +1,5 @@
 name: Bug report
-description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://docs.langbot.app/en/workshop/network-details.html
+description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://link.langbot.app/en/docs/network
 title: "[Bug]: "
 labels: ["bug?"]
 body:

.pre-commit-config.yaml

@@ -9,16 +9,14 @@ repos:
       # Run the formatter of backend.
       - id: ruff-format
 
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.1.0
-    hooks:
-      - id: prettier
-        types_or: [javascript, jsx, ts, tsx, css, scss]
-        additional_dependencies:
-          - prettier@3.1.0
-
   - repo: local
     hooks:
+      - id: prettier
+        name: prettier
+        entry: npx --prefix web prettier --write --ignore-unknown
+        language: system
+        types_or: [javascript, jsx, ts, tsx, css, scss]
+
       - id: lint-staged
         name: lint-staged
         entry: cd web && pnpm lint-staged

README.md

@@ -19,9 +19,9 @@ English / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Website</a> ｜
-<a href="https://docs.langbot.app/en/insight/features">Features</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Features</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide">Docs</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Docs</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app/cloud">Cloud</a> ｜
 <a href="https://space.langbot.app">Plugin Market</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Roadmap</a>
@@ -45,7 +45,7 @@ LangBot is an **open-source, production-grade platform** for building AI-powered
 - **Web Management Panel** — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
 - **Multi-Pipeline Architecture** — Different bots for different scenarios, with comprehensive monitoring and exception handling.
 
-[→ Learn more about all features](https://docs.langbot.app/en/insight/features)
+[→ Learn more about all features](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -76,7 +76,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**More options:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt) · [Kubernetes](./docker/README_K8S.md)
+**More options:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -124,7 +124,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | Gateway | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | Gateway | ✅ |
 
-[→ View all integrations](https://docs.langbot.app/en/insight/features)
+[→ View all integrations](https://link.langbot.app/en/docs/features)
 
 ---
 

README_CN.md

@@ -21,9 +21,9 @@
 [![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
 
 <a href="https://langbot.app">官网</a> ｜
-<a href="https://docs.langbot.app/zh/insight/features.html">特性</a> ｜
+<a href="https://link.langbot.app/zh/docs/features">特性</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">文档</a> ｜
+<a href="https://link.langbot.app/zh/docs/guide">文档</a> ｜
-<a href="https://docs.langbot.app/zh/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/zh/docs/api">API</a> ｜
 <a href="https://space.langbot.app/cloud">Cloud</a> ｜
 <a href="https://space.langbot.app">插件市场</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">路线图</a>
@@ -34,8 +34,6 @@
 
 ---
 
-## 什么是 LangBot？
-
 LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时通信机器人。它将大语言模型（LLM）连接到各种聊天平台，帮助你创建能够对话、执行任务、并集成到现有工作流程中的智能 Agent。
 
 ### 核心能力
@@ -43,11 +41,11 @@ LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时
 - **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
 - **全平台支持** — 一套代码，覆盖 QQ、微信、企业微信、飞书、钉钉、Discord、Telegram、Slack、LINE、KOOK 等平台。
 - **生产就绪** — 访问控制、限速、敏感词过滤、全面监控与异常处理，已被多家企业采用。
-- **插件生态** — 数百个插件，事件驱动架构，组件扩展，适配 [MCP 协议](https://modelcontextprotocol.io/)。
+- **插件生态** — 数百个插件，跨进程的事件驱动架构，组件扩展，适配 [MCP 协议](https://modelcontextprotocol.io/)。
 - **Web 管理面板** — 通过浏览器直观地配置、管理和监控机器人，无需手动编辑配置文件。
 - **多流水线架构** — 不同机器人用于不同场景，具备全面的监控和异常处理能力。
 
-[→ 了解更多功能特性](https://docs.langbot.app/zh/insight/features.html)
+[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
 
 ---
 
@@ -78,7 +76,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**更多方式：** [Docker](https://docs.langbot.app/zh/deploy/langbot/docker.html) · [手动部署](https://docs.langbot.app/zh/deploy/langbot/manual.html) · [宝塔面板](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手动部署](https://link.langbot.app/zh/docs/manual-deploy) · [宝塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -127,7 +125,7 @@ docker compose up -d
 | [小马算力](https://www.tokenpony.cn/453z1) | 聚合平台 | ✅ |
 | [百宝箱Tbox](https://www.tbox.cn/open) | 智能体平台 | ✅ |
 
-[→ 查看完整集成列表](https://docs.langbot.app/zh/insight/features.html)
+[→ 查看完整集成列表](https://link.langbot.app/zh/docs/features)
 
 ### TTS（语音合成）
 

README_ES.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Inicio</a> ｜
-<a href="https://docs.langbot.app/en/insight/features.html">Características</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Características</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Documentación</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Documentación</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app">Mercado de Plugins</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Hoja de Ruta</a>
 
@@ -44,7 +44,7 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
 - **Panel de Gestión Web** — Configure, gestione y monitoree sus bots a través de una interfaz de navegador intuitiva. Sin necesidad de editar YAML.
 - **Arquitectura Multi-Pipeline** — Diferentes bots para diferentes escenarios, con monitoreo completo y manejo de excepciones.
 
-[→ Conocer más sobre todas las funcionalidades](https://docs.langbot.app/en/insight/features.html)
+[→ Conocer más sobre todas las funcionalidades](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Más opciones:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**Más opciones:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |
 
-[→ Ver todas las integraciones](https://docs.langbot.app/en/insight/features.html)
+[→ Ver todas las integraciones](https://link.langbot.app/en/docs/features)
 
 ---
 

README_FR.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Accueil</a> ｜
-<a href="https://docs.langbot.app/en/insight/features.html">Fonctionnalités</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Fonctionnalités</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Documentation</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Documentation</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app">Marché des Plugins</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Feuille de Route</a>
 
@@ -44,7 +44,7 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
 - **Panneau de Gestion Web** — Configurez, gérez et surveillez vos bots via une interface navigateur intuitive. Aucune édition de YAML requise.
 - **Architecture Multi-Pipeline** — Différents bots pour différents scénarios, avec surveillance complète et gestion des exceptions.
 
-[→ En savoir plus sur toutes les fonctionnalités](https://docs.langbot.app/en/insight/features.html)
+[→ En savoir plus sur toutes les fonctionnalités](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Plus d'options :** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Manuel](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**Plus d'options :** [Docker](https://link.langbot.app/en/docs/docker) · [Manuel](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plateforme GPU | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plateforme GPU | ✅ |
 
-[→ Voir toutes les intégrations](https://docs.langbot.app/en/insight/features.html)
+[→ Voir toutes les intégrations](https://link.langbot.app/en/docs/features)
 
 ---
 

README_JP.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">ホーム</a> ｜
-<a href="https://docs.langbot.app/ja/insight/features.html">機能</a> ｜
+<a href="https://link.langbot.app/ja/docs/features">機能</a> ｜
-<a href="https://docs.langbot.app/ja/insight/guide.html">ドキュメント</a> ｜
+<a href="https://link.langbot.app/ja/docs/guide">ドキュメント</a> ｜
-<a href="https://docs.langbot.app/ja/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/ja/docs/api">API</a> ｜
 <a href="https://space.langbot.app">プラグインマーケット</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">ロードマップ</a>
 
@@ -44,7 +44,7 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
 - **Web管理パネル** — 直感的なブラウザインターフェースからボットの設定、管理、監視が可能。YAML編集は不要。
 - **マルチパイプラインアーキテクチャ** — 異なるシナリオに異なるボットを配置し、包括的な監視と例外処理を実現。
 
-[→ すべての機能について詳しく見る](https://docs.langbot.app/ja/insight/features.html)
+[→ すべての機能について詳しく見る](https://link.langbot.app/ja/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**その他:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [手動デプロイ](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**その他:** [Docker](https://link.langbot.app/en/docs/docker) · [手動デプロイ](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | ゲートウェイ | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | ゲートウェイ | ✅ |
 
-[→ すべての統合を表示](https://docs.langbot.app/en/insight/features.html)
+[→ すべての統合を表示](https://link.langbot.app/en/docs/features)
 
 ---
 

README_KO.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">홈</a> ｜
-<a href="https://docs.langbot.app/en/insight/features.html">기능</a> ｜
+<a href="https://link.langbot.app/en/docs/features">기능</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">문서</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">문서</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app">플러그인 마켓</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">로드맵</a>
 
@@ -44,7 +44,7 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
 - **웹 관리 패널** — 직관적인 브라우저 인터페이스로 봇을 구성, 관리 및 모니터링. YAML 편집 불필요.
 - **멀티 파이프라인 아키텍처** — 다양한 시나리오에 맞는 다양한 봇 구성, 종합 모니터링 및 예외 처리.
 
-[→ 모든 기능 자세히 보기](https://docs.langbot.app/en/insight/features.html)
+[→ 모든 기능 자세히 보기](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**더 많은 옵션:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [수동 배포](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**더 많은 옵션:** [Docker](https://link.langbot.app/en/docs/docker) · [수동 배포](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |
 
-[→ 모든 통합 보기](https://docs.langbot.app/en/insight/features.html)
+[→ 모든 통합 보기](https://link.langbot.app/en/docs/features)
 
 ---
 

README_RU.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Главная</a> ｜
-<a href="https://docs.langbot.app/en/insight/features.html">Возможности</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Возможности</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Документация</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Документация</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app">Магазин плагинов</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Дорожная карта</a>
 
@@ -44,7 +44,7 @@ LangBot — это **платформа с открытым исходным к
 - **Веб-панель управления** — Настраивайте, управляйте и мониторьте ваших ботов через интуитивный браузерный интерфейс. Ручное редактирование YAML не требуется.
 - **Мультиконвейерная архитектура** — Разные боты для разных сценариев с комплексным мониторингом и обработкой исключений.
 
-[→ Подробнее обо всех возможностях](https://docs.langbot.app/en/insight/features.html)
+[→ Подробнее обо всех возможностях](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Другие варианты:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Ручная установка](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**Другие варианты:** [Docker](https://link.langbot.app/en/docs/docker) · [Ручная установка](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Платформа GPU | ✅ |
 | [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Платформа GPU | ✅ |
 
-[→ Смотреть все интеграции](https://docs.langbot.app/en/insight/features.html)
+[→ Смотреть все интеграции](https://link.langbot.app/en/docs/features)
 
 ---
 

README_TW.md

@@ -21,9 +21,9 @@
 [![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
 
 <a href="https://langbot.app">官網</a> ｜
-<a href="https://docs.langbot.app/zh/insight/features.html">特性</a> ｜
+<a href="https://link.langbot.app/zh/docs/features">特性</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">文件</a> ｜
+<a href="https://link.langbot.app/zh/docs/guide">文件</a> ｜
-<a href="https://docs.langbot.app/zh/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/zh/docs/api">API</a> ｜
 <a href="https://space.langbot.app">外掛市場</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">路線圖</a>
 
@@ -46,7 +46,7 @@ LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時
 - **Web 管理面板** — 透過瀏覽器直觀地配置、管理和監控機器人，無需手動編輯設定檔。
 - **多流水線架構** — 不同機器人用於不同場景，具備全面的監控和異常處理能力。
 
-[→ 了解更多功能特性](https://docs.langbot.app/zh/insight/features.html)
+[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
 
 ---
 
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**更多方式：** [Docker](https://docs.langbot.app/zh/deploy/langbot/docker.html) · [手動部署](https://docs.langbot.app/zh/deploy/langbot/manual.html) · [寶塔面板](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手動部署](https://link.langbot.app/zh/docs/manual-deploy) · [寶塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -139,7 +139,7 @@ docker compose up -d
 |-----------|------|
 | 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |
 
-[→ 查看完整整合列表](https://docs.langbot.app/zh/insight/features.html)
+[→ 查看完整整合列表](https://link.langbot.app/zh/docs/features)
 
 ---
 

README_VI.md

@@ -19,9 +19,9 @@
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Trang chủ</a> ｜
-<a href="https://docs.langbot.app/en/insight/features.html">Tính năng</a> ｜
+<a href="https://link.langbot.app/en/docs/features">Tính năng</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Tài liệu</a> ｜
+<a href="https://link.langbot.app/en/docs/guide">Tài liệu</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://link.langbot.app/en/docs/api">API</a> ｜
 <a href="https://space.langbot.app">Chợ Plugin</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Lộ trình</a>
 
@@ -44,7 +44,7 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
 - **Bảng quản lý Web** — Cấu hình, quản lý và giám sát bot thông qua giao diện trình duyệt trực quan. Không cần chỉnh sửa YAML.
 - **Kiến trúc đa Pipeline** — Các bot khác nhau cho các kịch bản khác nhau, với giám sát toàn diện và xử lý ngoại lệ.
 
-[→ Tìm hiểu thêm về tất cả tính năng](https://docs.langbot.app/en/insight/features.html)
+[→ Tìm hiểu thêm về tất cả tính năng](https://link.langbot.app/en/docs/features)
 
 ---
 
@@ -75,7 +75,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Thêm tùy chọn:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Thủ công](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
+**Thêm tùy chọn:** [Docker](https://link.langbot.app/en/docs/docker) · [Thủ công](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -123,7 +123,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |
 
-[→ Xem tất cả tích hợp](https://docs.langbot.app/en/insight/features.html)
+[→ Xem tất cả tích hợp](https://link.langbot.app/en/docs/features)
 
 ---
 

docker/README_K8S.md

@@ -312,7 +312,7 @@ spec:
 ### 参考资源
 
 - [LangBot 官方文档](https://docs.langbot.app)
-- [Docker 部署文档](https://docs.langbot.app/zh/deploy/langbot/docker.html)
+- [Docker 部署文档](https://link.langbot.app/zh/docs/docker)
 - [Kubernetes 官方文档](https://kubernetes.io/docs/)
 
 ---
@@ -625,5 +625,5 @@ spec:
 ### References
 
 - [LangBot Official Documentation](https://docs.langbot.app)
-- [Docker Deployment Guide](https://docs.langbot.app/zh/deploy/langbot/docker.html)
+- [Docker Deployment Guide](https://link.langbot.app/zh/docs/docker)
 - [Kubernetes Official Documentation](https://kubernetes.io/docs/)

docker/docker-compose.yaml

@@ -34,4 +34,4 @@ services:
 
 networks:
   langbot_network:
-    driver: bridge
+    driver: bridge

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.8.7"
+version = "4.9.5"
 description = "Production-grade platform for building agentic IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]
@@ -61,10 +61,10 @@ dependencies = [
     "html2text>=2024.2.26",
     "langchain>=0.2.0",
     "langchain-text-splitters>=0.0.1",
-    "chromadb>=0.4.24",
+    "chromadb>=1.0.0,<2.0.0",
     "qdrant-client (>=1.15.1,<2.0.0)",
-    "pyseekdb==1.0.0b7",
+    "pyseekdb==1.1.0.post3",
-    "langbot-plugin==0.3.0rc1",
+    "langbot-plugin==0.3.6",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "tboxsdk>=0.0.10",

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building agentic IM bots"""
 
-__version__ = '4.8.7'
+__version__ = '4.9.5'

src/langbot/libs/dingtalk_api/api.py

@@ -272,15 +272,30 @@ class DingTalkClient:
 
                 message_data['Type'] = 'audio'
             elif incoming_message.message_type == 'file':
-                down_list = incoming_message.get_down_list()
+                # 获取原始数据字典并提取嵌套的文件信息
-                if len(down_list) >= 2:
+                raw_data = incoming_message.to_dict()
-                    message_data['File'] = await self.get_file_url(down_list[0])
+                file_info = raw_data.get('content', {})
-                    message_data['Name'] = down_list[1]
+
+                # 兼容处理：如果 content 仍为 JSON 字符串则进行解析
+                if isinstance(file_info, str):
+                    try:
+                        file_info = json.loads(file_info)
+                    except (json.JSONDecodeError, TypeError):
+                        file_info = {}
+
+                download_code = file_info.get('downloadCode')
+                file_name = file_info.get('fileName')
+
+                if download_code and file_name:
+                    # 转换 downloadCode 为可下载的真实 URL
+                    message_data['File'] = await self.get_file_url(download_code)
+                    message_data['Name'] = file_name
                 else:
                     if self.logger:
-                        await self.logger.error(f'get_down_list() returned fewer than 2 elements: {down_list}')
+                        await self.logger.error(f'Failed to extract file info from message content: {file_info}')
                     message_data['File'] = None
                     message_data['Name'] = None
+
                 message_data['Type'] = 'file'
 
             copy_message_data = message_data.copy()

src/langbot/libs/openclaw_weixin_api/__init__.py

@@ -0,0 +1,3 @@
+from .client import OpenClawWeixinClient as OpenClawWeixinClient
+from .types import ApiError as ApiError
+from .types import LoginResult as LoginResult

src/langbot/libs/openclaw_weixin_api/client.py

@@ -0,0 +1,807 @@
+"""Async HTTP client for the OpenClaw WeChat API.
+
+Implements the iLink Bot API protocol.
+Reference: https://github.com/epiral/weixin-bot
+
+Endpoints: getUpdates (long-poll), sendMessage, getUploadUrl, getConfig, sendTyping.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+import io
+import logging
+import os
+import struct
+import typing
+import uuid
+from typing import Optional
+from urllib.parse import quote
+
+import aiohttp
+
+from .types import (
+    ApiError,
+    CDNMedia,
+    FileItem,
+    GetConfigResponse,
+    GetUpdatesResponse,
+    GetUploadUrlResponse,
+    ImageItem,
+    LoginResult,
+    MessageItem,
+    QRCodeResponse,
+    QRStatusResponse,
+    RefMessage,
+    TextItem,
+    VideoItem,
+    VoiceItem,
+    WeixinMessage,
+)
+
+logger = logging.getLogger('openclaw-weixin-sdk')
+
+DEFAULT_BASE_URL = 'https://ilinkai.weixin.qq.com'
+CDN_BASE_URL = 'https://novac2c.cdn.weixin.qq.com/c2c'
+
+CHANNEL_VERSION = '1.0.0'
+
+DEFAULT_API_TIMEOUT = 15
+DEFAULT_LONG_POLL_TIMEOUT = 40
+DEFAULT_CONFIG_TIMEOUT = 10
+DEFAULT_QR_POLL_TIMEOUT = 35
+
+SESSION_EXPIRED_ERRCODE = -14
+
+DEFAULT_BOT_TYPE = '3'
+
+# Maximum text length per message chunk (WeChat limit)
+MAX_TEXT_CHUNK_SIZE = 2000
+
+
+def _random_wechat_uin() -> str:
+    """Generate the X-WECHAT-UIN header: random uint32 -> decimal string -> base64."""
+    rand_bytes = os.urandom(4)
+    uint32_val = struct.unpack('>I', rand_bytes)[0]
+    return base64.b64encode(str(uint32_val).encode('utf-8')).decode('utf-8')
+
+
+def _build_base_info() -> dict:
+    """Build the base_info payload included in every API request."""
+    return {'channel_version': CHANNEL_VERSION}
+
+
+def _chunk_text(text: str, max_size: int = MAX_TEXT_CHUNK_SIZE) -> list[str]:
+    """Split long text into chunks that fit within WeChat's message size limit."""
+    if len(text) <= max_size:
+        return [text]
+    chunks = []
+    while text:
+        chunks.append(text[:max_size])
+        text = text[max_size:]
+    return chunks
+
+
+class OpenClawWeixinClient:
+    """Async client for the OpenClaw WeChat HTTP JSON API."""
+
+    def __init__(self, base_url: str, token: str):
+        self.base_url = base_url.rstrip('/')
+        self.token = token
+        self._session: Optional[aiohttp.ClientSession] = None
+
+    async def _get_session(self) -> aiohttp.ClientSession:
+        if self._session is None or self._session.closed:
+            self._session = aiohttp.ClientSession()
+        return self._session
+
+    async def close(self):
+        if self._session and not self._session.closed:
+            await self._session.close()
+
+    def _build_headers(self) -> dict[str, str]:
+        headers = {
+            'Content-Type': 'application/json',
+            'AuthorizationType': 'ilink_bot_token',
+            'X-WECHAT-UIN': _random_wechat_uin(),
+        }
+        if self.token:
+            headers['Authorization'] = f'Bearer {self.token}'
+        return headers
+
+    async def _post(self, endpoint: str, payload: dict, timeout: float = DEFAULT_API_TIMEOUT) -> dict:
+        """Make a POST request and return the JSON response.
+
+        Raises ApiError on HTTP errors or when the response contains a non-zero errcode.
+        """
+        payload['base_info'] = _build_base_info()
+
+        session = await self._get_session()
+        url = f'{self.base_url}/{endpoint}'
+        headers = self._build_headers()
+
+        async with session.post(
+            url, json=payload, headers=headers, timeout=aiohttp.ClientTimeout(total=timeout)
+        ) as resp:
+            if resp.status != 200:
+                text = await resp.text()
+                raise ApiError(
+                    f'OpenClaw API error {resp.status}: {text}',
+                    status=resp.status,
+                )
+            data = await resp.json(content_type=None)
+
+        # Check for application-level errors in the response body
+        errcode = data.get('errcode') or data.get('ret')
+        if errcode and errcode != 0:
+            raise ApiError(
+                data.get('errmsg') or f'API errcode {errcode}',
+                status=200,
+                code=errcode,
+                payload=data,
+            )
+
+        return data
+
+    async def get_updates(
+        self, get_updates_buf: str = '', timeout: float = DEFAULT_LONG_POLL_TIMEOUT
+    ) -> GetUpdatesResponse:
+        """Long-poll for new messages.
+
+        Note: This method does NOT raise ApiError for errcode responses —
+        it returns them in the GetUpdatesResponse so the caller can handle
+        session expiry and other errors with full context.
+        """
+        try:
+            # Bypass the errcode check in _post since get_updates needs
+            # to return error info (e.g. session expired) to the caller.
+            payload: dict = {'get_updates_buf': get_updates_buf}
+            payload['base_info'] = _build_base_info()
+
+            session = await self._get_session()
+            url = f'{self.base_url}/ilink/bot/getupdates'
+            headers = self._build_headers()
+
+            async with session.post(
+                url,
+                json=payload,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=timeout),
+            ) as resp:
+                if resp.status != 200:
+                    text = await resp.text()
+                    raise ApiError(
+                        f'OpenClaw API error {resp.status}: {text}',
+                        status=resp.status,
+                    )
+                data = await resp.json(content_type=None)
+
+        except (asyncio.TimeoutError, aiohttp.ServerTimeoutError):
+            return GetUpdatesResponse(ret=0, msgs=[], get_updates_buf=get_updates_buf)
+        except ApiError:
+            raise
+        except Exception as e:
+            if 'timeout' in str(e).lower():
+                return GetUpdatesResponse(ret=0, msgs=[], get_updates_buf=get_updates_buf)
+            raise
+
+        return _parse_get_updates_response(data)
+
+    async def send_message(
+        self,
+        to_user_id: str,
+        item_list: list[MessageItem],
+        context_token: str = '',
+    ) -> None:
+        """Send a message to a user."""
+        items_payload = [_message_item_to_dict(item) for item in item_list]
+
+        payload = {
+            'msg': {
+                'from_user_id': '',
+                'to_user_id': to_user_id,
+                'client_id': f'langbot-{uuid.uuid4().hex[:16]}',
+                'message_type': WeixinMessage.TYPE_BOT,
+                'message_state': WeixinMessage.STATE_FINISH,
+                'item_list': items_payload,
+                'context_token': context_token or None,
+            }
+        }
+        await self._post('ilink/bot/sendmessage', payload)
+
+    async def send_text(self, to_user_id: str, text: str, context_token: str = '') -> None:
+        """Send a plain text message, automatically chunking if too long."""
+        chunks = _chunk_text(text)
+        for chunk in chunks:
+            item = MessageItem(type=MessageItem.TEXT, text_item=TextItem(text=chunk))
+            await self.send_message(to_user_id, [item], context_token)
+
+    async def get_config(self, ilink_user_id: str, context_token: str = '') -> GetConfigResponse:
+        """Get bot config including typing_ticket."""
+        data = await self._post(
+            'ilink/bot/getconfig',
+            {'ilink_user_id': ilink_user_id, 'context_token': context_token or None},
+            timeout=DEFAULT_CONFIG_TIMEOUT,
+        )
+        return GetConfigResponse(
+            ret=data.get('ret'),
+            errmsg=data.get('errmsg'),
+            typing_ticket=data.get('typing_ticket'),
+        )
+
+    async def send_typing(self, ilink_user_id: str, typing_ticket: str, status: int = 1) -> None:
+        """Send typing indicator. status: 1=typing, 2=cancel."""
+        await self._post(
+            'ilink/bot/sendtyping',
+            {
+                'ilink_user_id': ilink_user_id,
+                'typing_ticket': typing_ticket,
+                'status': status,
+            },
+            timeout=DEFAULT_CONFIG_TIMEOUT,
+        )
+
+    async def stop_typing(self, ilink_user_id: str, typing_ticket: str) -> None:
+        """Cancel the typing indicator for a user."""
+        await self.send_typing(ilink_user_id, typing_ticket, status=2)
+
+    async def download_media(
+        self,
+        media: CDNMedia,
+    ) -> bytes:
+        """Download and decrypt a file from the WeChat CDN.
+
+        Args:
+            media: CDNMedia object with encrypt_query_param and aes_key.
+
+        Returns:
+            Decrypted file bytes.
+        """
+        from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+        from cryptography.hazmat.primitives.padding import PKCS7
+
+        if not media.encrypt_query_param:
+            raise ApiError('CDN media has no encrypt_query_param', status=0)
+        if not media.aes_key:
+            raise ApiError('CDN media has no aes_key', status=0)
+
+        # Derive 16-byte AES key
+        # aes_key is base64-encoded; the decoded content may be:
+        #   - raw 16 bytes (direct AES key)
+        #   - 32-char hex string (decode hex to get 16 bytes)
+        raw = base64.b64decode(media.aes_key)
+        if len(raw) == 16:
+            aes_key = raw
+        elif len(raw) == 32:
+            # Hex-encoded 16-byte key
+            aes_key = bytes.fromhex(raw.decode('utf-8'))
+        else:
+            raise ApiError(f'Invalid AES key length: {len(raw)} (expected 16 or 32)', status=0)
+
+        # Download encrypted bytes from CDN
+        session = await self._get_session()
+        cdn_url = f'{CDN_BASE_URL}/download?encrypted_query_param={quote(media.encrypt_query_param, safe="")}'
+
+        async with session.get(cdn_url, timeout=aiohttp.ClientTimeout(total=120)) as resp:
+            if resp.status != 200:
+                text = await resp.text()
+                raise ApiError(f'CDN download failed: {resp.status} {text}', status=resp.status)
+            encrypted = await resp.read()
+
+        # Decrypt AES-128-ECB with PKCS7 padding
+        cipher = Cipher(algorithms.AES(aes_key), modes.ECB())
+        decryptor = cipher.decryptor()
+        padded = decryptor.update(encrypted) + decryptor.finalize()
+
+        unpadder = PKCS7(128).unpadder()
+        return unpadder.update(padded) + unpadder.finalize()
+
+    async def upload_media(
+        self,
+        file_bytes: bytes,
+        to_user_id: str,
+        media_type: int,
+    ) -> CDNMedia:
+        """Encrypt and upload media to WeChat CDN.
+
+        Args:
+            file_bytes: Raw file bytes to upload.
+            to_user_id: Recipient user ID.
+            media_type: 1=IMAGE, 2=VIDEO, 3=FILE, 4=VOICE.
+
+        Returns:
+            CDNMedia with encrypt_query_param and aes_key for use in sendMessage.
+        """
+        import hashlib
+
+        from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+        from cryptography.hazmat.primitives.padding import PKCS7
+
+        # 1. Generate random 16-byte AES key
+        raw_key = os.urandom(16)
+        aes_key_hex = raw_key.hex()  # 32-char hex string
+
+        # 2. Encode key for CDNMedia: base64(hex_string) — same for all media types
+        # Matches official SDK: Buffer.from(aeskey_hex).toString("base64")
+        encoded_key = base64.b64encode(aes_key_hex.encode('utf-8')).decode('utf-8')
+
+        # 3. Encrypt file with AES-128-ECB + PKCS7
+        padder = PKCS7(128).padder()
+        padded = padder.update(file_bytes) + padder.finalize()
+        cipher = Cipher(algorithms.AES(raw_key), modes.ECB())
+        encryptor = cipher.encryptor()
+        encrypted = encryptor.update(padded) + encryptor.finalize()
+
+        # 4. Get upload URL
+        raw_md5 = hashlib.md5(file_bytes).hexdigest()
+        filekey = os.urandom(16).hex()  # 32-char hex, matches official SDK
+
+        upload_resp = await self.get_upload_url(
+            filekey=filekey,
+            media_type=media_type,
+            to_user_id=to_user_id,
+            rawsize=len(file_bytes),
+            rawfilemd5=raw_md5,
+            filesize=len(encrypted),
+            aeskey=aes_key_hex,  # hex string, as expected by the API
+        )
+
+        if not upload_resp.upload_param:
+            raise ApiError('Failed to get upload URL', status=0)
+
+        # 5. Upload to CDN
+        # upload_param is an opaque token from the server — pass it as-is
+        session = await self._get_session()
+        cdn_url = f'{CDN_BASE_URL}/upload?encrypted_query_param={quote(upload_resp.upload_param, safe="")}&filekey={quote(filekey, safe="")}'
+        logger.debug(
+            'CDN upload: url=%s raw_size=%d encrypted_size=%d md5=%s aeskey=%s',
+            cdn_url,
+            len(file_bytes),
+            len(encrypted),
+            raw_md5,
+            encoded_key,
+        )
+
+        async with session.post(
+            cdn_url,
+            data=encrypted,
+            headers={'Content-Type': 'application/octet-stream'},
+            timeout=aiohttp.ClientTimeout(total=120),
+        ) as resp:
+            if resp.status != 200:
+                text = await resp.text()
+                logger.error('CDN upload failed: status=%d url=%s body=%s', resp.status, cdn_url, text[:500])
+                raise ApiError(f'CDN upload failed: {resp.status} {text}', status=resp.status)
+            download_param = resp.headers.get('x-encrypted-param', '')
+
+        if not download_param:
+            raise ApiError('CDN upload succeeded but no x-encrypted-param returned', status=0)
+
+        return CDNMedia(
+            encrypt_query_param=download_param,
+            aes_key=encoded_key,
+            encrypt_type=1,
+        )
+
+    async def send_image(
+        self,
+        to_user_id: str,
+        image_bytes: bytes,
+        context_token: str = '',
+    ) -> None:
+        """Upload an image to CDN and send it."""
+        media = await self.upload_media(image_bytes, to_user_id, media_type=1)
+        item = MessageItem(
+            type=MessageItem.IMAGE,
+            image_item=ImageItem(
+                media=media,
+                aeskey=media.aes_key,
+            ),
+        )
+        await self.send_message(to_user_id, [item], context_token)
+
+    async def send_file(
+        self,
+        to_user_id: str,
+        file_bytes: bytes,
+        file_name: str,
+        context_token: str = '',
+    ) -> None:
+        """Upload a file to CDN and send it."""
+        import hashlib
+
+        media = await self.upload_media(file_bytes, to_user_id, media_type=3)
+        item = MessageItem(
+            type=MessageItem.FILE,
+            file_item=FileItem(
+                media=media,
+                file_name=file_name,
+                md5=hashlib.md5(file_bytes).hexdigest(),
+                len=str(len(file_bytes)),
+            ),
+        )
+        await self.send_message(to_user_id, [item], context_token)
+
+    async def send_voice(
+        self,
+        to_user_id: str,
+        voice_bytes: bytes,
+        playtime: int = 0,
+        context_token: str = '',
+    ) -> None:
+        """Upload a voice message to CDN and send it."""
+        media = await self.upload_media(voice_bytes, to_user_id, media_type=4)
+        item = MessageItem(
+            type=MessageItem.VOICE,
+            voice_item=VoiceItem(
+                media=media,
+                playtime=playtime,
+            ),
+        )
+        await self.send_message(to_user_id, [item], context_token)
+
+    async def get_upload_url(
+        self,
+        filekey: str,
+        media_type: int,
+        to_user_id: str,
+        rawsize: int,
+        rawfilemd5: str,
+        filesize: int,
+        thumb_rawsize: Optional[int] = None,
+        thumb_rawfilemd5: Optional[str] = None,
+        thumb_filesize: Optional[int] = None,
+        aeskey: Optional[str] = None,
+    ) -> GetUploadUrlResponse:
+        """Get a pre-signed CDN upload URL."""
+        payload: dict = {
+            'filekey': filekey,
+            'media_type': media_type,
+            'to_user_id': to_user_id,
+            'rawsize': rawsize,
+            'rawfilemd5': rawfilemd5,
+            'filesize': filesize,
+            'no_need_thumb': True,
+        }
+        if thumb_rawsize is not None:
+            payload['thumb_rawsize'] = thumb_rawsize
+        if thumb_rawfilemd5 is not None:
+            payload['thumb_rawfilemd5'] = thumb_rawfilemd5
+        if thumb_filesize is not None:
+            payload['thumb_filesize'] = thumb_filesize
+        if aeskey is not None:
+            payload['aeskey'] = aeskey
+
+        data = await self._post('ilink/bot/getuploadurl', payload)
+        logger.debug('get_upload_url response: %s', data)
+        return GetUploadUrlResponse(
+            upload_param=data.get('upload_param'),
+            thumb_upload_param=data.get('thumb_upload_param'),
+        )
+
+    # -----------------------------------------------------------------------
+    # QR Code Login
+    # -----------------------------------------------------------------------
+
+    async def fetch_qrcode(self, bot_type: str = DEFAULT_BOT_TYPE) -> QRCodeResponse:
+        """Fetch a QR code for WeChat login authorization (GET, no auth needed)."""
+        session = await self._get_session()
+        url = f'{self.base_url}/ilink/bot/get_bot_qrcode?bot_type={bot_type}'
+
+        async with session.get(url, timeout=aiohttp.ClientTimeout(total=DEFAULT_API_TIMEOUT)) as resp:
+            if resp.status != 200:
+                text = await resp.text()
+                raise ApiError(
+                    f'Failed to fetch QR code: {resp.status} {text}',
+                    status=resp.status,
+                )
+            data = await resp.json(content_type=None)
+
+        logger.debug(
+            'fetch_qrcode response: qrcode=%s, img=%s', data.get('qrcode'), bool(data.get('qrcode_img_content'))
+        )
+
+        return QRCodeResponse(
+            qrcode=data.get('qrcode'),
+            qrcode_img_content=data.get('qrcode_img_content'),
+        )
+
+    async def _fetch_qr_image_base64(self, url: str) -> str:
+        """Generate a QR code image from the URL and return a data URI string.
+
+        The qrcode_img_content URL points to an HTML page (not a raw image),
+        so we generate the QR code locally using the qrcode library.
+        """
+        import qrcode
+
+        qr = qrcode.QRCode(error_correction=qrcode.constants.ERROR_CORRECT_L)
+        qr.add_data(url)
+        qr.make(fit=True)
+        img = qr.make_image(fill_color='black', back_color='white')
+
+        buf = io.BytesIO()
+        img.save(buf, format='PNG')
+        b64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+        return f'data:image/png;base64,{b64}'
+
+    async def poll_qrcode_status(self, qrcode: str) -> QRStatusResponse:
+        """Long-poll the QR code scan status (GET with iLink-App-ClientVersion header)."""
+        session = await self._get_session()
+        url = f'{self.base_url}/ilink/bot/get_qrcode_status?qrcode={quote(qrcode, safe="")}'
+        headers = {'iLink-App-ClientVersion': '1'}
+
+        try:
+            async with session.get(
+                url, headers=headers, timeout=aiohttp.ClientTimeout(total=DEFAULT_QR_POLL_TIMEOUT)
+            ) as resp:
+                if resp.status != 200:
+                    text = await resp.text()
+                    raise ApiError(
+                        f'Failed to poll QR status: {resp.status} {text}',
+                        status=resp.status,
+                    )
+                data = await resp.json(content_type=None)
+                logger.debug('QR status poll response: %s', data)
+        except (asyncio.TimeoutError, aiohttp.ServerTimeoutError):
+            return QRStatusResponse(status='wait')
+
+        return QRStatusResponse(
+            status=data.get('status'),
+            bot_token=data.get('bot_token'),
+            ilink_bot_id=data.get('ilink_bot_id'),
+            baseurl=data.get('baseurl'),
+            ilink_user_id=data.get('ilink_user_id'),
+        )
+
+    async def login(
+        self,
+        max_retries: int = 5,
+        poll_timeout_ms: int = 480_000,
+        on_qrcode: Optional[typing.Callable[[str, str], typing.Any]] = None,
+        on_status: Optional[typing.Callable[[str], typing.Any]] = None,
+    ) -> LoginResult:
+        """Complete QR code login flow with auto-retry on expiry.
+
+        Args:
+            max_retries: Max number of QR code refreshes on expiry.
+            poll_timeout_ms: Timeout per QR code in milliseconds.
+            on_qrcode: Callback(qr_image_base64, qr_url) called each time a
+                        new QR code is fetched. Use this to display the QR code.
+            on_status: Callback(status_str) called on each status poll change.
+
+        Returns:
+            LoginResult with token, base_url, and account_id.
+
+        Raises:
+            ApiError: On unrecoverable API errors.
+            Exception: If all retries are exhausted.
+        """
+        last_qr_base64: Optional[str] = None
+
+        for attempt in range(max_retries):
+            qr_resp = await self.fetch_qrcode()
+            if not qr_resp.qrcode or not qr_resp.qrcode_img_content:
+                raise ApiError('Failed to get QR code from server', status=0)
+
+            # Convert QR image to base64 and notify caller
+            last_qr_base64 = await self._fetch_qr_image_base64(qr_resp.qrcode_img_content)
+            if on_qrcode:
+                try:
+                    result = on_qrcode(last_qr_base64, qr_resp.qrcode_img_content)
+                    if asyncio.iscoroutine(result) or asyncio.isfuture(result):
+                        await result
+                except Exception as e:
+                    logger.warning('on_qrcode callback error: %s', e)
+
+            # Poll until confirmed / expired / timeout
+            loop = asyncio.get_running_loop()
+            deadline = loop.time() + poll_timeout_ms / 1000.0
+
+            while loop.time() < deadline:
+                try:
+                    status_resp = await self.poll_qrcode_status(qr_resp.qrcode)
+                except Exception as e:
+                    logger.error('Error polling QR status: %s', e)
+                    await asyncio.sleep(2)
+                    continue
+
+                if on_status:
+                    try:
+                        cb_result = on_status(status_resp.status or 'unknown')
+                        if asyncio.iscoroutine(cb_result) or asyncio.isfuture(cb_result):
+                            await cb_result
+                    except Exception as e:
+                        logger.warning('on_status callback error: %s', e)
+
+                if status_resp.status == 'confirmed' and status_resp.bot_token:
+                    new_base_url = status_resp.baseurl or self.base_url
+                    # Update this client instance as well
+                    self.token = status_resp.bot_token
+                    self.base_url = new_base_url.rstrip('/')
+                    return LoginResult(
+                        token=status_resp.bot_token,
+                        base_url=new_base_url,
+                        account_id=status_resp.ilink_bot_id or '',
+                        qr_image_base64=last_qr_base64,
+                    )
+
+                if status_resp.status == 'expired':
+                    break  # retry with a new QR code
+
+                await asyncio.sleep(1)
+            else:
+                # While-loop ended without break → poll timeout, treat as expired
+                pass
+
+            remaining = max_retries - attempt - 1
+            if remaining > 0:
+                logger.info('QR code expired, refreshing... (%d retries left)', remaining)
+            else:
+                raise ApiError('QR code login failed: max retries exceeded', status=0)
+
+        # Should not reach here, but just in case
+        raise ApiError('QR code login failed', status=0)
+
+
+# ---------------------------------------------------------------------------
+# Parsing helpers
+# ---------------------------------------------------------------------------
+
+
+def _parse_cdn_media(data: Optional[dict]) -> Optional[CDNMedia]:
+    if not data:
+        return None
+    return CDNMedia(
+        encrypt_query_param=data.get('encrypt_query_param'),
+        aes_key=data.get('aes_key'),
+        encrypt_type=data.get('encrypt_type'),
+    )
+
+
+def _parse_message_item(data: dict) -> MessageItem:
+    item = MessageItem(
+        type=data.get('type'),
+        create_time_ms=data.get('create_time_ms'),
+        update_time_ms=data.get('update_time_ms'),
+        is_completed=data.get('is_completed'),
+        msg_id=data.get('msg_id'),
+    )
+
+    if data.get('text_item'):
+        item.text_item = TextItem(text=data['text_item'].get('text'))
+
+    if data.get('image_item'):
+        img = data['image_item']
+        item.image_item = ImageItem(
+            media=_parse_cdn_media(img.get('media')),
+            thumb_media=_parse_cdn_media(img.get('thumb_media')),
+            aeskey=img.get('aeskey'),
+            url=img.get('url'),
+            mid_size=img.get('mid_size'),
+        )
+
+    if data.get('voice_item'):
+        v = data['voice_item']
+        item.voice_item = VoiceItem(
+            media=_parse_cdn_media(v.get('media')),
+            encode_type=v.get('encode_type'),
+            playtime=v.get('playtime'),
+            text=v.get('text'),
+        )
+
+    if data.get('file_item'):
+        f = data['file_item']
+        item.file_item = FileItem(
+            media=_parse_cdn_media(f.get('media')),
+            file_name=f.get('file_name'),
+            md5=f.get('md5'),
+            len=f.get('len'),
+        )
+
+    if data.get('video_item'):
+        vid = data['video_item']
+        item.video_item = VideoItem(
+            media=_parse_cdn_media(vid.get('media')),
+            video_size=vid.get('video_size'),
+            play_length=vid.get('play_length'),
+            video_md5=vid.get('video_md5'),
+            thumb_media=_parse_cdn_media(vid.get('thumb_media')),
+        )
+
+    if data.get('ref_msg'):
+        ref = data['ref_msg']
+        item.ref_msg = RefMessage(
+            title=ref.get('title'),
+            message_item=_parse_message_item(ref['message_item']) if ref.get('message_item') else None,
+        )
+
+    return item
+
+
+def _parse_weixin_message(data: dict) -> WeixinMessage:
+    msg = WeixinMessage(
+        seq=data.get('seq'),
+        message_id=data.get('message_id'),
+        from_user_id=data.get('from_user_id'),
+        to_user_id=data.get('to_user_id'),
+        client_id=data.get('client_id'),
+        create_time_ms=data.get('create_time_ms'),
+        session_id=data.get('session_id'),
+        group_id=data.get('group_id'),
+        message_type=data.get('message_type'),
+        message_state=data.get('message_state'),
+        context_token=data.get('context_token'),
+    )
+    if data.get('item_list'):
+        msg.item_list = [_parse_message_item(item) for item in data['item_list']]
+    return msg
+
+
+def _parse_get_updates_response(data: dict) -> GetUpdatesResponse:
+    resp = GetUpdatesResponse(
+        ret=data.get('ret'),
+        errcode=data.get('errcode'),
+        errmsg=data.get('errmsg'),
+        get_updates_buf=data.get('get_updates_buf'),
+        longpolling_timeout_ms=data.get('longpolling_timeout_ms'),
+    )
+    if data.get('msgs'):
+        resp.msgs = [_parse_weixin_message(m) for m in data['msgs']]
+    return resp
+
+
+def _cdn_media_to_dict(media: Optional[CDNMedia]) -> Optional[dict]:
+    if not media:
+        return None
+    d: dict = {}
+    if media.encrypt_query_param is not None:
+        d['encrypt_query_param'] = media.encrypt_query_param
+    if media.aes_key is not None:
+        d['aes_key'] = media.aes_key
+    if media.encrypt_type is not None:
+        d['encrypt_type'] = media.encrypt_type
+    return d or None
+
+
+def _message_item_to_dict(item: MessageItem) -> dict:
+    d: dict = {'type': item.type}
+
+    if item.text_item:
+        d['text_item'] = {'text': item.text_item.text}
+
+    if item.image_item:
+        img_d: dict = {}
+        if item.image_item.media:
+            img_d['media'] = _cdn_media_to_dict(item.image_item.media)
+        if item.image_item.mid_size is not None:
+            img_d['mid_size'] = item.image_item.mid_size
+        d['image_item'] = img_d
+
+    if item.voice_item:
+        voice_d: dict = {}
+        if item.voice_item.media:
+            voice_d['media'] = _cdn_media_to_dict(item.voice_item.media)
+        if item.voice_item.playtime is not None:
+            voice_d['playtime'] = item.voice_item.playtime
+        d['voice_item'] = voice_d
+
+    if item.file_item:
+        file_d: dict = {}
+        if item.file_item.media:
+            file_d['media'] = _cdn_media_to_dict(item.file_item.media)
+        if item.file_item.file_name:
+            file_d['file_name'] = item.file_item.file_name
+        if item.file_item.len:
+            file_d['len'] = item.file_item.len
+        d['file_item'] = file_d
+
+    if item.video_item:
+        vid_d: dict = {}
+        if item.video_item.media:
+            vid_d['media'] = _cdn_media_to_dict(item.video_item.media)
+        if item.video_item.video_size is not None:
+            vid_d['video_size'] = item.video_item.video_size
+        d['video_item'] = vid_d
+
+    return d

src/langbot/libs/openclaw_weixin_api/types.py

@@ -0,0 +1,200 @@
+"""Type definitions for the OpenClaw WeChat API, mirroring the upstream protocol."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Optional
+
+SESSION_EXPIRED_ERRCODE = -14
+
+
+class ApiError(Exception):
+    """Structured error raised by the OpenClaw WeChat API."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        status: int = 0,
+        code: int | None = None,
+        payload: Any = None,
+    ):
+        super().__init__(message)
+        self.status = status
+        self.code = code
+        self.payload = payload
+
+    @property
+    def is_session_expired(self) -> bool:
+        return self.code == SESSION_EXPIRED_ERRCODE
+
+
+@dataclass
+class CDNMedia:
+    encrypt_query_param: Optional[str] = None
+    aes_key: Optional[str] = None
+    encrypt_type: Optional[int] = None
+
+
+@dataclass
+class TextItem:
+    text: Optional[str] = None
+
+
+@dataclass
+class ImageItem:
+    media: Optional[CDNMedia] = None
+    thumb_media: Optional[CDNMedia] = None
+    aeskey: Optional[str] = None
+    url: Optional[str] = None
+    mid_size: Optional[int] = None
+    thumb_size: Optional[int] = None
+    thumb_height: Optional[int] = None
+    thumb_width: Optional[int] = None
+    hd_size: Optional[int] = None
+    _downloaded_bytes: Optional[bytes] = field(default=None, repr=False)
+
+
+@dataclass
+class VoiceItem:
+    media: Optional[CDNMedia] = None
+    encode_type: Optional[int] = None
+    bits_per_sample: Optional[int] = None
+    sample_rate: Optional[int] = None
+    playtime: Optional[int] = None
+    text: Optional[str] = None
+    _downloaded_bytes: Optional[bytes] = field(default=None, repr=False)
+
+
+@dataclass
+class FileItem:
+    media: Optional[CDNMedia] = None
+    file_name: Optional[str] = None
+    md5: Optional[str] = None
+    len: Optional[str] = None
+    _downloaded_bytes: Optional[bytes] = field(default=None, repr=False)
+
+
+@dataclass
+class VideoItem:
+    media: Optional[CDNMedia] = None
+    video_size: Optional[int] = None
+    play_length: Optional[int] = None
+    video_md5: Optional[str] = None
+    thumb_media: Optional[CDNMedia] = None
+    thumb_size: Optional[int] = None
+    thumb_height: Optional[int] = None
+    thumb_width: Optional[int] = None
+    _downloaded_bytes: Optional[bytes] = field(default=None, repr=False)
+
+
+@dataclass
+class RefMessage:
+    message_item: Optional[MessageItem] = None
+    title: Optional[str] = None
+
+
+@dataclass
+class MessageItem:
+    """A single content item inside a WeixinMessage."""
+
+    # Item types
+    NONE = 0
+    TEXT = 1
+    IMAGE = 2
+    VOICE = 3
+    FILE = 4
+    VIDEO = 5
+
+    type: Optional[int] = None
+    create_time_ms: Optional[int] = None
+    update_time_ms: Optional[int] = None
+    is_completed: Optional[bool] = None
+    msg_id: Optional[str] = None
+    ref_msg: Optional[RefMessage] = None
+    text_item: Optional[TextItem] = None
+    image_item: Optional[ImageItem] = None
+    voice_item: Optional[VoiceItem] = None
+    file_item: Optional[FileItem] = None
+    video_item: Optional[VideoItem] = None
+
+
+@dataclass
+class WeixinMessage:
+    """Unified message from getUpdates or for sendMessage."""
+
+    # Message types
+    TYPE_USER = 1
+    TYPE_BOT = 2
+
+    # Message states
+    STATE_NEW = 0
+    STATE_GENERATING = 1
+    STATE_FINISH = 2
+
+    seq: Optional[int] = None
+    message_id: Optional[int] = None
+    from_user_id: Optional[str] = None
+    to_user_id: Optional[str] = None
+    client_id: Optional[str] = None
+    create_time_ms: Optional[int] = None
+    update_time_ms: Optional[int] = None
+    delete_time_ms: Optional[int] = None
+    session_id: Optional[str] = None
+    group_id: Optional[str] = None
+    message_type: Optional[int] = None
+    message_state: Optional[int] = None
+    item_list: Optional[list[MessageItem]] = None
+    context_token: Optional[str] = None
+
+
+@dataclass
+class GetUpdatesResponse:
+    ret: Optional[int] = None
+    errcode: Optional[int] = None
+    errmsg: Optional[str] = None
+    msgs: list[WeixinMessage] = field(default_factory=list)
+    get_updates_buf: Optional[str] = None
+    longpolling_timeout_ms: Optional[int] = None
+
+
+@dataclass
+class GetConfigResponse:
+    ret: Optional[int] = None
+    errmsg: Optional[str] = None
+    typing_ticket: Optional[str] = None
+
+
+@dataclass
+class GetUploadUrlResponse:
+    upload_param: Optional[str] = None
+    thumb_upload_param: Optional[str] = None
+
+
+@dataclass
+class QRCodeResponse:
+    """Response from get_bot_qrcode endpoint."""
+
+    qrcode: Optional[str] = None
+    qrcode_img_content: Optional[str] = None
+
+
+@dataclass
+class QRStatusResponse:
+    """Response from get_qrcode_status endpoint."""
+
+    status: Optional[str] = None  # "wait" | "scaned" | "confirmed" | "expired"
+    bot_token: Optional[str] = None
+    ilink_bot_id: Optional[str] = None
+    baseurl: Optional[str] = None
+    ilink_user_id: Optional[str] = None
+
+
+@dataclass
+class LoginResult:
+    """Result returned by the login flow."""
+
+    token: str
+    base_url: str
+    account_id: str
+    qr_image_base64: Optional[str] = None  # data URI of the last QR code shown

src/langbot/libs/wecom_ai_bot_api/api.py

@@ -6,7 +6,8 @@ import traceback
 import uuid
 import xml.etree.ElementTree as ET
 from dataclasses import dataclass, field
-from typing import Any, Callable, Optional
+import re
+from typing import Any, Callable, Optional, Tuple
 from urllib.parse import unquote
 
 import httpx
@@ -63,6 +64,9 @@ class StreamSession:
     # 缓存最近一次片段，处理重试或超时兜底
     last_chunk: Optional[StreamChunk] = None
 
+    # 反馈 ID，用于接收用户点赞/点踩反馈
+    feedback_id: Optional[str] = None
+
 
 class StreamSessionManager:
     """管理 stream 会话的生命周期，并负责队列的生产消费。"""
@@ -73,6 +77,7 @@ class StreamSessionManager:
         self.ttl = ttl  # 超时时间（秒），超过该时间未被访问的会话会被清理由 cleanup
         self._sessions: dict[str, StreamSession] = {}  # stream_id -> StreamSession 映射
         self._msg_index: dict[str, str] = {}  # msgid -> stream_id 映射，便于流水线根据消息 ID 找到会话
+        self._feedback_index: dict[str, str] = {}  # feedback_id -> stream_id 映射
 
     def get_stream_id_by_msg(self, msg_id: str) -> Optional[str]:
         if not msg_id:
@@ -82,6 +87,32 @@ class StreamSessionManager:
     def get_session(self, stream_id: str) -> Optional[StreamSession]:
         return self._sessions.get(stream_id)
 
+    def get_session_by_feedback_id(self, feedback_id: str) -> Optional[StreamSession]:
+        """根据 feedback_id 查找会话。
+
+        Args:
+            feedback_id: 企业微信反馈事件中的反馈 ID。
+
+        Returns:
+            Optional[StreamSession]: 找到的会话实例，未找到返回 None。
+        """
+        if not feedback_id:
+            return None
+        stream_id = self._feedback_index.get(feedback_id)
+        if stream_id:
+            return self._sessions.get(stream_id)
+        return None
+
+    def register_feedback_id(self, stream_id: str, feedback_id: str) -> None:
+        """注册 feedback_id 与 stream_id 的映射。
+
+        Args:
+            stream_id: 企业微信流式会话 ID。
+            feedback_id: 反馈 ID。
+        """
+        if feedback_id and stream_id:
+            self._feedback_index[feedback_id] = stream_id
+
     def create_or_get(self, msg_json: dict[str, Any]) -> tuple[StreamSession, bool]:
         """根据企业微信回调创建或获取会话。
 
@@ -199,6 +230,366 @@ class StreamSessionManager:
                 self._msg_index.pop(msg_id, None)
 
 
+def _decrypt_file(encrypted_data: bytes, aes_key_str: str) -> bytes:
+    """Decrypt AES-256-CBC encrypted file data.
+
+    Aligned with the official WeCom AI Bot Python SDK (crypto_utils.py).
+
+    Args:
+        encrypted_data: The raw encrypted bytes.
+        aes_key_str: Base64-encoded AES key (may lack padding).
+
+    Returns:
+        Decrypted bytes with PKCS#7 padding removed.
+    """
+    if not encrypted_data:
+        raise ValueError('encrypted_data is empty')
+    if not aes_key_str:
+        raise ValueError('aes_key is empty')
+
+    # Python's base64.b64decode requires proper padding (length % 4 == 0).
+    # Node.js Buffer.from tolerates missing '=', so we must pad manually.
+    remainder = len(aes_key_str) % 4
+    if remainder != 0:
+        aes_key_str = aes_key_str + '=' * (4 - remainder)
+    key = base64.b64decode(aes_key_str)
+
+    iv = key[:16]
+
+    cipher = AES.new(key, AES.MODE_CBC, iv)
+
+    # Ensure encrypted data is aligned to AES block size (16 bytes).
+    # Node.js setAutoPadding(false) silently handles unaligned data,
+    # but PyCryptodome will raise an error.
+    block_size = 16
+    data_remainder = len(encrypted_data) % block_size
+    if data_remainder != 0:
+        encrypted_data = encrypted_data + b'\x00' * (block_size - data_remainder)
+
+    decrypted = cipher.decrypt(encrypted_data)
+
+    # Remove PKCS#7 padding with validation
+    if len(decrypted) == 0:
+        raise ValueError('Decrypted data is empty')
+
+    pad_len = decrypted[-1]
+    if pad_len < 1 or pad_len > 32 or pad_len > len(decrypted):
+        raise ValueError(f'Invalid PKCS#7 padding value: {pad_len}')
+
+    # Verify all padding bytes are consistent
+    for i in range(len(decrypted) - pad_len, len(decrypted)):
+        if decrypted[i] != pad_len:
+            raise ValueError('Invalid PKCS#7 padding: padding bytes mismatch')
+
+    return decrypted[: len(decrypted) - pad_len]
+
+
+def _extract_filename(content_disposition: str) -> Optional[str]:
+    """Extract filename from a Content-Disposition header value."""
+    if not content_disposition:
+        return None
+    # RFC 5987: filename*=UTF-8''xxx
+    utf8_match = re.search(r"filename\*=UTF-8''([^;\s]+)", content_disposition, re.IGNORECASE)
+    if utf8_match:
+        return unquote(utf8_match.group(1))
+    # Standard: filename="xxx" or filename=xxx
+    match = re.search(r'filename="?([^";\s]+)"?', content_disposition, re.IGNORECASE)
+    if match:
+        return unquote(match.group(1))
+    return None
+
+
+def _bytes_to_data_uri(data: bytes) -> str:
+    """Convert raw bytes to a data URI with auto-detected MIME type."""
+    if data.startswith(b'\xff\xd8'):
+        mime_type = 'image/jpeg'
+    elif data.startswith(b'\x89PNG'):
+        mime_type = 'image/png'
+    elif data.startswith((b'GIF87a', b'GIF89a')):
+        mime_type = 'image/gif'
+    elif data.startswith(b'BM'):
+        mime_type = 'image/bmp'
+    elif data.startswith(b'II*\x00') or data.startswith(b'MM\x00*'):
+        mime_type = 'image/tiff'
+    elif data[:4] == b'%PDF':
+        mime_type = 'application/pdf'
+    elif data[:4] == b'PK\x03\x04':
+        mime_type = 'application/zip'
+    else:
+        mime_type = 'application/octet-stream'
+
+    base64_str = base64.b64encode(data).decode('utf-8')
+    return f'data:{mime_type};base64,{base64_str}'
+
+
+async def download_encrypted_file(
+    download_url: str, aes_key: str, logger: EventLogger
+) -> Tuple[Optional[bytes], Optional[str]]:
+    """Download an AES-encrypted file from WeChat Work and decrypt it.
+
+    Args:
+        download_url: The encrypted file download URL.
+        aes_key: The AES key for decryption (base64-encoded, per-message aeskey
+                 or platform EncodingAESKey).
+        logger: Logger instance.
+
+    Returns:
+        A tuple of (decrypted_bytes, filename) or (None, None) on failure.
+    """
+    if not download_url:
+        return None, None
+    if not aes_key:
+        await logger.error('download_encrypted_file: aes_key is empty, cannot decrypt')
+        return None, None
+
+    filename: Optional[str] = None
+    try:
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.get(download_url)
+            if response.status_code != 200:
+                await logger.error(f'Failed to download file (HTTP {response.status_code}): {response.text[:200]}')
+                return None, None
+            encrypted_bytes = response.content
+            filename = _extract_filename(response.headers.get('content-disposition', ''))
+    except Exception:
+        await logger.error(f'Failed to download file: {traceback.format_exc()}')
+        return None, None
+
+    try:
+        decrypted = _decrypt_file(encrypted_bytes, aes_key)
+        return decrypted, filename
+    except Exception:
+        await logger.error(f'Failed to decrypt file: {traceback.format_exc()}')
+        return None, None
+
+
+async def parse_wecom_bot_message(
+    msg_json: dict[str, Any], encoding_aes_key: str, logger: EventLogger
+) -> dict[str, Any]:
+    """Parse a decrypted WeChat Work AI Bot message JSON into a unified message dict.
+
+    This is the shared message parsing logic used by both webhook and WebSocket modes.
+
+    Args:
+        msg_json: The decrypted message JSON from WeChat Work.
+        encoding_aes_key: AES key for file decryption.
+        logger: Logger instance.
+
+    Returns:
+        A dict suitable for constructing a WecomBotEvent.
+    """
+    message_data: dict[str, Any] = {}
+
+    msg_type = msg_json.get('msgtype', '')
+    if msg_type:
+        message_data['msgtype'] = msg_type
+
+    if msg_json.get('chattype', '') == 'single':
+        message_data['type'] = 'single'
+    elif msg_json.get('chattype', '') == 'group':
+        message_data['type'] = 'group'
+
+    max_inline_file_size = 5 * 1024 * 1024
+
+    async def _safe_download(url: str, per_msg_aeskey: str = '') -> Tuple[Optional[bytes], Optional[str]]:
+        """Download and decrypt a file, preferring per-message aeskey over platform key."""
+        if not url:
+            return None, None
+        key = per_msg_aeskey or encoding_aes_key
+        if not key:
+            await logger.warning('No AES key available for file decryption, skipping download')
+            return None, None
+        return await download_encrypted_file(url, key, logger)
+
+    async def _safe_download_as_data_uri(url: str, per_msg_aeskey: str = '') -> Optional[str]:
+        """Download, decrypt, and convert to data URI for backward compatibility."""
+        data, _filename = await _safe_download(url, per_msg_aeskey)
+        if data:
+            return _bytes_to_data_uri(data)
+        return None
+
+    if msg_type == 'text':
+        message_data['content'] = msg_json.get('text', {}).get('content')
+    elif msg_type == 'markdown':
+        message_data['content'] = msg_json.get('markdown', {}).get('content') or msg_json.get('text', {}).get(
+            'content', ''
+        )
+    elif msg_type == 'image':
+        image_info = msg_json.get('image', {})
+        picurl = image_info.get('url', '')
+        per_msg_aeskey = image_info.get('aeskey', '')
+        base64_data = await _safe_download_as_data_uri(picurl, per_msg_aeskey)
+        if base64_data:
+            message_data['picurl'] = base64_data
+            message_data['images'] = [base64_data]
+    elif msg_type == 'voice':
+        voice_info = msg_json.get('voice', {}) or {}
+        download_url = voice_info.get('url')
+        per_msg_aeskey = voice_info.get('aeskey', '')
+        message_data['voice'] = {
+            'url': download_url,
+            'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
+            'filesize': voice_info.get('filesize') or voice_info.get('size'),
+            'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
+        }
+        if voice_info.get('content'):
+            message_data['content'] = voice_info.get('content')
+        if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
+            voice_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
+            if voice_base64:
+                message_data['voice']['base64'] = voice_base64
+    elif msg_type == 'video':
+        video_info = msg_json.get('video', {}) or {}
+        download_url = video_info.get('url')
+        per_msg_aeskey = video_info.get('aeskey', '')
+        video_data = {
+            'url': download_url,
+            'filesize': video_info.get('filesize') or video_info.get('size'),
+            'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
+            'md5sum': video_info.get('md5sum') or video_info.get('md5'),
+            'filename': video_info.get('filename') or video_info.get('name'),
+        }
+        if (video_data.get('filesize') or 0) <= max_inline_file_size:
+            video_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
+            if video_base64:
+                video_data['base64'] = video_base64
+        message_data['video'] = video_data
+    elif msg_type == 'file':
+        file_info = msg_json.get('file', {}) or {}
+        download_url = file_info.get('url') or file_info.get('fileurl')
+        per_msg_aeskey = file_info.get('aeskey', '')
+        file_data = {
+            'filename': file_info.get('filename') or file_info.get('name'),
+            'filesize': file_info.get('filesize') or file_info.get('size'),
+            'md5sum': file_info.get('md5sum') or file_info.get('md5'),
+            'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
+            'download_url': download_url,
+            'extra': file_info,
+        }
+        if (file_data.get('filesize') or 0) <= max_inline_file_size:
+            file_bytes, dl_filename = await _safe_download(download_url, per_msg_aeskey)
+            if file_bytes:
+                file_data['base64'] = _bytes_to_data_uri(file_bytes)
+                if dl_filename and not file_data.get('filename'):
+                    file_data['filename'] = dl_filename
+        message_data['file'] = file_data
+    elif msg_type == 'link':
+        message_data['link'] = msg_json.get('link', {})
+        if not message_data.get('content'):
+            title = message_data['link'].get('title', '')
+            desc = message_data['link'].get('description') or message_data['link'].get('digest', '')
+            message_data['content'] = '\n'.join(filter(None, [title, desc]))
+    elif msg_type == 'mixed':
+        items = msg_json.get('mixed', {}).get('msg_item', [])
+        texts = []
+        images = []
+        files = []
+        voices = []
+        videos = []
+        links = []
+        for item in items:
+            item_type = item.get('msgtype')
+            if item_type == 'text':
+                texts.append(item.get('text', {}).get('content', ''))
+            elif item_type == 'image':
+                img_info = item.get('image', {})
+                img_url = img_info.get('url')
+                img_aeskey = img_info.get('aeskey', '')
+                base64_data = await _safe_download_as_data_uri(img_url, img_aeskey)
+                if base64_data:
+                    images.append(base64_data)
+            elif item_type == 'file':
+                file_info = item.get('file', {}) or {}
+                download_url = file_info.get('url') or file_info.get('fileurl')
+                item_aeskey = file_info.get('aeskey', '')
+                file_data = {
+                    'filename': file_info.get('filename') or file_info.get('name'),
+                    'filesize': file_info.get('filesize') or file_info.get('size'),
+                    'md5sum': file_info.get('md5sum') or file_info.get('md5'),
+                    'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
+                    'download_url': download_url,
+                    'extra': file_info,
+                }
+                if (file_data.get('filesize') or 0) <= max_inline_file_size:
+                    file_bytes, dl_filename = await _safe_download(download_url, item_aeskey)
+                    if file_bytes:
+                        file_data['base64'] = _bytes_to_data_uri(file_bytes)
+                        if dl_filename and not file_data.get('filename'):
+                            file_data['filename'] = dl_filename
+                files.append(file_data)
+            elif item_type == 'voice':
+                voice_info = item.get('voice', {}) or {}
+                download_url = voice_info.get('url')
+                item_aeskey = voice_info.get('aeskey', '')
+                voice_data = {
+                    'url': download_url,
+                    'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
+                    'filesize': voice_info.get('filesize') or voice_info.get('size'),
+                    'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
+                }
+                if voice_info.get('content'):
+                    texts.append(voice_info.get('content'))
+                if (voice_data.get('filesize') or 0) <= max_inline_file_size:
+                    voice_base64 = await _safe_download_as_data_uri(download_url, item_aeskey)
+                    if voice_base64:
+                        voice_data['base64'] = voice_base64
+                voices.append(voice_data)
+            elif item_type == 'video':
+                video_info = item.get('video', {}) or {}
+                download_url = video_info.get('url')
+                item_aeskey = video_info.get('aeskey', '')
+                video_data = {
+                    'url': download_url,
+                    'filesize': video_info.get('filesize') or video_info.get('size'),
+                    'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
+                    'md5sum': video_info.get('md5sum') or video_info.get('md5'),
+                    'filename': video_info.get('filename') or video_info.get('name'),
+                }
+                if (video_data.get('filesize') or 0) <= max_inline_file_size:
+                    video_base64 = await _safe_download_as_data_uri(download_url, item_aeskey)
+                    if video_base64:
+                        video_data['base64'] = video_base64
+                videos.append(video_data)
+            elif item_type == 'link':
+                links.append(item.get('link', {}))
+
+        if texts:
+            message_data['content'] = ' '.join(texts)
+        if images:
+            message_data['images'] = images
+            message_data['picurl'] = images[0]
+        if files:
+            message_data['files'] = files
+            message_data['file'] = files[0]
+        if voices:
+            message_data['voices'] = voices
+            message_data['voice'] = voices[0]
+        if videos:
+            message_data['videos'] = videos
+            message_data['video'] = videos[0]
+        if links:
+            message_data['link'] = links[0]
+        if items:
+            message_data['attachments'] = items
+    else:
+        message_data['raw_msg'] = msg_json
+
+    from_info = msg_json.get('from', {})
+    message_data['userid'] = from_info.get('userid', '')
+    message_data['username'] = from_info.get('alias', '') or from_info.get('name', '') or from_info.get('userid', '')
+
+    if msg_json.get('chattype', '') == 'group':
+        message_data['chatid'] = msg_json.get('chatid', '')
+        message_data['chatname'] = msg_json.get('chatname', '') or msg_json.get('chatid', '')
+
+    message_data['msgid'] = msg_json.get('msgid', '')
+
+    if msg_json.get('aibotid'):
+        message_data['aibotid'] = msg_json.get('aibotid', '')
+
+    return message_data
+
+
 class WecomBotClient:
     def __init__(self, Token: str, EnCodingAESKey: str, Corpid: str, logger: EventLogger, unified_mode: bool = False):
         """企业微信智能机器人客户端。
@@ -236,14 +627,27 @@ class WecomBotClient:
         self.stream_sessions = StreamSessionManager(logger=logger)
         self.stream_poll_timeout = 0.5
 
+        self._feedback_callback: Optional[Callable] = None
+
+    def set_feedback_callback(self, callback: Callable) -> None:
+        """设置反馈回调函数。
+
+        Args:
+            callback: 反馈回调函数，签名: async def callback(feedback_id, feedback_type, feedback_content, inaccurate_reasons, session)
+        """
+        self._feedback_callback = callback
+
     @staticmethod
-    def _build_stream_payload(stream_id: str, content: str, finish: bool) -> dict[str, Any]:
+    def _build_stream_payload(
+        stream_id: str, content: str, finish: bool, feedback_id: Optional[str] = None
+    ) -> dict[str, Any]:
         """按照企业微信协议拼装返回报文。
 
         Args:
             stream_id: 企业微信会话 ID。
             content: 推送的文本内容。
             finish: 是否为最终片段。
+            feedback_id: 反馈 ID，用于接收用户点赞/点踩反馈。
 
         Returns:
             dict[str, Any]: 可直接加密返回的 payload。
@@ -251,13 +655,16 @@ class WecomBotClient:
         Example:
             组装 `{'msgtype': 'stream', 'stream': {'id': 'sid', ...}}` 结构。
         """
+        stream_payload = {
+            'id': stream_id,
+            'finish': finish,
+            'content': content,
+        }
+        if feedback_id:
+            stream_payload['feedback'] = {'id': feedback_id}
         return {
             'msgtype': 'stream',
-            'stream': {
+            'stream': stream_payload,
-                'id': stream_id,
-                'finish': finish,
-                'content': content,
-            },
         }
 
     async def _encrypt_and_reply(self, payload: dict[str, Any], nonce: str) -> tuple[Response, int]:
@@ -313,9 +720,14 @@ class WecomBotClient:
         """
         session, is_new = self.stream_sessions.create_or_get(msg_json)
 
+        feedback_id = str(uuid.uuid4())
+        session.feedback_id = feedback_id
+        self.stream_sessions.register_feedback_id(session.stream_id, feedback_id)
+
         message_data = await self.get_message(msg_json)
         if message_data:
             message_data['stream_id'] = session.stream_id
+            message_data['feedback_id'] = feedback_id
             try:
                 event = wecombotevent.WecomBotEvent(message_data)
             except Exception:
@@ -324,7 +736,7 @@ class WecomBotClient:
                 if is_new:
                     asyncio.create_task(self._dispatch_event(event))
 
-        payload = self._build_stream_payload(session.stream_id, '', False)
+        payload = self._build_stream_payload(session.stream_id, '', False, feedback_id)
         return await self._encrypt_and_reply(payload, nonce)
 
     async def _handle_post_followup_response(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
@@ -449,202 +861,80 @@ class WecomBotClient:
 
         msg_json = json.loads(decrypted_xml)
 
+        event = msg_json.get('event', {})
+        event_type = event.get('eventtype', '')
+
+        if event_type == 'feedback_event':
+            return await self._handle_feedback_event(msg_json, nonce)
+
         if msg_json.get('msgtype') == 'stream':
             return await self._handle_post_followup_response(msg_json, nonce)
 
         return await self._handle_post_initial_response(msg_json, nonce)
 
-    async def get_message(self, msg_json):
+    async def _handle_feedback_event(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
-        message_data = {}
+        """处理企业微信用户反馈事件（点赞/点踩）。
 
-        msg_type = msg_json.get('msgtype', '')
+        Args:
-        if msg_type:
+            msg_json: 解密后的企业微信反馈事件 JSON。
-            message_data['msgtype'] = msg_type
+            nonce: 企业微信回调参数 nonce。
 
-        if msg_json.get('chattype', '') == 'single':
+        Returns:
-            message_data['type'] = 'single'
+            Tuple[Response, int]: Quart Response 及状态码。
-        elif msg_json.get('chattype', '') == 'group':
-            message_data['type'] = 'group'
 
-        max_inline_file_size = 5 * 1024 * 1024  # avoid decoding very large payloads by default
+        Note:
+            企业微信协议要求：反馈事件目前仅支持回复空包。
+        """
+        try:
+            feedback_event = msg_json.get('event', {}).get('feedback_event', {})
+            feedback_id = feedback_event.get('id', '')
+            feedback_type = feedback_event.get('type', 0)
+            feedback_content = feedback_event.get('content', '')
+            inaccurate_reasons = feedback_event.get('inaccurate_reason_list', [])
 
-        async def _safe_download(url: str):
+            await self.logger.info(
-            if not url:
+                f'收到用户反馈事件: feedback_id={feedback_id}, type={feedback_type}, '
-                return None
+                f'content={feedback_content}, reasons={inaccurate_reasons}'
-            return await self.download_url_to_base64(url, self.EnCodingAESKey)
-
-        if msg_type == 'text':
-            message_data['content'] = msg_json.get('text', {}).get('content')
-        elif msg_type == 'markdown':
-            message_data['content'] = msg_json.get('markdown', {}).get('content') or msg_json.get('text', {}).get(
-                'content', ''
             )
-        elif msg_type == 'image':
-            picurl = msg_json.get('image', {}).get('url', '')
-            base64_data = await _safe_download(picurl)
-            if base64_data:
-                message_data['picurl'] = base64_data
-                message_data['images'] = [base64_data]
-        elif msg_type == 'voice':
-            voice_info = msg_json.get('voice', {}) or {}
-            download_url = voice_info.get('url')
-            message_data['voice'] = {
-                'url': download_url,
-                'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
-                'filesize': voice_info.get('filesize') or voice_info.get('size'),
-                'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
-            }
-            # 企业微信智能转写文本（如果已有）直接复用，避免重复转写
-            if voice_info.get('content'):
-                message_data['content'] = voice_info.get('content')
-            if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
-                voice_base64 = await _safe_download(download_url)
-                if voice_base64:
-                    message_data['voice']['base64'] = voice_base64
-        elif msg_type == 'video':
-            video_info = msg_json.get('video', {}) or {}
-            download_url = video_info.get('url')
-            video_data = {
-                'url': download_url,
-                'filesize': video_info.get('filesize') or video_info.get('size'),
-                'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
-                'md5sum': video_info.get('md5sum') or video_info.get('md5'),
-                'filename': video_info.get('filename') or video_info.get('name'),
-            }
-            if (video_data.get('filesize') or 0) <= max_inline_file_size:
-                video_base64 = await _safe_download(download_url)
-                if video_base64:
-                    video_data['base64'] = video_base64
-            message_data['video'] = video_data
-        elif msg_type == 'file':
-            file_info = msg_json.get('file', {}) or {}
-            download_url = file_info.get('url') or file_info.get('fileurl')
-            file_data = {
-                'filename': file_info.get('filename') or file_info.get('name'),
-                'filesize': file_info.get('filesize') or file_info.get('size'),
-                'md5sum': file_info.get('md5sum') or file_info.get('md5'),
-                'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
-                'download_url': download_url,
-                'extra': file_info,
-            }
-            if (file_data.get('filesize') or 0) <= max_inline_file_size:
-                file_base64 = await _safe_download(download_url)
-                if file_base64:
-                    file_data['base64'] = file_base64
-            message_data['file'] = file_data
-        elif msg_type == 'link':
-            message_data['link'] = msg_json.get('link', {})
-            if not message_data.get('content'):
-                title = message_data['link'].get('title', '')
-                desc = message_data['link'].get('description') or message_data['link'].get('digest', '')
-                message_data['content'] = '\n'.join(filter(None, [title, desc]))
-        elif msg_type == 'mixed':
-            items = msg_json.get('mixed', {}).get('msg_item', [])
-            texts = []
-            images = []
-            files = []
-            voices = []
-            videos = []
-            links = []
-            for item in items:
-                item_type = item.get('msgtype')
-                if item_type == 'text':
-                    texts.append(item.get('text', {}).get('content', ''))
-                elif item_type == 'image':
-                    img_url = item.get('image', {}).get('url')
-                    base64_data = await _safe_download(img_url)
-                    if base64_data:
-                        images.append(base64_data)
-                elif item_type == 'file':
-                    file_info = item.get('file', {}) or {}
-                    download_url = file_info.get('url') or file_info.get('fileurl')
-                    file_data = {
-                        'filename': file_info.get('filename') or file_info.get('name'),
-                        'filesize': file_info.get('filesize') or file_info.get('size'),
-                        'md5sum': file_info.get('md5sum') or file_info.get('md5'),
-                        'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
-                        'download_url': download_url,
-                        'extra': file_info,
-                    }
-                    if (file_data.get('filesize') or 0) <= max_inline_file_size:
-                        file_base64 = await _safe_download(download_url)
-                        if file_base64:
-                            file_data['base64'] = file_base64
-                    files.append(file_data)
-                elif item_type == 'voice':
-                    voice_info = item.get('voice', {}) or {}
-                    download_url = voice_info.get('url')
-                    voice_data = {
-                        'url': download_url,
-                        'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
-                        'filesize': voice_info.get('filesize') or voice_info.get('size'),
-                        'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
-                    }
-                    if voice_info.get('content'):
-                        texts.append(voice_info.get('content'))
-                    if (voice_data.get('filesize') or 0) <= max_inline_file_size:
-                        voice_base64 = await _safe_download(download_url)
-                        if voice_base64:
-                            voice_data['base64'] = voice_base64
-                    voices.append(voice_data)
-                elif item_type == 'video':
-                    video_info = item.get('video', {}) or {}
-                    download_url = video_info.get('url')
-                    video_data = {
-                        'url': download_url,
-                        'filesize': video_info.get('filesize') or video_info.get('size'),
-                        'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
-                        'md5sum': video_info.get('md5sum') or video_info.get('md5'),
-                        'filename': video_info.get('filename') or video_info.get('name'),
-                    }
-                    if (video_data.get('filesize') or 0) <= max_inline_file_size:
-                        video_base64 = await _safe_download(download_url)
-                        if video_base64:
-                            video_data['base64'] = video_base64
-                    videos.append(video_data)
-                elif item_type == 'link':
-                    links.append(item.get('link', {}))
 
-            if texts:
+            session = self.stream_sessions.get_session_by_feedback_id(feedback_id)
-                message_data['content'] = ' '.join(texts)  # 拼接所有 text
+            if session:
-            if images:
+                await self.logger.info(
-                message_data['images'] = images
+                    f'反馈关联到会话: stream_id={session.stream_id}, msg_id={session.msg_id}, user_id={session.user_id}'
-                message_data['picurl'] = images[0]  # 只保留第一个 image
+                )
-            if files:
+                for handler in self._message_handlers.get('feedback', []):
-                message_data['files'] = files
+                    try:
-                message_data['file'] = files[0]
+                        await handler(
-            if voices:
+                            feedback_id=feedback_id,
-                message_data['voices'] = voices
+                            feedback_type=feedback_type,
-                message_data['voice'] = voices[0]
+                            feedback_content=feedback_content,
-            if videos:
+                            inaccurate_reasons=inaccurate_reasons,
-                message_data['videos'] = videos
+                            session=session,
-                message_data['video'] = videos[0]
+                        )
-            if links:
+                    except Exception:
-                message_data['link'] = links[0]
+                        await self.logger.error(traceback.format_exc())
-            if items:
-                message_data['attachments'] = items
-        else:
-            message_data['raw_msg'] = msg_json
 
-        # Extract user information
+                if self._feedback_callback:
-        from_info = msg_json.get('from', {})
+                    try:
-        message_data['userid'] = from_info.get('userid', '')
+                        await self._feedback_callback(
-        message_data['username'] = (
+                            feedback_id=feedback_id,
-            from_info.get('alias', '') or from_info.get('name', '') or from_info.get('userid', '')
+                            feedback_type=feedback_type,
-        )
+                            feedback_content=feedback_content,
+                            inaccurate_reasons=inaccurate_reasons,
+                            session=session,
+                        )
+                    except Exception:
+                        await self.logger.error(traceback.format_exc())
+            else:
+                await self.logger.warning(f'未找到 feedback_id={feedback_id} 对应的会话')
 
-        # Extract chat/group information
+        except Exception:
-        if msg_json.get('chattype', '') == 'group':
+            await self.logger.error(traceback.format_exc())
-            message_data['chatid'] = msg_json.get('chatid', '')
-            # Try to get group name if available
-            message_data['chatname'] = msg_json.get('chatname', '') or msg_json.get('chatid', '')
 
-        message_data['msgid'] = msg_json.get('msgid', '')
+        return await self._encrypt_and_reply({}, nonce)
 
-        if msg_json.get('aibotid'):
+    async def get_message(self, msg_json):
-            message_data['aibotid'] = msg_json.get('aibotid', '')
+        return await parse_wecom_bot_message(msg_json, self.EnCodingAESKey, self.logger)
-
-        return message_data
 
     async def _handle_message(self, event: wecombotevent.WecomBotEvent):
         """
@@ -711,40 +1001,20 @@ class WecomBotClient:
 
         return decorator
 
+    def on_feedback(self):
+        def decorator(func: Callable):
+            if 'feedback' not in self._message_handlers:
+                self._message_handlers['feedback'] = []
+            self._message_handlers['feedback'].append(func)
+            return func
+
+        return decorator
+
     async def download_url_to_base64(self, download_url, encoding_aes_key):
-        async with httpx.AsyncClient() as client:
+        data, _filename = await download_encrypted_file(download_url, encoding_aes_key, self.logger)
-            response = await client.get(download_url)
+        if data:
-            if response.status_code != 200:
+            return _bytes_to_data_uri(data)
-                await self.logger.error(f'failed to get file: {response.text}')
+        return None
-                return None
-
-            encrypted_bytes = response.content
-
-        aes_key = base64.b64decode(encoding_aes_key + '=')  # base64 补齐
-        iv = aes_key[:16]
-
-        cipher = AES.new(aes_key, AES.MODE_CBC, iv)
-        decrypted = cipher.decrypt(encrypted_bytes)
-
-        pad_len = decrypted[-1]
-        decrypted = decrypted[:-pad_len]
-
-        if decrypted.startswith(b'\xff\xd8'):  # JPEG
-            mime_type = 'image/jpeg'
-        elif decrypted.startswith(b'\x89PNG'):  # PNG
-            mime_type = 'image/png'
-        elif decrypted.startswith((b'GIF87a', b'GIF89a')):  # GIF
-            mime_type = 'image/gif'
-        elif decrypted.startswith(b'BM'):  # BMP
-            mime_type = 'image/bmp'
-        elif decrypted.startswith(b'II*\x00') or decrypted.startswith(b'MM\x00*'):  # TIFF
-            mime_type = 'image/tiff'
-        else:
-            mime_type = 'application/octet-stream'
-
-        # 转 base64
-        base64_str = base64.b64encode(decrypted).decode('utf-8')
-        return f'data:{mime_type};base64,{base64_str}'
 
     async def run_task(self, host: str, port: int, *args, **kwargs):
         """

src/langbot/libs/wecom_ai_bot_api/wecombotevent.py

@@ -133,3 +133,17 @@ class WecomBotEvent(dict):
         AI Bot ID
         """
         return self.get('aibotid', '')
+
+    @property
+    def feedback_id(self) -> str:
+        """
+        反馈 ID，用于关联用户点赞/点踩反馈
+        """
+        return self.get('feedback_id', '')
+
+    @property
+    def stream_id(self) -> str:
+        """
+        流式消息 ID
+        """
+        return self.get('stream_id', '')

src/langbot/libs/wecom_ai_bot_api/ws_client.py

@@ -0,0 +1,596 @@
+"""WeChat Work AI Bot WebSocket long connection client.
+
+Implements the WebSocket protocol for receiving messages and sending replies
+via a persistent connection to wss://openws.work.weixin.qq.com, as an
+alternative to the HTTP callback (webhook) mode.
+
+Protocol reference: https://developer.work.weixin.qq.com/document/path/101463
+Official Node.js SDK: https://github.com/WecomTeam/aibot-node-sdk
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import secrets
+import time
+import traceback
+from typing import Any, Callable, Optional
+
+import aiohttp
+
+from langbot.libs.wecom_ai_bot_api import wecombotevent
+from langbot.libs.wecom_ai_bot_api.api import parse_wecom_bot_message
+from langbot.pkg.platform.logger import EventLogger
+
+DEFAULT_WS_URL = 'wss://openws.work.weixin.qq.com'
+
+# WebSocket frame command constants
+CMD_SUBSCRIBE = 'aibot_subscribe'
+CMD_HEARTBEAT = 'ping'
+CMD_MSG_CALLBACK = 'aibot_msg_callback'
+CMD_EVENT_CALLBACK = 'aibot_event_callback'
+CMD_RESPOND_MSG = 'aibot_respond_msg'
+CMD_RESPOND_WELCOME = 'aibot_respond_welcome_msg'
+CMD_RESPOND_UPDATE = 'aibot_respond_update_msg'
+CMD_SEND_MSG = 'aibot_send_msg'
+
+
+def _generate_req_id(prefix: str) -> str:
+    """Generate a unique request ID in the format: {prefix}_{timestamp}_{random}."""
+    ts = int(time.time() * 1000)
+    rand = secrets.token_hex(4)
+    return f'{prefix}_{ts}_{rand}'
+
+
+class WecomBotWsClient:
+    """WeChat Work AI Bot WebSocket long connection client.
+
+    Provides message receiving, streaming reply, proactive message sending,
+    and event callback handling over a persistent WebSocket connection.
+    """
+
+    def __init__(
+        self,
+        bot_id: str,
+        secret: str,
+        logger: EventLogger,
+        encoding_aes_key: str = '',
+        ws_url: str = DEFAULT_WS_URL,
+        heartbeat_interval: float = 30.0,
+        max_reconnect_attempts: int = -1,
+        reconnect_base_delay: float = 1.0,
+        reconnect_max_delay: float = 30.0,
+    ):
+        self.bot_id = bot_id
+        self.secret = secret
+        self.logger = logger
+        self.encoding_aes_key = encoding_aes_key
+        self.ws_url = ws_url
+        self.heartbeat_interval = heartbeat_interval
+        self.max_reconnect_attempts = max_reconnect_attempts
+        self.reconnect_base_delay = reconnect_base_delay
+        self.reconnect_max_delay = reconnect_max_delay
+
+        self._ws: Optional[aiohttp.ClientWebSocketResponse] = None
+        self._session: Optional[aiohttp.ClientSession] = None
+        self._running = False
+        self._heartbeat_task: Optional[asyncio.Task] = None
+        self._missed_pong_count = 0
+        self._max_missed_pong = 2
+        self._reconnect_attempts = 0
+
+        # Message handler registry (same pattern as WecomBotClient)
+        self._message_handlers: dict[str, list[Callable]] = {}
+        # Message deduplication
+        self._msg_id_map: dict[str, int] = {}
+
+        # Pending ACK futures: req_id -> Future[dict]
+        self._pending_acks: dict[str, asyncio.Future] = {}
+        # Per-req_id serial reply queues
+        self._reply_queues: dict[str, asyncio.Queue] = {}
+        self._reply_workers: dict[str, asyncio.Task] = {}
+        self._reply_ack_timeout = 5.0
+
+        # Stream ID tracking for WebSocket mode
+        self._stream_ids: dict[str, str] = {}  # msg_id -> req_id|stream_id
+        # Dedup: skip sending when content hasn't changed
+        self._stream_last_content: dict[str, str] = {}  # msg_id -> last content sent
+
+    # ── Public API ──────────────────────────────────────────────────
+
+    async def connect(self):
+        """Connect to WebSocket server with automatic reconnection.
+
+        This method blocks until disconnect() is called or max reconnect
+        attempts are exhausted.
+        """
+        self._running = True
+        self._reconnect_attempts = 0
+
+        while self._running:
+            try:
+                await self._connect_once()
+            except Exception:
+                if not self._running:
+                    break
+                await self.logger.error(f'WebSocket connection error: {traceback.format_exc()}')
+
+            if not self._running:
+                break
+
+            # Reconnect with exponential backoff
+            if self.max_reconnect_attempts != -1 and self._reconnect_attempts >= self.max_reconnect_attempts:
+                await self.logger.error(f'Max reconnect attempts reached ({self.max_reconnect_attempts}), giving up')
+                break
+
+            self._reconnect_attempts += 1
+            delay = min(
+                self.reconnect_base_delay * (2 ** (self._reconnect_attempts - 1)),
+                self.reconnect_max_delay,
+            )
+            await self.logger.info(f'Reconnecting in {delay:.1f}s (attempt {self._reconnect_attempts})...')
+            await asyncio.sleep(delay)
+
+    async def disconnect(self):
+        """Gracefully disconnect from the WebSocket server."""
+        self._running = False
+        if self._heartbeat_task and not self._heartbeat_task.done():
+            self._heartbeat_task.cancel()
+        for task in self._reply_workers.values():
+            if not task.done():
+                task.cancel()
+        if self._ws and not self._ws.closed:
+            await self._ws.close()
+        self._ws = None
+        if self._session and not self._session.closed:
+            await self._session.close()
+        self._session = None
+
+    def on_message(self, msg_type: str) -> Callable:
+        """Decorator to register a message handler.
+
+        Same interface as WecomBotClient.on_message for compatibility.
+
+        Args:
+            msg_type: 'single', 'group', or specific message type.
+        """
+
+        def decorator(func: Callable[[wecombotevent.WecomBotEvent], Any]):
+            if msg_type not in self._message_handlers:
+                self._message_handlers[msg_type] = []
+            self._message_handlers[msg_type].append(func)
+            return func
+
+        return decorator
+
+    async def reply_stream(
+        self,
+        req_id: str,
+        stream_id: str,
+        content: str,
+        finish: bool = False,
+    ) -> Optional[dict]:
+        """Send a streaming reply frame.
+
+        Args:
+            req_id: The req_id from the original message frame (must be passed through).
+            stream_id: The stream ID for this streaming session.
+            content: The content to send (supports Markdown).
+            finish: Whether this is the final chunk.
+
+        Returns:
+            The ACK frame dict, or None on failure.
+        """
+        body = {
+            'msgtype': 'stream',
+            'stream': {
+                'id': stream_id,
+                'finish': finish,
+                'content': content,
+            },
+        }
+        return await self._send_reply(req_id, body)
+
+    async def reply_text(self, req_id: str, content: str) -> Optional[dict]:
+        """Send a non-streaming text reply.
+
+        Args:
+            req_id: The req_id from the original message frame.
+            content: The text content to reply.
+
+        Returns:
+            The ACK frame dict, or None on failure.
+        """
+        body = {
+            'msgtype': 'markdown',
+            'markdown': {
+                'content': content,
+            },
+        }
+        return await self._send_reply(req_id, body)
+
+    async def send_message(self, chat_id: str, content: str, msgtype: str = 'markdown') -> Optional[dict]:
+        """Proactively send a message to a specified chat.
+
+        Args:
+            chat_id: The chat ID (userid for single chat, chatid for group chat).
+            content: The message content.
+            msgtype: Message type, 'markdown' by default.
+
+        Returns:
+            The ACK frame dict, or None on failure.
+        """
+        req_id = _generate_req_id(CMD_SEND_MSG)
+        body: dict[str, Any] = {
+            'chatid': chat_id,
+            'msgtype': msgtype,
+        }
+        if msgtype == 'markdown':
+            body['markdown'] = {'content': content}
+        elif msgtype == 'text':
+            body['text'] = {'content': content}
+        return await self._send_reply(req_id, body, cmd=CMD_SEND_MSG)
+
+    async def push_stream_chunk(self, msg_id: str, content: str, is_final: bool = False) -> bool:
+        """Push a streaming chunk for a given message ID.
+
+        Compatible interface with WecomBotClient.push_stream_chunk.
+
+        Args:
+            msg_id: The original message ID.
+            content: The cumulative content from the pipeline.
+            is_final: Whether this is the final chunk.
+
+        Returns:
+            True if the stream session exists and chunk was sent.
+        """
+        key = self._stream_ids.get(msg_id)
+        if not key:
+            return False
+        req_id, stream_id = key.split('|', 1)
+        try:
+            # Skip sending if content hasn't changed (e.g. during tool call argument streaming)
+            if not is_final and content == self._stream_last_content.get(msg_id):
+                return True
+            await self.reply_stream(req_id, stream_id, content, finish=is_final)
+            self._stream_last_content[msg_id] = content
+            if is_final:
+                self._stream_ids.pop(msg_id, None)
+                self._stream_last_content.pop(msg_id, None)
+            return True
+        except Exception:
+            await self.logger.error(f'Failed to push stream chunk: {traceback.format_exc()}')
+            return False
+
+    async def set_message(self, msg_id: str, content: str):
+        """Fallback: send content as a final stream chunk or direct reply.
+
+        Compatible interface with WecomBotClient.set_message.
+        """
+        handled = await self.push_stream_chunk(msg_id, content, is_final=True)
+        if not handled:
+            await self.logger.warning(f'No active stream for msg_id={msg_id}, message dropped')
+
+    # ── Connection lifecycle ────────────────────────────────────────
+
+    async def _connect_once(self):
+        """Establish a single WebSocket connection, authenticate, and listen."""
+        await self.logger.info(f'Connecting to {self.ws_url}...')
+
+        self._session = aiohttp.ClientSession()
+        try:
+            self._ws = await self._session.ws_connect(self.ws_url)
+            self._missed_pong_count = 0
+            self._reconnect_attempts = 0
+            await self.logger.info('WebSocket connected, sending auth...')
+
+            await self._send_auth()
+
+            # Wait for auth response
+            auth_ok = await self._wait_for_auth()
+            if not auth_ok:
+                await self.logger.error('Authentication failed')
+                return
+
+            await self.logger.info('Authenticated successfully')
+
+            # Start heartbeat
+            self._heartbeat_task = asyncio.create_task(self._heartbeat_loop())
+
+            try:
+                await self._listen_loop()
+            finally:
+                if self._heartbeat_task and not self._heartbeat_task.done():
+                    self._heartbeat_task.cancel()
+                self._clear_pending_acks('Connection closed')
+        finally:
+            if self._ws and not self._ws.closed:
+                await self._ws.close()
+            self._ws = None
+            if self._session and not self._session.closed:
+                await self._session.close()
+            self._session = None
+
+    async def _send_auth(self):
+        """Send the authentication frame."""
+        frame = {
+            'cmd': CMD_SUBSCRIBE,
+            'headers': {'req_id': _generate_req_id(CMD_SUBSCRIBE)},
+            'body': {
+                'bot_id': self.bot_id,
+                'secret': self.secret,
+            },
+        }
+        await self._send_frame(frame)
+
+    async def _wait_for_auth(self) -> bool:
+        """Wait for and validate the authentication response."""
+        try:
+            msg = await asyncio.wait_for(self._ws.receive(), timeout=10.0)
+            if msg.type in (aiohttp.WSMsgType.TEXT,):
+                frame = json.loads(msg.data)
+                req_id = frame.get('headers', {}).get('req_id', '')
+                if req_id.startswith(CMD_SUBSCRIBE) and frame.get('errcode') == 0:
+                    return True
+                await self.logger.error(f'Auth response: errcode={frame.get("errcode")}, errmsg={frame.get("errmsg")}')
+                return False
+            elif msg.type in (aiohttp.WSMsgType.ERROR, aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.CLOSING):
+                await self.logger.error(f'WebSocket closed during auth: {msg.type}')
+                return False
+            await self.logger.error(f'Unexpected message type during auth: {msg.type}')
+            return False
+        except asyncio.TimeoutError:
+            await self.logger.error('Auth response timeout')
+            return False
+
+    async def _heartbeat_loop(self):
+        """Periodically send heartbeat pings."""
+        try:
+            while self._running and self._ws and not self._ws.closed:
+                await asyncio.sleep(self.heartbeat_interval)
+                if not self._running or not self._ws or self._ws.closed:
+                    break
+
+                if self._missed_pong_count >= self._max_missed_pong:
+                    await self.logger.warning(
+                        f'No heartbeat ack for {self._missed_pong_count} consecutive pings, connection considered dead'
+                    )
+                    await self._ws.close()
+                    break
+
+                self._missed_pong_count += 1
+                frame = {
+                    'cmd': CMD_HEARTBEAT,
+                    'headers': {'req_id': _generate_req_id(CMD_HEARTBEAT)},
+                }
+                try:
+                    await self._send_frame(frame)
+                except Exception:
+                    break
+        except asyncio.CancelledError:
+            pass
+
+    async def _listen_loop(self):
+        """Listen for incoming WebSocket frames and dispatch them."""
+        async for msg in self._ws:
+            if not self._running:
+                break
+            if msg.type == aiohttp.WSMsgType.TEXT:
+                try:
+                    frame = json.loads(msg.data)
+                    await self._handle_frame(frame)
+                except json.JSONDecodeError:
+                    await self.logger.error(f'Failed to parse WebSocket message: {str(msg.data)[:200]}')
+                except Exception:
+                    await self.logger.error(f'Error handling frame: {traceback.format_exc()}')
+            elif msg.type == aiohttp.WSMsgType.BINARY:
+                try:
+                    frame = json.loads(msg.data)
+                    await self._handle_frame(frame)
+                except Exception:
+                    await self.logger.error(f'Error handling binary frame: {traceback.format_exc()}')
+            elif msg.type in (aiohttp.WSMsgType.ERROR, aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.CLOSING):
+                await self.logger.warning(f'WebSocket connection closed: {msg.type}')
+                break
+
+    # ── Frame handling ──────────────────────────────────────────────
+
+    async def _handle_frame(self, frame: dict):
+        """Route an incoming frame to the appropriate handler."""
+        cmd = frame.get('cmd', '')
+
+        # Message push
+        if cmd == CMD_MSG_CALLBACK:
+            asyncio.create_task(self._handle_message_callback(frame))
+            return
+
+        # Event push
+        if cmd == CMD_EVENT_CALLBACK:
+            asyncio.create_task(self._handle_event_callback(frame))
+            return
+
+        # No cmd → response/ACK frame, dispatch by req_id prefix
+        req_id = frame.get('headers', {}).get('req_id', '')
+
+        # Check pending ACKs first
+        if req_id in self._pending_acks:
+            future = self._pending_acks.pop(req_id)
+            if not future.done():
+                future.set_result(frame)
+            return
+
+        # Heartbeat response
+        if req_id.startswith(CMD_HEARTBEAT):
+            if frame.get('errcode') == 0:
+                self._missed_pong_count = 0
+            return
+
+        # Unknown frame
+        await self.logger.warning(f'Unknown frame: {json.dumps(frame, ensure_ascii=False)[:200]}')
+
+    async def _handle_message_callback(self, frame: dict):
+        """Handle an incoming message callback frame."""
+        try:
+            body = frame.get('body', {})
+            req_id = frame.get('headers', {}).get('req_id', '')
+
+            # Parse message using shared logic
+            message_data = await parse_wecom_bot_message(body, self.encoding_aes_key, self.logger)
+            if not message_data:
+                return
+
+            # Generate stream_id for this message and store the mapping
+            stream_id = _generate_req_id('stream')
+            msg_id = message_data.get('msgid', '')
+            if msg_id:
+                self._stream_ids[msg_id] = f'{req_id}|{stream_id}'
+            message_data['stream_id'] = stream_id
+            message_data['req_id'] = req_id
+
+            event = wecombotevent.WecomBotEvent(message_data)
+            await self._dispatch_event(event)
+        except Exception:
+            await self.logger.error(f'Error in message callback: {traceback.format_exc()}')
+
+    async def _handle_event_callback(self, frame: dict):
+        """Handle an incoming event callback frame (enter_chat, template_card_event, etc.)."""
+        try:
+            body = frame.get('body', {})
+            req_id = frame.get('headers', {}).get('req_id', '')
+
+            event_info = body.get('event', {})
+            event_type = event_info.get('eventtype', '')
+
+            message_data = {
+                'msgtype': 'event',
+                'type': body.get('chattype', 'single'),
+                'event': event_info,
+                'eventtype': event_type,
+                'msgid': body.get('msgid', ''),
+                'aibotid': body.get('aibotid', ''),
+                'req_id': req_id,
+            }
+
+            from_info = body.get('from', {})
+            message_data['userid'] = from_info.get('userid', '')
+            message_data['username'] = from_info.get('alias', '') or from_info.get('userid', '')
+
+            if body.get('chatid'):
+                message_data['chatid'] = body.get('chatid', '')
+
+            event = wecombotevent.WecomBotEvent(message_data)
+
+            # Dispatch to event-specific handlers
+            if event_type in self._message_handlers:
+                for handler in self._message_handlers[event_type]:
+                    await handler(event)
+
+            # Also dispatch to generic 'event' handlers
+            if 'event' in self._message_handlers:
+                for handler in self._message_handlers['event']:
+                    await handler(event)
+
+        except Exception:
+            await self.logger.error(f'Error in event callback: {traceback.format_exc()}')
+
+    async def _dispatch_event(self, event: wecombotevent.WecomBotEvent):
+        """Dispatch a message event to registered handlers with deduplication."""
+        try:
+            message_id = event.message_id
+            if message_id in self._msg_id_map:
+                self._msg_id_map[message_id] += 1
+                return
+            self._msg_id_map[message_id] = 1
+
+            msg_type = event.type
+            if msg_type in self._message_handlers:
+                for handler in self._message_handlers[msg_type]:
+                    await handler(event)
+        except Exception:
+            await self.logger.error(f'Error dispatching event: {traceback.format_exc()}')
+
+    # ── Reply sending with serial queue ─────────────────────────────
+
+    async def _send_reply(
+        self,
+        req_id: str,
+        body: dict,
+        cmd: str = CMD_RESPOND_MSG,
+    ) -> Optional[dict]:
+        """Send a reply frame and wait for ACK.
+
+        Replies with the same req_id are serialized to maintain ordering.
+        """
+        if not self._ws or self._ws.closed:
+            return None
+
+        frame = {
+            'cmd': cmd,
+            'headers': {'req_id': req_id},
+            'body': body,
+        }
+
+        # Ensure serial delivery per req_id
+        if req_id not in self._reply_queues:
+            self._reply_queues[req_id] = asyncio.Queue()
+            self._reply_workers[req_id] = asyncio.create_task(self._reply_queue_worker(req_id))
+
+        future: asyncio.Future = asyncio.get_event_loop().create_future()
+        await self._reply_queues[req_id].put((frame, future))
+        return await future
+
+    async def _reply_queue_worker(self, req_id: str):
+        """Process reply queue items serially for a given req_id."""
+        queue = self._reply_queues[req_id]
+        try:
+            while self._running:
+                try:
+                    frame, future = await asyncio.wait_for(queue.get(), timeout=60.0)
+                except asyncio.TimeoutError:
+                    # Queue idle, clean up worker
+                    break
+
+                try:
+                    ack = await self._send_and_wait_ack(frame)
+                    if not future.done():
+                        future.set_result(ack)
+                except Exception as e:
+                    if not future.done():
+                        future.set_exception(e)
+        except asyncio.CancelledError:
+            pass
+        finally:
+            self._reply_queues.pop(req_id, None)
+            self._reply_workers.pop(req_id, None)
+
+    async def _send_and_wait_ack(self, frame: dict) -> Optional[dict]:
+        """Send a frame and wait for the corresponding ACK."""
+        req_id = frame['headers']['req_id']
+        ack_future: asyncio.Future = asyncio.get_event_loop().create_future()
+        self._pending_acks[req_id] = ack_future
+
+        try:
+            await self._send_frame(frame)
+            result = await asyncio.wait_for(ack_future, timeout=self._reply_ack_timeout)
+            if result.get('errcode', 0) != 0:
+                await self.logger.warning(
+                    f'Reply ACK error: errcode={result.get("errcode")}, errmsg={result.get("errmsg")}'
+                )
+            return result
+        except asyncio.TimeoutError:
+            self._pending_acks.pop(req_id, None)
+            await self.logger.warning(f'Reply ACK timeout ({self._reply_ack_timeout}s) for req_id={req_id}')
+            return None
+
+    async def _send_frame(self, frame: dict):
+        """Send a JSON frame over the WebSocket connection."""
+        if self._ws and not self._ws.closed:
+            await self._ws.send_str(json.dumps(frame, ensure_ascii=False))
+
+    def _clear_pending_acks(self, reason: str):
+        """Reject all pending ACK futures on disconnection."""
+        for req_id, future in self._pending_acks.items():
+            if not future.done():
+                future.set_exception(ConnectionError(reason))
+        self._pending_acks.clear()

src/langbot/libs/wecom_api/api.py

@@ -4,6 +4,7 @@ import base64
 import binascii
 import httpx
 import traceback
+from urllib.parse import quote
 from quart import Quart
 import xml.etree.ElementTree as ET
 from typing import Callable, Dict, Any
@@ -67,6 +68,31 @@ class WecomClient:
                 await self.logger.error(f'获取accesstoken失败:{response.json()}')
                 raise Exception(f'未获取access token: {data}')
 
+    async def get_user_info(self, userid: str) -> dict:
+        """
+        Get user information by user ID using the application secret.
+
+        Args:
+            userid: The user ID to look up.
+
+        Returns:
+            dict: User information including 'name' field.
+        """
+        if not await self.check_access_token():
+            self.access_token = await self.get_access_token(self.secret)
+
+        url = self.base_url + '/user/get?access_token=' + self.access_token + '&userid=' + quote(userid)
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url)
+            data = response.json()
+            if data.get('errcode') == 40014 or data.get('errcode') == 42001:
+                self.access_token = await self.get_access_token(self.secret)
+                return await self.get_user_info(userid)
+            if data.get('errcode', 0) != 0:
+                await self.logger.error(f'获取用户信息失败:{data}')
+                return {}
+            return data
+
     async def get_users(self):
         if not self.check_access_token_for_contacts():
             self.access_token_for_contacts = await self.get_access_token(self.secret_for_contacts)

src/langbot/libs/wecom_customer_service_api/api.py

@@ -10,6 +10,7 @@ from typing import Callable
 from .wecomcsevent import WecomCSEvent
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import aiofiles
+import time
 
 
 class WecomCSClient:
@@ -34,6 +35,10 @@ class WecomCSClient:
         self.unified_mode = unified_mode
         self.app = Quart(__name__)
 
+        # Customer info cache: {external_userid: (info_dict, timestamp)}
+        self._customer_cache: dict[str, tuple[dict, float]] = {}
+        self._cache_ttl = 60  # Cache TTL in seconds (1 minute)
+
         # 只有在非统一模式下才注册独立路由
         if not self.unified_mode:
             self.app.add_url_rule(
@@ -378,3 +383,53 @@ class WecomCSClient:
     async def get_media_id(self, image: platform_message.Image):
         media_id = await self.upload_to_work(image=image)
         return media_id
+
+    async def get_customer_info(self, external_userid: str) -> dict | None:
+        """
+        Get customer information by external_userid with caching.
+
+        Uses a 1-minute cache to avoid repeated API calls for the same user.
+
+        Args:
+            external_userid: The external user ID of the customer.
+
+        Returns:
+            Customer info dict with 'nickname', 'avatar', etc., or None if not found.
+        """
+        # Check cache first
+        current_time = time.time()
+        if external_userid in self._customer_cache:
+            cached_info, cached_time = self._customer_cache[external_userid]
+            if current_time - cached_time < self._cache_ttl:
+                return cached_info
+
+        # Cache miss or expired, fetch from API
+        if not await self.check_access_token():
+            self.access_token = await self.get_access_token(self.secret)
+
+        url = f'{self.base_url}/kf/customer/batchget?access_token={self.access_token}'
+
+        payload = {
+            'external_userid_list': [external_userid],
+        }
+
+        async with httpx.AsyncClient() as client:
+            response = await client.post(url, json=payload)
+            data = response.json()
+
+            if data.get('errcode') in [40014, 42001]:
+                self.access_token = await self.get_access_token(self.secret)
+                return await self.get_customer_info(external_userid)
+
+            if data.get('errcode', 0) != 0:
+                if self.logger:
+                    await self.logger.warning(f'Failed to get customer info: {data}')
+                return None
+
+            customer_list = data.get('customer_list', [])
+            if customer_list:
+                customer_info = customer_list[0]
+                # Store in cache
+                self._customer_cache[external_userid] = (customer_info, current_time)
+                return customer_info
+            return None

src/langbot/pkg/api/http/controller/groups/files.py

@@ -13,9 +13,9 @@ from .. import group
 @group.group_class('files', '/api/v1/files')
 class FilesRouterGroup(group.RouterGroup):
     async def initialize(self) -> None:
-        @self.route('/image/<image_key>', methods=['GET'], auth_type=group.AuthType.NONE)
+        @self.route('/image/<path:image_key>', methods=['GET'], auth_type=group.AuthType.NONE)
         async def _(image_key: str) -> quart.Response:
-            if '/' in image_key or '\\' in image_key:
+            if '..' in image_key or '\\' in image_key:
                 return quart.Response(status=404)
 
             if not await self.ap.storage_mgr.storage_provider.exists(image_key):

src/langbot/pkg/api/http/controller/groups/knowledge/migration.py

@@ -0,0 +1,372 @@
+import asyncio
+import json
+
+import httpx
+import quart
+import sqlalchemy
+
+from ... import group
+from ......core import taskmgr
+from ......entity.persistence import metadata as persistence_metadata
+from langbot_plugin.runtime.plugin.mgr import PluginInstallSource
+
+LANGRAG_PLUGIN_AUTHOR = 'langbot-team'
+LANGRAG_PLUGIN_NAME = 'LangRAG'
+LANGRAG_PLUGIN_ID = f'{LANGRAG_PLUGIN_AUTHOR}/{LANGRAG_PLUGIN_NAME}'
+DEFAULT_SPACE_URL = 'https://space.langbot.app'
+
+# Old Retriever plugin_name -> New Connector plugin_name
+EXTERNAL_PLUGIN_NAME_MAPPING = {
+    'DifyDatasetsRetriever': 'DifyDatasetsConnector',
+    'RAGFlowRetriever': 'RAGFlowConnector',
+    'FastGPTRetriever': 'FastGPTConnector',
+}
+
+# Per-plugin: which old retriever_config fields belong to creation_settings.
+# Remaining fields go to retrieval_settings.
+# None means ALL fields go to creation_settings (no retrieval_schema).
+EXTERNAL_PLUGIN_CREATION_FIELDS: dict[str, set[str] | None] = {
+    'langbot-team/DifyDatasetsConnector': {'api_base_url', 'dify_apikey', 'dataset_id'},
+    'langbot-team/RAGFlowConnector': {'api_base_url', 'api_key', 'dataset_ids'},
+    'langbot-team/FastGPTConnector': None,  # all fields -> creation_settings
+}
+
+
+@group.group_class('knowledge/migration', '/api/v1/knowledge/migration')
+class KnowledgeMigrationRouterGroup(group.RouterGroup):
+    async def _get_migration_flag(self) -> bool:
+        """Check if rag_plugin_migration_needed flag is set."""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_metadata.Metadata).where(
+                persistence_metadata.Metadata.key == 'rag_plugin_migration_needed'
+            )
+        )
+        row = result.first()
+        return row is not None and row.value == 'true'
+
+    async def _set_migration_flag(self, value: str):
+        """Set rag_plugin_migration_needed flag."""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_metadata.Metadata)
+            .where(persistence_metadata.Metadata.key == 'rag_plugin_migration_needed')
+            .values(value=value)
+        )
+
+    async def _table_exists(self, table_name: str) -> bool:
+        """Check if a table exists."""
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);'
+                ).bindparams(table_name=table_name)
+            )
+            return result.scalar()
+        else:
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams(
+                    table_name=table_name
+                )
+            )
+            return result.first() is not None
+
+    async def _install_plugin_from_marketplace(
+        self, plugin_id: str, task_context: taskmgr.TaskContext, space_url: str
+    ) -> None:
+        """Install a single plugin from the marketplace."""
+        p_author, p_name = plugin_id.split('/', 1)
+        self.ap.logger.info(f'RAG migration: installing plugin {plugin_id} from marketplace...')
+        task_context.trace(f'Installing plugin {plugin_id} from marketplace...')
+
+        async with httpx.AsyncClient(trust_env=True, timeout=15) as client:
+            resp = await client.get(f'{space_url}/api/v1/marketplace/plugins/{p_author}/{p_name}')
+            resp.raise_for_status()
+            p_data = resp.json().get('data', {}).get('plugin', {})
+            p_version = p_data.get('latest_version')
+            if not p_version:
+                raise Exception(f'Could not determine latest version for {plugin_id}')
+
+        await self.ap.plugin_connector.install_plugin(
+            PluginInstallSource.MARKETPLACE,
+            {
+                'plugin_author': p_author,
+                'plugin_name': p_name,
+                'plugin_version': p_version,
+            },
+            task_context=task_context,
+        )
+        self.ap.logger.info(f'RAG migration: plugin {plugin_id} install request sent.')
+
+    async def _execute_rag_migration(self, task_context: taskmgr.TaskContext, install_plugin: bool = True):
+        """Execute RAG migration: install required plugins and restore backup data."""
+        warnings = []
+
+        # Collect all plugins we need: LangRAG (always) + connector plugins (from external KBs)
+        needed_plugins: dict[str, str] = {
+            LANGRAG_PLUGIN_ID: LANGRAG_PLUGIN_NAME,
+        }
+
+        has_external = await self._table_exists('external_knowledge_bases')
+        if has_external:
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('SELECT DISTINCT plugin_author, plugin_name FROM external_knowledge_bases;')
+            )
+            for row in result.fetchall():
+                plugin_author = row[0] or ''
+                plugin_name = row[1] or ''
+                mapped_name = EXTERNAL_PLUGIN_NAME_MAPPING.get(plugin_name, plugin_name)
+                plugin_id = f'{plugin_author}/{mapped_name}'
+                if plugin_id not in needed_plugins:
+                    needed_plugins[plugin_id] = mapped_name
+
+        self.ap.logger.info(f'RAG migration: plugins needed: {list(needed_plugins.keys())}')
+
+        if install_plugin:
+            # Step 1: Install all required plugins from marketplace
+            task_context.trace('Installing required plugins...', action='install-plugin')
+            space_url = self.ap.instance_config.data.get('space', {}).get('url', DEFAULT_SPACE_URL).rstrip('/')
+
+            for plugin_id in needed_plugins:
+                try:
+                    await self._install_plugin_from_marketplace(plugin_id, task_context, space_url)
+                except Exception as e:
+                    self.ap.logger.warning(f'RAG migration: plugin {plugin_id} install returned: {e}')
+                    task_context.trace(f'Plugin install note ({plugin_id}): {e}')
+
+            # Step 2: Wait for all plugins to become available as knowledge engines
+            task_context.trace(
+                f'Waiting for plugins to become available: {list(needed_plugins.keys())}...',
+                action='wait-plugin',
+            )
+            max_retries = 30
+            engine_id_set: set[str] = set()
+            for i in range(max_retries):
+                try:
+                    engines = await self.ap.plugin_connector.list_knowledge_engines()
+                    engine_id_set = {e.get('plugin_id') for e in engines}
+                except Exception:
+                    pass
+                if all(pid in engine_id_set for pid in needed_plugins):
+                    self.ap.logger.info(f'RAG migration: all plugins ready: {engine_id_set}')
+                    task_context.trace('All required plugins are ready.')
+                    break
+                if i == max_retries - 1:
+                    still_missing = [pid for pid in needed_plugins if pid not in engine_id_set]
+                    warning = f'Plugin(s) {still_missing} did not become available after {max_retries} retries'
+                    self.ap.logger.warning(f'RAG migration: {warning}')
+                    warnings.append(warning)
+                    task_context.trace(warning)
+                await asyncio.sleep(2)
+        else:
+            try:
+                engines = await self.ap.plugin_connector.list_knowledge_engines()
+                engine_id_set = {e.get('plugin_id') for e in engines}
+            except Exception:
+                engine_id_set = set()
+
+        # Step 3: Restore internal knowledge bases from backup
+        task_context.trace('Restoring internal knowledge bases...', action='restore-internal')
+        if await self._table_exists('knowledge_bases_backup'):
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('SELECT * FROM knowledge_bases_backup;')
+            )
+            rows = result.fetchall()
+            columns = result.keys()
+
+            for row in rows:
+                row_dict = dict(zip(columns, row))
+                kb_uuid = row_dict.get('uuid')
+                name = row_dict.get('name', 'Untitled')
+                description = row_dict.get('description', '')
+                emoji = row_dict.get('emoji', '\U0001f4da')
+                embedding_model_uuid = row_dict.get('embedding_model_uuid', '')
+                top_k = row_dict.get('top_k', 5)
+                created_at = row_dict.get('created_at')
+                updated_at = row_dict.get('updated_at')
+
+                creation_settings = json.dumps({'embedding_model_uuid': embedding_model_uuid})
+                retrieval_settings = json.dumps({'top_k': top_k})
+
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'INSERT INTO knowledge_bases '
+                        '(uuid, name, description, emoji, created_at, updated_at, '
+                        'knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings) '
+                        'VALUES (:uuid, :name, :description, :emoji, :created_at, :updated_at, '
+                        ':plugin_id, :collection_id, :creation_settings, :retrieval_settings);'
+                    ).bindparams(
+                        uuid=kb_uuid,
+                        name=name,
+                        description=description,
+                        emoji=emoji,
+                        created_at=created_at,
+                        updated_at=updated_at,
+                        plugin_id=LANGRAG_PLUGIN_ID,
+                        collection_id=kb_uuid,
+                        creation_settings=creation_settings,
+                        retrieval_settings=retrieval_settings,
+                    )
+                )
+
+                try:
+                    config = {'embedding_model_uuid': embedding_model_uuid}
+                    await self.ap.plugin_connector.rag_on_kb_create(LANGRAG_PLUGIN_ID, kb_uuid, config)
+                    task_context.trace(f'Restored internal KB: {name} ({kb_uuid})')
+                except Exception as e:
+                    warning = f'Failed to notify plugin for KB {name} ({kb_uuid}): {e}'
+                    warnings.append(warning)
+                    task_context.trace(warning)
+
+            await self.ap.rag_mgr.load_knowledge_bases_from_db()
+
+        # Step 4: Restore external knowledge bases
+        task_context.trace('Restoring external knowledge bases...', action='restore-external')
+        if has_external:
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('SELECT * FROM external_knowledge_bases;')
+            )
+            rows = result.fetchall()
+            columns = result.keys()
+
+            self.ap.logger.info(
+                f'RAG migration: {len(rows)} external KB(s) to restore. Available engines: {engine_id_set}'
+            )
+            task_context.trace(f'Found {len(rows)} external KB(s). Available engines: {engine_id_set}')
+
+            for row in rows:
+                row_dict = dict(zip(columns, row))
+                kb_uuid = row_dict.get('uuid')
+                name = row_dict.get('name', 'Untitled')
+                description = row_dict.get('description', '')
+                emoji = row_dict.get('emoji', '\U0001f517')
+                plugin_author = row_dict.get('plugin_author', '')
+                plugin_name = row_dict.get('plugin_name', '')
+                retriever_config = row_dict.get('retriever_config', {})
+                created_at = row_dict.get('created_at')
+
+                mapped_plugin_name = EXTERNAL_PLUGIN_NAME_MAPPING.get(plugin_name, plugin_name)
+                external_plugin_id = f'{plugin_author}/{mapped_plugin_name}'
+
+                self.ap.logger.info(
+                    f'RAG migration: processing external KB "{name}" ({kb_uuid}), '
+                    f'plugin: {plugin_author}/{plugin_name} -> {external_plugin_id}'
+                )
+
+                if isinstance(retriever_config, str):
+                    try:
+                        retriever_config = json.loads(retriever_config)
+                    except (json.JSONDecodeError, TypeError):
+                        retriever_config = {}
+
+                creation_fields = EXTERNAL_PLUGIN_CREATION_FIELDS.get(external_plugin_id)
+                if creation_fields is None:
+                    creation_settings_dict = retriever_config
+                    retrieval_settings_dict = {}
+                else:
+                    creation_settings_dict = {k: v for k, v in retriever_config.items() if k in creation_fields}
+                    retrieval_settings_dict = {k: v for k, v in retriever_config.items() if k not in creation_fields}
+
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'INSERT INTO knowledge_bases '
+                        '(uuid, name, description, emoji, created_at, updated_at, '
+                        'knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings) '
+                        'VALUES (:uuid, :name, :description, :emoji, :created_at, :updated_at, '
+                        ':plugin_id, :collection_id, :creation_settings, :retrieval_settings);'
+                    ).bindparams(
+                        uuid=kb_uuid,
+                        name=name,
+                        description=description,
+                        emoji=emoji,
+                        created_at=created_at,
+                        updated_at=created_at,
+                        plugin_id=external_plugin_id,
+                        collection_id=kb_uuid,
+                        creation_settings=json.dumps(creation_settings_dict),
+                        retrieval_settings=json.dumps(retrieval_settings_dict),
+                    )
+                )
+
+                if external_plugin_id not in engine_id_set:
+                    warning = (
+                        f'External KB "{name}" ({kb_uuid}) record saved, but plugin {external_plugin_id} '
+                        f'is not installed yet. Install the connector plugin to use it.'
+                    )
+                    warnings.append(warning)
+                    task_context.trace(warning)
+                else:
+                    try:
+                        await self.ap.plugin_connector.rag_on_kb_create(
+                            external_plugin_id, kb_uuid, creation_settings_dict
+                        )
+                        task_context.trace(f'Restored external KB: {name} ({kb_uuid})')
+                    except Exception as e:
+                        warning = f'Failed to notify plugin for external KB {name} ({kb_uuid}): {e}'
+                        warnings.append(warning)
+                        task_context.trace(warning)
+
+            await self.ap.rag_mgr.load_knowledge_bases_from_db()
+
+        # Step 5: Clear migration flag
+        await self._set_migration_flag('false')
+        task_context.trace('RAG migration completed.', action='done')
+
+        if warnings:
+            task_context.trace(f'Completed with {len(warnings)} warning(s).')
+
+    async def initialize(self) -> None:
+        @self.route('/status', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> str:
+            needed = await self._get_migration_flag()
+
+            internal_kb_count = 0
+            external_kb_count = 0
+
+            if needed:
+                if await self._table_exists('knowledge_bases_backup'):
+                    result = await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text('SELECT COUNT(*) FROM knowledge_bases_backup;')
+                    )
+                    internal_kb_count = result.scalar() or 0
+
+                if await self._table_exists('external_knowledge_bases'):
+                    result = await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text('SELECT COUNT(*) FROM external_knowledge_bases;')
+                    )
+                    external_kb_count = result.scalar() or 0
+
+            return self.success(
+                data={
+                    'needed': needed,
+                    'internal_kb_count': internal_kb_count,
+                    'external_kb_count': external_kb_count,
+                }
+            )
+
+        @self.route('/execute', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> str:
+            needed = await self._get_migration_flag()
+            if not needed:
+                return self.http_status(400, -1, 'RAG migration is not needed')
+
+            data = await quart.request.get_json(silent=True) or {}
+            install_plugin = data.get('install_plugin', True)
+
+            ctx = taskmgr.TaskContext.new()
+            wrapper = self.ap.task_mgr.create_user_task(
+                self._execute_rag_migration(task_context=ctx, install_plugin=install_plugin),
+                kind='rag-migration',
+                name='rag-migration-execute',
+                label='Migrating knowledge bases to plugin architecture',
+                context=ctx,
+            )
+
+            return self.success(data={'task_id': wrapper.id})
+
+        @self.route('/dismiss', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> str:
+            needed = await self._get_migration_flag()
+            if not needed:
+                return self.http_status(400, -1, 'RAG migration is not needed')
+
+            await self._set_migration_flag('false')
+            return self.success()

src/langbot/pkg/api/http/controller/groups/monitoring.py

@@ -456,6 +456,31 @@ class MonitoringRouterGroup(group.RouterGroup):
                     'platform',
                     'user_id',
                 ]
+            elif export_type == 'feedback':
+                data = await self.ap.monitoring_service.export_feedback(
+                    bot_ids=bot_ids if bot_ids else None,
+                    pipeline_ids=pipeline_ids if pipeline_ids else None,
+                    start_time=start_time,
+                    end_time=end_time,
+                    limit=limit,
+                )
+                headers = [
+                    'id',
+                    'timestamp',
+                    'feedback_id',
+                    'feedback_type',
+                    'feedback_content',
+                    'inaccurate_reasons',
+                    'bot_id',
+                    'bot_name',
+                    'pipeline_id',
+                    'pipeline_name',
+                    'session_id',
+                    'message_id',
+                    'stream_id',
+                    'user_id',
+                    'platform',
+                ]
             else:
                 return self.error(message=f'Invalid export type: {export_type}', code=400)
 
@@ -486,3 +511,63 @@ class MonitoringRouterGroup(group.RouterGroup):
             )
 
             return response, 200
+
+        @self.route('/feedback/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_feedback_stats() -> str:
+            """Get feedback statistics"""
+            # Parse query parameters
+            bot_ids = quart.request.args.getlist('botId')
+            pipeline_ids = quart.request.args.getlist('pipelineId')
+            start_time_str = quart.request.args.get('startTime')
+            end_time_str = quart.request.args.get('endTime')
+
+            # Parse datetime
+            start_time = parse_iso_datetime(start_time_str)
+            end_time = parse_iso_datetime(end_time_str)
+
+            stats = await self.ap.monitoring_service.get_feedback_stats(
+                bot_ids=bot_ids if bot_ids else None,
+                pipeline_ids=pipeline_ids if pipeline_ids else None,
+                start_time=start_time,
+                end_time=end_time,
+            )
+
+            return self.success(data=stats)
+
+        @self.route('/feedback', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_feedback() -> str:
+            """Get feedback list"""
+            # Parse query parameters
+            bot_ids = quart.request.args.getlist('botId')
+            pipeline_ids = quart.request.args.getlist('pipelineId')
+            feedback_type_str = quart.request.args.get('feedbackType')
+            start_time_str = quart.request.args.get('startTime')
+            end_time_str = quart.request.args.get('endTime')
+            limit = int(quart.request.args.get('limit', 100))
+            offset = int(quart.request.args.get('offset', 0))
+
+            # Parse datetime
+            start_time = parse_iso_datetime(start_time_str)
+            end_time = parse_iso_datetime(end_time_str)
+
+            # Parse feedback type
+            feedback_type = int(feedback_type_str) if feedback_type_str else None
+
+            feedback_list, total = await self.ap.monitoring_service.get_feedback_list(
+                bot_ids=bot_ids if bot_ids else None,
+                pipeline_ids=pipeline_ids if pipeline_ids else None,
+                feedback_type=feedback_type,
+                start_time=start_time,
+                end_time=end_time,
+                limit=limit,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'feedback': feedback_list,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )

src/langbot/pkg/api/http/controller/groups/plugins.py

@@ -265,6 +265,8 @@ class PluginsRouterGroup(group.RouterGroup):
                 return self.http_status(400, -1, 'Missing asset_url parameter')
 
             ctx = taskmgr.TaskContext.new()
+            ctx.metadata['plugin_name'] = f'{owner}/{repo}'
+            ctx.metadata['install_source'] = 'github'
             install_info = {
                 'asset_url': asset_url,
                 'owner': owner,
@@ -295,12 +297,17 @@ class PluginsRouterGroup(group.RouterGroup):
 
             data = await quart.request.json
 
+            plugin_author = data.get('plugin_author', '')
+            plugin_name = data.get('plugin_name', '')
+
             ctx = taskmgr.TaskContext.new()
+            ctx.metadata['plugin_name'] = f'{plugin_author}/{plugin_name}'
+            ctx.metadata['install_source'] = 'marketplace'
             wrapper = self.ap.task_mgr.create_user_task(
                 self.ap.plugin_connector.install_plugin(PluginInstallSource.MARKETPLACE, data, task_context=ctx),
                 kind='plugin-operation',
                 name='plugin-install-marketplace',
-                label=f'Installing plugin from marketplace ...{data}',
+                label=f'Installing plugin from marketplace {plugin_author}/{plugin_name}',
                 context=ctx,
             )
 
@@ -323,11 +330,13 @@ class PluginsRouterGroup(group.RouterGroup):
             }
 
             ctx = taskmgr.TaskContext.new()
+            ctx.metadata['plugin_name'] = file.filename or 'local plugin'
+            ctx.metadata['install_source'] = 'local'
             wrapper = self.ap.task_mgr.create_user_task(
                 self.ap.plugin_connector.install_plugin(PluginInstallSource.LOCAL, data, task_context=ctx),
                 kind='plugin-operation',
                 name='plugin-install-local',
-                label=f'Installing plugin from local ...{file.filename}',
+                label=f'Installing plugin from local {file.filename}',
                 context=ctx,
             )
 

src/langbot/pkg/api/http/controller/groups/system.py

@@ -1,7 +1,11 @@
+import json
+
 import quart
+import sqlalchemy
 
 from .. import group
 from .....utils import constants
+from .....entity.persistence.metadata import Metadata
 
 
 @group.group_class('system', '/api/v1/system')
@@ -9,6 +13,24 @@ class SystemRouterGroup(group.RouterGroup):
     async def initialize(self) -> None:
         @self.route('/info', methods=['GET'], auth_type=group.AuthType.NONE)
         async def _() -> str:
+            # Read wizard_status and wizard_progress from metadata table
+            wizard_status = 'none'
+            wizard_progress = None
+            try:
+                result = await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.select(Metadata).where(Metadata.key.in_(['wizard_status', 'wizard_progress']))
+                )
+                for row in result:
+                    if row.key == 'wizard_status':
+                        wizard_status = row.value
+                    elif row.key == 'wizard_progress':
+                        try:
+                            wizard_progress = json.loads(row.value)
+                        except (json.JSONDecodeError, TypeError):
+                            wizard_progress = None
+            except Exception:
+                pass
+
             return self.success(
                 data={
                     'version': constants.semantic_version,
@@ -27,17 +49,83 @@ class SystemRouterGroup(group.RouterGroup):
                         'disable_models_service', False
                     ),
                     'limitation': self.ap.instance_config.data.get('system', {}).get('limitation', {}),
+                    'wizard_status': wizard_status,
+                    'wizard_progress': wizard_progress,
                 }
             )
 
+        @self.route('/wizard/completed', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> str:
+            """Mark wizard status in metadata table and clear progress.
+
+            Accepts JSON body: { "status": "skipped" | "completed" }
+            """
+            data = await quart.request.get_json(silent=True) or {}
+            status = data.get('status', 'completed')
+            if status not in ('skipped', 'completed'):
+                return self.http_status(400, 400, f'Invalid wizard status: {status}')
+
+            try:
+                result = await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.select(Metadata).where(Metadata.key == 'wizard_status')
+                )
+                if result.first():
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.update(Metadata).where(Metadata.key == 'wizard_status').values(value=status)
+                    )
+                else:
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.insert(Metadata).values(key='wizard_status', value=status)
+                    )
+
+                # Clear wizard progress when wizard is completed/skipped
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.delete(Metadata).where(Metadata.key == 'wizard_progress')
+                )
+            except Exception as e:
+                return self.http_status(500, 500, f'Failed to update wizard status: {e}')
+
+            return self.success(data={})
+
+        @self.route('/wizard/progress', methods=['PUT'], auth_type=group.AuthType.USER_TOKEN)
+        async def _() -> str:
+            """Save wizard progress to metadata table.
+
+            Accepts JSON body with wizard state fields:
+            { "step": int, "selected_adapter": str|null, "created_bot_uuid": str|null,
+              "bot_saved": bool, "selected_runner": str|null }
+            """
+            data = await quart.request.get_json(silent=True) or {}
+            progress_json = json.dumps(data, ensure_ascii=False)
+
+            try:
+                result = await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.select(Metadata).where(Metadata.key == 'wizard_progress')
+                )
+                if result.first():
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.update(Metadata).where(Metadata.key == 'wizard_progress').values(value=progress_json)
+                    )
+                else:
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.insert(Metadata).values(key='wizard_progress', value=progress_json)
+                    )
+            except Exception as e:
+                return self.http_status(500, 500, f'Failed to save wizard progress: {e}')
+
+            return self.success(data={})
+
         @self.route('/tasks', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
         async def _() -> str:
             task_type = quart.request.args.get('type')
+            task_kind = quart.request.args.get('kind')
 
             if task_type == '':
                 task_type = None
+            if task_kind == '':
+                task_kind = None
 
-            return self.success(data=self.ap.task_mgr.get_tasks_dict(task_type))
+            return self.success(data=self.ap.task_mgr.get_tasks_dict(task_type, task_kind))
 
         @self.route('/tasks/<task_id>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
         async def _(task_id: str) -> str:

src/langbot/pkg/api/http/controller/main.py

@@ -105,6 +105,28 @@ class HTTPController:
             ):
                 if os.path.exists(os.path.join(frontend_path, path + '.html')):
                     path += '.html'
+                elif path.startswith('home/'):
+                    # SPA fallback for /home/* sub-routes.
+                    # Entity detail views use query params (e.g. /home/bots?id=uuid),
+                    # so the pre-rendered list page is served directly via path + '.html'.
+                    # This fallback handles any remaining unmatched sub-paths.
+                    segments = path.rstrip('/').split('/')
+
+                    # Walk up parent segments looking for matching .html files
+                    for i in range(len(segments) - 1, 0, -1):
+                        parent_path = '/'.join(segments[:i]) + '.html'
+                        if os.path.exists(os.path.join(frontend_path, parent_path)):
+                            response = await quart.send_from_directory(frontend_path, parent_path, mimetype='text/html')
+                            response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
+                            response.headers['Pragma'] = 'no-cache'
+                            response.headers['Expires'] = '0'
+                            return response
+                    # Final fallback to index.html for /home/* routes
+                    response = await quart.send_from_directory(frontend_path, 'index.html', mimetype='text/html')
+                    response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
+                    response.headers['Pragma'] = 'no-cache'
+                    response.headers['Expires'] = '0'
+                    return response
                 else:
                     return await quart.send_from_directory(frontend_path, '404.html')
 

src/langbot/pkg/api/http/service/bot.py

@@ -70,12 +70,17 @@ class BotService:
             'lark',
         ]:
             webhook_prefix = self.ap.instance_config.data['api'].get('webhook_prefix', 'http://127.0.0.1:5300')
+            extra_webhook_prefix = self.ap.instance_config.data['api'].get('extra_webhook_prefix', '')
             webhook_url = f'/bots/{bot_uuid}'
             adapter_runtime_values['webhook_url'] = webhook_url
             adapter_runtime_values['webhook_full_url'] = f'{webhook_prefix}{webhook_url}'
+            adapter_runtime_values['extra_webhook_full_url'] = (
+                f'{extra_webhook_prefix}{webhook_url}' if extra_webhook_prefix else ''
+            )
         else:
             adapter_runtime_values['webhook_url'] = None
             adapter_runtime_values['webhook_full_url'] = None
+            adapter_runtime_values['extra_webhook_full_url'] = None
 
         persistence_bot['adapter_runtime_values'] = adapter_runtime_values
 

src/langbot/pkg/api/http/service/model.py

@@ -105,11 +105,16 @@ class LLMModelsService:
                 )
             )
             pipeline = result.first()
-            if pipeline is not None and pipeline.config['ai']['local-agent']['model'] == '':
+            if pipeline is not None:
-                pipeline_config = pipeline.config
+                model_config = pipeline.config.get('ai', {}).get('local-agent', {}).get('model', {})
-                pipeline_config['ai']['local-agent']['model'] = model_data['uuid']
+                if not model_config.get('primary', ''):
-                pipeline_data = {'config': pipeline_config}
+                    pipeline_config = pipeline.config
-                await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
+                    pipeline_config['ai']['local-agent']['model'] = {
+                        'primary': model_data['uuid'],
+                        'fallbacks': [],
+                    }
+                    pipeline_data = {'config': pipeline_config}
+                    await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
 
         return model_data['uuid']
 

src/langbot/pkg/api/http/service/monitoring.py

@@ -16,6 +16,57 @@ class MonitoringService:
     def __init__(self, ap: app.Application) -> None:
         self.ap = ap
 
+    # ========== Cleanup Methods ==========
+
+    async def cleanup_expired_records(self, retention_days: int) -> dict[str, int]:
+        """Delete monitoring records older than the specified retention period.
+
+        Args:
+            retention_days: Number of days to retain records.
+
+        Returns:
+            A dict mapping table name to the number of deleted rows.
+        """
+        cutoff = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) - datetime.timedelta(
+            days=retention_days
+        )
+
+        tables_and_columns: list[tuple[str, type, sqlalchemy.Column]] = [
+            (
+                'monitoring_messages',
+                persistence_monitoring.MonitoringMessage,
+                persistence_monitoring.MonitoringMessage.timestamp,
+            ),
+            (
+                'monitoring_llm_calls',
+                persistence_monitoring.MonitoringLLMCall,
+                persistence_monitoring.MonitoringLLMCall.timestamp,
+            ),
+            (
+                'monitoring_embedding_calls',
+                persistence_monitoring.MonitoringEmbeddingCall,
+                persistence_monitoring.MonitoringEmbeddingCall.timestamp,
+            ),
+            (
+                'monitoring_errors',
+                persistence_monitoring.MonitoringError,
+                persistence_monitoring.MonitoringError.timestamp,
+            ),
+            (
+                'monitoring_sessions',
+                persistence_monitoring.MonitoringSession,
+                persistence_monitoring.MonitoringSession.last_activity,
+            ),
+        ]
+
+        deleted_counts: dict[str, int] = {}
+
+        for table_name, model_cls, ts_column in tables_and_columns:
+            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.delete(model_cls).where(ts_column < cutoff))
+            deleted_counts[table_name] = result.rowcount
+
+        return deleted_counts
+
     # ========== Recording Methods ==========
 
     async def record_message(
@@ -30,6 +81,7 @@ class MonitoringService:
         level: str = 'info',
         platform: str | None = None,
         user_id: str | None = None,
+        user_name: str | None = None,
         runner_name: str | None = None,
         variables: str | None = None,
         role: str = 'user',
@@ -49,6 +101,7 @@ class MonitoringService:
             'level': level,
             'platform': platform,
             'user_id': user_id,
+            'user_name': user_name,
             'runner_name': runner_name,
             'variables': variables,
             'role': role,
@@ -152,6 +205,7 @@ class MonitoringService:
         pipeline_name: str,
         platform: str | None = None,
         user_id: str | None = None,
+        user_name: str | None = None,
     ) -> None:
         """Record a new session"""
         session_data = {
@@ -166,6 +220,7 @@ class MonitoringService:
             'is_active': True,
             'platform': platform,
             'user_id': user_id,
+            'user_name': user_name,
         }
 
         await self.ap.persistence_mgr.execute_async(
@@ -1128,3 +1183,261 @@ class MonitoringService:
             }
             for row in rows
         ]
+
+    # ========== Feedback Methods ==========
+
+    async def record_feedback(
+        self,
+        feedback_id: str,
+        feedback_type: int,
+        feedback_content: str | None = None,
+        inaccurate_reasons: list[str] | None = None,
+        bot_id: str | None = None,
+        bot_name: str | None = None,
+        pipeline_id: str | None = None,
+        pipeline_name: str | None = None,
+        session_id: str | None = None,
+        message_id: str | None = None,
+        stream_id: str | None = None,
+        user_id: str | None = None,
+        platform: str | None = None,
+    ) -> str:
+        """Record user feedback (like/dislike) from AI Bot conversation.
+
+        Args:
+            feedback_id: Unique feedback identifier from platform (e.g., WeChat Work)
+            feedback_type: 1 = like (thumbs up), 2 = dislike (thumbs down)
+            feedback_content: Optional user feedback text
+            inaccurate_reasons: List of reasons for inaccurate response (for dislike)
+            bot_id: Bot ID
+            bot_name: Bot name
+            pipeline_id: Pipeline ID
+            pipeline_name: Pipeline name
+            session_id: Session ID
+            message_id: Message ID
+            stream_id: Stream ID (for WeChat Work streaming messages)
+            user_id: User ID
+            platform: Platform name (e.g., 'wecom')
+
+        Returns:
+            The record ID
+        """
+        import json
+
+        record_id = str(uuid.uuid4())
+        record_data = {
+            'id': record_id,
+            'timestamp': datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+            'feedback_id': feedback_id,
+            'feedback_type': feedback_type,
+            'feedback_content': feedback_content,
+            'inaccurate_reasons': json.dumps(inaccurate_reasons, ensure_ascii=False) if inaccurate_reasons else None,
+            'bot_id': bot_id,
+            'bot_name': bot_name,
+            'pipeline_id': pipeline_id,
+            'pipeline_name': pipeline_name,
+            'session_id': session_id,
+            'message_id': message_id,
+            'stream_id': stream_id,
+            'user_id': user_id,
+            'platform': platform,
+        }
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_monitoring.MonitoringFeedback).values(record_data)
+        )
+
+        return record_id
+
+    async def get_feedback_stats(
+        self,
+        bot_ids: list[str] | None = None,
+        pipeline_ids: list[str] | None = None,
+        start_time: datetime.datetime | None = None,
+        end_time: datetime.datetime | None = None,
+    ) -> dict:
+        """Get feedback statistics.
+
+        Returns:
+            Dictionary with total likes, dislikes, and breakdown by bot/pipeline
+        """
+        conditions = []
+
+        if bot_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
+        if pipeline_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
+        if start_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
+        if end_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
+
+        # Get total likes (feedback_type = 1)
+        likes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
+            persistence_monitoring.MonitoringFeedback.feedback_type == 1
+        )
+        if conditions:
+            likes_query = likes_query.where(sqlalchemy.and_(*conditions))
+        likes_result = await self.ap.persistence_mgr.execute_async(likes_query)
+        total_likes = likes_result.scalar() or 0
+
+        # Get total dislikes (feedback_type = 2)
+        dislikes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
+            persistence_monitoring.MonitoringFeedback.feedback_type == 2
+        )
+        if conditions:
+            dislikes_query = dislikes_query.where(sqlalchemy.and_(*conditions))
+        dislikes_result = await self.ap.persistence_mgr.execute_async(dislikes_query)
+        total_dislikes = dislikes_result.scalar() or 0
+
+        # Get total feedback count
+        total_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
+        if conditions:
+            total_query = total_query.where(sqlalchemy.and_(*conditions))
+        total_result = await self.ap.persistence_mgr.execute_async(total_query)
+        total_feedback = total_result.scalar() or 0
+
+        # Calculate satisfaction rate
+        satisfaction_rate = (total_likes / total_feedback * 100) if total_feedback > 0 else 0
+
+        # Get feedback by bot
+        bot_stats_query = sqlalchemy.select(
+            persistence_monitoring.MonitoringFeedback.bot_id,
+            persistence_monitoring.MonitoringFeedback.bot_name,
+            sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id).label('total'),
+            sqlalchemy.func.sum(
+                sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 1, 1), else_=0)
+            ).label('likes'),
+            sqlalchemy.func.sum(
+                sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 2, 1), else_=0)
+            ).label('dislikes'),
+        ).group_by(
+            persistence_monitoring.MonitoringFeedback.bot_id,
+            persistence_monitoring.MonitoringFeedback.bot_name,
+        )
+        if conditions:
+            bot_stats_query = bot_stats_query.where(sqlalchemy.and_(*conditions))
+        bot_stats_result = await self.ap.persistence_mgr.execute_async(bot_stats_query)
+        bot_stats = [
+            {
+                'bot_id': row.bot_id,
+                'bot_name': row.bot_name,
+                'total': row.total,
+                'likes': row.likes or 0,
+                'dislikes': row.dislikes or 0,
+            }
+            for row in bot_stats_result.all()
+        ]
+
+        return {
+            'total_feedback': total_feedback,
+            'total_likes': total_likes,
+            'total_dislikes': total_dislikes,
+            'satisfaction_rate': round(satisfaction_rate, 2),
+            'by_bot': bot_stats,
+        }
+
+    async def get_feedback_list(
+        self,
+        bot_ids: list[str] | None = None,
+        pipeline_ids: list[str] | None = None,
+        feedback_type: int | None = None,
+        start_time: datetime.datetime | None = None,
+        end_time: datetime.datetime | None = None,
+        limit: int = 100,
+        offset: int = 0,
+    ) -> tuple[list[dict], int]:
+        """Get feedback list with filters."""
+        conditions = []
+
+        if bot_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
+        if pipeline_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
+        if feedback_type is not None:
+            conditions.append(persistence_monitoring.MonitoringFeedback.feedback_type == feedback_type)
+        if start_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
+        if end_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
+
+        # Get total count
+        count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
+        if conditions:
+            count_query = count_query.where(sqlalchemy.and_(*conditions))
+        count_result = await self.ap.persistence_mgr.execute_async(count_query)
+        total = count_result.scalar() or 0
+
+        # Get feedback list
+        query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
+            persistence_monitoring.MonitoringFeedback.timestamp.desc()
+        )
+        if conditions:
+            query = query.where(sqlalchemy.and_(*conditions))
+        query = query.limit(limit).offset(offset)
+
+        result = await self.ap.persistence_mgr.execute_async(query)
+        rows = result.all()
+
+        return (
+            [
+                self.ap.persistence_mgr.serialize_model(
+                    persistence_monitoring.MonitoringFeedback, row[0] if isinstance(row, tuple) else row
+                )
+                for row in rows
+            ],
+            total,
+        )
+
+    async def export_feedback(
+        self,
+        bot_ids: list[str] | None = None,
+        pipeline_ids: list[str] | None = None,
+        start_time: datetime.datetime | None = None,
+        end_time: datetime.datetime | None = None,
+        limit: int = 100000,
+    ) -> list[dict]:
+        """Export feedback as list of dictionaries for CSV conversion."""
+        conditions = []
+
+        if bot_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
+        if pipeline_ids:
+            conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
+        if start_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
+        if end_time:
+            conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
+
+        query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
+            persistence_monitoring.MonitoringFeedback.timestamp.desc()
+        )
+        if conditions:
+            query = query.where(sqlalchemy.and_(*conditions))
+        query = query.limit(limit)
+
+        result = await self.ap.persistence_mgr.execute_async(query)
+        rows = result.all()
+
+        return [
+            {
+                'id': row[0].id if isinstance(row, tuple) else row.id,
+                'timestamp': self._format_timestamp(row[0].timestamp if isinstance(row, tuple) else row.timestamp),
+                'feedback_id': row[0].feedback_id if isinstance(row, tuple) else row.feedback_id,
+                'feedback_type': 'like'
+                if (row[0].feedback_type if isinstance(row, tuple) else row.feedback_type) == 1
+                else 'dislike',
+                'feedback_content': row[0].feedback_content if isinstance(row, tuple) else row.feedback_content,
+                'inaccurate_reasons': row[0].inaccurate_reasons if isinstance(row, tuple) else row.inaccurate_reasons,
+                'bot_id': row[0].bot_id if isinstance(row, tuple) else row.bot_id,
+                'bot_name': row[0].bot_name if isinstance(row, tuple) else row.bot_name,
+                'pipeline_id': row[0].pipeline_id if isinstance(row, tuple) else row.pipeline_id,
+                'pipeline_name': row[0].pipeline_name if isinstance(row, tuple) else row.pipeline_name,
+                'session_id': row[0].session_id if isinstance(row, tuple) else row.session_id,
+                'message_id': row[0].message_id if isinstance(row, tuple) else row.message_id,
+                'stream_id': row[0].stream_id if isinstance(row, tuple) else row.stream_id,
+                'user_id': row[0].user_id if isinstance(row, tuple) else row.user_id,
+                'platform': row[0].platform if isinstance(row, tuple) else row.platform,
+            }
+            for row in rows
+        ]

src/langbot/pkg/core/app.py

@@ -9,6 +9,7 @@ from ..platform import botmgr as im_mgr
 from ..platform.webhook_pusher import WebhookPusher
 from ..provider.session import sessionmgr as llm_session_mgr
 from ..provider.modelmgr import modelmgr as llm_model_mgr
+
 from langbot.pkg.provider.tools import toolmgr as llm_tool_mgr
 from ..config import manager as config_mgr
 from ..command import cmdmgr
@@ -30,6 +31,7 @@ from ..api.http.service import mcp as mcp_service
 from ..api.http.service import apikey as apikey_service
 from ..api.http.service import webhook as webhook_service
 from ..api.http.service import monitoring as monitoring_service
+
 from ..discover import engine as discover_engine
 from ..storage import mgr as storagemgr
 from ..utils import logcache
@@ -186,6 +188,34 @@ class Application:
                 scopes=[core_entities.LifecycleControlScope.APPLICATION],
             )
 
+            # Start monitoring data cleanup task if enabled
+            monitoring_cfg = self.instance_config.data.get('monitoring', {})
+            auto_cleanup_cfg = monitoring_cfg.get('auto_cleanup', {})
+            if auto_cleanup_cfg.get('enabled', True):
+                retention_days = auto_cleanup_cfg.get('retention_days', 30)
+                check_interval_hours = auto_cleanup_cfg.get('check_interval_hours', 1)
+
+                async def monitoring_cleanup_loop():
+                    check_interval_seconds = check_interval_hours * 3600
+                    while True:
+                        try:
+                            deleted = await self.monitoring_service.cleanup_expired_records(retention_days)
+                            total_deleted = sum(deleted.values())
+                            if total_deleted > 0:
+                                self.logger.info(
+                                    f'Monitoring auto-cleanup: deleted {total_deleted} expired records '
+                                    f'(retention={retention_days}d): {deleted}'
+                                )
+                        except Exception as e:
+                            self.logger.warning(f'Monitoring auto-cleanup error: {e}')
+                        await asyncio.sleep(check_interval_seconds)
+
+                self.task_mgr.create_task(
+                    monitoring_cleanup_loop(),
+                    name='monitoring-cleanup',
+                    scopes=[core_entities.LifecycleControlScope.APPLICATION],
+                )
+
             self.task_mgr.create_task(
                 never_ending(),
                 name='never-ending-task',

src/langbot/pkg/core/stages/load_config.py

@@ -74,20 +74,26 @@ def _apply_env_overrides_to_config(cfg: dict) -> dict:
         current = cfg
 
         for i, key in enumerate(keys):
-            if not isinstance(current, dict) or key not in current:
+            if not isinstance(current, dict):
                 break
 
             if i == len(keys) - 1:
-                # At the final key - check if it's a scalar value
+                # At the final key
-                if isinstance(current[key], (dict, list)):
+                if key in current:
-                    # Skip dict and list types
+                    if isinstance(current[key], (dict, list)):
-                    pass
+                        # Skip dict and list types
+                        pass
+                    else:
+                        # Valid scalar value - convert and set it
+                        converted_value = convert_value(env_value, current[key])
+                        current[key] = converted_value
                 else:
-                    # Valid scalar value - convert and set it
+                    # Key doesn't exist yet - create it as string
-                    converted_value = convert_value(env_value, current[key])
+                    current[key] = env_value
-                    current[key] = converted_value
             else:
-                # Navigate deeper
+                # Navigate deeper - create intermediate dict if needed
+                if key not in current:
+                    current[key] = {}
                 current = current[key]
 
     return cfg
@@ -146,16 +152,50 @@ class LoadConfigStage(stage.BootingStage):
         await ap.instance_config.dump_config()
 
         # load or generate instance id
-        ap.instance_id = await config.load_json_config(
+        # Priority:
-            'data/labels/instance_id.json',
+        # 1. system.instance_id from config.yaml (can be set via SYSTEM__INSTANCE_ID env var)
-            template_data={
+        # 2. data/labels/instance_id.json (if file exists)
-                'instance_id': f'instance_{str(uuid.uuid4())}',
+        # 3. Generate new and save to file
-                'instance_create_ts': int(time.time()),
+        config_instance_id = ap.instance_config.data.get('system', {}).get('instance_id', '')
-            },
-            completion=False,
-        )
 
-        constants.instance_id = ap.instance_id.data['instance_id']
+        if config_instance_id:
+            # Use the instance_id from config.yaml
+            constants.instance_id = config_instance_id
+            # Still load/create the file for backward compat, but don't use its value
+            ap.instance_id = await config.load_json_config(
+                'data/labels/instance_id.json',
+                template_data={
+                    'instance_id': f'instance_{str(uuid.uuid4())}',
+                    'instance_create_ts': int(time.time()),
+                },
+                completion=False,
+            )
+        else:
+            # Try loading file-based instance id
+            instance_id_path = os.path.join('data', 'labels', 'instance_id.json')
+            if os.path.exists(instance_id_path):
+                # File exists, read it
+                ap.instance_id = await config.load_json_config(
+                    'data/labels/instance_id.json',
+                    template_data={
+                        'instance_id': '',
+                        'instance_create_ts': 0,
+                    },
+                    completion=False,
+                )
+                constants.instance_id = ap.instance_id.data['instance_id']
+            else:
+                # Neither config nor file, generate new and save to file
+                new_id = f'instance_{str(uuid.uuid4())}'
+                ap.instance_id = await config.load_json_config(
+                    'data/labels/instance_id.json',
+                    template_data={
+                        'instance_id': new_id,
+                        'instance_create_ts': int(time.time()),
+                    },
+                    completion=False,
+                )
+                constants.instance_id = new_id
         constants.edition = ap.instance_config.data.get('system', {}).get('edition', 'community')
 
         print(f'LangBot instance id: {constants.instance_id}')

src/langbot/pkg/core/taskmgr.py

@@ -17,9 +17,13 @@ class TaskContext:
     log: str
     """Log"""
 
+    metadata: dict
+    """Structured metadata for progress reporting"""
+
     def __init__(self):
         self.current_action = 'default'
         self.log = ''
+        self.metadata = {}
 
     def _log(self, msg: str):
         self.log += msg + '\n'
@@ -38,7 +42,7 @@ class TaskContext:
         self._log(f'{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | {self.current_action} | {msg}')
 
     def to_dict(self) -> dict:
-        return {'current_action': self.current_action, 'log': self.log}
+        return {'current_action': self.current_action, 'log': self.log, 'metadata': self.metadata}
 
     @staticmethod
     def new() -> TaskContext:
@@ -211,9 +215,14 @@ class AsyncTaskManager:
     def get_tasks_dict(
         self,
         type: str = None,
+        kind: str = None,
     ) -> dict:
         return {
-            'tasks': [t.to_dict() for t in self.tasks if type is None or t.task_type == type],
+            'tasks': [
+                t.to_dict()
+                for t in self.tasks
+                if (type is None or t.task_type == type) and (kind is None or t.kind == kind)
+            ],
             'id_index': TaskWrapper._id_index,
         }
 

src/langbot/pkg/discover/engine.py

@@ -17,11 +17,23 @@ class I18nString(pydantic.BaseModel):
     """英文"""
 
     zh_Hans: typing.Optional[str] = None
-    """中文"""
+    """简体中文"""
+
+    zh_Hant: typing.Optional[str] = None
+    """繁体中文"""
 
     ja_JP: typing.Optional[str] = None
     """日文"""
 
+    th_TH: typing.Optional[str] = None
+    """泰文"""
+
+    vi_VN: typing.Optional[str] = None
+    """越南文"""
+
+    es_ES: typing.Optional[str] = None
+    """西班牙文"""
+
     def to_dict(self) -> dict:
         """转换为字典"""
         dic = {}
@@ -29,8 +41,16 @@ class I18nString(pydantic.BaseModel):
             dic['en_US'] = self.en_US
         if self.zh_Hans is not None:
             dic['zh_Hans'] = self.zh_Hans
+        if self.zh_Hant is not None:
+            dic['zh_Hant'] = self.zh_Hant
         if self.ja_JP is not None:
             dic['ja_JP'] = self.ja_JP
+        if self.th_TH is not None:
+            dic['th_TH'] = self.th_TH
+        if self.vi_VN is not None:
+            dic['vi_VN'] = self.vi_VN
+        if self.es_ES is not None:
+            dic['es_ES'] = self.es_ES
         return dic
 
 

src/langbot/pkg/entity/persistence/monitoring.py

@@ -20,6 +20,7 @@ class MonitoringMessage(Base):
     level = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # info, warning, error, debug
     platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # User display name
     runner_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # Runner name for this query
     variables = sqlalchemy.Column(sqlalchemy.Text, nullable=True)  # Query variables as JSON string
     role = sqlalchemy.Column(sqlalchemy.String(50), nullable=True, default='user')  # user, assistant
@@ -64,6 +65,7 @@ class MonitoringSession(Base):
     is_active = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True, index=True)
     platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # User display name
 
 
 class MonitoringError(Base):
@@ -104,3 +106,26 @@ class MonitoringEmbeddingCall(Base):
     session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
     message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
     call_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)  # embedding, retrieve
+
+
+class MonitoringFeedback(Base):
+    """User feedback records (like/dislike) from AI Bot conversations"""
+
+    __tablename__ = 'monitoring_feedback'
+
+    id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    feedback_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
+    feedback_type = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)  # 1=like, 2=dislike
+    feedback_content = sqlalchemy.Column(sqlalchemy.Text, nullable=True)  # User feedback text
+    inaccurate_reasons = sqlalchemy.Column(sqlalchemy.Text, nullable=True)  # JSON list of inaccurate reasons
+    # Context fields
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    stream_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # e.g., wecom

src/langbot/pkg/persistence/mgr.py

@@ -2,18 +2,16 @@ from __future__ import annotations
 
 import datetime
 import typing
-import json
+
-import uuid
 
 import sqlalchemy.ext.asyncio as sqlalchemy_asyncio
 import sqlalchemy
 
 from . import database, migration
-from ..entity.persistence import base, pipeline, metadata, model as persistence_model
+from ..entity.persistence import base, metadata, model as persistence_model
 from ..entity import persistence
 from ..core import app
 from ..utils import constants, importutil
-from ..api.http.service import pipeline as pipeline_service
 from . import databases, migrations
 
 importutil.import_modules_in_pkg(databases)
@@ -78,7 +76,6 @@ class PersistenceManager:
 
             self.ap.logger.info(f'Successfully upgraded database to version {last_migration_number}.')
 
-        await self.write_default_pipeline()
         await self.write_space_model_providers()
 
     async def create_tables(self):
@@ -101,29 +98,6 @@ class PersistenceManager:
             if row is None:
                 await self.execute_async(sqlalchemy.insert(metadata.Metadata).values(item))
 
-    async def write_default_pipeline(self):
-        # write default pipeline
-        result = await self.execute_async(sqlalchemy.select(pipeline.LegacyPipeline))
-        default_pipeline_uuid = None
-        if result.first() is None:
-            self.ap.logger.info('Creating default pipeline...')
-
-            pipeline_config = json.loads(importutil.read_resource_file('templates/default-pipeline-config.json'))
-
-            default_pipeline_uuid = str(uuid.uuid4())
-            pipeline_data = {
-                'uuid': default_pipeline_uuid,
-                'for_version': self.ap.ver_mgr.get_current_version(),
-                'stages': pipeline_service.default_stage_order,
-                'is_default': True,
-                'name': 'ChatPipeline',
-                'description': 'Default pipeline, new bots will be bound to this pipeline | 默认提供的流水线，您配置的机器人将自动绑定到此流水线',
-                'config': pipeline_config,
-                'extensions_preferences': {},
-            }
-
-            await self.execute_async(sqlalchemy.insert(pipeline.LegacyPipeline).values(pipeline_data))
-
     async def write_space_model_providers(self):
         space_models_gateway_api_url = self.ap.instance_config.data.get('space', {}).get(
             'models_gateway_api_url', 'https://api.langbot.cloud/v1'

src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py

@@ -1,5 +1,3 @@
-import json
-
 import sqlalchemy
 from .. import migration
 
@@ -9,20 +7,22 @@ class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration):
     """Migrate to unified Knowledge Engine plugin architecture.
 
     Changes:
-    - Add knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings columns to knowledge_bases
+    - Backup existing knowledge_bases data to knowledge_bases_backup
-    - Migrate existing top_k values into retrieval_settings JSON
+    - Clear knowledge_bases table and add new plugin architecture columns
-    - Migrate existing embedding_model_uuid into creation_settings JSON
+    - Drop old columns (PostgreSQL only; SQLite leaves them unmapped)
-    - Drop embedding_model_uuid and top_k columns (PostgreSQL only; SQLite leaves them unmapped)
+    - Preserve external_knowledge_bases table as-is for future migration
-    - Drop external_knowledge_bases table (no longer needed; external KB data is not migrated)
+    - Set rag_plugin_migration_needed flag in metadata if old data exists
     """
 
     async def upgrade(self):
         """Upgrade"""
+        has_internal_data = await self._backup_knowledge_bases()
+        has_external_data = await self._check_external_knowledge_bases()
+        await self._clear_knowledge_bases()
         await self._add_columns_to_knowledge_bases()
-        await self._migrate_top_k_to_retrieval_settings()
-        await self._migrate_embedding_model_uuid_to_creation_settings()
         await self._drop_old_columns()
-        await self._drop_external_knowledge_bases_table()
+        if has_internal_data or has_external_data:
+            await self._set_migration_flag()
 
     async def _get_table_columns(self, table_name: str) -> list[str]:
         """Get column names from a table (works for both SQLite and PostgreSQL)."""
@@ -57,6 +57,50 @@ class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration):
             )
             return result.first() is not None
 
+    async def _backup_knowledge_bases(self) -> bool:
+        """Backup knowledge_bases data. Returns True if data was backed up."""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text('SELECT COUNT(*) FROM knowledge_bases;'))
+        count = result.scalar()
+        if count == 0:
+            return False
+
+        # Drop backup table if it already exists (from a previous failed migration)
+        if await self._table_exists('knowledge_bases_backup'):
+            await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE knowledge_bases_backup;'))
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('CREATE TABLE knowledge_bases_backup AS SELECT * FROM knowledge_bases;')
+        )
+        self.ap.logger.info(
+            'Backed up %d knowledge base(s) to knowledge_bases_backup table.',
+            count,
+        )
+        return True
+
+    async def _check_external_knowledge_bases(self) -> bool:
+        """Check if external_knowledge_bases table exists and has data.
+
+        The table is preserved as-is (not dropped) for future migration.
+        """
+        if not await self._table_exists('external_knowledge_bases'):
+            return False
+
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT COUNT(*) FROM external_knowledge_bases;')
+        )
+        count = result.scalar()
+        if count > 0:
+            self.ap.logger.info(
+                'Found %d external knowledge base(s) in external_knowledge_bases table. '
+                'Table preserved for future migration.',
+                count,
+            )
+        return count > 0
+
+    async def _clear_knowledge_bases(self):
+        """Clear all rows from knowledge_bases table (preserve table structure)."""
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DELETE FROM knowledge_bases;'))
+
     async def _add_columns_to_knowledge_bases(self):
         """Add new RAG plugin architecture columns to knowledge_bases table."""
         columns = await self._get_table_columns('knowledge_bases')
@@ -74,73 +118,6 @@ class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration):
                     sqlalchemy.text(f'ALTER TABLE knowledge_bases ADD COLUMN {col_name} {col_type};')
                 )
 
-        # For existing knowledge bases without knowledge_engine_plugin_id,
-        # set collection_id = uuid (same default as new KBs)
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('UPDATE knowledge_bases SET collection_id = uuid WHERE collection_id IS NULL;')
-        )
-
-    async def _migrate_top_k_to_retrieval_settings(self):
-        """Migrate existing top_k values into retrieval_settings JSON."""
-        columns = await self._get_table_columns('knowledge_bases')
-        if 'top_k' not in columns:
-            return
-
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text(
-                'SELECT uuid, top_k FROM knowledge_bases WHERE top_k IS NOT NULL AND retrieval_settings IS NULL;'
-            )
-        )
-        rows = result.fetchall()
-
-        for row in rows:
-            kb_uuid = row[0]
-            top_k = row[1]
-            retrieval_settings = json.dumps({'top_k': top_k})
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('UPDATE knowledge_bases SET retrieval_settings = :rs WHERE uuid = :uuid;').bindparams(
-                    rs=retrieval_settings, uuid=kb_uuid
-                )
-            )
-
-    async def _migrate_embedding_model_uuid_to_creation_settings(self):
-        """Migrate existing embedding_model_uuid into creation_settings JSON."""
-        columns = await self._get_table_columns('knowledge_bases')
-        if 'embedding_model_uuid' not in columns:
-            return
-
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text(
-                'SELECT uuid, embedding_model_uuid, creation_settings FROM knowledge_bases '
-                "WHERE embedding_model_uuid IS NOT NULL AND embedding_model_uuid != '';"
-            )
-        )
-        rows = result.fetchall()
-
-        for row in rows:
-            kb_uuid = row[0]
-            emb_uuid = row[1]
-            existing_settings = row[2]
-
-            if existing_settings and isinstance(existing_settings, str):
-                try:
-                    settings = json.loads(existing_settings)
-                except (json.JSONDecodeError, TypeError):
-                    settings = {}
-            elif isinstance(existing_settings, dict):
-                settings = existing_settings
-            else:
-                settings = {}
-
-            if 'embedding_model_uuid' not in settings:
-                settings['embedding_model_uuid'] = emb_uuid
-                new_settings = json.dumps(settings)
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE knowledge_bases SET creation_settings = :cs WHERE uuid = :uuid;'
-                    ).bindparams(cs=new_settings, uuid=kb_uuid)
-                )
-
     async def _drop_old_columns(self):
         """Drop embedding_model_uuid and top_k columns (PostgreSQL only).
 
@@ -162,22 +139,22 @@ class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration):
                 sqlalchemy.text('ALTER TABLE knowledge_bases DROP COLUMN top_k;')
             )
 
-    async def _drop_external_knowledge_bases_table(self):
+    async def _set_migration_flag(self):
-        """Drop the external_knowledge_bases table if it exists."""
+        """Set rag_plugin_migration_needed flag in metadata table."""
-        if await self._table_exists('external_knowledge_bases'):
+        # Check if the key already exists
-            # Log existing external KBs before dropping, so users are aware of data loss
+        result = await self.ap.persistence_mgr.execute_async(
-            rows = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text("SELECT value FROM metadata WHERE key = 'rag_plugin_migration_needed';")
-                sqlalchemy.text('SELECT * FROM external_knowledge_bases;')
+        )
+        row = result.first()
+        if row is not None:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("UPDATE metadata SET value = 'true' WHERE key = 'rag_plugin_migration_needed';")
             )
-            existing = rows.fetchall()
+        else:
-            if existing:
+            await self.ap.persistence_mgr.execute_async(
-                self.ap.logger.warning(
+                sqlalchemy.text("INSERT INTO metadata (key, value) VALUES ('rag_plugin_migration_needed', 'true');")
-                    'Dropping external_knowledge_bases table with %d existing record(s). '
+            )
-                    'These external KB configurations will be removed: %s',
+        self.ap.logger.info('Set rag_plugin_migration_needed=true in metadata.')
-                    len(existing),
-                    [dict(row._mapping) for row in existing],
-                )
-            await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE external_knowledge_bases;'))
 
     async def downgrade(self):
         """Downgrade"""

src/langbot/pkg/persistence/migrations/dbm021_merge_exception_handling.py

@@ -0,0 +1,74 @@
+from .. import migration
+
+import sqlalchemy
+import json
+
+
+@migration.migration_class(21)
+class DBMigrateMergeExceptionHandling(migration.DBMigration):
+    """Merge hide-exception and block-failed-request-output into a single exception-handling select option,
+    and add failure-hint field.
+
+    Conversion logic:
+    - block-failed-request-output=true  ->  exception-handling: hide
+    - hide-exception=true               ->  exception-handling: show-hint
+    - hide-exception=false              ->  exception-handling: show-error
+    """
+
+    async def upgrade(self):
+        """Upgrade"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
+        )
+        pipelines = result.fetchall()
+
+        current_version = self.ap.ver_mgr.get_current_version()
+
+        for pipeline_row in pipelines:
+            uuid = pipeline_row[0]
+            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
+
+            if 'output' not in config:
+                config['output'] = {}
+            if 'misc' not in config['output']:
+                config['output']['misc'] = {}
+
+            misc = config['output']['misc']
+
+            # Determine new exception-handling value from legacy fields
+            hide_exception = misc.get('hide-exception', True)
+            block_failed = misc.get('block-failed-request-output', False)
+
+            if block_failed:
+                exception_handling = 'hide'
+            elif hide_exception:
+                exception_handling = 'show-hint'
+            else:
+                exception_handling = 'show-error'
+
+            misc['exception-handling'] = exception_handling
+
+            # Add failure-hint with default value
+            misc['failure-hint'] = 'Request failed.'
+
+            # Remove legacy fields
+            misc.pop('hide-exception', None)
+
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass

src/langbot/pkg/persistence/migrations/dbm022_monitoring_user_name.py

@@ -0,0 +1,73 @@
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(22)
+class DBMigrateMonitoringUserId(migration.DBMigration):
+    """Add user_id and user_name columns to monitoring_sessions table
+
+    This migration adds the missing user_id column and also ensures user_name
+    column exists (in case migration 21 failed or was skipped).
+    """
+
+    async def _table_exists(self, table_name: str) -> bool:
+        """Check if a table exists (works for both SQLite and PostgreSQL)."""
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);'
+                ).bindparams(table_name=table_name)
+            )
+            return bool(result.scalar())
+        else:
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams(
+                    table_name=table_name
+                )
+            )
+            return result.first() is not None
+
+    async def _get_table_columns(self, table_name: str) -> list[str]:
+        """Get column names from a table (works for both SQLite and PostgreSQL)."""
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    'SELECT column_name FROM information_schema.columns WHERE table_name = :table_name;'
+                ).bindparams(table_name=table_name)
+            )
+            return [row[0] for row in result.fetchall()]
+        else:
+            if not table_name.isidentifier():
+                raise ValueError(f'Invalid table name: {table_name}')
+            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});'))
+            return [row[1] for row in result.fetchall()]
+
+    async def _add_column_if_not_exists(self, table_name: str, column_name: str, column_type: str):
+        """Add a column to a table if it does not already exist."""
+        columns = await self._get_table_columns(table_name)
+        if column_name in columns:
+            self.ap.logger.debug('%s column already exists in %s.', column_name, table_name)
+            return
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text(f'ALTER TABLE {table_name} ADD COLUMN {column_name} {column_type};')
+        )
+        self.ap.logger.info('Added %s column to %s table.', column_name, table_name)
+
+    async def upgrade(self):
+        # Check if monitoring_sessions table exists
+        if not await self._table_exists('monitoring_sessions'):
+            self.ap.logger.warning('monitoring_sessions table does not exist, skipping migration.')
+            return
+
+        # Add user_id column to monitoring_sessions table
+        await self._add_column_if_not_exists('monitoring_sessions', 'user_id', 'VARCHAR(255)')
+
+        # Add user_name column to monitoring_sessions table (in case migration 21 failed)
+        await self._add_column_if_not_exists('monitoring_sessions', 'user_name', 'VARCHAR(255)')
+
+        # Add user_name column to monitoring_messages table (in case migration 21 failed)
+        if await self._table_exists('monitoring_messages'):
+            await self._add_column_if_not_exists('monitoring_messages', 'user_name', 'VARCHAR(255)')
+
+    async def downgrade(self):
+        pass

src/langbot/pkg/persistence/migrations/dbm023_model_fallback_config.py

@@ -0,0 +1,102 @@
+from .. import migration
+
+import sqlalchemy
+import json
+
+
+@migration.migration_class(23)
+class DBMigrateModelFallbackConfig(migration.DBMigration):
+    """Convert model field from plain UUID string to object with primary/fallbacks"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
+        )
+        pipelines = result.fetchall()
+
+        current_version = self.ap.ver_mgr.get_current_version()
+
+        for pipeline_row in pipelines:
+            uuid = pipeline_row[0]
+            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
+
+            if 'ai' not in config or 'local-agent' not in config['ai']:
+                continue
+
+            local_agent = config['ai']['local-agent']
+            changed = False
+
+            # Convert model from string to object
+            model_value = local_agent.get('model', '')
+            if isinstance(model_value, str):
+                local_agent['model'] = {
+                    'primary': model_value,
+                    'fallbacks': [],
+                }
+                changed = True
+
+            # Remove leftover fallback-models field if present
+            if 'fallback-models' in local_agent:
+                del local_agent['fallback-models']
+                changed = True
+
+            if not changed:
+                continue
+
+            # Update using raw SQL with compatibility for both SQLite and PostgreSQL
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )
+
+    async def downgrade(self):
+        """Downgrade"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
+        )
+        pipelines = result.fetchall()
+
+        current_version = self.ap.ver_mgr.get_current_version()
+
+        for pipeline_row in pipelines:
+            uuid = pipeline_row[0]
+            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
+
+            if 'ai' not in config or 'local-agent' not in config['ai']:
+                continue
+
+            local_agent = config['ai']['local-agent']
+
+            # Convert model from object back to string
+            model_value = local_agent.get('model', '')
+            if isinstance(model_value, dict):
+                local_agent['model'] = model_value.get('primary', '')
+            else:
+                continue
+
+            # Update using raw SQL with compatibility for both SQLite and PostgreSQL
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(
+                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
+                    ),
+                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
+                )

src/langbot/pkg/persistence/migrations/dbm024_wecombot_websocket_mode.py

@@ -0,0 +1,49 @@
+from .. import migration
+
+import sqlalchemy
+import json
+
+
+@migration.migration_class(24)
+class DBMigrateWecomBotWebSocketMode(migration.DBMigration):
+    """Add enable-webhook field to existing wecombot adapter configs.
+
+    Existing wecombot bots were all using webhook mode, so we set
+    enable-webhook=true to preserve their behavior after the new
+    WebSocket long connection mode is introduced as default.
+    """
+
+    async def upgrade(self):
+        """Upgrade"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text("SELECT uuid, adapter_config FROM bots WHERE adapter = 'wecombot'")
+        )
+        bots = result.fetchall()
+
+        for bot_row in bots:
+            bot_uuid = bot_row[0]
+            adapter_config = json.loads(bot_row[1]) if isinstance(bot_row[1], str) else bot_row[1]
+
+            if 'enable-webhook' in adapter_config:
+                continue
+
+            # Determine mode based on existing config: if webhook fields are present, keep webhook mode
+            has_webhook_config = bool(
+                adapter_config.get('Token') and adapter_config.get('EncodingAESKey') and adapter_config.get('Corpid')
+            )
+            adapter_config['enable-webhook'] = has_webhook_config
+
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('UPDATE bots SET adapter_config = :config::jsonb WHERE uuid = :uuid'),
+                    {'config': json.dumps(adapter_config), 'uuid': bot_uuid},
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('UPDATE bots SET adapter_config = :config WHERE uuid = :uuid'),
+                    {'config': json.dumps(adapter_config), 'uuid': bot_uuid},
+                )
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass

src/langbot/pkg/pipeline/config_coercion.py

@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+# metadata type -> coercion function
+_COERCE_MAP = {
+    'integer': lambda v: int(v),
+    'number': lambda v: float(v),
+    'float': lambda v: float(v),
+}
+
+
+def _coerce_bool(v):
+    if isinstance(v, bool):
+        return v
+    if isinstance(v, str):
+        if v.lower() == 'true':
+            return True
+        if v.lower() == 'false':
+            return False
+        raise ValueError(f'Cannot convert string {v!r} to bool')
+    return bool(v)
+
+
+def _coerce_value(value, expected_type: str):
+    """Convert a single value to the expected type.
+
+    Returns the converted value, or the original value if no conversion needed.
+    """
+    if value is None:
+        return value
+
+    if expected_type == 'boolean':
+        if isinstance(value, bool):
+            return value
+        return _coerce_bool(value)
+
+    coerce_fn = _COERCE_MAP.get(expected_type)
+    if coerce_fn is None:
+        return value
+
+    # Already the correct type
+    if expected_type == 'integer' and isinstance(value, int) and not isinstance(value, bool):
+        return value
+    if expected_type in ('number', 'float') and isinstance(value, (int, float)) and not isinstance(value, bool):
+        return float(value)
+
+    return coerce_fn(value)
+
+
+def coerce_pipeline_config(
+    config: dict,
+    *metadata_list: dict,
+) -> None:
+    """Coerce pipeline config values according to metadata type definitions.
+
+    Walks each metadata dict (trigger, safety, ai, output) and converts
+    config values in-place so that strings coming from the JSON column are
+    cast to their declared types (integer, number/float, boolean).
+
+    Args:
+        config: The pipeline config dict to modify in-place.
+        *metadata_list: Metadata dicts loaded from the YAML templates.
+    """
+    for meta in metadata_list:
+        section_name = meta.get('name')
+        if not section_name or section_name not in config:
+            continue
+
+        section = config[section_name]
+        if not isinstance(section, dict):
+            continue
+
+        for stage_def in meta.get('stages', []):
+            stage_name = stage_def.get('name')
+            if not stage_name or stage_name not in section:
+                continue
+
+            stage_config = section[stage_name]
+            if not isinstance(stage_config, dict):
+                continue
+
+            for field_def in stage_def.get('config', []):
+                field_name = field_def.get('name')
+                field_type = field_def.get('type')
+                if not field_name or not field_type or field_name not in stage_config:
+                    continue
+
+                old_value = stage_config[field_name]
+                try:
+                    new_value = _coerce_value(old_value, field_type)
+                    if new_value is not old_value:
+                        stage_config[field_name] = new_value
+                except (ValueError, TypeError) as e:
+                    logger.warning(
+                        'Failed to coerce config %s.%s.%s (%r) to %s: %s',
+                        section_name,
+                        stage_name,
+                        field_name,
+                        old_value,
+                        field_type,
+                        e,
+                    )

src/langbot/pkg/pipeline/longtext/longtext.py

@@ -22,13 +22,10 @@ class LongTextProcessStage(stage.PipelineStage):
     """
 
     strategy_impl: strategy.LongTextStrategy | None
-    is_split: bool
 
     async def initialize(self, pipeline_config: dict):
         config = pipeline_config['output']['long-text-processing']
 
-        self.is_split = config['strategy'] == 'split'
-
         if config['strategy'] == 'none':
             self.strategy_impl = None
             return
@@ -93,23 +90,8 @@ class LongTextProcessStage(stage.PipelineStage):
             len(str(query.resp_message_chain[-1]))
             > query.pipeline_config['output']['long-text-processing']['threshold']
         ):
-            if self.is_split:
+            query.resp_message_chain[-1] = platform_message.MessageChain(
-                original_text = str(query.resp_message_chain[-1])
+                await self.strategy_impl.process(str(query.resp_message_chain[-1]), query)
-                threshold = query.pipeline_config['output']['long-text-processing']['threshold']
+            )
-                segments = self.strategy_impl.split_text(original_text, threshold)
-                # Replace the last chain with the first segment, store extra segments separately
-                # to avoid interfering with existing multi-chain scenarios (e.g. agent tool calls)
-                query.resp_message_chain[-1] = platform_message.MessageChain(
-                    [platform_message.Plain(text=segments[0])]
-                )
-                if len(segments) > 1:
-                    query.set_variable(
-                        '_longtext_split_extra_chains',
-                        [platform_message.MessageChain([platform_message.Plain(text=seg)]) for seg in segments[1:]],
-                    )
-            else:
-                query.resp_message_chain[-1] = platform_message.MessageChain(
-                    await self.strategy_impl.process(str(query.resp_message_chain[-1]), query)
-                )
 
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/pipeline/longtext/strategies/split.py

@@ -1,224 +0,0 @@
-from __future__ import annotations
-
-import re
-
-from .. import strategy as strategy_model
-
-import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-
-
-@strategy_model.strategy_class('split')
-class SplitStrategy(strategy_model.LongTextStrategy):
-    """Split long text into multiple message segments with Markdown awareness."""
-
-    async def process(self, message: str, query: pipeline_query.Query) -> list[platform_message.MessageComponent]:
-        segments = self.split_text(
-            message,
-            query.pipeline_config['output']['long-text-processing']['threshold'],
-        )
-        return [platform_message.Plain(text=segments[0])] if segments else []
-
-    def split_text(self, text: str, max_length: int) -> list[str]:
-        """Split text into segments respecting Markdown structure.
-
-        Priority:
-            1. Markdown structural boundaries (headings, code blocks, horizontal rules)
-            2. Paragraph breaks (blank lines)
-            3. List item boundaries
-            4. Line breaks
-            5. Hard cut (fallback)
-        """
-        if len(text) <= max_length:
-            return [text]
-
-        blocks = self._parse_markdown_blocks(text)
-        return self._merge_blocks(blocks, max_length)
-
-    def _parse_markdown_blocks(self, text: str) -> list[str]:
-        """Parse text into Markdown-aware blocks.
-
-        Keeps code blocks intact and splits the rest by structural elements.
-        """
-        blocks: list[str] = []
-        lines = text.split('\n')
-        current_block: list[str] = []
-        in_code_block = False
-
-        for line in lines:
-            stripped = line.strip()
-
-            # Toggle fenced code block state
-            if stripped.startswith('```'):
-                if in_code_block:
-                    # End of code block - close it as one block
-                    current_block.append(line)
-                    blocks.append('\n'.join(current_block))
-                    current_block = []
-                    in_code_block = False
-                    continue
-                else:
-                    # Start of code block - flush current block first
-                    if current_block:
-                        blocks.append('\n'.join(current_block))
-                        current_block = []
-                    current_block.append(line)
-                    in_code_block = True
-                    continue
-
-            if in_code_block:
-                current_block.append(line)
-                continue
-
-            # Heading (# ...) - start a new block
-            if re.match(r'^#{1,6}\s', stripped):
-                if current_block:
-                    blocks.append('\n'.join(current_block))
-                    current_block = []
-                current_block.append(line)
-                continue
-
-            # Horizontal rule (---, ***, ___) - start a new block
-            if re.match(r'^(-{3,}|\*{3,}|_{3,})\s*$', stripped):
-                if current_block:
-                    blocks.append('\n'.join(current_block))
-                    current_block = []
-                blocks.append(line)
-                continue
-
-            # Blank line - paragraph boundary
-            if stripped == '':
-                if current_block:
-                    current_block.append(line)
-                    blocks.append('\n'.join(current_block))
-                    current_block = []
-                continue
-
-            current_block.append(line)
-
-        # Flush remaining (including unclosed code blocks)
-        if current_block:
-            blocks.append('\n'.join(current_block))
-
-        return [b for b in blocks if b.strip()]
-
-    def _merge_blocks(self, blocks: list[str], max_length: int) -> list[str]:
-        """Merge small blocks greedily until approaching max_length.
-
-        If a single block exceeds max_length, split it by lines as fallback.
-        """
-        segments: list[str] = []
-        current = ''
-
-        for block in blocks:
-            candidate = (current + '\n\n' + block) if current else block
-
-            if len(candidate) <= max_length:
-                current = candidate
-            else:
-                # Flush current segment
-                if current:
-                    segments.append(current)
-
-                # Check if this single block fits
-                if len(block) <= max_length:
-                    current = block
-                else:
-                    # Block too large - split it by lines
-                    for part in self._split_large_block(block, max_length):
-                        segments.append(part)
-                    current = ''
-
-        if current:
-            segments.append(current)
-
-        return [s for s in segments if s.strip()]
-
-    def _split_large_block(self, block: str, max_length: int) -> list[str]:
-        """Split an oversized block by lines, preserving code block fences.
-
-        For single-line plain text (no newlines), falls back to splitting at
-        natural language boundaries (spaces, punctuation).
-        """
-        lines = block.split('\n')
-
-        # Single long line with no newlines - use plain text splitting
-        if len(lines) == 1:
-            return self._split_plain_text(block, max_length)
-
-        is_code_block = lines[0].strip().startswith('```')
-
-        segments: list[str] = []
-        current_lines: list[str] = []
-        current_len = 0
-
-        # For code blocks, track the opening fence to re-apply on continuations
-        code_fence = lines[0] if is_code_block else ''
-
-        for i, line in enumerate(lines):
-            line_len = len(line) + 1  # +1 for newline
-
-            # Single line exceeds limit on its own - split it first
-            if line_len > max_length:
-                if current_lines:
-                    seg = '\n'.join(current_lines)
-                    if is_code_block and not seg.rstrip().endswith('```'):
-                        seg += '\n```'
-                    segments.append(seg)
-                    current_lines = []
-                    current_len = 0
-
-                for part in self._split_plain_text(line, max_length):
-                    segments.append(part)
-                continue
-
-            if current_len + line_len > max_length and current_lines:
-                segment = '\n'.join(current_lines)
-                # Close code block fence if splitting mid-code-block
-                if is_code_block and not segment.rstrip().endswith('```'):
-                    segment += '\n```'
-                segments.append(segment)
-
-                current_lines = []
-                current_len = 0
-                # Re-open code block fence for continuation
-                if is_code_block and i < len(lines) - 1 and not line.strip().startswith('```'):
-                    current_lines.append(code_fence)
-                    current_len = len(code_fence) + 1
-
-            current_lines.append(line)
-            current_len += line_len
-
-        if current_lines:
-            segments.append('\n'.join(current_lines))
-
-        return segments
-
-    def _split_plain_text(self, text: str, max_length: int) -> list[str]:
-        """Split a long plain text string (no newlines) at word/space boundaries."""
-        if len(text) <= max_length:
-            return [text]
-
-        segments: list[str] = []
-        remaining = text
-
-        while remaining:
-            if len(remaining) <= max_length:
-                segments.append(remaining)
-                break
-
-            chunk = remaining[:max_length]
-            min_pos = int(max_length * 0.3)
-
-            # Try to find a space to split at
-            pos = chunk.rfind(' ')
-            if pos >= min_pos:
-                split_pos = pos
-            else:
-                # Hard cut as last resort
-                split_pos = max_length
-
-            segments.append(remaining[:split_pos].rstrip())
-            remaining = remaining[split_pos:].lstrip()
-
-        return [s for s in segments if s]

src/langbot/pkg/pipeline/monitoring_helper.py

@@ -34,6 +34,15 @@ class MonitoringHelper:
             # Check if session exists, if not, record session start
             session_id = f'{query.launcher_type}_{query.launcher_id}'
 
+            # Get sender name from message event
+            sender_name = None
+            if hasattr(query, 'message_event'):
+                if hasattr(query.message_event, 'sender'):
+                    if hasattr(query.message_event.sender, 'nickname'):
+                        sender_name = query.message_event.sender.nickname
+                    elif hasattr(query.message_event.sender, 'member_name'):
+                        sender_name = query.message_event.sender.member_name
+
             # Try to record message
             # Use JSON serialization to preserve message chain structure (including image URLs, etc.)
             if hasattr(query, 'message_chain') and hasattr(query.message_chain, 'model_dump'):
@@ -57,6 +66,7 @@ class MonitoringHelper:
                 if hasattr(query.launcher_type, 'value')
                 else str(query.launcher_type),
                 user_id=query.sender_id,
+                user_name=sender_name,
                 runner_name=runner_name,
                 variables=None,  # Will be updated in record_query_success
             )
@@ -80,6 +90,7 @@ class MonitoringHelper:
                     if hasattr(query.launcher_type, 'value')
                     else str(query.launcher_type),
                     user_id=query.sender_id,
+                    user_name=sender_name,
                 )
 
             return message_id
@@ -128,6 +139,15 @@ class MonitoringHelper:
         try:
             session_id = f'{query.launcher_type}_{query.launcher_id}'
 
+            # Get sender name from message event
+            sender_name = None
+            if hasattr(query, 'message_event'):
+                if hasattr(query.message_event, 'sender'):
+                    if hasattr(query.message_event.sender, 'nickname'):
+                        sender_name = query.message_event.sender.nickname
+                    elif hasattr(query.message_event.sender, 'member_name'):
+                        sender_name = query.message_event.sender.member_name
+
             # Extract response content from resp_message_chain
             if hasattr(query, 'resp_message_chain') and query.resp_message_chain:
                 # Serialize the last response message chain
@@ -162,6 +182,7 @@ class MonitoringHelper:
                 if hasattr(query.launcher_type, 'value')
                 else str(query.launcher_type),
                 user_id=query.sender_id,
+                user_name=sender_name,
                 runner_name=runner_name,
                 role='assistant',
             )
@@ -183,6 +204,15 @@ class MonitoringHelper:
         try:
             session_id = f'{query.launcher_type}_{query.launcher_id}'
 
+            # Get sender name from message event
+            sender_name = None
+            if hasattr(query, 'message_event'):
+                if hasattr(query.message_event, 'sender'):
+                    if hasattr(query.message_event.sender, 'nickname'):
+                        sender_name = query.message_event.sender.nickname
+                    elif hasattr(query.message_event.sender, 'member_name'):
+                        sender_name = query.message_event.sender.member_name
+
             # Record error message
             message_id = await ap.monitoring_service.record_message(
                 bot_id=bot_id,
@@ -197,6 +227,7 @@ class MonitoringHelper:
                 if hasattr(query.launcher_type, 'value')
                 else str(query.launcher_type),
                 user_id=query.sender_id,
+                user_name=sender_name,
                 runner_name=runner_name,
             )
 

src/langbot/pkg/pipeline/pipelinemgr.py

@@ -13,6 +13,7 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.events as events
 from ..utils import importutil
+from .config_coercion import coerce_pipeline_config
 
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -420,6 +421,14 @@ class PipelineManager:
         elif isinstance(pipeline_entity, dict):
             pipeline_entity = persistence_pipeline.LegacyPipeline(**pipeline_entity)
 
+        coerce_pipeline_config(
+            pipeline_entity.config,
+            getattr(self.ap, 'pipeline_config_meta_trigger', {'name': 'trigger', 'stages': []}),
+            getattr(self.ap, 'pipeline_config_meta_safety', {'name': 'safety', 'stages': []}),
+            getattr(self.ap, 'pipeline_config_meta_ai', {'name': 'ai', 'stages': []}),
+            getattr(self.ap, 'pipeline_config_meta_output', {'name': 'output', 'stages': []}),
+        )
+
         # initialize stage containers according to pipeline_entity.stages
         stage_containers: list[StageInstContainer] = []
         for stage_name in pipeline_entity.stages:

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -36,17 +36,36 @@ class PreProcessor(stage.PipelineStage):
         session = await self.ap.sess_mgr.get_session(query)
 
         # When not local-agent, llm_model is None
-        try:
+        llm_model = None
-            llm_model = (
+        if selected_runner == 'local-agent':
-                await self.ap.model_mgr.get_model_by_uuid(query.pipeline_config['ai']['local-agent']['model'])
+            # Read model config — new format is { primary: str, fallbacks: [str] },
-                if selected_runner == 'local-agent'
+            # but handle legacy plain string for backward compatibility
-                else None
+            model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
-            )
+            if isinstance(model_config, str):
-        except ValueError:
+                # Legacy format: plain UUID string
-            self.ap.logger.warning(
+                primary_uuid = model_config
-                f'LLM model {query.pipeline_config["ai"]["local-agent"]["model"] + " "}not found or not configured'
+                fallback_uuids = []
-            )
+            else:
-            llm_model = None
+                primary_uuid = model_config.get('primary', '')
+                fallback_uuids = model_config.get('fallbacks', [])
+
+            if primary_uuid:
+                try:
+                    llm_model = await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
+                except ValueError:
+                    self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')
+
+            # Resolve fallback model UUIDs
+            if fallback_uuids:
+                valid_fallbacks = []
+                for fb_uuid in fallback_uuids:
+                    try:
+                        await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
+                        valid_fallbacks.append(fb_uuid)
+                    except ValueError:
+                        self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
+                if valid_fallbacks:
+                    query.variables['_fallback_model_uuids'] = valid_fallbacks
 
         conversation = await self.ap.sess_mgr.get_conversation(
             query,
@@ -61,20 +80,28 @@ class PreProcessor(stage.PipelineStage):
         query.prompt = conversation.prompt.copy()
         query.messages = conversation.messages.copy()
 
-        if selected_runner == 'local-agent' and llm_model:
+        if selected_runner == 'local-agent':
             query.use_funcs = []
-            query.use_llm_model_uuid = llm_model.model_entity.uuid
+            if llm_model:
+                query.use_llm_model_uuid = llm_model.model_entity.uuid
 
-            if llm_model.model_entity.abilities.__contains__('func_call'):
+                if llm_model.model_entity.abilities.__contains__('func_call'):
-                # Get bound plugins and MCP servers for filtering tools
+                    # Get bound plugins and MCP servers for filtering tools
+                    bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
+                    bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
+                    query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
+
+                    self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
+                    self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
+                    self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
+
+            # If primary model doesn't support func_call but fallback models exist,
+            # load tools anyway since fallback models may support them
+            if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
                 bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
                 bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
                 query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
 
-                self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
-                self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
-                self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
-
         sender_name = ''
 
         if isinstance(query.message_event, platform_events.GroupMessage):
@@ -149,6 +176,16 @@ class PreProcessor(stage.PipelineStage):
         query.variables['user_message_text'] = plain_text
 
         query.user_message = provider_message.Message(role='user', content=content_list)
+
+        # Extract knowledge base UUIDs into query variables so plugins can modify them
+        # during PromptPreProcessing before the runner performs retrieval.
+        kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
+        if not kb_uuids:
+            old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
+            if old_kb_uuid and old_kb_uuid != '__none__':
+                kb_uuids = [old_kb_uuid]
+        query.variables['_knowledge_base_uuids'] = list(kb_uuids)
+
         # =========== 触发事件 PromptPreProcessing
 
         event = events.PromptPreProcessing(

src/langbot/pkg/pipeline/process/handlers/chat.py

@@ -149,12 +149,19 @@ class ChatMessageHandler(handler.MessageHandler):
                 self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
                 traceback.print_exc()
 
-                hide_exception_info = query.pipeline_config['output']['misc']['hide-exception']
+                exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
+
+                if exception_handling == 'show-error':
+                    user_notice = f'{e}'
+                elif exception_handling == 'show-hint':
+                    user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
+                else:  # hide
+                    user_notice = None
 
                 yield entities.StageProcessResult(
                     result_type=entities.ResultType.INTERRUPT,
                     new_query=query,
-                    user_notice='请求失败' if hide_exception_info else f'{e}',
+                    user_notice=user_notice,
                     error_notice=f'{e}',
                     debug_notice=traceback.format_exc(),
                 )

src/langbot/pkg/pipeline/respback/respback.py

@@ -55,15 +55,4 @@ class SendResponseBackStage(stage.PipelineStage):
                 quote_origin=quote_origin,
             )
 
-        # Send extra chains produced by long text split strategy
-        extra_chains = query.get_variable('_longtext_split_extra_chains')
-        if extra_chains:
-            for chain in extra_chains:
-                await query.adapter.reply_message(
-                    message_source=query.message_event,
-                    message=chain,
-                    quote_origin=False,
-                )
-            query.set_variable('_longtext_split_extra_chains', None)
-
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/platform/botmgr.py

@@ -9,6 +9,7 @@ from ..core import app, entities as core_entities, taskmgr
 from ..discover import engine
 
 from ..entity.persistence import bot as persistence_bot
+from ..entity.persistence import pipeline as persistence_pipeline
 
 from ..entity.errors import platform as platform_errors
 
@@ -141,6 +142,50 @@ class RuntimeBot:
         self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
         self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
 
+        # Register feedback listener (only effective on adapters that support it)
+        async def on_feedback(
+            event: platform_events.FeedbackEvent,
+            adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
+        ):
+            try:
+                # Resolve pipeline name
+                pipeline_name = ''
+                if self.bot_entity.use_pipeline_uuid:
+                    try:
+                        pipeline_result = await self.ap.persistence_mgr.execute_async(
+                            sqlalchemy.select(persistence_pipeline.LegacyPipeline.name).where(
+                                persistence_pipeline.LegacyPipeline.uuid == self.bot_entity.use_pipeline_uuid
+                            )
+                        )
+                        pipeline_row = pipeline_result.first()
+                        if pipeline_row:
+                            pipeline_name = pipeline_row[0]
+                    except Exception:
+                        pass
+
+                await self.ap.monitoring_service.record_feedback(
+                    feedback_id=event.feedback_id,
+                    feedback_type=event.feedback_type,
+                    feedback_content=event.feedback_content,
+                    inaccurate_reasons=event.inaccurate_reasons,
+                    bot_id=self.bot_entity.uuid,
+                    bot_name=self.bot_entity.name,
+                    pipeline_id=self.bot_entity.use_pipeline_uuid or '',
+                    pipeline_name=pipeline_name,
+                    session_id=event.session_id,
+                    message_id=event.message_id,
+                    stream_id=event.stream_id,
+                    user_id=event.user_id,
+                    platform=adapter.__class__.__name__,
+                )
+                await self.logger.info(
+                    f'Recorded feedback: feedback_id={event.feedback_id}, type={event.feedback_type}'
+                )
+            except Exception:
+                await self.logger.error(f'Failed to record feedback: {traceback.format_exc()}')
+
+        self.adapter.register_listener(platform_events.FeedbackEvent, on_feedback)
+
     async def run(self):
         async def exception_wrapper():
             try:
@@ -282,6 +327,8 @@ class PlatformManager:
         return runtime_bot
 
     async def get_bot_by_uuid(self, bot_uuid: str) -> RuntimeBot | None:
+        if self.websocket_proxy_bot and self.websocket_proxy_bot.bot_entity.uuid == bot_uuid:
+            return self.websocket_proxy_bot
         for bot in self.bots:
             if bot.bot_entity.uuid == bot_uuid:
                 return bot

src/langbot/pkg/platform/sources/aiocqhttp.yaml

@@ -5,19 +5,29 @@ metadata:
   label:
     en_US: OneBot v11
     zh_Hans: OneBot v11
+    zh_Hant: OneBot v11
   description:
-    en_US: OneBot v11 Adapter
+    en_US: OneBot v11 Adapter, used for QQ bots
-    zh_Hans: OneBot v11 适配器，请查看文档了解使用方式
+    zh_Hans: OneBot v11 适配器，用于接入 QQ 机器人协议端，请查看文档了解使用方式
+    zh_Hant: OneBot v11 適配器，用於接入 QQ 機器人協定端，請查看文件了解使用方式
   icon: onebot.png
 spec:
+  categories:
+    - protocol
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/aiocqhttp
+    en: https://link.langbot.app/en/platforms/aiocqhttp
+    ja: https://link.langbot.app/ja/platforms/aiocqhttp
   config:
     - name: host
       label:
         en_US: Host
         zh_Hans: 主机
+        zh_Hant: 主機
       description:
         en_US: The host that OneBot v11 listens on for reverse WebSocket connections. Unless you know what you're doing, use 0.0.0.0
         zh_Hans: OneBot v11 监听的反向 WS 主机，除非你知道自己在做什么，否则请写 0.0.0.0
+        zh_Hant: OneBot v11 監聽的反向 WS 主機，除非你知道自己在做什麼，否則請填 0.0.0.0
       type: string
       required: true
       default: 0.0.0.0
@@ -25,9 +35,11 @@ spec:
       label:
         en_US: Port
         zh_Hans: 端口
+        zh_Hant: 連接埠
       description:
         en_US: Port
         zh_Hans: 监听的端口
+        zh_Hant: 監聽的連接埠
       type: integer
       required: true
       default: 2280
@@ -35,9 +47,11 @@ spec:
       label:
         en_US: Access Token
         zh_Hans: 访问令牌
+        zh_Hant: 存取令牌
       description:
         en_US: Custom connection token for the protocol endpoint. If the protocol endpoint is not set, don't fill it
         zh_Hans: 自定义的与协议端的连接令牌，若协议端未设置，则不填
+        zh_Hant: 自訂的與協定端的連線令牌，若協定端未設定，則不填
       type: string
       required: false
       default: ""

src/langbot/pkg/platform/sources/dingtalk.yaml

@@ -5,16 +5,25 @@ metadata:
   label:
     en_US: DingTalk
     zh_Hans: 钉钉
+    zh_Hant: 釘釘
   description:
     en_US: DingTalk Adapter
     zh_Hans: 钉钉适配器，请查看文档了解使用方式
+    zh_Hant: 釘釘適配器，請查看文件了解使用方式
   icon: dingtalk.svg
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/dingtalk
+    en: https://link.langbot.app/en/platforms/dingtalk
+    ja: https://link.langbot.app/ja/platforms/dingtalk
   config:
     - name: client_id
       label:
         en_US: Client ID
         zh_Hans: 客户端ID
+        zh_Hant: 用戶端ID
       type: string
       required: true
       default: ""
@@ -22,6 +31,7 @@ spec:
       label:
         en_US: Client Secret
         zh_Hans: 客户端密钥
+        zh_Hant: 用戶端密鑰
       type: string
       required: true
       default: ""
@@ -29,6 +39,7 @@ spec:
       label:
         en_US: Robot Code
         zh_Hans: 机器人代码
+        zh_Hant: 機器人代碼
       type: string
       required: true
       default: ""
@@ -36,6 +47,7 @@ spec:
       label:
         en_US: Robot Name
         zh_Hans: 机器人名称
+        zh_Hant: 機器人名稱
       type: string
       required: true
       default: ""
@@ -43,6 +55,7 @@ spec:
       label:
         en_US: Markdown Card
         zh_Hans: 是否使用 Markdown 卡片
+        zh_Hant: 是否使用 Markdown 卡片
       type: boolean
       required: false
       default: true
@@ -50,9 +63,11 @@ spec:
       label:
         en_US: Enable Stream Reply Mode
         zh_Hans: 启用钉钉卡片流式回复模式
+        zh_Hant: 啟用釘釘卡片串流回覆模式
       description:
         en_US: If enabled, the bot will use the stream of lark reply mode
         zh_Hans: 如果启用，将使用钉钉卡片流式方式来回复内容
+        zh_Hant: 如果啟用，將使用釘釘卡片串流方式來回覆內容
       type: boolean
       required: true
       default: false
@@ -60,6 +75,7 @@ spec:
       label:
         en_US: Card Auto Layout
         zh_Hans: 卡片宽屏自动布局
+        zh_Hant: 卡片寬螢幕自動佈局
       type: boolean
       required: false
       default: false
@@ -67,6 +83,7 @@ spec:
       label:
         en_US: card template id
         zh_Hans: 卡片模板ID
+        zh_Hant: 卡片範本ID
       type: string
       required: true
       default: "填写你的卡片template_id"

src/langbot/pkg/platform/sources/discord.yaml

@@ -5,16 +5,38 @@ metadata:
   label:
     en_US: Discord
     zh_Hans: Discord
+    zh_Hant: Discord
+    ja_JP: Discord
+    th_TH: Discord
+    vi_VN: Discord
+    es_ES: Discord
   description:
     en_US: Discord Adapter
-    zh_Hans: Discord 适配器，请查看文档了解使用方式
+    zh_Hans: Discord 适配器，需要可连接 Discord 服务器的网络环境
+    zh_Hant: Discord 適配器，需要可連線 Discord 伺服器的網路環境
+    ja_JP: Discord アダプター、Discord サーバーに接続可能なネットワーク環境が必要です
+    th_TH: อะแดปเตอร์ Discord ต้องการสภาพแวดล้อมเครือข่ายที่สามารถเชื่อมต่อกับเซิร์ฟเวอร์ Discord ได้
+    vi_VN: Bộ điều hợp Discord, cần môi trường mạng có thể kết nối với máy chủ Discord
+    es_ES: Adaptador de Discord, requiere un entorno de red con acceso al servidor de Discord
   icon: discord.svg
 spec:
+  categories:
+    - popular
+    - global
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/discord
+    en: https://link.langbot.app/en/platforms/discord
+    ja: https://link.langbot.app/ja/platforms/discord
   config:
     - name: client_id
       label:
         en_US: Client ID
         zh_Hans: 客户端ID
+        zh_Hant: 用戶端ID
+        ja_JP: クライアント ID
+        th_TH: รหัสไคลเอนต์
+        vi_VN: ID khách hàng
+        es_ES: ID de cliente
       type: string
       required: true
       default: ""
@@ -22,6 +44,11 @@ spec:
       label:
         en_US: Token
         zh_Hans: 令牌
+        zh_Hant: 令牌
+        ja_JP: トークン
+        th_TH: โทเค็น
+        vi_VN: Mã thông báo
+        es_ES: Token
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/kook.yaml

@@ -5,16 +5,25 @@ metadata:
   label:
     en_US: KOOK
     zh_Hans: KOOK
+    zh_Hant: KOOK
   description:
     en_US: KOOK Adapter (formerly KaiHeiLa)
     zh_Hans: KOOK 适配器(原开黑啦)，支持频道消息和私聊消息
+    zh_Hant: KOOK 適配器（原開黑啦），支援頻道訊息和私聊訊息
   icon: kook.png
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/kook
+    en: https://link.langbot.app/en/platforms/kook
+    ja: https://link.langbot.app/ja/platforms/kook
   config:
     - name: token
       label:
         en_US: Bot Token
         zh_Hans: 机器人令牌
+        zh_Hant: 機器人令牌
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/lark.py

@@ -575,6 +575,127 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
 
 
 class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
+    _processed_thread_quote_cache: typing.ClassVar[dict[str, float]] = {}
+    _processed_thread_quote_cache_max_size: typing.ClassVar[int] = 4096
+    _processed_thread_quote_cache_ttl_seconds: typing.ClassVar[int] = 86400
+
+    @classmethod
+    def _prune_processed_thread_quote_cache(cls, now: typing.Optional[float] = None) -> None:
+        if now is None:
+            now = time.time()
+
+        expire_before = now - cls._processed_thread_quote_cache_ttl_seconds
+        while cls._processed_thread_quote_cache:
+            oldest_key, oldest_ts = next(iter(cls._processed_thread_quote_cache.items()))
+            if oldest_ts >= expire_before:
+                break
+            cls._processed_thread_quote_cache.pop(oldest_key, None)
+
+        while len(cls._processed_thread_quote_cache) > cls._processed_thread_quote_cache_max_size:
+            oldest_key = next(iter(cls._processed_thread_quote_cache))
+            cls._processed_thread_quote_cache.pop(oldest_key, None)
+
+    @classmethod
+    def _mark_thread_quote_processed(cls, thread_id: str) -> None:
+        now = time.time()
+        cls._prune_processed_thread_quote_cache(now)
+        cls._processed_thread_quote_cache[thread_id] = now
+
+    @classmethod
+    def _extract_quote_message_id(cls, message: EventMessage) -> typing.Optional[str]:
+        """
+        Extract the message ID to quote from the given message.
+
+        Rules:
+        - First thread reply in a topic: return parent_id and mark topic as processed
+        - Follow-up thread replies in the same topic: return None
+        - Non-thread message: return parent_id if valid (non-empty, different from message_id)
+
+        Thread reply state is kept in a bounded TTL cache to avoid unbounded memory growth.
+        """
+        parent_id = getattr(message, 'parent_id', None)
+        if not parent_id:
+            return None
+
+        message_id = getattr(message, 'message_id', None)
+        if parent_id == message_id:
+            return None
+
+        thread_id = getattr(message, 'thread_id', None)
+        if thread_id:
+            cls._prune_processed_thread_quote_cache()
+            if thread_id in cls._processed_thread_quote_cache:
+                return None
+            cls._mark_thread_quote_processed(thread_id)
+
+        return parent_id
+
+    @staticmethod
+    def _build_event_message_from_message_item(message_item: Message) -> typing.Optional[EventMessage]:
+        """
+        Build EventMessage from SDK typed Message item.
+
+        Returns None if body or content is missing.
+        """
+        body = getattr(message_item, 'body', None)
+        if not body:
+            return None
+
+        content = getattr(body, 'content', None)
+        if not content:
+            return None
+
+        event_data = {
+            'message_id': message_item.message_id,
+            'message_type': message_item.msg_type,
+            'content': content,
+            'create_time': message_item.create_time,
+            'mentions': getattr(message_item, 'mentions', []) or [],
+        }
+
+        # Preserve thread-related fields
+        if hasattr(message_item, 'parent_id') and message_item.parent_id:
+            event_data['parent_id'] = message_item.parent_id
+        if hasattr(message_item, 'root_id') and message_item.root_id:
+            event_data['root_id'] = message_item.root_id
+        if hasattr(message_item, 'thread_id') and message_item.thread_id:
+            event_data['thread_id'] = message_item.thread_id
+        if hasattr(message_item, 'chat_id') and message_item.chat_id:
+            event_data['chat_id'] = message_item.chat_id
+
+        return EventMessage(event_data)
+
+    @staticmethod
+    async def _fetch_quoted_message(
+        quote_message_id: str,
+        api_client: lark_oapi.Client,
+    ) -> typing.Optional[platform_message.MessageChain]:
+        """
+        Fetch the quoted message and convert to MessageChain.
+
+        Returns None if:
+        - API call fails
+        - Response items is empty
+        - Message item normalization fails
+        """
+        request = GetMessageRequest.builder().message_id(quote_message_id).build()
+        response = await api_client.im.v1.message.aget(request)
+
+        if not response.success():
+            return None
+
+        items = getattr(response.data, 'items', None)
+        if not items:
+            return None
+
+        message_item = items[0]
+        event_message = LarkEventConverter._build_event_message_from_message_item(message_item)
+        if event_message is None:
+            return None
+
+        quote_chain = await LarkMessageConverter.target2yiri(event_message, api_client)
+        return quote_chain
+
     @staticmethod
     async def yiri2target(
         event: platform_events.MessageEvent,
@@ -587,6 +708,23 @@ class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
     ) -> platform_events.Event:
         message_chain = await LarkMessageConverter.target2yiri(event.event.message, api_client)
 
+        # Check for quote/reply message
+        quote_message_id = LarkEventConverter._extract_quote_message_id(event.event.message)
+        if quote_message_id:
+            quote_chain = await LarkEventConverter._fetch_quoted_message(quote_message_id, api_client)
+            if quote_chain:
+                # Filter out Source component from quoted chain, keep only content
+                quote_origin = platform_message.MessageChain(
+                    [comp for comp in quote_chain if not isinstance(comp, platform_message.Source)]
+                )
+                if quote_origin:
+                    message_chain.append(
+                        platform_message.Quote(
+                            message_id=quote_message_id,
+                            origin=quote_origin,
+                        )
+                    )
+
         if event.event.message.chat_type == 'p2p':
             return platform_events.FriendMessage(
                 sender=platform_entities.Friend(
@@ -770,6 +908,32 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             self.request_tenant_access_token(tenant_key)
         return self.tenant_access_tokens.get(tenant_key)['token'] if self.tenant_access_tokens.get(tenant_key) else None
 
+    def get_launcher_id(self, event: platform_events.MessageEvent) -> str | None:
+        """
+        Get topic-scoped launcher_id for thread-aware session isolation.
+
+        For group thread messages, returns "{group_id}_{thread_id}"
+        to ensure conversation context stays stable per topic.
+
+        Returns None for non-thread messages or P2P messages.
+        """
+        source_event = getattr(event.source_platform_object, 'event', None)
+        if not source_event:
+            return None
+
+        message = getattr(source_event, 'message', None)
+        if not message:
+            return None
+
+        thread_id = getattr(message, 'thread_id', None)
+        if not thread_id:
+            return None
+
+        if isinstance(event, platform_events.GroupMessage):
+            return f'{event.group.id}_{thread_id}'
+
+        return None
+
     def build_api_client(self, config):
         app_id = config['app_id']
         app_secret = config['app_secret']

src/langbot/pkg/platform/sources/lark.yaml

@@ -5,16 +5,30 @@ metadata:
   label:
     en_US: Lark
     zh_Hans: 飞书
+    zh_Hant: 飛書
+    ja_JP: Lark
   description:
-    en_US: Lark Adapter
+    en_US: Lark Adapter, supports both long connection and Webhook modes. Please refer to the documentation for usage details.
-    zh_Hans: 飞书适配器，请查看文档了解使用方式
+    zh_Hans: 飞书适配器，支持长连接和 Webhook 两种接入方式，请查看文档了解使用方式
+    zh_Hant: 飛書適配器，支援長連線和 Webhook 兩種接入方式，請查看文件了解使用方式
+    ja_JP: Lark アダプター、長期接続およびWebhookモードの両方をサポートしています。使用方法の詳細については、ドキュメントを参照してください。
   icon: lark.svg
 spec:
+  categories:
+    - popular
+    - china
+    - global
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/lark
+    en: https://link.langbot.app/en/platforms/lark
+    ja: https://link.langbot.app/ja/platforms/lark
   config:
     - name: app_id
       label:
         en_US: App ID
         zh_Hans: 应用ID
+        zh_Hant: 應用ID
+        ja_JP: アプリ ID
       type: string
       required: true
       default: ""
@@ -22,6 +36,8 @@ spec:
       label:
         en_US: App Secret
         zh_Hans: 应用密钥
+        zh_Hant: 應用密鑰
+        ja_JP: アプリシークレット
       type: string
       required: true
       default: ""
@@ -29,9 +45,13 @@ spec:
       label:
         en_US: Bot Name
         zh_Hans: 机器人名称
+        zh_Hant: 機器人名稱
+        ja_JP: ボット名
       description:
         en_US: Must be the same as the name of the bot in Lark, otherwise the bot will not be able to receive messages in the group
         zh_Hans: 必须与飞书机器人名称一致，否则机器人将无法在群内正常接收消息
+        zh_Hant: 必須與飛書機器人名稱一致，否則機器人將無法在群組內正常接收訊息
+        ja_JP: Lark のボット名と一致する必要があります。一致しない場合、グループ内でメッセージを受信できません
       type: string
       required: true
       default: ""
@@ -39,29 +59,63 @@ spec:
       label:
         en_US: Enable Webhook Mode
         zh_Hans: 启用Webhook模式
+        zh_Hant: 啟用 Webhook 模式
+        ja_JP: Webhook モードを有効化
       description:
         en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
         zh_Hans: 如果启用，机器人将使用 Webhook 模式接收消息。否则，将使用 WS 长连接模式
+        zh_Hant: 如果啟用，機器人將使用 Webhook 模式接收訊息。否則，將使用 WS 長連線模式
+        ja_JP: 有効にすると、ボットは Webhook モードでメッセージを受信します。無効の場合は WS 長期接続モードを使用します
       type: boolean
       required: true
       default: false
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+        ja_JP: Webhook コールバック URL
+      description:
+        en_US: Copy this URL and paste it into your Lark app's webhook configuration
+        zh_Hans: 复制此地址并粘贴到飞书应用的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到飛書應用的 Webhook 設定中
+        ja_JP: この URL をコピーして Lark アプリの Webhook 設定に貼り付けてください
+      type: webhook-url
+      required: false
+      default: ""
+      show_if:
+        field: enable-webhook
+        operator: eq
+        value: true
     - name: encrypt-key
       label:
         en_US: Encrypt Key
         zh_Hans: 加密密钥
+        zh_Hant: 加密密鑰
+        ja_JP: 暗号化キー
       description:
         en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
         zh_Hans: 仅在启用 Webhook 模式时有效，请填写加密密钥
+        zh_Hant: 僅在啟用 Webhook 模式時有效，請填寫加密密鑰
+        ja_JP: Webhook モードが有効な場合にのみ有効です。暗号化キーを入力してください
       type: string
       required: true
       default: ""
+      show_if:
+        field: enable-webhook
+        operator: eq
+        value: true
     - name: enable-stream-reply
       label:
         en_US: Enable Stream Reply Mode
         zh_Hans: 启用飞书流式回复模式
+        zh_Hant: 啟用飛書串流回覆模式
+        ja_JP: ストリーミング返信モードを有効化
       description:
         en_US: If enabled, the bot will use the stream of lark reply mode
         zh_Hans: 如果启用，将使用飞书流式方式来回复内容
+        zh_Hant: 如果啟用，將使用飛書串流方式來回覆內容
+        ja_JP: 有効にすると、ボットはストリーミングモードでメッセージに返信します
       type: boolean
       required: true
       default: false
@@ -69,28 +123,40 @@ spec:
       label:
         en_US: App Type
         zh_Hans: 应用类型
+        zh_Hant: 應用類型
+        ja_JP: アプリタイプ
       description:
         en_US: Default to self-built application, refer to https://open.feishu.cn/document/platform-overveiw/overview
         zh_Hans: 默认为企业自建应用，参考 https://open.feishu.cn/document/platform-overveiw/overview
+        zh_Hant: 預設為企業自建應用，參考 https://open.feishu.cn/document/platform-overveiw/overview
+        ja_JP: デフォルトはカスタムアプリです。詳細は https://open.feishu.cn/document/platform-overveiw/overview を参照してください
       type: select
       options:
         - name: self
           label:
             en_US: Self-built Application
             zh_Hans: 自建应用
+            zh_Hant: 自建應用
+            ja_JP: カスタムアプリ
         - name: isv
           label:
             en_US: Store Application
             zh_Hans: 商店应用
+            zh_Hant: 商店應用
+            ja_JP: ストアアプリ
       required: false
       default: self
     - name: bot_added_welcome
       label:
         en_US: Bot Welcome Message
         zh_Hans: 机器人进群欢迎语
+        zh_Hant: 機器人進群歡迎語
+        ja_JP: ボット参加時のウェルカムメッセージ
       description:
         en_US: Welcome message when the bot is added to a group, supports Markdown format
         zh_Hans: 机器人进群欢迎语，支持 Markdown 格式
+        zh_Hant: 機器人進群歡迎語，支援 Markdown 格式
+        ja_JP: ボットがグループに追加された際のウェルカムメッセージ。Markdown 形式に対応しています
       type: text
       required: false
       default: ""

src/langbot/pkg/platform/sources/line.yaml

@@ -5,20 +5,56 @@ metadata:
   label:
     en_US: LINE
     zh_Hans: LINE
+    zh_Hant: LINE
+    th_TH: LINE
+    vi_VN: LINE
+    es_ES: LINE
   description:
-    en_US: LINE Adapter
+    en_US: LINE Adapter, requires a public URL to receive LINE message pushes, please refer to the documentation for usage details
-    zh_Hans: LINE适配器，请查看文档了解使用方式
+    zh_Hans: LINE适配器，需要公网地址以接收 LINE 消息推送，请查看文档了解使用方式
-    ja_JP: LINEアダプター、ドキュメントを参照してください
+    zh_Hant: LINE 適配器，需要公網地址以接收 LINE 訊息推送，請查看文件了解使用方式
-    zh_Hant: LINE適配器，請查看文檔了解使用方式
+    ja_JP: LINEアダプター、LINEのメッセージプッシュを受信するためにパブリックURLが必要です。使用方法の詳細については、ドキュメントを参照してください。
+    th_TH: อะแดปเตอร์ LINE ต้องการ URL สาธารณะเพื่อรับการแจ้งเตือนข้อความจาก LINE โปรดดูเอกสารประกอบสำหรับรายละเอียดการใช้งาน
+    vi_VN: Bộ điều hợp LINE, cần URL công cộng để nhận thông báo tin nhắn LINE, vui lòng xem tài liệu để biết chi tiết cách sử dụng
+    es_ES: Adaptador de LINE, requiere una URL pública para recibir notificaciones de mensajes de LINE, consulte la documentación para obtener detalles de uso
   icon: line.png
 spec:
+  categories:
+    - global
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/line
+    en: https://link.langbot.app/en/platforms/line
+    ja: https://link.langbot.app/ja/platforms/line
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        ja_JP: Webhook コールバック URL
+        zh_Hant: Webhook 回調地址
+        th_TH: URL การเรียกกลับ Webhook
+        vi_VN: URL gọi lại Webhook
+        es_ES: URL de devolución de llamada Webhook
+      description:
+        en_US: Copy this URL and paste it into your LINE channel's webhook configuration
+        zh_Hans: 复制此地址并粘贴到 LINE 频道的 Webhook 配置中
+        ja_JP: この URL をコピーして LINE チャンネルの Webhook 設定に貼り付けてください
+        zh_Hant: 複製此地址並貼到 LINE 頻道的 Webhook 設定中
+        th_TH: คัดลอก URL นี้แล้ววางในการตั้งค่า Webhook ของช่อง LINE ของคุณ
+        vi_VN: Sao chép URL này và dán vào cấu hình webhook của kênh LINE của bạn
+        es_ES: Copie esta URL y péguela en la configuración de webhook de su canal LINE
+      type: webhook-url
+      required: false
+      default: ""
     - name: channel_access_token
       label:
         en_US: Channel access token
         zh_Hans: 频道访问令牌
         ja_JP: チャンネルアクセストークン
-        zh_Hant: 頻道訪問令牌
+        zh_Hant: 頻道存取令牌
+        th_TH: โทเค็นการเข้าถึงช่อง
+        vi_VN: Mã truy cập kênh
+        es_ES: Token de acceso del canal
       type: string
       required: true
       default: ""
@@ -27,12 +63,18 @@ spec:
         en_US: Channel secret
         zh_Hans: 消息密钥
         ja_JP: チャンネルシークレット
-        zh_Hant: 消息密钥
+        zh_Hant: 訊息密鑰
+        th_TH: รหัสลับช่อง
+        vi_VN: Khóa bí mật kênh
+        es_ES: Secreto del canal
       description:
         en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
         zh_Hans: 请填写加密密钥
         ja_JP: Webhookモードが有効な場合にのみ、暗号化キーを入力してください
-        zh_Hant: 請填寫加密密钥
+        zh_Hant: 請填寫加密密鑰
+        th_TH: กรุณากรอกคีย์เข้ารหัส
+        vi_VN: Vui lòng điền khóa mã hóa
+        es_ES: Por favor, introduzca la clave de cifrado
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/officialaccount.yaml

@@ -5,23 +5,44 @@ metadata:
   label:
     en_US: Official Account
     zh_Hans: 微信公众号
+    zh_Hant: 微信公眾號
   description:
     en_US: Official Account Adapter
-    zh_Hans: 微信公众号适配器，请查看文档了解使用方式
+    zh_Hans: 微信公众号适配器，需要公网地址以接收消息推送，请查看文档了解使用方式
+    zh_Hant: 微信公眾號適配器，需要公網地址以接收訊息推送，請查看文件了解使用方式
   icon: officialaccount.png
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/officialaccount
+    en: https://link.langbot.app/en/platforms/officialaccount
+    ja: https://link.langbot.app/ja/platforms/officialaccount
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+      description:
+        en_US: Copy this URL and paste it into your Official Account webhook configuration
+        zh_Hans: 复制此地址并粘贴到微信公众号的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到微信公眾號的 Webhook 設定中
+      type: webhook-url
+      required: false
+      default: ""
     - name: token
       label:
         en_US: Token
         zh_Hans: 令牌
-      type: string
+        zh_Hant: 令牌
       required: true
       default: ""
     - name: EncodingAESKey
       label:
         en_US: EncodingAESKey
         zh_Hans: 消息加解密密钥
+        zh_Hant: 訊息加解密密鑰
       type: string
       required: true
       default: ""
@@ -29,6 +50,7 @@ spec:
       label:
         en_US: App ID
         zh_Hans: 应用ID
+        zh_Hant: 應用ID
       type: string
       required: true
       default: ""
@@ -36,6 +58,7 @@ spec:
       label:
         en_US: App Secret
         zh_Hans: 应用密钥
+        zh_Hant: 應用密鑰
       type: string
       required: true
       default: ""
@@ -43,6 +66,7 @@ spec:
       label:
         en_US: Mode
         zh_Hans: 接入模式
+        zh_Hant: 接入模式
       type: string
       required: true
       default: "drop"
@@ -50,6 +74,7 @@ spec:
       label:
         en_US: Loading Message
         zh_Hans: 加载消息
+        zh_Hant: 載入訊息
       type: string
       required: true
       default: "AI正在思考中，请发送任意内容获取回复。"
@@ -57,9 +82,11 @@ spec:
       label:
         en_US: API Base URL
         zh_Hans: API 基础 URL
+        zh_Hant: API 基礎 URL
       description:
         en_US: API Base URL, used for accessing the Official Account API. If you are deploying in an internal network environment and accessing the Official Account API through a reverse proxy, please fill in this item according to the documentation.
         zh_Hans: 可选，若您部署在内网环境并通过反向代理访问微信公众号 API，可根据文档修改此项
+        zh_Hant: 可選，若您部署在內網環境並透過反向代理存取微信公眾號 API，可根據文件修改此項
       type: string
       required: false
       default: "https://api.weixin.qq.com"

src/langbot/pkg/platform/sources/openclaw_weixin.py

@@ -0,0 +1,577 @@
+"""OpenClaw WeChat adapter for LangBot.
+
+Uses the OpenClaw WeChat HTTP JSON API (long-poll getUpdates + sendMessage)
+to integrate personal WeChat accounts with LangBot.
+
+Reference: https://github.com/epiral/weixin-bot
+"""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+import traceback
+import typing
+
+import pydantic
+import sqlalchemy
+
+from langbot.libs.openclaw_weixin_api.client import (
+    DEFAULT_BASE_URL,
+    SESSION_EXPIRED_ERRCODE,
+    OpenClawWeixinClient,
+)
+from langbot.libs.openclaw_weixin_api.types import (
+    MessageItem,
+    WeixinMessage,
+)
+from langbot.pkg.entity.persistence import bot as persistence_bot
+
+import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
+import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_platform_logger
+import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
+import langbot_plugin.api.entities.builtin.platform.events as platform_events
+import langbot_plugin.api.entities.builtin.platform.message as platform_message
+
+
+class OpenClawWeixinMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
+    """Converts between LangBot MessageChain and OpenClaw WeChat message items."""
+
+    @staticmethod
+    async def yiri2target(message_chain: platform_message.MessageChain) -> list[dict]:
+        """Convert LangBot MessageChain to a list of OpenClaw message item dicts."""
+        items = []
+        for component in message_chain:
+            if isinstance(component, platform_message.Plain):
+                items.append({'type': MessageItem.TEXT, 'text_item': {'text': component.text}})
+            elif isinstance(component, platform_message.Image):
+                # OpenClaw WeChat only supports text messages without CDN upload.
+                # For images, we send a placeholder text with the URL if available.
+                if component.url:
+                    items.append(
+                        {
+                            'type': MessageItem.TEXT,
+                            'text_item': {'text': f'[Image: {component.url}]'},
+                        }
+                    )
+                elif component.base64:
+                    items.append(
+                        {
+                            'type': MessageItem.TEXT,
+                            'text_item': {'text': '[Image]'},
+                        }
+                    )
+            elif isinstance(component, platform_message.File):
+                if component.name:
+                    items.append(
+                        {
+                            'type': MessageItem.TEXT,
+                            'text_item': {'text': f'[File: {component.name}]'},
+                        }
+                    )
+            elif isinstance(component, platform_message.Forward):
+                for node in component.node_list:
+                    if node.message_chain:
+                        items.extend(await OpenClawWeixinMessageConverter.yiri2target(node.message_chain))
+        return items
+
+    @staticmethod
+    async def target2yiri(
+        msg: WeixinMessage,
+    ) -> platform_message.MessageChain:
+        """Convert an OpenClaw WeixinMessage to LangBot MessageChain."""
+        components: list[platform_message.MessageComponent] = []
+
+        if not msg.item_list:
+            return platform_message.MessageChain(components)
+
+        for item in msg.item_list:
+            if item.type == MessageItem.TEXT and item.text_item and item.text_item.text:
+                text = item.text_item.text
+
+                # Handle quoted messages
+                if item.ref_msg:
+                    ref_parts = []
+                    if item.ref_msg.title:
+                        ref_parts.append(item.ref_msg.title)
+                    if item.ref_msg.message_item:
+                        ref_item = item.ref_msg.message_item
+                        if ref_item.text_item and ref_item.text_item.text:
+                            ref_parts.append(ref_item.text_item.text)
+                    if ref_parts:
+                        components.append(
+                            platform_message.Quote(
+                                sender_id='',
+                                origin=platform_message.MessageChain(
+                                    [platform_message.Plain(text=' | '.join(ref_parts))]
+                                ),
+                            )
+                        )
+
+                components.append(platform_message.Plain(text=text))
+
+            elif item.type == MessageItem.IMAGE and item.image_item:
+                if hasattr(item.image_item, '_downloaded_bytes') and item.image_item._downloaded_bytes:
+                    b64 = base64.b64encode(item.image_item._downloaded_bytes).decode('utf-8')
+                    components.append(platform_message.Image(base64=f'data:image/jpeg;base64,{b64}'))
+                else:
+                    components.append(platform_message.Unknown(text='[Image]'))
+
+            elif item.type == MessageItem.VOICE and item.voice_item:
+                # Voice with speech-to-text: use the transcribed text
+                if item.voice_item.text:
+                    components.append(platform_message.Plain(text=item.voice_item.text))
+                else:
+                    components.append(platform_message.Unknown(text='[Voice]'))
+
+            # TODO: enable after full testing
+            # elif item.type == MessageItem.VOICE and item.voice_item:
+            #     if item.voice_item.text:
+            #         components.append(platform_message.Plain(text=item.voice_item.text))
+            #     elif hasattr(item.voice_item, '_downloaded_bytes') and item.voice_item._downloaded_bytes:
+            #         b64 = base64.b64encode(item.voice_item._downloaded_bytes).decode('utf-8')
+            #         components.append(
+            #             platform_message.Voice(
+            #                 base64=b64,
+            #                 length=item.voice_item.playtime or 0,
+            #             )
+            #         )
+            #     else:
+            #         components.append(
+            #             platform_message.Voice(
+            #                 length=item.voice_item.playtime or 0,
+            #             )
+            #         )
+
+            elif item.type == MessageItem.FILE and item.file_item:
+                components.append(platform_message.Unknown(text=f'[File: {item.file_item.file_name or ""}]'))
+
+            # TODO: enable after full testing
+            # elif item.type == MessageItem.FILE and item.file_item:
+            #     file_name = item.file_item.file_name or ''
+            #     file_size = int(item.file_item.len) if item.file_item.len else 0
+            #     if hasattr(item.file_item, '_downloaded_bytes') and item.file_item._downloaded_bytes:
+            #         b64 = base64.b64encode(item.file_item._downloaded_bytes).decode('utf-8')
+            #         components.append(
+            #             platform_message.File(
+            #                 name=file_name,
+            #                 size=file_size,
+            #                 base64=b64,
+            #             )
+            #         )
+            #     else:
+            #         components.append(
+            #             platform_message.File(
+            #                 name=file_name,
+            #                 size=file_size,
+            #             )
+            #         )
+
+            elif item.type == MessageItem.VIDEO and item.video_item:
+                components.append(platform_message.Unknown(text='[Video]'))
+
+            # TODO: enable after full testing
+            # elif item.type == MessageItem.VIDEO and item.video_item:
+            #     if hasattr(item.video_item, '_downloaded_bytes') and item.video_item._downloaded_bytes:
+            #         b64 = base64.b64encode(item.video_item._downloaded_bytes).decode('utf-8')
+            #         components.append(
+            #             platform_message.File(
+            #                 name='video.mp4',
+            #                 size=item.video_item.video_size or 0,
+            #                 base64=b64,
+            #             )
+            #         )
+            #     else:
+            #         components.append(
+            #             platform_message.File(
+            #                 name='video.mp4',
+            #                 size=item.video_item.video_size or 0,
+            #             )
+            #         )
+
+            else:
+                components.append(platform_message.Unknown(text='[Unknown message type]'))
+
+        return platform_message.MessageChain(components)
+
+
+class OpenClawWeixinEventConverter(abstract_platform_adapter.AbstractEventConverter):
+    """Converts OpenClaw WeChat messages to LangBot events."""
+
+    @staticmethod
+    async def yiri2target(event: platform_events.MessageEvent) -> dict:
+        return event.source_platform_object
+
+    @staticmethod
+    async def target2yiri(msg: WeixinMessage) -> typing.Optional[platform_events.MessageEvent]:
+        """Convert an inbound WeixinMessage to a LangBot event."""
+        if msg.message_type != WeixinMessage.TYPE_USER:
+            return None
+
+        from_user_id = msg.from_user_id or ''
+        if not from_user_id:
+            return None
+
+        message_chain = await OpenClawWeixinMessageConverter.target2yiri(msg)
+        if not message_chain:
+            return None
+
+        timestamp = (msg.create_time_ms or 0) / 1000.0
+
+        return platform_events.FriendMessage(
+            sender=platform_entities.Friend(
+                id=from_user_id,
+                nickname=from_user_id,
+                remark='',
+            ),
+            message_chain=message_chain,
+            time=timestamp,
+            source_platform_object=msg,
+        )
+
+
+class OpenClawWeixinAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
+    """LangBot adapter for OpenClaw WeChat (long-poll based)."""
+
+    name: str = 'openclaw-weixin'
+
+    client: OpenClawWeixinClient = pydantic.Field(exclude=True)
+
+    config: dict
+
+    message_converter: OpenClawWeixinMessageConverter = OpenClawWeixinMessageConverter()
+    event_converter: OpenClawWeixinEventConverter = OpenClawWeixinEventConverter()
+
+    # context_token cache: from_user_id -> context_token
+    _context_tokens: dict[str, str] = pydantic.PrivateAttr(default_factory=dict)
+
+    _polling: bool = pydantic.PrivateAttr(default=False)
+    _poll_task: typing.Optional[asyncio.Task] = pydantic.PrivateAttr(default=None)
+    _bot_uuid: typing.Optional[str] = pydantic.PrivateAttr(default=None)
+
+    listeners: typing.Dict[
+        typing.Type[platform_events.Event],
+        typing.Callable[[platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None],
+    ] = {}
+
+    def __init__(self, config: dict, logger: abstract_platform_logger.AbstractEventLogger):
+        client = OpenClawWeixinClient(
+            base_url=config.get('base_url', DEFAULT_BASE_URL),
+            token=config.get('token', ''),
+        )
+        super().__init__(
+            config=config,
+            logger=logger,
+            client=client,
+            bot_account_id='',
+            listeners={},
+            name='openclaw-weixin',
+        )
+
+    def set_bot_uuid(self, bot_uuid: str):
+        """Called by BotManager to provide the bot's UUID for config persistence."""
+        self._bot_uuid = bot_uuid
+
+    async def _persist_config(self) -> None:
+        """Persist current self.config to the database so token survives restart."""
+        if not self._bot_uuid:
+            return
+        try:
+            ap = self.logger.ap
+            await ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_bot.Bot)
+                .where(persistence_bot.Bot.uuid == self._bot_uuid)
+                .values(adapter_config=self.config)
+            )
+        except Exception as e:
+            await self.logger.warning(f'Failed to persist adapter config: {e}')
+
+    async def _do_login(self) -> None:
+        """Run the QR code login flow via client.login() and update config."""
+        adapter_logger = self.logger
+
+        async def _on_qrcode(qr_base64: str, _qr_url: str):
+            await adapter_logger.info(
+                f'Please scan the QR code to login WeChat: {_qr_url}',
+                images=[platform_message.Image(base64=qr_base64)],
+            )
+
+        login_result = await self.client.login(
+            on_qrcode=_on_qrcode,
+        )
+
+        # client.login() already updates client.token and client.base_url
+        self.config['token'] = login_result.token
+        self.config['base_url'] = login_result.base_url
+        if login_result.account_id:
+            self.config['account_id'] = login_result.account_id
+
+        await self.logger.info(f'WeChat login successful! account_id={login_result.account_id}')
+
+        # Persist token to database so it survives restart
+        await self._persist_config()
+
+    async def send_message(
+        self,
+        target_type: str,
+        target_id: str,
+        message: platform_message.MessageChain,
+    ):
+        """Send a message to a user."""
+        context_token = self._context_tokens.get(target_id, '')
+
+        for component in message:
+            try:
+                if isinstance(component, platform_message.Plain):
+                    if component.text:
+                        await self.client.send_text(target_id, component.text, context_token)
+
+                elif isinstance(component, platform_message.Image):
+                    img_bytes, _ = await component.get_bytes()
+                    await self.client.send_image(target_id, img_bytes, context_token)
+
+                elif isinstance(component, platform_message.File):
+                    file_bytes = await self._get_component_bytes(component)
+                    if file_bytes:
+                        await self.client.send_file(target_id, file_bytes, component.name or 'file', context_token)
+
+                elif isinstance(component, platform_message.Voice):
+                    voice_bytes = await self._get_component_bytes(component)
+                    if voice_bytes:
+                        await self.client.send_voice(target_id, voice_bytes, component.length or 0, context_token)
+
+                elif isinstance(component, platform_message.Forward):
+                    for node in component.node_list:
+                        if node.message_chain:
+                            await self.send_message(target_type, target_id, node.message_chain)
+
+            except Exception:
+                await self.logger.error(
+                    f'Failed to send component {type(component).__name__}: {traceback.format_exc()}'
+                )
+
+    async def reply_message(
+        self,
+        message_source: platform_events.MessageEvent,
+        message: platform_message.MessageChain,
+        quote_origin: bool = False,
+    ):
+        """Reply to a received message."""
+        source_msg = message_source.source_platform_object
+        if isinstance(source_msg, WeixinMessage):
+            target_id = source_msg.from_user_id or ''
+            if target_id:
+                await self.send_message('friend', target_id, message)
+
+    async def is_muted(self, group_id: int) -> bool:
+        return False
+
+    @staticmethod
+    async def _get_component_bytes(component: platform_message.MessageComponent) -> typing.Optional[bytes]:
+        """Extract raw bytes from a File or Voice component."""
+        b64_val = getattr(component, 'base64', None)
+        url_val = getattr(component, 'url', None)
+        path_val = getattr(component, 'path', None)
+
+        if b64_val:
+            return base64.b64decode(b64_val)
+        elif url_val and url_val.startswith(('http://', 'https://')):
+            import aiohttp
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url_val) as resp:
+                    if resp.status == 200:
+                        return await resp.read()
+        elif path_val:
+            import asyncio
+
+            with open(path_val, 'rb') as f:
+                return await asyncio.to_thread(f.read)
+        return None
+
+    def register_listener(
+        self,
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[
+            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter],
+            None,
+        ],
+    ):
+        self.listeners[event_type] = callback
+
+    def unregister_listener(
+        self,
+        event_type: typing.Type[platform_events.Event],
+        callback: typing.Callable[
+            [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter],
+            None,
+        ],
+    ):
+        self.listeners.pop(event_type, None)
+
+    async def run_async(self):
+        """Start the adapter. If no token is configured, trigger QR code login first."""
+        base_url = self.config.get('base_url', DEFAULT_BASE_URL)
+        token = self.config.get('token', '')
+
+        await self.logger.info('OpenClaw WeChat adapter starting...')
+
+        # QR code login flow when no token is provided
+        if not token:
+            await self.logger.info('No token configured, starting QR code login...')
+            try:
+                await self._do_login()
+            except Exception as e:
+                await self.logger.error(f'QR code login failed: {e}')
+                raise
+
+        # Rebuild client with the (possibly updated) config
+        self.client = OpenClawWeixinClient(
+            base_url=self.config.get('base_url', base_url),
+            token=self.config.get('token', token),
+        )
+        self.bot_account_id = self.config.get('account_id', 'openclaw-weixin')
+        self._polling = True
+
+        # Start the long-poll loop
+        self._poll_task = asyncio.create_task(self._poll_loop())
+        await self.logger.info('OpenClaw WeChat adapter running')
+
+        try:
+            await self._poll_task
+        except asyncio.CancelledError:
+            pass
+
+    async def _poll_loop(self):
+        """Long-poll loop: call getUpdates continuously.
+
+        Error handling follows the weixin-bot SDK pattern:
+        - Exponential backoff (1s -> 10s max) on failures
+        - Session expired (errcode -14) triggers automatic re-login
+        """
+        get_updates_buf = ''
+        poll_timeout = float(self.config.get('poll_timeout', 35))
+
+        backoff_delay = 1.0
+        max_backoff = 10.0
+
+        while self._polling:
+            try:
+                resp = await self.client.get_updates(
+                    get_updates_buf=get_updates_buf,
+                    timeout=poll_timeout + 5,
+                )
+
+                if resp.longpolling_timeout_ms and resp.longpolling_timeout_ms > 0:
+                    poll_timeout = resp.longpolling_timeout_ms / 1000.0
+
+                is_api_error = (resp.ret is not None and resp.ret != 0) or (
+                    resp.errcode is not None and resp.errcode != 0
+                )
+                if is_api_error:
+                    is_session_expired = resp.errcode == SESSION_EXPIRED_ERRCODE or resp.ret == SESSION_EXPIRED_ERRCODE
+
+                    if is_session_expired:
+                        await self.logger.error('OpenClaw WeChat session expired, attempting re-login...')
+                        try:
+                            await self._do_login()
+                            # Rebuild client with new credentials
+                            self.client = OpenClawWeixinClient(
+                                base_url=self.config.get('base_url', DEFAULT_BASE_URL),
+                                token=self.config.get('token', ''),
+                            )
+                            self._context_tokens.clear()
+                            get_updates_buf = ''
+                            backoff_delay = 1.0
+                            continue
+                        except Exception:
+                            await self.logger.error(f'Re-login failed: {traceback.format_exc()}')
+                            break
+
+                    await self.logger.error(
+                        f'OpenClaw getUpdates failed: ret={resp.ret} errcode={resp.errcode} errmsg={resp.errmsg}'
+                    )
+                    await asyncio.sleep(backoff_delay)
+                    backoff_delay = min(backoff_delay * 2, max_backoff)
+                    continue
+
+                backoff_delay = 1.0
+
+                if resp.get_updates_buf:
+                    get_updates_buf = resp.get_updates_buf
+
+                for msg in resp.msgs:
+                    try:
+                        await self._handle_inbound_message(msg)
+                    except Exception:
+                        await self.logger.error(f'Error handling message: {traceback.format_exc()}')
+
+            except asyncio.CancelledError:
+                break
+            except Exception:
+                await self.logger.error(f'OpenClaw poll error: {traceback.format_exc()}')
+                await asyncio.sleep(backoff_delay)
+                backoff_delay = min(backoff_delay * 2, max_backoff)
+
+    async def _handle_inbound_message(self, msg: WeixinMessage):
+        """Process a single inbound message from getUpdates."""
+        if msg.context_token and msg.from_user_id:
+            self._context_tokens[msg.from_user_id] = msg.context_token
+
+        # Download CDN media (files, images) before converting to LangBot events
+        await self._download_media_items(msg)
+
+        event = await OpenClawWeixinEventConverter.target2yiri(msg)
+        if event is None:
+            return
+
+        if type(event) in self.listeners:
+            await self.listeners[type(event)](event, self)
+
+    async def _download_media_items(self, msg: WeixinMessage):
+        """Download CDN media for image items in the message."""
+        if not msg.item_list:
+            return
+
+        for item in msg.item_list:
+            try:
+                if item.type == MessageItem.IMAGE and item.image_item:
+                    if (
+                        item.image_item.media
+                        and item.image_item.media.encrypt_query_param
+                        and item.image_item.media.aes_key
+                    ):
+                        img_bytes = await self.client.download_media(item.image_item.media)
+                        item.image_item._downloaded_bytes = img_bytes
+
+                # TODO: enable after full testing
+                # elif item.type == MessageItem.FILE and item.file_item and item.file_item.media:
+                #     if item.file_item.media.encrypt_query_param and item.file_item.media.aes_key:
+                #         file_bytes = await self.client.download_media(item.file_item.media)
+                #         item.file_item._downloaded_bytes = file_bytes
+                #
+                # elif item.type == MessageItem.VOICE and item.voice_item and item.voice_item.media:
+                #     if item.voice_item.media.encrypt_query_param and item.voice_item.media.aes_key:
+                #         voice_bytes = await self.client.download_media(item.voice_item.media)
+                #         item.voice_item._downloaded_bytes = voice_bytes
+                #
+                # elif item.type == MessageItem.VIDEO and item.video_item and item.video_item.media:
+                #     if item.video_item.media.encrypt_query_param and item.video_item.media.aes_key:
+                #         video_bytes = await self.client.download_media(item.video_item.media)
+                #         item.video_item._downloaded_bytes = video_bytes
+
+            except Exception:
+                await self.logger.warning(f'Failed to download CDN media: {traceback.format_exc()}')
+
+    async def kill(self) -> bool:
+        """Stop the adapter."""
+        self._polling = False
+        if self._poll_task and not self._poll_task.done():
+            self._poll_task.cancel()
+            try:
+                await self._poll_task
+            except asyncio.CancelledError:
+                pass
+        await self.client.close()
+        await self.logger.info('OpenClaw WeChat adapter stopped')
+        return True

src/langbot/pkg/platform/sources/openclaw_weixin.yaml

@@ -0,0 +1,74 @@
+apiVersion: v1
+kind: MessagePlatformAdapter
+metadata:
+  name: openclaw-weixin
+  label:
+    en_US: OpenClaw WeChat
+    zh_Hans: 个人微信机器人
+    zh_Hant: 個人微信機器人
+  description:
+    en_US: OpenClaw WeChat adapter, supports personal WeChat via QR code login
+    zh_Hans: 微信官方个人助手，扫码即可登录使用
+    zh_Hant: 微信官方個人助手，掃碼即可登入使用
+  icon: wechat.png
+spec:
+  categories:
+    - popular
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/openclaw_weixin
+    en: https://link.langbot.app/en/platforms/openclaw_weixin
+    ja: https://link.langbot.app/ja/platforms/openclaw_weixin
+  config:
+    - name: base_url
+      label:
+        en_US: API Base URL
+        zh_Hans: API 基础地址
+        zh_Hant: API 基礎地址
+      description:
+        en_US: The base URL of the OpenClaw WeChat backend API
+        zh_Hans: OpenClaw 微信后端 API 的基础地址
+        zh_Hant: OpenClaw 微信後端 API 的基礎地址
+      type: string
+      required: true
+      default: "https://ilinkai.weixin.qq.com"
+    - name: token
+      label:
+        en_US: Token
+        zh_Hans: 令牌
+        zh_Hant: 令牌
+      description:
+        en_US: Bearer token obtained after QR code login authorization. Leave empty to trigger QR code login on startup.
+        zh_Hans: 扫码登录授权后获取的 Bearer 令牌。请留空并保存，将在启动时输出二维码到日志，扫码后即可自动登录。
+        zh_Hant: 掃碼登入授權後取得的 Bearer 令牌。請留空並儲存，將在啟動時輸出 QR Code 到日誌，掃碼後即可自動登入。
+      type: string
+      required: false
+      default: ""
+    - name: account_id
+      label:
+        en_US: Account ID
+        zh_Hans: 账号标识
+        zh_Hant: 帳號標識
+      description:
+        en_US: A label for this WeChat account (used for display purposes)
+        zh_Hans: 此微信账号的标识（用于显示）
+        zh_Hant: 此微信帳號的標識（用於顯示）
+      type: string
+      required: false
+      default: "openclaw-weixin"
+    - name: poll_timeout
+      label:
+        en_US: Poll Timeout (seconds)
+        zh_Hans: 轮询超时（秒）
+        zh_Hant: 輪詢逾時（秒）
+      description:
+        en_US: Long-poll timeout for getUpdates, the server may hold the request up to this duration
+        zh_Hans: getUpdates 长轮询超时时间，服务端最多持有请求的时长
+        zh_Hant: getUpdates 長輪詢逾時時間，伺服端最多持有請求的時長
+      type: integer
+      required: false
+      default: 35
+execution:
+  python:
+    path: ./openclaw_weixin.py
+    attr: OpenClawWeixinAdapter

src/langbot/pkg/platform/sources/qqofficial.yaml

@@ -5,16 +5,37 @@ metadata:
   label:
     en_US: QQ Official API
     zh_Hans: QQ 官方 API
+    zh_Hant: QQ 官方 API
   description:
     en_US: QQ Official API (Webhook)
-    zh_Hans: QQ 官方 API (Webhook)，请查看文档了解使用方式
+    zh_Hans: QQ 官方 API (Webhook)，需要公网地址以接收消息推送，请查看文档了解使用方式
+    zh_Hant: QQ 官方 API (Webhook)，需要公網地址以接收訊息推送，請查看文件了解使用方式
   icon: qqofficial.svg
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/qqofficial
+    en: https://link.langbot.app/en/platforms/qqofficial
+    ja: https://link.langbot.app/ja/platforms/qqofficial
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+      description:
+        en_US: Copy this URL and paste it into your QQ Official API webhook configuration
+        zh_Hans: 复制此地址并粘贴到 QQ 官方 API 的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到 QQ 官方 API 的 Webhook 設定中
+      type: webhook-url
+      required: false
+      default: ""
     - name: appid
       label:
         en_US: App ID
         zh_Hans: 应用ID
+        zh_Hant: 應用ID
       type: string
       required: true
       default: ""
@@ -22,6 +43,7 @@ spec:
       label:
         en_US: Secret
         zh_Hans: 密钥
+        zh_Hant: 密鑰
       type: string
       required: true
       default: ""
@@ -29,6 +51,7 @@ spec:
       label:
         en_US: Token
         zh_Hans: 令牌
+        zh_Hant: 令牌
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/satori.yaml

@@ -5,36 +5,70 @@ metadata:
   label:
     en_US: Satori
     zh_Hans: Satori
+    zh_Hant: Satori
+    th_TH: Satori
+    vi_VN: Satori
+    es_ES: Satori
   description:
     en_US: SatoriAdapter
-    zh_Hans: 古明地觉协议适配器
+    zh_Hans: Satori 协议适配器，支持多种平台的接入，请查看文档了解使用方式
+    zh_Hant: Satori 協定適配器，支援多種平台的接入，請查看文件了解使用方式
+    th_TH: อะแดปเตอร์โปรโตคอล Satori รองรับการเชื่อมต่อหลายแพลตฟอร์ม โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
+    vi_VN: Bộ điều hợp giao thức Satori, hỗ trợ kết nối nhiều nền tảng, vui lòng xem tài liệu để biết cách sử dụng
+    es_ES: Adaptador del protocolo Satori, soporta acceso a múltiples plataformas, consulte la documentación para obtener instrucciones de uso
   icon: satori.png
 spec:
+  categories:
+    - protocol
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/satori
+    en: https://link.langbot.app/en/platforms/satori
+    ja: https://link.langbot.app/ja/platforms/satori
   config:
     - name: platform
       label:
         en_US: Platform
         zh_Hans: 平台名称
+        zh_Hant: 平台名稱
+        th_TH: ชื่อแพลตฟอร์ม
+        vi_VN: Tên nền tảng
+        es_ES: Nombre de la plataforma
       type: string
       required: true
       default: "llonebot"
       description:
         en_US: The platform name (e.g., llonebot, discord, telegram)
         zh_Hans: 平台名称（如 llonebot, discord, telegram）
+        zh_Hant: 平台名稱（如 llonebot、discord、telegram）
+        th_TH: ชื่อแพลตฟอร์ม (เช่น llonebot, discord, telegram)
+        vi_VN: "Tên nền tảng (ví dụ: llonebot, discord, telegram)"
+        es_ES: El nombre de la plataforma (p. ej., llonebot, discord, telegram)
     - name: host
       label:
         en_US: Host
         zh_Hans: 主机地址
+        zh_Hant: 主機地址
+        th_TH: ที่อยู่โฮสต์
+        vi_VN: Địa chỉ máy chủ
+        es_ES: Dirección del host
       type: string
       required: true
       default: "127.0.0.1"
       description:
         en_US: The host address of LLOneBot Satori server (e.g., 127.0.0.1, localhost, 192.168.1.100)
         zh_Hans: LLOneBot Satori服务器的主机地址（如 127.0.0.1, localhost, 192.168.1.100）
+        zh_Hant: LLOneBot Satori 伺服器的主機地址（如 127.0.0.1、localhost、192.168.1.100）
+        th_TH: ที่อยู่โฮสต์ของเซิร์ฟเวอร์ LLOneBot Satori (เช่น 127.0.0.1, localhost, 192.168.1.100)
+        vi_VN: "Địa chỉ máy chủ LLOneBot Satori (ví dụ: 127.0.0.1, localhost, 192.168.1.100)"
+        es_ES: La dirección del host del servidor LLOneBot Satori (p. ej., 127.0.0.1, localhost, 192.168.1.100)
     - name: port
       label:
         en_US: Port
         zh_Hans: 监听端口
+        zh_Hant: 監聽連接埠
+        th_TH: พอร์ต
+        vi_VN: Cổng
+        es_ES: Puerto
       type: integer
       required: true
       default: 5600
@@ -42,6 +76,10 @@ spec:
       label:
         en_US: Satori API Endpoint
         zh_Hans: Satori API 终结点
+        zh_Hant: Satori API 端點
+        th_TH: จุดปลาย Satori API
+        vi_VN: Điểm cuối Satori API
+        es_ES: Punto de acceso de la API Satori
       type: string
       required: true
       default: "http://localhost:5600/v1"
@@ -49,6 +87,10 @@ spec:
       label:
         en_US: Satori WebSocket Endpoint
         zh_Hans: Satori WebSocket 终结点
+        zh_Hant: Satori WebSocket 端點
+        th_TH: จุดปลาย Satori WebSocket
+        vi_VN: Điểm cuối Satori WebSocket
+        es_ES: Punto de acceso WebSocket de Satori
       type: string
       required: true
       default: "ws://localhost:5600/v1/events"
@@ -56,6 +98,10 @@ spec:
       label:
         en_US: Token
         zh_Hans: 令牌
+        zh_Hant: 令牌
+        th_TH: โทเค็น
+        vi_VN: Mã thông báo
+        es_ES: Token
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/slack.yaml

@@ -5,16 +5,58 @@ metadata:
   label:
     en_US: Slack
     zh_Hans: Slack
+    zh_Hant: Slack
+    ja_JP: Slack
+    th_TH: Slack
+    vi_VN: Slack
+    es_ES: Slack
   description:
     en_US: Slack Adapter
-    zh_Hans: Slack 适配器，请查看文档了解使用方式
+    zh_Hans: Slack 适配器，需要公网地址以接收 Slack 消息推送，请查看文档了解使用方式
+    zh_Hant: Slack 適配器，需要公網地址以接收 Slack 訊息推送，請查看文件了解使用方式
+    ja_JP: Slack アダプター、Slackのメッセージプッシュを受信するためにパブリックURLが必要です。使用方法の詳細については、ドキュメントを参照してください。
+    th_TH: อะแดปเตอร์ Slack ต้องการที่อยู่สาธารณะเพื่อรับการแจ้งเตือนข้อความจาก Slack โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
+    vi_VN: Bộ điều hợp Slack, cần địa chỉ công cộng để nhận thông báo tin nhắn từ Slack, vui lòng xem tài liệu để biết cách sử dụng
+    es_ES: Adaptador de Slack, requiere una dirección pública para recibir notificaciones de mensajes de Slack, consulte la documentación para obtener instrucciones de uso
   icon: slack.png
 spec:
+  categories:
+    - popular
+    - global
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/slack
+    en: https://link.langbot.app/en/platforms/slack
+    ja: https://link.langbot.app/ja/platforms/slack
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+        ja_JP: Webhook コールバック URL
+        th_TH: URL การเรียกกลับ Webhook
+        vi_VN: URL gọi lại Webhook
+        es_ES: URL de devolución de llamada Webhook
+      description:
+        en_US: Copy this URL and paste it into your Slack app's event subscription configuration
+        zh_Hans: 复制此地址并粘贴到 Slack 应用的事件订阅配置中
+        zh_Hant: 複製此地址並貼到 Slack 應用的事件訂閱設定中
+        ja_JP: この URL をコピーして Slack アプリのイベントサブスクリプション設定に貼り付けてください
+        th_TH: คัดลอก URL นี้แล้ววางในการตั้งค่าการสมัครรับเหตุการณ์ของแอป Slack ของคุณ
+        vi_VN: Sao chép URL này và dán vào cấu hình đăng ký sự kiện của ứng dụng Slack của bạn
+        es_ES: Copie esta URL y péguela en la configuración de suscripción de eventos de su aplicación Slack
+      type: webhook-url
+      required: false
+      default: ""
     - name: bot_token
       label:
         en_US: Bot Token
         zh_Hans: 机器人令牌
+        zh_Hant: 機器人令牌
+        ja_JP: ボットトークン
+        th_TH: โทเค็นบอท
+        vi_VN: Mã thông báo Bot
+        es_ES: Token del bot
       type: string
       required: true
       default: ""
@@ -22,6 +64,11 @@ spec:
       label:
         en_US: signing_secret
         zh_Hans: 密钥
+        zh_Hant: 密鑰
+        ja_JP: 署名シークレット
+        th_TH: คีย์ลายเซ็น
+        vi_VN: Khóa ký
+        es_ES: Secreto de firma
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/telegram.py

@@ -42,6 +42,25 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
                         photo_bytes = f.read()
 
                 components.append({'type': 'photo', 'photo': photo_bytes})
+            elif isinstance(component, platform_message.File):
+                file_bytes = None
+
+                if component.base64:
+                    # Strip data URI prefix if present (e.g. "data:application/pdf;base64,...")
+                    b64_data = component.base64
+                    if ';base64,' in b64_data:
+                        b64_data = b64_data.split(';base64,', 1)[1]
+                    file_bytes = base64.b64decode(b64_data)
+                elif component.url:
+                    session = httpclient.get_session()
+                    async with session.get(component.url) as response:
+                        file_bytes = await response.read()
+                elif component.path:
+                    with open(component.path, 'rb') as f:
+                        file_bytes = f.read()
+
+                file_name = getattr(component, 'name', None) or 'file'
+                components.append({'type': 'document', 'document': file_bytes, 'filename': file_name})
             elif isinstance(component, platform_message.Forward):
                 for node in component.node_list:
                     components.extend(await TelegramMessageConverter.yiri2target(node.message_chain, bot))
@@ -104,6 +123,27 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
                 )
             )
 
+        if message.document:
+            if message.caption:
+                message_components.extend(parse_message_text(message.caption))
+
+            file = await message.document.get_file()
+            file_name = message.document.file_name or 'document'
+            file_size = message.document.file_size or 0
+            file_format = message.document.mime_type or 'application/octet-stream'
+
+            file_bytes = None
+            async with httpclient.get_session(trust_env=True).get(file.file_path) as response:
+                file_bytes = await response.read()
+
+            message_components.append(
+                platform_message.File(
+                    name=file_name,
+                    size=file_size,
+                    base64=f'data:{file_format};base64,{base64.b64encode(file_bytes).decode("utf-8")}',
+                )
+            )
+
         return platform_message.MessageChain(message_components)
 
 
@@ -179,7 +219,10 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         application = ApplicationBuilder().token(config['token']).build()
         bot = application.bot
         application.add_handler(
-            MessageHandler(filters.TEXT | (filters.COMMAND) | filters.PHOTO | filters.VOICE, telegram_callback)
+            MessageHandler(
+                filters.TEXT | (filters.COMMAND) | filters.PHOTO | filters.VOICE | filters.Document.ALL,
+                telegram_callback,
+            )
         )
         super().__init__(
             config=config,
@@ -218,6 +261,13 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                     continue
                 args['photo'] = telegram.InputFile(photo)
                 await self.bot.send_photo(**args)
+            elif component_type == 'document':
+                doc = component.get('document')
+                if doc is None:
+                    continue
+                filename = component.get('filename', 'file')
+                args['document'] = telegram.InputFile(doc, filename=filename)
+                await self.bot.send_document(**args)
 
     async def reply_message(
         self,

src/langbot/pkg/platform/sources/telegram.yaml

@@ -5,23 +5,50 @@ metadata:
   label:
     en_US: Telegram
     zh_Hans: 电报
+    zh_Hant: Telegram
+    ja_JP: Telegram
+    th_TH: Telegram
+    vi_VN: Telegram
+    es_ES: Telegram
   description:
     en_US: Telegram Adapter
-    zh_Hans: 电报适配器，请查看文档了解使用方式
+    zh_Hans: Telegram 适配器，请查看文档了解使用方式
+    zh_Hant: Telegram 適配器，請查看文件了解使用方式
+    ja_JP: Telegram アダプター。使用方法の詳細については、ドキュメントを参照してください。
+    th_TH: อะแดปเตอร์ Telegram โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
+    vi_VN: Bộ điều hợp Telegram, vui lòng xem tài liệu để biết cách sử dụng
+    es_ES: Adaptador de Telegram, consulte la documentación para obtener instrucciones de uso
   icon: telegram.svg
 spec:
+  categories:
+    - popular
+    - global
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/telegram
+    en: https://link.langbot.app/en/platforms/telegram
+    ja: https://link.langbot.app/ja/platforms/telegram
   config:
     - name: token
       label:
         en_US: Token
         zh_Hans: 令牌
+        zh_Hant: 令牌
+        ja_JP: トークン
+        th_TH: โทเค็น
+        vi_VN: Mã thông báo
+        es_ES: Token
       type: string
       required: true
-      default: ""
+      default: "token_from_botfather"
     - name: markdown_card
       label:
         en_US: Markdown Card
         zh_Hans: 是否使用 Markdown 卡片
+        zh_Hant: 是否使用 Markdown 卡片
+        ja_JP: Markdown カードを使用
+        th_TH: การ์ด Markdown
+        vi_VN: Thẻ Markdown
+        es_ES: Tarjeta Markdown
       type: boolean
       required: false
       default: true
@@ -29,9 +56,19 @@ spec:
       label:
         en_US: Enable Stream Reply Mode
         zh_Hans: 启用电报流式回复模式
+        zh_Hant: 啟用 Telegram 串流回覆模式
+        ja_JP: ストリーミング返信モードを有効化
+        th_TH: เปิดใช้งานโหมดตอบกลับแบบสตรีม
+        vi_VN: Bật chế độ trả lời trực tuyến
+        es_ES: Habilitar modo de respuesta en streaming
       description:
         en_US: If enabled, the bot will use the stream of telegram reply mode
         zh_Hans: 如果启用，将使用电报流式方式来回复内容
+        zh_Hant: 如果啟用，將使用 Telegram 串流方式來回覆內容
+        ja_JP: 有効にすると、ボットはストリーミングモードでメッセージに返信します
+        th_TH: หากเปิดใช้งาน บอทจะใช้โหมดสตรีมของ Telegram ในการตอบกลับ
+        vi_VN: Nếu bật, bot sẽ sử dụng chế độ trả lời trực tuyến của Telegram
+        es_ES: Si está habilitado, el bot usará el modo de respuesta en streaming de Telegram
       type: boolean
       required: true
       default: false

src/langbot/pkg/platform/sources/websocket.yaml

@@ -5,11 +5,21 @@ metadata:
   label:
     en_US: "WebSocket Chat"
     zh_Hans: "WebSocket 聊天"
+    zh_Hant: "WebSocket 聊天"
+    th_TH: "แชท WebSocket"
+    vi_VN: "Trò chuyện WebSocket"
+    es_ES: "Chat WebSocket"
   description:
     en_US: "WebSocket adapter for bidirectional real-time communication"
     zh_Hans: "用于双向实时通信的 WebSocket 适配器"
+    zh_Hant: "用於雙向即時通訊的 WebSocket 適配器"
+    th_TH: "อะแดปเตอร์ WebSocket สำหรับการสื่อสารแบบเรียลไทม์สองทิศทาง"
+    vi_VN: "Bộ điều hợp WebSocket cho giao tiếp thời gian thực hai chiều"
+    es_ES: "Adaptador WebSocket para comunicación bidireccional en tiempo real"
   icon: ""
 spec:
+  categories:
+    - protocol
   config: []
 execution:
   python:

src/langbot/pkg/platform/sources/websocket_adapter.py

@@ -37,16 +37,24 @@ class WebSocketSession:
     id: str
     message_lists: dict[str, list[WebSocketMessage]] = {}
     """消息列表 {pipeline_uuid: [messages]}"""
+    stream_message_indexes: dict[str, dict[str, int]] = {}
+    """流式消息索引 {pipeline_uuid: {resp_message_id: message_index}}"""
 
     def __init__(self, id: str):
         self.id = id
         self.message_lists = {}
+        self.stream_message_indexes = {}
 
     def get_message_list(self, pipeline_uuid: str) -> list[WebSocketMessage]:
         if pipeline_uuid not in self.message_lists:
             self.message_lists[pipeline_uuid] = []
         return self.message_lists[pipeline_uuid]
 
+    def get_stream_message_indexes(self, pipeline_uuid: str) -> dict[str, int]:
+        if pipeline_uuid not in self.stream_message_indexes:
+            self.stream_message_indexes[pipeline_uuid] = {}
+        return self.stream_message_indexes[pipeline_uuid]
+
 
 class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
     """WebSocket适配器 - 支持双向实时通信"""
@@ -89,20 +97,46 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         target_id: str,
         message: platform_message.MessageChain,
     ) -> dict:
-        """发送消息 - 这里用于主动推送消息到前端"""
+        """发送消息 - 这里用于主动推送消息到前端
-        message_data = {
-            'type': 'bot_message',
-            'target_type': target_type,
-            'target_id': target_id,
-            'content': str(message),
-            'message_chain': [component.__dict__ for component in message],
-            'timestamp': datetime.now().isoformat(),
-        }
 
-        # 推送到所有相关连接
+        对于 WebSocket 适配器，我们需要将消息广播到正确的 pipeline 连接。
-        await self.outbound_message_queue.put(message_data)
+        target_id 可能是 launcher_id（如 websocket_xxx）或 pipeline_uuid。
+        我们需要尝试两种方式来确保消息能够送达。
+        """
+        # 获取当前的 pipeline_uuid
+        pipeline_uuid = self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid
+        session_type = 'group' if target_type == 'group' else 'person'
 
-        return message_data
+        # 选择会话
+        session = self.websocket_group_session if session_type == 'group' else self.websocket_person_session
+
+        # 生成唯一消息ID
+        msg_id = len(session.get_message_list(pipeline_uuid)) + 1
+
+        message_data = WebSocketMessage(
+            id=msg_id,
+            role='assistant',
+            content=str(message),
+            message_chain=[component.__dict__ for component in message],
+            timestamp=datetime.now().isoformat(),
+            is_final=True,
+        )
+
+        # 保存到历史记录
+        session.get_message_list(pipeline_uuid).append(message_data)
+
+        # 直接广播到当前pipeline的连接
+        await ws_connection_manager.broadcast_to_pipeline(
+            pipeline_uuid,
+            {
+                'type': 'response',
+                'session_type': session_type,
+                'data': message_data.model_dump(),
+            },
+            session_type=session_type,
+        )
+
+        return message_data.model_dump()
 
     async def reply_message(
         self,
@@ -169,10 +203,16 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         pipeline_uuid = self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid
         session_type = 'group' if isinstance(message_source, platform_events.GroupMessage) else 'person'
         message_list = session.get_message_list(pipeline_uuid)
+        stream_message_indexes = session.get_stream_message_indexes(pipeline_uuid)
 
-        # 检查是否是新的流式消息（通过bot_message对象判断）
+        # Streaming messages in LangBot have a stable resp_message_id during the same assistant reply.
-        # 如果列表为空，或者最后一条消息已经is_final=True，则创建新消息
+        # Use it as the primary key to avoid overwriting an old card from a previous reply.
-        if not message_list or message_list[-1].is_final:
+        resp_message_id = str(getattr(bot_message, 'resp_message_id', '') or '')
+        existing_index = stream_message_indexes.get(resp_message_id) if resp_message_id else None
+
+        message_is_final = is_final and bot_message.tool_calls is None
+
+        if existing_index is None or existing_index >= len(message_list):
             # 创建新消息
             msg_id = len(message_list) + 1
             message_data = WebSocketMessage(
@@ -181,27 +221,31 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
                 content=str(message),
                 message_chain=[component.__dict__ for component in message],
                 timestamp=datetime.now().isoformat(),
-                is_final=is_final and bot_message.tool_calls is None,
+                is_final=message_is_final,
             )
 
-            # 只有在is_final时才保存到历史记录
+            # 立即添加到历史记录（即使is_final=False），以便后续块可以更新它
-            if is_final and bot_message.tool_calls is None:
+            message_list.append(message_data)
-                message_list.append(message_data)
+            if resp_message_id:
+                stream_message_indexes[resp_message_id] = len(message_list) - 1
         else:
-            # 更新最后一条消息
+            # 更新同一条流式消息
-            msg_id = message_list[-1].id
+            old_message = message_list[existing_index]
+            msg_id = old_message.id
             message_data = WebSocketMessage(
                 id=msg_id,
                 role='assistant',
                 content=str(message),
                 message_chain=[component.__dict__ for component in message],
-                timestamp=message_list[-1].timestamp,  # 保持原始时间戳
+                timestamp=old_message.timestamp,  # 保持原始时间戳
-                is_final=is_final and bot_message.tool_calls is None,
+                is_final=message_is_final,
             )
 
-            # 如果是final，更新历史记录中的最后一条
+            # 更新历史记录中的对应消息
-            if is_final and bot_message.tool_calls is None:
+            message_list[existing_index] = message_data
-                message_list[-1] = message_data
+
+        if message_is_final and resp_message_id:
+            stream_message_indexes.pop(resp_message_id, None)
 
         # 直接广播到所有该pipeline的连接，包含session_type信息
         await ws_connection_manager.broadcast_to_pipeline(
@@ -410,6 +454,10 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         if session_type == 'person':
             if pipeline_uuid in self.websocket_person_session.message_lists:
                 self.websocket_person_session.message_lists[pipeline_uuid] = []
+            if pipeline_uuid in self.websocket_person_session.stream_message_indexes:
+                self.websocket_person_session.stream_message_indexes[pipeline_uuid] = {}
         else:
             if pipeline_uuid in self.websocket_group_session.message_lists:
                 self.websocket_group_session.message_lists[pipeline_uuid] = []
+            if pipeline_uuid in self.websocket_group_session.stream_message_indexes:
+                self.websocket_group_session.stream_message_indexes[pipeline_uuid] = {}

src/langbot/pkg/platform/sources/wechatpad.yaml

@@ -4,17 +4,26 @@ metadata:
   name: wechatpad
   label:
     en_US: WeChatPad
-    zh_CN: WeChatPad（个人微信ipad）
+    zh_Hans: WeChatPad（个人微信ipad）
+    zh_Hant: WeChatPad（個人微信iPad）
   description:
     en_US: WeChatPad Adapter
-    zh_CN: WeChatPad 适配器
+    zh_Hans: WeChatPad 适配器，基于WeChatPad的个人微信解决方案，请查看文档了解使用方式
+    zh_Hant: WeChatPad 適配器，基於 WeChatPad 的個人微信解決方案，請查看文件了解使用方式
   icon: wechatpad.png
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/wechatpad
+    en: https://link.langbot.app/en/platforms/wechatpad
+    ja: https://link.langbot.app/ja/platforms/wechatpad
   config:
     - name: wechatpad_url
       label:
         en_US: WeChatPad ERL
         zh_CN: WeChatPad URL
+        zh_Hant: WeChatPad URL
       type: string
       required: true
       default: ""
@@ -22,6 +31,7 @@ spec:
       label:
         en_US: WeChatPad_Ws
         zh_CN: WeChatPad_Ws
+        zh_Hant: WeChatPad_Ws
       type: string
       required: true
       default: ""
@@ -29,6 +39,7 @@ spec:
       label:
         en_US: Admin_Key
         zh_CN: 管理员密匙
+        zh_Hant: 管理員密鑰
       type: string
       required: true
       default: ""
@@ -36,6 +47,7 @@ spec:
       label:
         en_US: Token
         zh_CN: 令牌
+        zh_Hant: 令牌
       type: string
       required: true
       default: ""
@@ -43,6 +55,7 @@ spec:
       label:
         en_US: wxid
         zh_CN: wxid
+        zh_Hant: wxid
       type: string
       required: true
       default: ""

src/langbot/pkg/platform/sources/wecom.py

@@ -148,51 +148,54 @@ class WecomEventConverter(abstract_platform_adapter.AbstractEventConverter):
             pass
 
         if type(event) is platform_events.FriendMessage:
-            payload = {
+            return event.source_platform_object
-                'MsgType': 'text',
-                'Content': '',
-                'FromUserName': event.sender.id,
-                'ToUserName': bot_account_id,
-                'CreateTime': int(datetime.datetime.now().timestamp()),
-                'AgentID': event.sender.nickname,
-            }
-            wecom_event = WecomEvent.from_payload(payload=payload)
-            if not wecom_event:
-                raise ValueError('无法从 message_data 构造 WecomEvent 对象')
-
-            return wecom_event
 
     @staticmethod
-    async def target2yiri(event: WecomEvent):
+    async def target2yiri(event: WecomEvent, bot: WecomClient = None):
         """
         将 WecomEvent 转换为平台的 FriendMessage 对象。
 
         Args:
             event (WecomEvent): 企业微信事件。
+            bot (WecomClient): 企业微信客户端，用于获取用户信息。
 
         Returns:
             platform_events.FriendMessage: 转换后的 FriendMessage 对象。
         """
+        # Try to get the user's real name from the WeCom API
+        nickname = str(event.user_id)
+        if bot and event.user_id:
+            try:
+                user_info = await bot.get_user_info(event.user_id)
+                if user_info and user_info.get('name'):
+                    nickname = user_info.get('name')
+            except Exception:
+                pass  # Fall back to user_id as nickname
+
         # 转换消息链
         if event.type == 'text':
             yiri_chain = await WecomMessageConverter.target2yiri(event.message, event.message_id)
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=str(event.agent_id),
+                nickname=nickname,
                 remark='',
             )
 
-            return platform_events.FriendMessage(sender=friend, message_chain=yiri_chain, time=event.timestamp)
+            return platform_events.FriendMessage(
+                sender=friend, message_chain=yiri_chain, time=event.timestamp, source_platform_object=event
+            )
         elif event.type == 'image':
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=str(event.agent_id),
+                nickname=nickname,
                 remark='',
             )
 
             yiri_chain = await WecomMessageConverter.target2yiri_image(picurl=event.picurl, message_id=event.message_id)
 
-            return platform_events.FriendMessage(sender=friend, message_chain=yiri_chain, time=event.timestamp)
+            return platform_events.FriendMessage(
+                sender=friend, message_chain=yiri_chain, time=event.timestamp, source_platform_object=event
+            )
 
 
 class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
@@ -210,7 +213,6 @@ class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             'secret',
             'token',
             'EncodingAESKey',
-            'contacts_secret',
         ]
 
         missing_keys = [key for key in required_keys if key not in config]
@@ -223,7 +225,7 @@ class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             secret=config['secret'],
             token=config['token'],
             EncodingAESKey=config['EncodingAESKey'],
-            contacts_secret=config['contacts_secret'],
+            contacts_secret=config.get('contacts_secret', ''),  # Optional, kept for backward compatibility
             logger=logger,
             unified_mode=True,
             api_base_url=config.get('api_base_url', 'https://qyapi.weixin.qq.com/cgi-bin'),
@@ -248,18 +250,17 @@ class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
     ):
         Wecom_event = await WecomEventConverter.yiri2target(message_source, self.bot_account_id, self.bot)
         content_list = await WecomMessageConverter.yiri2target(message, self.bot)
-        fixed_user_id = Wecom_event.user_id
+        # user_id is the original FromUserName from WecomEvent
-        # 删掉开头的u
+        user_id = Wecom_event.user_id
-        fixed_user_id = fixed_user_id[1:]
         for content in content_list:
             if content['type'] == 'text':
-                await self.bot.send_private_msg(fixed_user_id, Wecom_event.agent_id, content['content'])
+                await self.bot.send_private_msg(user_id, Wecom_event.agent_id, content['content'])
             elif content['type'] == 'image':
-                await self.bot.send_image(fixed_user_id, Wecom_event.agent_id, content['media_id'])
+                await self.bot.send_image(user_id, Wecom_event.agent_id, content['media_id'])
             elif content['type'] == 'voice':
-                await self.bot.send_voice(fixed_user_id, Wecom_event.agent_id, content['media_id'])
+                await self.bot.send_voice(user_id, Wecom_event.agent_id, content['media_id'])
             elif content['type'] == 'file':
-                await self.bot.send_file(fixed_user_id, Wecom_event.agent_id, content['media_id'])
+                await self.bot.send_file(user_id, Wecom_event.agent_id, content['media_id'])
 
     async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
         content_list = await WecomMessageConverter.yiri2target(message, self.bot)
@@ -287,7 +288,7 @@ class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         async def on_message(event: WecomEvent):
             self.bot_account_id = event.receiver_id
             try:
-                return await callback(await self.event_converter.target2yiri(event), self)
+                return await callback(await self.event_converter.target2yiri(event, self.bot), self)
             except Exception:
                 await self.logger.error(f'Error in wecom callback: {traceback.format_exc()}')
 

src/langbot/pkg/platform/sources/wecom.yaml

@@ -5,16 +5,38 @@ metadata:
   label:
     en_US: WeCom
     zh_Hans: 企业微信
+    zh_Hant: 企業微信
   description:
     en_US: WeCom Adapter
-    zh_Hans: 企业微信适配器，请查看文档了解使用方式
+    zh_Hans: 企业微信内部机器人，请查看文档了解使用方式
+    zh_Hant: 企業微信內部機器人，請查看文件了解使用方式
   icon: wecom.png
 spec:
+  categories:
+    - popular
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/wecom
+    en: https://link.langbot.app/en/platforms/wecom
+    ja: https://link.langbot.app/ja/platforms/wecom
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+      description:
+        en_US: Copy this URL and paste it into your WeCom app's webhook configuration
+        zh_Hans: 复制此地址并粘贴到企业微信应用的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到企業微信應用的 Webhook 設定中
+      type: webhook-url
+      required: false
+      default: ""
     - name: corpid
       label:
         en_US: Corpid
         zh_Hans: 企业ID
+        zh_Hant: 企業ID
       type: string
       required: true
       default: ""
@@ -22,6 +44,7 @@ spec:
       label:
         en_US: Secret
         zh_Hans: 密钥 (Secret)
+        zh_Hant: 密鑰 (Secret)
       type: string
       required: true
       default: ""
@@ -29,6 +52,7 @@ spec:
       label:
         en_US: Token
         zh_Hans: 令牌 (Token)
+        zh_Hant: 令牌 (Token)
       type: string
       required: true
       default: ""
@@ -36,13 +60,7 @@ spec:
       label:
         en_US: EncodingAESKey
         zh_Hans: 消息加解密密钥 (EncodingAESKey)
-      type: string
+        zh_Hant: 訊息加解密密鑰 (EncodingAESKey)
-      required: true
-      default: ""
-    - name: contacts_secret
-      label:
-        en_US: Contacts Secret
-        zh_Hans: 通讯录密钥
       type: string
       required: true
       default: ""
@@ -50,9 +68,11 @@ spec:
       label:
         en_US: API Base URL
         zh_Hans: API 基础 URL
+        zh_Hant: API 基礎 URL
       description:
         en_US: API Base URL, used for accessing the WeCom API. If you are deploying in an internal network environment and accessing the WeCom Customer Service API through a reverse proxy, please fill in this item according to the documentation.
         zh_Hans: 可选，若您部署在内网环境并通过反向代理访问企业微信 API，可根据文档填写此项
+        zh_Hant: 可選，若您部署在內網環境並透過反向代理存取企業微信 API，可根據文件填寫此項
       type: string
       required: false
       default: "https://qyapi.weixin.qq.com/cgi-bin"

src/langbot/pkg/platform/sources/wecombot.py

@@ -11,6 +11,7 @@ import langbot_plugin.api.entities.builtin.platform.entities as platform_entitie
 from ..logger import EventLogger
 from langbot.libs.wecom_ai_bot_api.wecombotevent import WecomBotEvent
 from langbot.libs.wecom_ai_bot_api.api import WecomBotClient
+from langbot.libs.wecom_ai_bot_api.ws_client import WecomBotWsClient
 
 
 class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
@@ -23,14 +24,18 @@ class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverte
         return content
 
     @staticmethod
-    async def target2yiri(event: WecomBotEvent):
+    async def target2yiri(event: WecomBotEvent, bot_name: str = ''):
         yiri_msg_list = []
         if event.type == 'group':
             yiri_msg_list.append(platform_message.At(target=event.ai_bot_id))
+
         yiri_msg_list.append(platform_message.Source(id=event.message_id, time=datetime.datetime.now()))
 
         if event.content:
-            yiri_msg_list.append(platform_message.Plain(text=event.content))
+            content = event.content
+            if bot_name:
+                content = content.replace(f'@{bot_name}', '').strip()
+            yiri_msg_list.append(platform_message.Plain(text=content))
 
         images = []
         if event.images:
@@ -133,13 +138,15 @@ class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverte
 
 
 class WecomBotEventConverter(abstract_platform_adapter.AbstractEventConverter):
+    def __init__(self, bot_name: str = ''):
+        self.bot_name = bot_name
+
     @staticmethod
     async def yiri2target(event: platform_events.MessageEvent):
         return event.source_platform_object
 
-    @staticmethod
+    async def target2yiri(self, event: WecomBotEvent):
-    async def target2yiri(event: WecomBotEvent):
+        message_chain = await WecomBotMessageConverter.target2yiri(event, bot_name=self.bot_name)
-        message_chain = await WecomBotMessageConverter.target2yiri(event)
         if event.type == 'single':
             return platform_events.FriendMessage(
                 sender=platform_entities.Friend(
@@ -176,34 +183,53 @@ class WecomBotEventConverter(abstract_platform_adapter.AbstractEventConverter):
 
 
 class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    bot: WecomBotClient
+    bot: typing.Union[WecomBotClient, WecomBotWsClient]
     bot_account_id: str
     message_converter: WecomBotMessageConverter = WecomBotMessageConverter()
-    event_converter: WecomBotEventConverter = WecomBotEventConverter()
+    event_converter: WecomBotEventConverter
     config: dict
     bot_uuid: str = None
+    _ws_mode: bool = False
+    bot_name: str = ''
+    listeners: dict = {}
 
     def __init__(self, config: dict, logger: EventLogger):
-        required_keys = ['Token', 'EncodingAESKey', 'Corpid', 'BotId']
+        enable_webhook = config.get('enable-webhook', False)
-        missing_keys = [key for key in required_keys if key not in config]
+        bot_name = config.get('robot_name', '')
-        if missing_keys:
-            raise Exception(f'WecomBot 缺少配置项: {missing_keys}')
 
-        bot = WecomBotClient(
+        if not enable_webhook:
-            Token=config['Token'],
+            bot = WecomBotWsClient(
-            EnCodingAESKey=config['EncodingAESKey'],
+                bot_id=config['BotId'],
-            Corpid=config['Corpid'],
+                secret=config['Secret'],
-            logger=logger,
+                logger=logger,
-            unified_mode=True,
+                encoding_aes_key=config.get('EncodingAESKey', ''),
-        )
+            )
-        bot_account_id = config['BotId']
+        else:
+            # Webhook callback mode
+            required_keys = ['Token', 'EncodingAESKey', 'Corpid']
+            missing_keys = [key for key in required_keys if key not in config or not config[key]]
+            if missing_keys:
+                raise Exception(f'WecomBot webhook mode missing config: {missing_keys}')
 
+            bot = WecomBotClient(
+                Token=config['Token'],
+                EnCodingAESKey=config['EncodingAESKey'],
+                Corpid=config['Corpid'],
+                logger=logger,
+                unified_mode=True,
+            )
+
+        bot_account_id = config.get('BotId', '')
+        event_converter = WecomBotEventConverter(bot_name=bot_name)
         super().__init__(
             config=config,
             logger=logger,
             bot=bot,
             bot_account_id=bot_account_id,
+            bot_name=bot_name,
+            event_converter=event_converter,
         )
+        self.listeners = {}
 
     async def reply_message(
         self,
@@ -212,7 +238,17 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         quote_origin: bool = False,
     ):
         content = await self.message_converter.yiri2target(message)
-        await self.bot.set_message(message_source.source_platform_object.message_id, content)
+        _ws_mode = not self.config.get('enable-webhook', False)
+
+        if _ws_mode:
+            event = message_source.source_platform_object
+            req_id = event.get('req_id', '')
+            if req_id:
+                await self.bot.reply_text(req_id, content)
+            else:
+                await self.bot.set_message(event.message_id, content)
+        else:
+            await self.bot.set_message(message_source.source_platform_object.message_id, content)
 
     async def reply_message_chunk(
         self,
@@ -222,44 +258,44 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         quote_origin: bool = False,
         is_final: bool = False,
     ):
-        """将流水线增量输出写入企业微信 stream 会话。
-
-        Args:
-            message_source: 流水线提供的原始消息事件。
-            bot_message: 当前片段对应的模型元信息（未使用）。
-            message: 需要回复的消息链。
-            quote_origin: 是否引用原消息（企业微信暂不支持）。
-            is_final: 标记当前片段是否为最终回复。
-
-        Returns:
-            dict: 包含 `stream` 键，标识写入是否成功。
-
-        Example:
-            在流水线 `reply_message_chunk` 调用中自动触发，无需手动调用。
-        """
-        # 转换为纯文本（智能机器人当前协议仅支持文本流）
         content = await self.message_converter.yiri2target(message)
         msg_id = message_source.source_platform_object.message_id
+        _ws_mode = not self.config.get('enable-webhook', False)
 
-        # 将片段推送到 WecomBotClient 中的队列，返回值用于判断是否走降级逻辑
+        if _ws_mode:
-        success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
+            success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
-        if not success and is_final:
+            if not success and is_final:
-            # 未命中流式队列时使用旧有 set_message 兜底
+                event = message_source.source_platform_object
-            await self.bot.set_message(msg_id, content)
+                req_id = event.get('req_id', '')
-        return {'stream': success}
+                if req_id:
+                    await self.bot.reply_text(req_id, content)
+            return {'stream': success}
+        else:
+            success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
+            if not success and is_final:
+                await self.bot.set_message(msg_id, content)
+            return {'stream': success}
 
     async def is_stream_output_supported(self) -> bool:
-        """智能机器人侧默认开启流式能力。
+        """Whether streaming output is enabled for this bot instance."""
-
+        return self.config.get('enable-stream-reply', True)
-        Returns:
-            bool: 恒定返回 True。
-
-        Example:
-            流水线执行阶段会调用此方法以确认是否启用流式。"""
-        return True
 
     async def send_message(self, target_type, target_id, message):
-        pass
+        _ws_mode = not self.config.get('enable-webhook', False)
+        if _ws_mode:
+            content = await self.message_converter.yiri2target(message)
+            await self.bot.send_message(target_id, content)
+        else:
+            pass
+
+    async def on_message(self, event: WecomBotEvent):
+        try:
+            lb_event = await self.event_converter.target2yiri(event)
+            if lb_event:
+                await self.listeners[type(lb_event)](lb_event, self)
+        except Exception:
+            await self.logger.error(f'Error in wecombot callback: {traceback.format_exc()}')
+            print(traceback.format_exc())
 
     def register_listener(
         self,
@@ -268,18 +304,16 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             [platform_events.Event, abstract_platform_adapter.AbstractMessagePlatformAdapter], None
         ],
     ):
-        async def on_message(event: WecomBotEvent):
+        self.listeners[event_type] = callback
-            try:
-                return await callback(await self.event_converter.target2yiri(event), self)
-            except Exception:
-                await self.logger.error(f'Error in wecombot callback: {traceback.format_exc()}')
-                print(traceback.format_exc())
 
         try:
             if event_type == platform_events.FriendMessage:
-                self.bot.on_message('single')(on_message)
+                self.bot.on_message('single')(self.on_message)
             elif event_type == platform_events.GroupMessage:
-                self.bot.on_message('group')(on_message)
+                self.bot.on_message('group')(self.on_message)
+            elif event_type == platform_events.FeedbackEvent:
+                if hasattr(self.bot, 'on_feedback'):
+                    self.bot.on_feedback()(self._on_feedback)
         except Exception:
             print(traceback.format_exc())
 
@@ -287,30 +321,68 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         """设置 bot UUID（用于生成 webhook URL）"""
         self.bot_uuid = bot_uuid
 
+    async def _on_feedback(self, **kwargs):
+        """Handle feedback event from WeChat Work AI Bot SDK and dispatch as FeedbackEvent."""
+        try:
+            feedback_id = kwargs.get('feedback_id', '')
+            feedback_type = kwargs.get('feedback_type', 0)
+            feedback_content = kwargs.get('feedback_content', '') or None
+            inaccurate_reasons = kwargs.get('inaccurate_reasons', []) or None
+            session = kwargs.get('session')
+
+            session_id = None
+            user_id = None
+            message_id = None
+            stream_id = None
+            if session:
+                if session.chat_id:
+                    session_id = f'group_{session.chat_id}'
+                elif session.user_id:
+                    session_id = f'person_{session.user_id}'
+                user_id = session.user_id
+                message_id = session.msg_id
+                stream_id = session.stream_id
+
+            event = platform_events.FeedbackEvent(
+                feedback_id=feedback_id,
+                feedback_type=feedback_type,
+                feedback_content=feedback_content,
+                inaccurate_reasons=inaccurate_reasons,
+                user_id=user_id,
+                session_id=session_id,
+                message_id=message_id,
+                stream_id=stream_id,
+                source_platform_object=session,
+            )
+
+            if platform_events.FeedbackEvent in self.listeners:
+                await self.listeners[platform_events.FeedbackEvent](event, self)
+        except Exception:
+            await self.logger.error(f'Error in wecombot feedback callback: {traceback.format_exc()}')
+
     async def handle_unified_webhook(self, bot_uuid: str, path: str, request):
-        """处理统一 webhook 请求。
+        _ws_mode = not self.config.get('enable-webhook', False)
-
+        if _ws_mode:
-        Args:
+            return None
-            bot_uuid: Bot 的 UUID
-            path: 子路径（如果有的话）
-            request: Quart Request 对象
-
-        Returns:
-            响应数据
-        """
         return await self.bot.handle_unified_webhook(request)
 
     async def run_async(self):
-        # 统一 webhook 模式下，不启动独立的 Quart 应用
+        _ws_mode = not self.config.get('enable-webhook', False)
-        # 保持运行但不启动独立端口
+        if _ws_mode:
+            await self.bot.connect()
+        else:
 
-        async def keep_alive():
+            async def keep_alive():
-            while True:
+                while True:
-                await asyncio.sleep(1)
+                    await asyncio.sleep(1)
 
-        await keep_alive()
+            await keep_alive()
 
     async def kill(self) -> bool:
+        _ws_mode = not self.config.get('enable-webhook', False)
+        if _ws_mode:
+            await self.bot.disconnect()
+            return True
         return False
 
     async def unregister_listener(

src/langbot/pkg/platform/sources/wecombot.yaml

@@ -5,41 +5,125 @@ metadata:
   label:
     en_US: WeComBot
     zh_Hans: 企业微信智能机器人
+    zh_Hant: 企業微信智慧機器人
   description:
     en_US: WeComBot Adapter
-    zh_Hans: 企业微信智能机器人适配器，请查看文档了解使用方式
+    zh_Hans: 企业微信智能机器人，支持长连接和 Webhook 两种接入方式，请查看文档了解使用方式
+    zh_Hant: 企業微信智慧機器人，支援長連線和 Webhook 兩種接入方式，請查看文件了解使用方式
   icon: wecombot.png
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/wecombot
+    en: https://link.langbot.app/en/platforms/wecombot
+    ja: https://link.langbot.app/ja/platforms/wecombot
   config:
+    - name: BotId
+      label:
+        en_US: BotId
+        zh_Hans: 机器人ID (BotId)
+        zh_Hant: 機器人ID (BotId)
+      type: string
+      required: true
+      default: ""
+    - name: robot_name
+      label:
+        en_US: Robot Name
+        zh_Hans: 机器人名称
+        zh_Hant: 機器人名稱
+      type: string
+      required: true
+      default: ""
+    - name: enable-webhook
+      label:
+        en_US: Enable Webhook Mode
+        zh_Hans: 启用Webhook模式
+        zh_Hant: 啟用 Webhook 模式
+      description:
+        en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
+        zh_Hans: 如果启用，机器人将使用 Webhook 模式接收消息。否则，将使用 WS 长连接模式
+        zh_Hant: 如果啟用，機器人將使用 Webhook 模式接收訊息。否則，將使用 WS 長連線模式
+      type: boolean
+      required: true
+      default: false
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+      description:
+        en_US: Copy this URL and paste it into your WeComBot webhook configuration
+        zh_Hans: 复制此地址并粘贴到企业微信智能机器人的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到企業微信智慧機器人的 Webhook 設定中
+      type: webhook-url
+      required: false
+      default: ""
+      show_if:
+        field: enable-webhook
+        operator: eq
+        value: true
+    - name: Secret
+      label:
+        en_US: Secret
+        zh_Hans: 机器人密钥 (Secret)
+        zh_Hant: 機器人密鑰 (Secret)
+      description:
+        en_US: Required for WebSocket long connection mode
+        zh_Hans: 使用 WS 长连接模式时必填
+        zh_Hant: 使用 WS 長連線模式時必填
+      type: string
+      required: false
+      default: ""
     - name: Corpid
       label:
         en_US: Corpid
         zh_Hans: 企业ID
+        zh_Hant: 企業ID
+      description:
+        en_US: Required for Webhook mode
+        zh_Hans: 使用 Webhook 模式时必填
+        zh_Hant: 使用 Webhook 模式時必填
       type: string
-      required: true
+      required: false
       default: ""
     - name: Token
       label:
         en_US: Token
         zh_Hans: 令牌 (Token)
+        zh_Hant: 令牌 (Token)
+      description:
+        en_US: Required for Webhook mode
+        zh_Hans: 使用 Webhook 模式时必填
+        zh_Hant: 使用 Webhook 模式時必填
       type: string
-      required: true
+      required: false
       default: ""
     - name: EncodingAESKey
       label:
         en_US: EncodingAESKey
         zh_Hans: 消息加解密密钥 (EncodingAESKey)
-      type: string
+        zh_Hant: 訊息加解密密鑰 (EncodingAESKey)
-      required: true
+      description:
-      default: ""
+        en_US: Required for Webhook mode. Optional for WebSocket mode (used for file decryption)
-    - name: BotId
+        zh_Hans: 使用 Webhook 模式时必填。WebSocket 模式下可选（用于文件解密）
-      label:
+        zh_Hant: 使用 Webhook 模式時必填。WebSocket 模式下可選（用於檔案解密）
-        en_US: BotId
-        zh_Hans: 机器人ID
       type: string
       required: false
       default: ""
+    - name: enable-stream-reply
+      label:
+        en_US: Enable Stream Reply
+        zh_Hans: 启用流式回复
+        zh_Hant: 啟用串流回覆
+      description:
+        en_US: If enabled, the bot will use streaming mode to reply messages
+        zh_Hans: 如果启用，机器人将使用流式模式回复消息
+        zh_Hant: 如果啟用，機器人將使用串流模式回覆訊息
+      type: boolean
+      required: false
+      default: true
 execution:
   python:
     path: ./wecombot.py
-    attr: WecomBotAdapter
+    attr: WecomBotAdapter

src/langbot/pkg/platform/sources/wecomcs.py

@@ -81,22 +81,33 @@ class WecomEventConverter(abstract_platform_adapter.AbstractEventConverter):
             return event.source_platform_object
 
     @staticmethod
-    async def target2yiri(event: WecomCSEvent):
+    async def target2yiri(event: WecomCSEvent, bot: WecomCSClient = None):
         """
         将 WecomEvent 转换为平台的 FriendMessage 对象。
 
         Args:
             event (WecomEvent): 企业微信客服事件。
+            bot (WecomCSClient): 企业微信客服客户端，用于获取用户信息。
 
         Returns:
             platform_events.FriendMessage: 转换后的 FriendMessage 对象。
         """
+        # Try to get customer nickname from WeChat API
+        nickname = str(event.user_id)
+        if bot and event.user_id:
+            try:
+                customer_info = await bot.get_customer_info(event.user_id)
+                if customer_info and customer_info.get('nickname'):
+                    nickname = customer_info.get('nickname')
+            except Exception:
+                pass  # Fall back to user_id as nickname
+
         # 转换消息链
         if event.type == 'text':
             yiri_chain = await WecomMessageConverter.target2yiri(event.message, event.message_id)
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=str(event.user_id),
+                nickname=nickname,
                 remark='',
             )
 
@@ -106,7 +117,7 @@ class WecomEventConverter(abstract_platform_adapter.AbstractEventConverter):
         elif event.type == 'image':
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=str(event.user_id),
+                nickname=nickname,
                 remark='',
             )
 
@@ -187,7 +198,7 @@ class WecomCSAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         async def on_message(event: WecomCSEvent):
             self.bot_account_id = event.receiver_id
             try:
-                return await callback(await self.event_converter.target2yiri(event), self)
+                return await callback(await self.event_converter.target2yiri(event, self.bot), self)
             except Exception:
                 await self.logger.error(f'Error in wecomcs callback: {traceback.format_exc()}')
 

src/langbot/pkg/platform/sources/wecomcs.yaml

@@ -5,16 +5,37 @@ metadata:
   label:
     en_US: WeComCustomerService
     zh_Hans: 企业微信客服
+    zh_Hant: 企業微信客服
   description:
     en_US: WeComCSAdapter
-    zh_Hans: 企业微信客服适配器
+    zh_Hans: 企业微信对外客服机器人，需要公网地址以接收消息推送，请查看文档了解使用方式
+    zh_Hant: 企業微信對外客服機器人，需要公網地址以接收訊息推送，請查看文件了解使用方式
   icon: wecom.png
 spec:
+  categories:
+    - china
+  help_links:
+    zh: https://link.langbot.app/zh/platforms/wecomcs
+    en: https://link.langbot.app/en/platforms/wecomcs
+    ja: https://link.langbot.app/ja/platforms/wecomcs
   config:
+    - name: webhook_url
+      label:
+        en_US: Webhook Callback URL
+        zh_Hans: Webhook 回调地址
+        zh_Hant: Webhook 回調地址
+      description:
+        en_US: Copy this URL and paste it into your WeCom Customer Service webhook configuration
+        zh_Hans: 复制此地址并粘贴到企业微信客服的 Webhook 配置中
+        zh_Hant: 複製此地址並貼到企業微信客服的 Webhook 設定中
+      type: webhook-url
+      required: false
+      default: ""
     - name: corpid
       label:
         en_US: Corpid
         zh_Hans: 企业ID
+        zh_Hant: 企業ID
       type: string
       required: true
       default: ""
@@ -22,6 +43,7 @@ spec:
       label:
         en_US: Secret
         zh_Hans: 密钥
+        zh_Hant: 密鑰
       type: string
       required: true
       default: ""
@@ -29,6 +51,7 @@ spec:
       label:
         en_US: Token
         zh_Hans: 令牌
+        zh_Hant: 令牌
       type: string
       required: true
       default: ""
@@ -36,6 +59,7 @@ spec:
       label:
         en_US: EncodingAESKey
         zh_Hans: 消息加解密密钥
+        zh_Hant: 訊息加解密密鑰
       type: string
       required: true
       default: ""
@@ -43,9 +67,11 @@ spec:
       label:
         en_US: API Base URL
         zh_Hans: API 基础 URL
+        zh_Hant: API 基礎 URL
       description:
         en_US: API Base URL, used for accessing the WeCom API. If you are deploying in an internal network environment and accessing the WeCom Customer Service API through a reverse proxy, please fill in this item according to the documentation.
         zh_Hans: 可选，若您部署在内网环境并通过反向代理访问企业微信 API，可根据文档修改此项
+        zh_Hant: 可選，若您部署在內網環境並透過反向代理存取企業微信 API，可根據文件修改此項
       type: string
       required: false
       default: "https://qyapi.weixin.qq.com/cgi-bin"

src/langbot/pkg/plugin/connector.py

@@ -2,6 +2,9 @@
 from __future__ import annotations
 
 import asyncio
+import io
+import time
+import zipfile
 from typing import Any
 import typing
 import os
@@ -192,6 +195,30 @@ class PluginRuntimeConnector:
 
         return await self.handler.ping()
 
+    def _extract_deps_metadata(
+        self,
+        file_bytes: bytes,
+        task_context: taskmgr.TaskContext | None,
+    ):
+        """Extract dependency count from requirements.txt inside plugin zip."""
+        if task_context is None:
+            return
+        try:
+            with zipfile.ZipFile(io.BytesIO(file_bytes)) as zf:
+                for name in zf.namelist():
+                    if name.endswith('requirements.txt'):
+                        content = zf.read(name).decode('utf-8', errors='ignore')
+                        deps = [
+                            line.strip()
+                            for line in content.splitlines()
+                            if line.strip() and not line.strip().startswith('#')
+                        ]
+                        task_context.metadata['deps_total'] = len(deps)
+                        task_context.metadata['deps_list'] = deps
+                        break
+        except Exception:
+            pass
+
     async def install_plugin(
         self,
         install_source: PluginInstallSource,
@@ -201,23 +228,44 @@ class PluginRuntimeConnector:
         if install_source == PluginInstallSource.LOCAL:
             # transfer file before install
             file_bytes = install_info['plugin_file']
+            self._extract_deps_metadata(file_bytes, task_context)
             file_key = await self.handler.send_file(file_bytes, 'lbpkg')
             install_info['plugin_file_key'] = file_key
             del install_info['plugin_file']
             self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
         elif install_source == PluginInstallSource.GITHUB:
-            # download and transfer file
+            # download and transfer file with streaming progress
             try:
                 async with httpx.AsyncClient(
                     trust_env=True,
                     follow_redirects=True,
-                    timeout=20,
+                    timeout=60,
                 ) as client:
-                    response = await client.get(
+                    async with client.stream('GET', install_info['asset_url']) as response:
-                        install_info['asset_url'],
+                        response.raise_for_status()
-                    )
+                        total = int(response.headers.get('content-length', 0))
-                    response.raise_for_status()
+                        downloaded = 0
-                    file_bytes = response.content
+                        chunks: list[bytes] = []
+                        start_time = time.time()
+
+                        if task_context is not None:
+                            task_context.set_current_action('downloading plugin package')
+                            task_context.metadata['download_total'] = total
+                            task_context.metadata['download_current'] = 0
+                            task_context.metadata['download_speed'] = 0
+
+                        async for chunk in response.aiter_bytes(chunk_size=8192):
+                            chunks.append(chunk)
+                            downloaded += len(chunk)
+
+                            if task_context is not None:
+                                elapsed = time.time() - start_time
+                                task_context.metadata['download_current'] = downloaded
+                                task_context.metadata['download_total'] = total
+                                task_context.metadata['download_speed'] = downloaded / elapsed if elapsed > 0 else 0
+
+                    file_bytes = b''.join(chunks)
+                    self._extract_deps_metadata(file_bytes, task_context)
                     file_key = await self.handler.send_file(file_bytes, 'lbpkg')
                     install_info['plugin_file_key'] = file_key
                     self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
@@ -236,6 +284,11 @@ class PluginRuntimeConnector:
                 if task_context is not None:
                     task_context.trace(trace)
 
+            # Forward structured metadata from runtime
+            metadata = ret.get('metadata', None)
+            if metadata is not None and task_context is not None:
+                task_context.metadata.update(metadata)
+
     async def upgrade_plugin(
         self,
         plugin_author: str,

src/langbot/pkg/plugin/handler.py

@@ -314,11 +314,11 @@ class RuntimeConnectionHandler(handler.Handler):
 
         @self.action(PluginToRuntimeAction.GET_LLM_MODELS)
         async def get_llm_models(data: dict[str, Any]) -> handler.ActionResponse:
-            """Get llm models"""
+            """Get llm models, returns list of UUID strings"""
             llm_models = await self.ap.llm_model_service.get_llm_models(include_secret=False)
             return handler.ActionResponse.success(
                 data={
-                    'llm_models': llm_models,
+                    'llm_models': [m['uuid'] for m in llm_models],
                 },
             )
 
@@ -337,7 +337,14 @@ class RuntimeConnectionHandler(handler.Handler):
                 )
 
             messages_obj = [provider_message.Message.model_validate(message) for message in messages]
-            funcs_obj = [resource_tool.LLMTool.model_validate(func) for func in funcs]
+
+            # The func field is excluded during model_dump() in plugin side (marked as exclude=True),
+            # but it's a required field for LLMTool validation. We need to provide a placeholder
+            # function when reconstructing the LLMTool objects from serialized data.
+            async def _placeholder_func(**kwargs):
+                pass
+
+            funcs_obj = [resource_tool.LLMTool.model_validate({**func, 'func': _placeholder_func}) for func in funcs]
 
             result = await llm_model.provider.invoke_llm(
                 query=None,
@@ -524,6 +531,7 @@ class RuntimeConnectionHandler(handler.Handler):
             filters = data.get('filters')
             search_type = data.get('search_type', 'vector')
             query_text = data.get('query_text', '')
+            vector_weight = data.get('vector_weight')
             try:
                 results = await self.ap.rag_runtime_service.vector_search(
                     collection_id,
@@ -532,6 +540,7 @@ class RuntimeConnectionHandler(handler.Handler):
                     filters,
                     search_type,
                     query_text,
+                    vector_weight=vector_weight,
                 )
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
@@ -548,6 +557,18 @@ class RuntimeConnectionHandler(handler.Handler):
             except Exception as e:
                 return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
 
+        @self.action(PluginToRuntimeAction.VECTOR_LIST)
+        async def vector_list(data: dict[str, Any]) -> handler.ActionResponse:
+            collection_id = data['collection_id']
+            filters = data.get('filters')
+            limit = data.get('limit', 20)
+            offset = data.get('offset', 0)
+            try:
+                items, total = await self.ap.rag_runtime_service.vector_list(collection_id, filters, limit, offset)
+                return handler.ActionResponse.success(data={'items': items, 'total': total})
+            except Exception as e:
+                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
+
         @self.action(PluginToRuntimeAction.GET_KNOWLEDEGE_FILE_STREAM)
         async def get_knowledge_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
             storage_path = data['storage_path']
@@ -558,6 +579,16 @@ class RuntimeConnectionHandler(handler.Handler):
             except Exception as e:
                 return _make_rag_error_response(e, 'FileServiceError', storage_path=storage_path)
 
+        @self.action(PluginToRuntimeAction.LIST_PARSERS)
+        async def list_parsers(data: dict[str, Any]) -> handler.ActionResponse:
+            """Plugin requests host to list available parser plugins."""
+            mime_type = data.get('mime_type')
+            try:
+                parsers = await self.ap.knowledge_service.list_parsers(mime_type)
+                return handler.ActionResponse.success(data={'parsers': parsers})
+            except Exception as e:
+                return _make_rag_error_response(e, 'ParserDiscoveryError', mime_type=mime_type)
+
         @self.action(PluginToRuntimeAction.INVOKE_PARSER)
         async def invoke_parser(data: dict[str, Any]) -> handler.ActionResponse:
             """Plugin requests host to invoke a parser plugin."""
@@ -582,6 +613,139 @@ class RuntimeConnectionHandler(handler.Handler):
             except Exception as e:
                 return _make_rag_error_response(e, 'ParserError')
 
+        # ================= Knowledge Base Query APIs =================
+
+        @self.action(PluginToRuntimeAction.LIST_KNOWLEDGE_BASES)
+        async def list_knowledge_bases(data: dict[str, Any]) -> handler.ActionResponse:
+            """List all knowledge bases available in the LangBot instance (unrestricted)."""
+            knowledge_bases = []
+            for kb_uuid, kb in self.ap.rag_mgr.knowledge_bases.items():
+                knowledge_bases.append(
+                    {
+                        'uuid': kb.get_uuid(),
+                        'name': kb.get_name(),
+                        'description': kb.knowledge_base_entity.description or '',
+                    }
+                )
+            return handler.ActionResponse.success(data={'knowledge_bases': knowledge_bases})
+
+        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE)
+        async def retrieve_knowledge(data: dict[str, Any]) -> handler.ActionResponse:
+            """Retrieve documents from any knowledge base (unrestricted)."""
+            kb_id = data['kb_id']
+            query_text = data['query_text']
+            top_k = data.get('top_k', 5)
+            filters = data.get('filters', {})
+
+            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_id)
+            if not kb:
+                return handler.ActionResponse.error(
+                    message=f'Knowledge base {kb_id} not found',
+                )
+
+            try:
+                entries = await kb.retrieve(
+                    query_text,
+                    settings={
+                        'top_k': top_k,
+                        'filters': filters,
+                    },
+                )
+                results = [entry.model_dump(mode='json') for entry in entries]
+                return handler.ActionResponse.success(data={'results': results})
+            except Exception as e:
+                return _make_rag_error_response(e, 'RetrievalError', kb_id=kb_id)
+
+        @self.action(PluginToRuntimeAction.LIST_PIPELINE_KNOWLEDGE_BASES)
+        async def list_pipeline_knowledge_bases(data: dict[str, Any]) -> handler.ActionResponse:
+            """List knowledge bases configured for the current query's pipeline."""
+            query_id = data['query_id']
+
+            if query_id not in self.ap.query_pool.cached_queries:
+                return handler.ActionResponse.error(
+                    message=f'Query with query_id {query_id} not found',
+                )
+
+            query = self.ap.query_pool.cached_queries[query_id]
+
+            kb_uuids = []
+            if query.pipeline_config:
+                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
+                kb_uuids = local_agent_config.get('knowledge-bases', [])
+                # Backward compatibility
+                if not kb_uuids:
+                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
+                    if old_kb_uuid and old_kb_uuid != '__none__':
+                        kb_uuids = [old_kb_uuid]
+
+            knowledge_bases = []
+            for kb_uuid in kb_uuids:
+                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+                if kb:
+                    knowledge_bases.append(
+                        {
+                            'uuid': kb.get_uuid(),
+                            'name': kb.get_name(),
+                            'description': kb.knowledge_base_entity.description or '',
+                        }
+                    )
+
+            return handler.ActionResponse.success(data={'knowledge_bases': knowledge_bases})
+
+        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE_BASE)
+        async def retrieve_knowledge_base(data: dict[str, Any]) -> handler.ActionResponse:
+            """Retrieve documents from a knowledge base within the pipeline's scope."""
+            query_id = data['query_id']
+            kb_id = data['kb_id']
+            query_text = data['query_text']
+            top_k = data.get('top_k', 5)
+            filters = data.get('filters', {})
+
+            if query_id not in self.ap.query_pool.cached_queries:
+                return handler.ActionResponse.error(
+                    message=f'Query with query_id {query_id} not found',
+                )
+
+            query = self.ap.query_pool.cached_queries[query_id]
+
+            # Validate kb_id is in pipeline's allowed list
+            allowed_kb_uuids = []
+            if query.pipeline_config:
+                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
+                allowed_kb_uuids = local_agent_config.get('knowledge-bases', [])
+                if not allowed_kb_uuids:
+                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
+                    if old_kb_uuid and old_kb_uuid != '__none__':
+                        allowed_kb_uuids = [old_kb_uuid]
+
+            if kb_id not in allowed_kb_uuids:
+                return handler.ActionResponse.error(
+                    message=f'Knowledge base {kb_id} is not configured for this pipeline',
+                )
+
+            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_id)
+            if not kb:
+                return handler.ActionResponse.error(
+                    message=f'Knowledge base {kb_id} not found',
+                )
+
+            try:
+                session_name = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+                entries = await kb.retrieve(
+                    query_text,
+                    settings={
+                        'top_k': top_k,
+                        'filters': filters,
+                        'session_name': session_name,
+                        'bot_uuid': query.bot_uuid or '',
+                        'sender_id': str(query.sender_id),
+                    },
+                )
+                results = [entry.model_dump(mode='json') for entry in entries]
+                return handler.ActionResponse.success(data={'results': results})
+            except Exception as e:
+                return _make_rag_error_response(e, 'RetrievalError', kb_id=kb_id)
+
         @self.action(CommonAction.PING)
         async def ping(data: dict[str, Any]) -> handler.ActionResponse:
             """Ping"""
@@ -888,7 +1052,7 @@ class RuntimeConnectionHandler(handler.Handler):
         result = await self.call_action(
             LangBotToRuntimeAction.RAG_INGEST_DOCUMENT,
             {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data},
-            timeout=300,  # Ingestion can be slow
+            timeout=1200,  # Ingestion can be slow for large documents
         )
         return result
 

src/langbot/pkg/provider/modelmgr/requesters/anthropicmsgs.py

@@ -288,10 +288,10 @@ class AnthropicMessages(requester.ProviderAPIRequester):
             think_started = False
             think_ended = False
             finish_reason = False
-            content = ''
             tool_name = ''
             tool_id = ''
             async for chunk in await self.client.messages.create(**args):
+                content = ''
                 tool_call = {'id': None, 'function': {'name': None, 'arguments': None}, 'type': 'function'}
                 if isinstance(
                     chunk, anthropic.types.raw_content_block_start_event.RawContentBlockStartEvent

src/langbot/pkg/provider/runners/difysvapi.py

@@ -441,6 +441,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
         is_final = False
         think_start = False
         think_end = False
+        yielded_final = False
 
         remove_think = self.pipeline_config['output'].get('misc', '').get('remove-think')
 
@@ -493,13 +494,19 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                     if answer:
                         basic_mode_pending_chunk = answer
 
-            if (is_final or message_idx % 8 == 0) and (basic_mode_pending_chunk != '' or is_final):
+            if (
+                not yielded_final
+                and (is_final or message_idx % 8 == 0)
+                and (basic_mode_pending_chunk != '' or is_final)
+            ):
                 # content, _ = self._process_thinking_content(basic_mode_pending_chunk)
                 yield provider_message.MessageChunk(
                     role='assistant',
                     content=basic_mode_pending_chunk,
                     is_final=is_final,
                 )
+                if is_final:
+                    yielded_final = True
 
         if chunk is None:
             raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')

src/langbot/pkg/provider/runners/localagent.py

@@ -4,6 +4,7 @@ import json
 import copy
 import typing
 from .. import runner
+from ..modelmgr import requester as modelmgr_requester
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 import langbot_plugin.api.entities.builtin.rag.context as rag_context
@@ -26,29 +27,114 @@ Respond in the same language as the user's input.
 
 @runner.runner_class('local-agent')
 class LocalAgentRunner(runner.RequestRunner):
-    """本地Agent请求运行器"""
+    """Local agent request runner"""
 
-    class ToolCallTracker:
+    async def _get_model_candidates(
-        """工具调用追踪器"""
+        self,
+        query: pipeline_query.Query,
+    ) -> list[modelmgr_requester.RuntimeLLMModel]:
+        """Build ordered list of models to try: primary model + fallback models."""
+        candidates = []
 
-        def __init__(self):
+        # Primary model
-            self.active_calls: dict[str, dict] = {}
+        if query.use_llm_model_uuid:
-            self.completed_calls: list[provider_message.ToolCall] = []
+            try:
+                primary = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
+                candidates.append(primary)
+            except ValueError:
+                self.ap.logger.warning(f'Primary model {query.use_llm_model_uuid} not found')
+
+        # Fallback models
+        fallback_uuids = (query.variables or {}).get('_fallback_model_uuids', [])
+        for fb_uuid in fallback_uuids:
+            try:
+                fb_model = await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
+                candidates.append(fb_model)
+            except ValueError:
+                self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
+
+        return candidates
+
+    async def _invoke_with_fallback(
+        self,
+        query: pipeline_query.Query,
+        candidates: list[modelmgr_requester.RuntimeLLMModel],
+        messages: list,
+        funcs: list,
+        remove_think: bool,
+    ) -> tuple[provider_message.Message, modelmgr_requester.RuntimeLLMModel]:
+        """Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
+        last_error = None
+        for model in candidates:
+            try:
+                msg = await model.provider.invoke_llm(
+                    query,
+                    model,
+                    messages,
+                    funcs if model.model_entity.abilities.__contains__('func_call') else [],
+                    extra_args=model.model_entity.extra_args,
+                    remove_think=remove_think,
+                )
+                return msg, model
+            except Exception as e:
+                last_error = e
+                self.ap.logger.warning(f'Model {model.model_entity.name} failed: {e}, trying next fallback...')
+        raise last_error or RuntimeError('No model candidates available')
+
+    async def _invoke_stream_with_fallback(
+        self,
+        query: pipeline_query.Query,
+        candidates: list[modelmgr_requester.RuntimeLLMModel],
+        messages: list,
+        funcs: list,
+        remove_think: bool,
+    ) -> tuple[typing.AsyncGenerator, modelmgr_requester.RuntimeLLMModel]:
+        """Try streaming invocation with sequential fallback. Returns (stream_generator, model_used).
+
+        Fallback is only possible before any chunks have been yielded to the client.
+        Once streaming starts, the model is committed.
+        """
+        last_error = None
+        for model in candidates:
+            try:
+                stream = model.provider.invoke_llm_stream(
+                    query,
+                    model,
+                    messages,
+                    funcs if model.model_entity.abilities.__contains__('func_call') else [],
+                    extra_args=model.model_entity.extra_args,
+                    remove_think=remove_think,
+                )
+                # Attempt to get the first chunk to verify the stream works
+                first_chunk = await stream.__anext__()
+
+                async def _chain_stream(first, rest):
+                    yield first
+                    async for chunk in rest:
+                        yield chunk
+
+                return _chain_stream(first_chunk, stream), model
+            except StopAsyncIteration:
+                # Empty stream — treat as success (model returned nothing)
+                async def _empty_stream():
+                    return
+                    yield  # make it a generator
+
+                return _empty_stream(), model
+            except Exception as e:
+                last_error = e
+                self.ap.logger.warning(f'Model {model.model_entity.name} stream failed: {e}, trying next fallback...')
+        raise last_error or RuntimeError('No model candidates available')
 
     async def run(
         self, query: pipeline_query.Query
     ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """运行请求"""
+        """Run request"""
         pending_tool_calls = []
 
-        # Get knowledge bases list (new field)
+        # Get knowledge bases list from query variables (set by PreProcessor,
-        kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
+        # may have been modified by plugins during PromptPreProcessing)
-
+        kb_uuids = query.variables.get('_knowledge_base_uuids', [])
-        # Fallback to old field for backward compatibility
-        if not kb_uuids:
-            old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
-            if old_kb_uuid and old_kb_uuid != '__none__':
-                kb_uuids = [old_kb_uuid]
 
         user_message = copy.deepcopy(query.user_message)
 
@@ -74,7 +160,14 @@ class LocalAgentRunner(runner.RequestRunner):
                     self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping')
                     continue
 
-                result = await kb.retrieve(user_message_text)
+                result = await kb.retrieve(
+                    user_message_text,
+                    settings={
+                        'bot_uuid': query.bot_uuid or '',
+                        'sender_id': str(query.sender_id),
+                        'session_name': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
+                    },
+                )
 
                 if result:
                     all_results.extend(result)
@@ -113,51 +206,51 @@ class LocalAgentRunner(runner.RequestRunner):
 
         remove_think = query.pipeline_config['output'].get('misc', '').get('remove-think')
 
-        use_llm_model = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
+        # Build ordered candidate list (primary + fallbacks)
+        candidates = await self._get_model_candidates(query)
+        if not candidates:
+            raise RuntimeError('No LLM model configured for local-agent runner')
 
         self.ap.logger.debug(
-            f'localagent req: query={query.query_id} req_messages={req_messages} use_llm_model={query.use_llm_model_uuid}'
+            f'localagent req: query={query.query_id} req_messages={req_messages} '
+            f'candidates={[m.model_entity.name for m in candidates]}'
         )
 
         if not is_stream:
-            # 非流式输出，直接请求
+            # Non-streaming: invoke with fallback
-
+            msg, use_llm_model = await self._invoke_with_fallback(
-            msg = await use_llm_model.provider.invoke_llm(
                 query,
-                use_llm_model,
+                candidates,
                 req_messages,
                 query.use_funcs,
-                extra_args=use_llm_model.model_entity.extra_args,
+                remove_think,
-                remove_think=remove_think,
             )
             yield msg
             final_msg = msg
         else:
-            # 流式输出，需要处理工具调用
+            # Streaming: invoke with fallback
             tool_calls_map: dict[str, provider_message.ToolCall] = {}
             msg_idx = 0
-            accumulated_content = ''  # 从开始累积的所有内容
+            accumulated_content = ''
             last_role = 'assistant'
             msg_sequence = 1
-            async for msg in use_llm_model.provider.invoke_llm_stream(
+
+            stream_src, use_llm_model = await self._invoke_stream_with_fallback(
                 query,
-                use_llm_model,
+                candidates,
                 req_messages,
                 query.use_funcs,
-                extra_args=use_llm_model.model_entity.extra_args,
+                remove_think,
-                remove_think=remove_think,
+            )
-            ):
+            async for msg in stream_src:
                 msg_idx = msg_idx + 1
 
-                # 记录角色
                 if msg.role:
                     last_role = msg.role
 
-                # 累积内容
                 if msg.content:
                     accumulated_content += msg.content
 
-                # 处理工具调用
                 if msg.tool_calls:
                     for tool_call in msg.tool_calls:
                         if tool_call.id not in tool_calls_map:
@@ -169,21 +262,18 @@ class LocalAgentRunner(runner.RequestRunner):
                                 ),
                             )
                         if tool_call.function and tool_call.function.arguments:
-                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                             tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-                # continue
+
-                # 每8个chunk或最后一个chunk时，输出所有累积的内容
                 if msg_idx % 8 == 0 or msg.is_final:
                     msg_sequence += 1
                     yield provider_message.MessageChunk(
                         role=last_role,
-                        content=accumulated_content,  # 输出所有累积内容
+                        content=accumulated_content,
                         tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
                         is_final=msg.is_final,
                         msg_sequence=msg_sequence,
                     )
 
-            # 创建最终消息用于后续处理
             final_msg = provider_message.MessageChunk(
                 role=last_role,
                 content=accumulated_content,
@@ -198,7 +288,8 @@ class LocalAgentRunner(runner.RequestRunner):
 
         req_messages.append(final_msg)
 
-        # 持续请求，只要还有待处理的工具调用就继续处理调用
+        # Once a model succeeds, commit to it for the tool call loop
+        # (no fallback mid-conversation — different models may interpret tool results differently)
         while pending_tool_calls:
             for tool_call in pending_tool_calls:
                 try:
@@ -239,7 +330,6 @@ class LocalAgentRunner(runner.RequestRunner):
 
                     req_messages.append(msg)
                 except Exception as e:
-                    # 工具调用出错，添加一个报错信息到 req_messages
                     err_msg = provider_message.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
 
                     yield err_msg
@@ -247,39 +337,38 @@ class LocalAgentRunner(runner.RequestRunner):
                     req_messages.append(err_msg)
 
             self.ap.logger.debug(
-                f'localagent req: query={query.query_id} req_messages={req_messages} use_llm_model={query.use_llm_model_uuid}'
+                f'localagent req: query={query.query_id} req_messages={req_messages} '
+                f'use_llm_model={use_llm_model.model_entity.name}'
             )
 
             if is_stream:
                 tool_calls_map = {}
                 msg_idx = 0
-                accumulated_content = ''  # 从开始累积的所有内容
+                accumulated_content = ''
                 last_role = 'assistant'
                 msg_sequence = first_end_sequence
 
-                async for msg in use_llm_model.provider.invoke_llm_stream(
+                tool_stream_src = use_llm_model.provider.invoke_llm_stream(
                     query,
                     use_llm_model,
                     req_messages,
-                    query.use_funcs,
+                    query.use_funcs if use_llm_model.model_entity.abilities.__contains__('func_call') else [],
                     extra_args=use_llm_model.model_entity.extra_args,
                     remove_think=remove_think,
-                ):
+                )
+                async for msg in tool_stream_src:
                     msg_idx += 1
 
-                    # 记录角色
                     if msg.role:
                         last_role = msg.role
 
-                    # 第一次请求工具调用时的内容
+                    # Prepend first-round content on first chunk of tool-call round
                     if msg_idx == 1:
                         accumulated_content = first_content if first_content is not None else accumulated_content
 
-                    # 累积内容
                     if msg.content:
                         accumulated_content += msg.content
 
-                    # 处理工具调用
                     if msg.tool_calls:
                         for tool_call in msg.tool_calls:
                             if tool_call.id not in tool_calls_map:
@@ -291,15 +380,13 @@ class LocalAgentRunner(runner.RequestRunner):
                                     ),
                                 )
                             if tool_call.function and tool_call.function.arguments:
-                                # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                                 tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
-                    # 每8个chunk或最后一个chunk时，输出所有累积的内容
                     if msg_idx % 8 == 0 or msg.is_final:
                         msg_sequence += 1
                         yield provider_message.MessageChunk(
                             role=last_role,
-                            content=accumulated_content,  # 输出所有累积内容
+                            content=accumulated_content,
                             tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
                             is_final=msg.is_final,
                             msg_sequence=msg_sequence,
@@ -312,12 +399,12 @@ class LocalAgentRunner(runner.RequestRunner):
                     msg_sequence=msg_sequence,
                 )
             else:
-                # 处理完所有调用，再次请求
+                # Non-streaming: use committed model directly (no fallback in tool loop)
                 msg = await use_llm_model.provider.invoke_llm(
                     query,
                     use_llm_model,
                     req_messages,
-                    query.use_funcs,
+                    query.use_funcs if use_llm_model.model_entity.abilities.__contains__('func_call') else [],
                     extra_args=use_llm_model.model_entity.extra_args,
                     remove_think=remove_think,
                 )

src/langbot/pkg/rag/knowledge/kbmgr.py

@@ -321,13 +321,19 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
         if not plugin_id:
             raise ValueError(f'No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.')
 
+        # Session context (e.g. session_name) stays in retrieval_settings
+        # for plugins that need it. Do NOT move them into filters, as filters
+        # are passed directly to vector_search by some plugins (e.g. LangRAG)
+        # and would cause empty results when the metadata field doesn't exist.
+        filters = settings.pop('filters', {})
+
         retrieval_context = {
             'query': query,
             'knowledge_base_id': kb.uuid,
             'collection_id': kb.collection_id or kb.uuid,
             'retrieval_settings': settings,
             'creation_settings': kb.creation_settings or {},
-            'filters': settings.pop('filters', {}),
+            'filters': filters,
         }
 
         result = await self.ap.plugin_connector.call_rag_retrieve(

src/langbot/pkg/rag/service/runtime.py

@@ -41,6 +41,7 @@ class RAGRuntimeService:
         filters: dict[str, Any] | None = None,
         search_type: str = 'vector',
         query_text: str = '',
+        vector_weight: float | None = None,
     ) -> list[dict[str, Any]]:
         """Handle VECTOR_SEARCH action."""
         return await self.ap.vector_db_mgr.search(
@@ -50,6 +51,7 @@ class RAGRuntimeService:
             filter=filters,
             search_type=search_type,
             query_text=query_text,
+            vector_weight=vector_weight,
         )
 
     async def vector_delete(
@@ -75,6 +77,31 @@ class RAGRuntimeService:
             count = await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
         return count
 
+    async def vector_list(
+        self,
+        collection_id: str,
+        filters: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        """Handle VECTOR_LIST action.
+
+        Args:
+            collection_id: The collection to list from.
+            filters: Optional metadata filters.
+            limit: Maximum number of items to return.
+            offset: Number of items to skip.
+
+        Returns:
+            Tuple of (items, total).
+        """
+        return await self.ap.vector_db_mgr.list_by_filter(
+            collection_name=collection_id,
+            filter=filters,
+            limit=limit,
+            offset=offset,
+        )
+
     async def get_file_stream(self, storage_path: str) -> bytes:
         """Handle GET_KNOWLEDEGE_FILE_STREAM action.
 

src/langbot/pkg/utils/constants.py

@@ -2,7 +2,7 @@ import langbot
 
 semantic_version = f'v{langbot.__version__}'
 
-required_database_version = 19
+required_database_version = 24
 """Tag the version of the database schema, used to check if the database needs to be migrated"""
 
 debug_mode = False

src/langbot/pkg/utils/version.py

@@ -203,7 +203,7 @@ class VersionManager:
         try:
             if await self.ap.ver_mgr.is_new_version_available():
                 return (
-                    'New version available:\n有新版本可用，根据文档更新: \nhttps://docs.langbot.app/zh/deploy/update.html',
+                    'New version available:\n有新版本可用，根据文档更新: \nhttps://link.langbot.app/zh/docs/update',
                     logging.INFO,
                 )
 

src/langbot/pkg/vector/filter_utils.py

@@ -49,17 +49,25 @@ def normalize_filter(
 def strip_unsupported_fields(
     triples: list[tuple[str, str, Any]],
     supported_fields: set[str],
+    field_aliases: dict[str, str] | None = None,
 ) -> list[tuple[str, str, Any]]:
     """Return only triples whose field is in *supported_fields*.
 
+    If *field_aliases* is provided, aliased field names are mapped to the
+    canonical backend name before the support check.  For example,
+    ``{'uuid': 'chunk_uuid'}`` allows callers to use ``uuid`` which is
+    transparently rewritten to ``chunk_uuid``.
+
     Dropped fields are logged at WARNING level so the caller knows they were
     silently ignored (useful for Milvus / pgvector which only store a fixed
     schema).
     """
+    aliases = field_aliases or {}
     kept: list[tuple[str, str, Any]] = []
     for field, op, value in triples:
-        if field in supported_fields:
+        resolved = aliases.get(field, field)
-            kept.append((field, op, value))
+        if resolved in supported_fields:
+            kept.append((resolved, op, value))
         else:
             logger.warning(
                 'Filter field %r is not supported by this backend and will be ignored (supported: %s)',

src/langbot/pkg/vector/mgr.py

@@ -97,10 +97,11 @@ class VectorDBManager:
         filter: dict | None = None,
         search_type: str = 'vector',
         query_text: str = '',
+        vector_weight: float | None = None,
     ) -> list[dict]:
         """Proxy: Search vectors.
 
-        Returns a list of dicts with keys: 'id', 'score', 'metadata'.
+        Returns a list of dicts with keys: 'id', 'distance', 'metadata'.
         The underlying VectorDatabase.search returns Chroma-style format:
         { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]] }
         """
@@ -111,6 +112,7 @@ class VectorDBManager:
             search_type=search_type,
             query_text=query_text,
             filter=filter,
+            vector_weight=vector_weight,
         )
 
         if not results or 'ids' not in results or not results['ids']:
@@ -130,7 +132,7 @@ class VectorDBManager:
             parsed_results.append(
                 {
                     'id': id_val,
-                    'score': r_dists[i] if r_dists and i < len(r_dists) else 0.0,
+                    'distance': r_dists[i] if r_dists and i < len(r_dists) else 0.0,
                     'metadata': r_metas[i] if r_metas and i < len(r_metas) else {},
                 }
             )
@@ -157,3 +159,17 @@ class VectorDBManager:
             Number of deleted vectors (best-effort; some backends return 0).
         """
         return await self.vector_db.delete_by_filter(collection_name, filter)
+
+    async def list_by_filter(
+        self,
+        collection_name: str,
+        filter: dict | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict], int]:
+        """Proxy: List vectors by metadata filter with pagination.
+
+        Returns:
+            Tuple of (items, total).
+        """
+        return await self.vector_db.list_by_filter(collection_name, filter, limit, offset)

src/langbot/pkg/vector/vdb.py

@@ -53,6 +53,7 @@ class VectorDatabase(abc.ABC):
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for the most similar vectors in the specified collection.
 
@@ -70,6 +71,8 @@ class VectorDatabase(abc.ABC):
                     {"file_id": "abc"}
                     {"created_at": {"$gte": 1700000000}}
                     {"file_type": {"$in": ["pdf", "docx"]}}
+            vector_weight: Weight for vector search in hybrid mode (0.0–1.0).
+                ``None`` means use equal weights (backward compatible).
         """
         pass
 
@@ -92,6 +95,28 @@ class VectorDatabase(abc.ABC):
         """
         pass
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        """List vectors matching the given metadata filter with pagination.
+
+        Args:
+            collection: Collection name.
+            filter: Optional metadata filter dict in canonical format.
+            limit: Maximum number of items to return.
+            offset: Number of items to skip.
+
+        Returns:
+            Tuple of (items, total) where items is a list of dicts with
+            keys 'id', 'document', 'metadata', and total is the best-effort
+            count of all matching vectors (-1 if unknown).
+        """
+        return [], -1
+
     @abc.abstractmethod
     async def get_or_create_collection(self, collection: str):
         """Get or create collection."""

src/langbot/pkg/vector/vdbs/chroma.py

@@ -2,11 +2,14 @@ from __future__ import annotations
 import asyncio
 from typing import Any
 from chromadb import PersistentClient
-from langbot.pkg.vector.vdb import VectorDatabase
+from langbot.pkg.vector.vdb import VectorDatabase, SearchType
 from langbot.pkg.core import app
 import chromadb
 import chromadb.errors
 
+# RRF smoothing constant (standard value from the literature)
+_RRF_K = 60
+
 
 class ChromaVectorDatabase(VectorDatabase):
     def __init__(self, ap: app.Application, base_path: str = './data/chroma'):
@@ -14,6 +17,10 @@ class ChromaVectorDatabase(VectorDatabase):
         self.client = PersistentClient(path=base_path)
         self._collections = {}
 
+    @classmethod
+    def supported_search_types(cls) -> list[SearchType]:
+        return [SearchType.VECTOR, SearchType.FULL_TEXT, SearchType.HYBRID]
+
     async def get_or_create_collection(self, collection: str) -> chromadb.Collection:
         if collection not in self._collections:
             self._collections[collection] = await asyncio.to_thread(
@@ -34,8 +41,8 @@ class ChromaVectorDatabase(VectorDatabase):
         kwargs: dict[str, Any] = dict(embeddings=embeddings_list, ids=ids, metadatas=metadatas)
         if documents is not None:
             kwargs['documents'] = documents
-        await asyncio.to_thread(col.add, **kwargs)
+        await asyncio.to_thread(col.upsert, **kwargs)
-        self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")
+        self.ap.logger.info(f"Upserted {len(ids)} embeddings to Chroma collection '{collection}'.")
 
     async def search(
         self,
@@ -45,8 +52,28 @@ class ChromaVectorDatabase(VectorDatabase):
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> dict[str, Any]:
         col = await self.get_or_create_collection(collection)
+
+        if search_type == SearchType.FULL_TEXT:
+            return await self._full_text_search(col, collection, k, query_text, filter)
+        elif search_type == SearchType.HYBRID:
+            return await self._hybrid_search(
+                col, collection, query_embedding, k, query_text, filter, vector_weight=vector_weight
+            )
+
+        # Default: vector search
+        return await self._vector_search(col, collection, query_embedding, k, filter)
+
+    async def _vector_search(
+        self,
+        col: chromadb.Collection,
+        collection: str,
+        query_embedding: list[float],
+        k: int,
+        filter: dict[str, Any] | None,
+    ) -> dict[str, Any]:
         query_kwargs: dict[str, Any] = dict(
             query_embeddings=query_embedding,
             n_results=k,
@@ -55,9 +82,154 @@ class ChromaVectorDatabase(VectorDatabase):
         if filter:
             query_kwargs['where'] = filter
         results = await asyncio.to_thread(col.query, **query_kwargs)
-        self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
+        self.ap.logger.info(
+            f"Chroma vector search in '{collection}' returned {len(results.get('ids', [[]])[0])} results."
+        )
         return results
 
+    async def _full_text_search(
+        self,
+        col: chromadb.Collection,
+        collection: str,
+        k: int,
+        query_text: str,
+        filter: dict[str, Any] | None,
+    ) -> dict[str, Any]:
+        if not query_text:
+            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
+
+        get_kwargs: dict[str, Any] = dict(
+            where_document={'$contains': query_text},
+            include=['metadatas', 'documents'],
+            limit=k,
+        )
+        if filter:
+            get_kwargs['where'] = filter
+        results = await asyncio.to_thread(col.get, **get_kwargs)
+
+        # col.get returns flat lists; wrap into column-major format.
+        # Distances are all 0.0 because Chroma's local $contains is a boolean
+        # filter with no relevance scoring.  Chroma's BM25 sparse embedding
+        # function (ChromaBm25EmbeddingFunction) can generate scored sparse
+        # vectors, but sparse vector *indexing* is only available on Chroma
+        # Cloud, not locally.  For ranked results, use hybrid mode or apply a
+        # reranker in a downstream stage.
+        ids = results.get('ids', [])
+        metadatas = results.get('metadatas', []) or [None] * len(ids)
+        documents = results.get('documents', []) or [None] * len(ids)
+        distances = [0.0] * len(ids)
+
+        self.ap.logger.info(f"Chroma full-text search in '{collection}' returned {len(ids)} results.")
+        return {'ids': [ids], 'metadatas': [metadatas], 'distances': [distances], 'documents': [documents]}
+
+    async def _hybrid_search(
+        self,
+        col: chromadb.Collection,
+        collection: str,
+        query_embedding: list[float],
+        k: int,
+        query_text: str,
+        filter: dict[str, Any] | None,
+        vector_weight: float | None = None,
+    ) -> dict[str, Any]:
+        # Fall back to pure vector search when no text is provided
+        if not query_text:
+            return await self._vector_search(col, collection, query_embedding, k, filter)
+
+        # Run vector search and full-text search in parallel
+        vector_task = self._vector_search(col, collection, query_embedding, k, filter)
+        text_task = self._full_text_search(col, collection, k, query_text, filter)
+        vector_results, text_results = await asyncio.gather(vector_task, text_task)
+
+        vector_ids = vector_results.get('ids', [[]])[0]
+        text_ids = text_results.get('ids', [[]])[0]
+
+        if not vector_ids and not text_ids:
+            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
+
+        # RRF fusion
+        weights = None
+        if vector_weight is not None:
+            weights = [vector_weight, 1.0 - vector_weight]
+        self.ap.logger.info(
+            f"Chroma hybrid fusion config in '{collection}': "
+            f'vector_weight={vector_weight}, weights={weights or [1.0, 1.0]}, '
+            f'vector_hits={len(vector_ids)}, text_hits={len(text_ids)}'
+        )
+        fused = self._rrf_fuse([vector_ids, text_ids], k, weights=weights)
+        if not fused:
+            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
+
+        fused_ids = [doc_id for doc_id, _ in fused]
+
+        # Fetch full metadata and documents for fused results
+        fetched = await asyncio.to_thread(col.get, ids=fused_ids, include=['metadatas', 'documents'])
+
+        # col.get returns results in arbitrary order; re-order to match fused ranking
+        fetched_map: dict[str, tuple] = {}
+        for i, fid in enumerate(fetched.get('ids', [])):
+            meta = (fetched.get('metadatas') or [None] * len(fetched['ids']))[i]
+            doc = (fetched.get('documents') or [None] * len(fetched['ids']))[i]
+            fetched_map[fid] = (meta, doc)
+
+        ordered_ids = []
+        ordered_metas = []
+        ordered_docs = []
+        ordered_dists = []
+
+        # Normalize RRF scores to 0~1 distances via min-max scaling.
+        # Raw RRF scores are tiny (e.g. 0.016~0.033 with k=60) so a naive
+        # ``1 - score`` would compress all distances into a narrow 0.96~0.98
+        # band with almost no discriminative power.  Min-max normalization
+        # spreads them across the full 0~1 range (0.0 = best match).
+        max_score = fused[0][1]
+        min_score = fused[-1][1]
+        score_range = max_score - min_score
+
+        for doc_id, score in fused:
+            if doc_id in fetched_map:
+                meta, doc = fetched_map[doc_id]
+                ordered_ids.append(doc_id)
+                ordered_metas.append(meta)
+                ordered_docs.append(doc)
+                if score_range > 0:
+                    ordered_dists.append(1.0 - (score - min_score) / score_range)
+                else:
+                    ordered_dists.append(0.0)
+
+        self.ap.logger.info(
+            f"Chroma hybrid search in '{collection}' returned {len(ordered_ids)} results "
+            f'(vector={len(vector_ids)}, text={len(text_ids)}).'
+        )
+        return {
+            'ids': [ordered_ids],
+            'metadatas': [ordered_metas],
+            'distances': [ordered_dists],
+            'documents': [ordered_docs],
+        }
+
+    @staticmethod
+    def _rrf_fuse(result_lists: list[list[str]], k: int, weights: list[float] | None = None) -> list[tuple[str, float]]:
+        """Reciprocal Rank Fusion over multiple ranked ID lists.
+
+        Returns a list of (doc_id, rrf_score) sorted by descending score,
+        truncated to *k* entries.
+
+        Args:
+            result_lists: Ranked ID lists from different search methods.
+            k: Number of results to return.
+            weights: Per-list weights.  ``None`` means equal weight (1.0 each).
+        """
+        if weights is None:
+            weights = [1.0] * len(result_lists)
+        scores: dict[str, float] = {}
+        for list_idx, ranked_ids in enumerate(result_lists):
+            w = weights[list_idx]
+            for rank, doc_id in enumerate(ranked_ids):
+                scores[doc_id] = scores.get(doc_id, 0.0) + w / (_RRF_K + rank + 1)
+        sorted_results = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+        return sorted_results[:k]
+
     async def delete_by_file_id(self, collection: str, file_id: str) -> None:
         col = await self.get_or_create_collection(collection)
         await asyncio.to_thread(col.delete, where={'file_id': file_id})
@@ -69,6 +241,41 @@ class ChromaVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' by filter")
         return 0  # Chroma delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        col = await self.get_or_create_collection(collection)
+        get_kwargs: dict[str, Any] = dict(
+            include=['metadatas', 'documents'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            get_kwargs['where'] = filter
+        results = await asyncio.to_thread(col.get, **get_kwargs)
+
+        ids = results.get('ids', [])
+        metadatas = results.get('metadatas', []) or [None] * len(ids)
+        documents = results.get('documents', []) or [None] * len(ids)
+
+        items = []
+        for i, vid in enumerate(ids):
+            items.append(
+                {
+                    'id': vid,
+                    'document': documents[i] if i < len(documents) else None,
+                    'metadata': metadatas[i] if i < len(metadatas) else {},
+                }
+            )
+
+        # Chroma col.count() gives total in collection; filtered count not available
+        total = await asyncio.to_thread(col.count) if not filter else -1
+        return items, total
+
     async def delete_collection(self, collection: str):
         if collection in self._collections:
             del self._collections[collection]

src/langbot/pkg/vector/vdbs/milvus.py

@@ -11,11 +11,14 @@ from langbot.pkg.core import app
 # silently dropped with a warning.
 _MILVUS_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
 
+# Callers use canonical metadata key 'uuid' but Milvus stores it as 'chunk_uuid'.
+_MILVUS_FIELD_ALIASES = {'uuid': 'chunk_uuid'}
+
 
 def _build_milvus_expr(filter_dict: dict[str, Any]) -> str:
     """Translate canonical filter dict into a Milvus boolean expression string."""
     triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS)
+    triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS, _MILVUS_FIELD_ALIASES)
     if not triples:
         return ''
 
@@ -252,6 +255,7 @@ class MilvusVectorDatabase(VectorDatabase):
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for similar vectors in Milvus collection
 
@@ -340,6 +344,62 @@ class MilvusVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' by filter")
         return 0  # Milvus delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        collection = self._normalize_collection_name(collection)
+        await self.get_or_create_collection(collection)
+
+        query_kwargs: dict[str, Any] = dict(
+            collection_name=collection,
+            output_fields=['text', 'file_id', 'chunk_uuid'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            expr = _build_milvus_expr(filter)
+            if expr:
+                query_kwargs['filter'] = expr
+
+        results = await asyncio.to_thread(self.client.query, **query_kwargs)
+
+        items = []
+        for row in results:
+            items.append(
+                {
+                    'id': row.get('id', ''),
+                    'document': row.get('text'),
+                    'metadata': {
+                        'text': row.get('text', ''),
+                        'file_id': row.get('file_id', ''),
+                        'uuid': row.get('chunk_uuid', ''),
+                    },
+                }
+            )
+
+        # Milvus query with count(*)
+        total = -1
+        try:
+            count_kwargs: dict[str, Any] = dict(
+                collection_name=collection,
+                output_fields=['count(*)'],
+            )
+            if filter:
+                expr = _build_milvus_expr(filter)
+                if expr:
+                    count_kwargs['filter'] = expr
+            count_result = await asyncio.to_thread(self.client.query, **count_kwargs)
+            if count_result:
+                total = count_result[0].get('count(*)', -1)
+        except Exception:
+            pass
+
+        return items, total
+
     async def delete_collection(self, collection: str):
         """Delete a Milvus collection
 

src/langbot/pkg/vector/vdbs/pgvector_db.py

@@ -13,6 +13,9 @@ Base = declarative_base()
 # pgvector schema only stores these metadata fields.
 _PG_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
 
+# Callers use canonical metadata key 'uuid' but pgvector stores it as 'chunk_uuid'.
+_PG_FIELD_ALIASES = {'uuid': 'chunk_uuid'}
+
 # Map schema field names to SQLAlchemy columns (resolved lazily from PgVectorEntry).
 _PG_COLUMN_MAP = {
     'text': 'text',
@@ -37,7 +40,7 @@ class PgVectorEntry(Base):
 def _build_pg_conditions(filter_dict: dict[str, Any]) -> list:
     """Translate canonical filter dict into a list of SQLAlchemy conditions."""
     triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS)
+    triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS, _PG_FIELD_ALIASES)
 
     conditions = []
     for field, op, value in triples:
@@ -189,6 +192,7 @@ class PgVectorDatabase(VectorDatabase):
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for similar vectors using cosine distance
 
@@ -309,6 +313,65 @@ class PgVectorDatabase(VectorDatabase):
                 self.ap.logger.error(f'Error deleting from pgvector by filter: {e}')
                 raise
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        await self.get_or_create_collection(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                from sqlalchemy import select, func
+
+                stmt = (
+                    select(
+                        PgVectorEntry.id,
+                        PgVectorEntry.text,
+                        PgVectorEntry.file_id,
+                        PgVectorEntry.chunk_uuid,
+                    )
+                    .filter(PgVectorEntry.collection == collection)
+                    .offset(offset)
+                    .limit(limit)
+                )
+
+                count_stmt = (
+                    select(func.count()).select_from(PgVectorEntry).filter(PgVectorEntry.collection == collection)
+                )
+
+                if filter:
+                    for cond in _build_pg_conditions(filter):
+                        stmt = stmt.filter(cond)
+                        count_stmt = count_stmt.filter(cond)
+
+                result = await session.execute(stmt)
+                rows = result.fetchall()
+
+                count_result = await session.execute(count_stmt)
+                total = count_result.scalar() or 0
+
+                items = []
+                for row in rows:
+                    items.append(
+                        {
+                            'id': row.id,
+                            'document': row.text or '',
+                            'metadata': {
+                                'text': row.text or '',
+                                'file_id': row.file_id or '',
+                                'uuid': row.chunk_uuid or '',
+                            },
+                        }
+                    )
+
+                return items, total
+            except Exception as e:
+                self.ap.logger.error(f'Error listing from pgvector: {e}')
+                raise
+
     async def delete_collection(self, collection: str):
         """Delete all vectors in a collection
 

src/langbot/pkg/vector/vdbs/qdrant.py

@@ -100,6 +100,7 @@ class QdrantVectorDatabase(VectorDatabase):
         search_type: str = 'vector',
         query_text: str = '',
         filter: dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> dict[str, Any]:
         exists = await self.client.collection_exists(collection)
         if not exists:
@@ -150,6 +151,97 @@ class QdrantVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' by filter")
         return 0  # Qdrant delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[dict[str, Any]], int]:
+        exists = await self.client.collection_exists(collection)
+        if not exists:
+            return [], 0
+
+        qdrant_filter = _build_qdrant_filter(filter) if filter else None
+
+        # Qdrant scroll uses cursor-based pagination (offset = point ID),
+        # not numeric skip.  To support numeric offset we scroll through
+        # `offset + limit` items and discard the first `offset`.
+        remaining_to_skip = offset
+        remaining_to_collect = limit
+        cursor: int | str | None = None
+        collected: list[dict[str, Any]] = []
+
+        while remaining_to_skip > 0 or remaining_to_collect > 0:
+            batch_size = remaining_to_skip + remaining_to_collect if remaining_to_skip > 0 else remaining_to_collect
+            scroll_kwargs: dict[str, Any] = dict(
+                collection_name=collection,
+                limit=min(batch_size, 256),
+                with_payload=True if remaining_to_skip == 0 else False,
+                with_vectors=False,
+            )
+            if qdrant_filter:
+                scroll_kwargs['scroll_filter'] = qdrant_filter
+            if cursor is not None:
+                scroll_kwargs['offset'] = cursor
+
+            points, next_cursor = await self.client.scroll(**scroll_kwargs)
+            if not points:
+                break
+
+            for point in points:
+                if remaining_to_skip > 0:
+                    remaining_to_skip -= 1
+                    continue
+                if remaining_to_collect <= 0:
+                    break
+                # Re-fetch payload if we skipped it during the skip phase
+                payload = point.payload or {}
+                collected.append(
+                    {
+                        'id': str(point.id),
+                        'document': payload.get('text') or payload.get('document'),
+                        'metadata': payload,
+                    }
+                )
+                remaining_to_collect -= 1
+
+            if next_cursor is None:
+                break
+            cursor = next_cursor
+
+        # If we skipped without payload, re-fetch the collected items' payloads
+        # (only needed when offset > 0 and items were collected in a skip batch)
+        if offset > 0 and collected:
+            refetch_ids = [item['id'] for item in collected if not item.get('metadata')]
+            if refetch_ids:
+                fetched_points = await self.client.retrieve(
+                    collection_name=collection,
+                    ids=refetch_ids,
+                    with_payload=True,
+                    with_vectors=False,
+                )
+                payload_map = {str(p.id): p.payload or {} for p in fetched_points}
+                for item in collected:
+                    if item['id'] in payload_map:
+                        payload = payload_map[item['id']]
+                        item['metadata'] = payload
+                        item['document'] = payload.get('text') or payload.get('document')
+
+        # Use count() for accurate total (supports filter)
+        total = -1
+        try:
+            count_result = await self.client.count(
+                collection_name=collection,
+                count_filter=qdrant_filter,
+                exact=True,
+            )
+            total = count_result.count
+        except Exception:
+            pass
+
+        return collected, total
+
     async def delete_collection(self, collection: str):
         try:
             await self.client.delete_collection(collection)

src/langbot/pkg/vector/vdbs/seekdb.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import asyncio
+from decimal import Decimal
+import re
 from typing import Any, Dict, List
 
 
@@ -101,8 +103,28 @@ class SeekDBVectorDatabase(VectorDatabase):
             }
         )
 
+    def _normalize_collection_name(self, collection: str) -> str:
+        """SeekDB only accepts [a-zA-Z0-9_], while LangBot uses UUID-like KB IDs."""
+        normalized = re.sub(r'[^A-Za-z0-9_]', '_', collection)
+        if normalized != collection:
+            self.ap.logger.info(f"Normalized SeekDB collection name: '{collection}' -> '{normalized}'")
+        return normalized
+
+    def _json_safe(self, value: Any) -> Any:
+        """Convert SeekDB result values into JSON-serializable Python primitives."""
+        if isinstance(value, Decimal):
+            return float(value)
+        if isinstance(value, dict):
+            return {k: self._json_safe(v) for k, v in value.items()}
+        if isinstance(value, list):
+            return [self._json_safe(v) for v in value]
+        if isinstance(value, tuple):
+            return [self._json_safe(v) for v in value]
+        return value
+
     async def _get_or_create_collection_internal(self, collection: str, vector_size: int = None) -> Any:
         """Internal method to get or create a collection with proper configuration."""
+        collection = self._normalize_collection_name(collection)
         if collection in self._collections:
             return self._collections[collection]
 
@@ -173,6 +195,7 @@ class SeekDBVectorDatabase(VectorDatabase):
         if not embeddings_list:
             return
 
+        collection = self._normalize_collection_name(collection)
         # Ensure collection exists with correct dimension
         vector_size = len(embeddings_list[0])
         coll = await self._get_or_create_collection_internal(collection, vector_size)
@@ -194,6 +217,7 @@ class SeekDBVectorDatabase(VectorDatabase):
         search_type: str = 'vector',
         query_text: str = '',
         filter: Dict[str, Any] | None = None,
+        vector_weight: float | None = None,
     ) -> Dict[str, Any]:
         """Search for the most similar vectors in the specified collection.
 
@@ -210,6 +234,7 @@ class SeekDBVectorDatabase(VectorDatabase):
         Returns:
             Dictionary with 'ids', 'metadatas', 'distances' keys
         """
+        collection = self._normalize_collection_name(collection)
         # Check if collection exists
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
@@ -271,6 +296,17 @@ class SeekDBVectorDatabase(VectorDatabase):
                     query_cfg['where'] = filter
                     knn_cfg['where'] = filter
 
+                # Apply vector_weight via pyseekdb's native boost parameter
+                if vector_weight is not None:
+                    knn_cfg['boost'] = vector_weight
+                    query_cfg['boost'] = 1.0 - vector_weight
+                self.ap.logger.info(
+                    f"SeekDB hybrid fusion config in '{collection}': "
+                    f'vector_weight={vector_weight}, '
+                    f'knn_boost={knn_cfg.get("boost", 1.0)}, '
+                    f'query_boost={query_cfg.get("boost", 1.0)}'
+                )
+
                 results = await asyncio.to_thread(
                     coll.hybrid_search,
                     query=query_cfg,
@@ -279,6 +315,9 @@ class SeekDBVectorDatabase(VectorDatabase):
                     n_results=k,
                     include=['documents', 'metadatas'],
                 )
+                self.ap.logger.info(
+                    f"SeekDB hybrid search in '{collection}' returned {len(results.get('ids', [[]])[0])} results."
+                )
         else:
             # Default: vector search via query()
             query_kwargs = {'n_results': k, 'query_embeddings': query_embedding}
@@ -286,6 +325,7 @@ class SeekDBVectorDatabase(VectorDatabase):
                 query_kwargs['where'] = filter
             results = await asyncio.to_thread(coll.query, **query_kwargs)
 
+        results = self._json_safe(results)
         self.ap.logger.info(
             f"SeekDB {search_type} search in '{collection}' returned {len(results.get('ids', [[]])[0])} results"
         )
@@ -299,6 +339,7 @@ class SeekDBVectorDatabase(VectorDatabase):
             collection: Collection name
             file_id: File ID to delete
         """
+        collection = self._normalize_collection_name(collection)
         # Check if collection exists
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
@@ -325,6 +366,7 @@ class SeekDBVectorDatabase(VectorDatabase):
             collection: Collection name
             filter: Chroma-style ``where`` filter dict
         """
+        collection = self._normalize_collection_name(collection)
         exists = await asyncio.to_thread(self.client.has_collection, collection)
         if not exists:
             self.ap.logger.warning(f"SeekDB collection '{collection}' not found for deletion")
@@ -340,12 +382,59 @@ class SeekDBVectorDatabase(VectorDatabase):
         self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' by filter")
         return 0  # SeekDB delete does not return a count
 
+    async def list_by_filter(
+        self,
+        collection: str,
+        filter: Dict[str, Any] | None = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[Dict[str, Any]], int]:
+        collection = self._normalize_collection_name(collection)
+        exists = await asyncio.to_thread(self.client.has_collection, collection)
+        if not exists:
+            return [], 0
+
+        if collection not in self._collections:
+            coll = await asyncio.to_thread(self.client.get_collection, collection, embedding_function=None)
+            self._collections[collection] = coll
+        else:
+            coll = self._collections[collection]
+
+        get_kwargs: Dict[str, Any] = dict(
+            include=['metadatas', 'documents'],
+            limit=limit,
+            offset=offset,
+        )
+        if filter:
+            get_kwargs['where'] = filter
+
+        results = await asyncio.to_thread(coll.get, **get_kwargs)
+
+        results = self._json_safe(results)
+        ids = results.get('ids', [])
+        metadatas = results.get('metadatas', []) or [None] * len(ids)
+        documents = results.get('documents', []) or [None] * len(ids)
+
+        items = []
+        for i, vid in enumerate(ids):
+            items.append(
+                {
+                    'id': vid,
+                    'document': documents[i] if i < len(documents) else None,
+                    'metadata': metadatas[i] if i < len(metadatas) else {},
+                }
+            )
+
+        total = await asyncio.to_thread(coll.count) if not filter else -1
+        return items, total
+
     async def delete_collection(self, collection: str):
         """Delete the entire collection.
 
         Args:
             collection: Collection name
         """
+        collection = self._normalize_collection_name(collection)
         # Remove from cache
         if collection in self._collections:
             del self._collections[collection]

src/langbot/templates/config.yaml

@@ -2,6 +2,7 @@ admins: []
 api:
     port: 5300
     webhook_prefix: 'http://127.0.0.1:5300'
+    extra_webhook_prefix: ''
 command:
     enable: true
     prefix:
@@ -15,6 +16,7 @@ proxy:
     http: ''
     https: ''
 system:
+    instance_id: ''
     edition: community
     recovery_key: ''
     allow_modify_login_info: true
@@ -76,6 +78,14 @@ plugin:
     runtime_ws_url: 'ws://langbot_plugin_runtime:5400/control/ws'
     enable_marketplace: true
     display_plugin_debug_url: 'ws://localhost:5401/plugin/debug/ws'
+monitoring:
+    auto_cleanup:
+        # Enable automatic cleanup of expired monitoring records
+        enabled: true
+        # Retention period in days, records older than this will be deleted
+        retention_days: 30
+        # Cleanup check interval in hours
+        check_interval_hours: 1
 space:
     # Space service URL for OAuth and API
     url: 'https://space.langbot.app'

src/langbot/templates/default-pipeline-config.json

@@ -41,7 +41,10 @@
             "runner": "local-agent"
         },
         "local-agent": {
-            "model": "",
+            "model": {
+                "primary": "",
+                "fallbacks": []
+            },
             "max-round": 10,
             "prompt": [
                 {
@@ -95,11 +98,12 @@
             "max": 0
         },
         "misc": {
-            "hide-exception": true,
+            "exception-handling": "show-hint",
+            "failure-hint": "Request failed.",
             "at-sender": true,
             "quote-origin": true,
             "track-function-calls": false,
             "remove-think": false
         }
     }
-}
+}

src/langbot/templates/metadata/pipeline/ai.yaml

@@ -23,30 +23,30 @@ stages:
             label:
               en_US: Local Agent
               zh_Hans: 内置 Agent
-          - name: tbox-app-api
-            label:
-              en_US: Tbox App API
-              zh_Hans: 蚂蚁百宝箱平台 API
           - name: dify-service-api
             label:
               en_US: Dify Service API
               zh_Hans: Dify 服务 API
-          - name: dashscope-app-api
-            label:
-              en_US: Aliyun Dashscope App API
-              zh_Hans: 阿里云百炼平台 API
           - name: n8n-service-api
             label:
               en_US: n8n Workflow API
               zh_Hans: n8n 工作流 API
-          - name: langflow-api
-            label:
-              en_US: Langflow API
-              zh_Hans: Langflow API
           - name: coze-api
             label:
               en_US: Coze API
               zh_Hans: 扣子 API
+          - name: tbox-app-api
+            label:
+              en_US: Tbox App API
+              zh_Hans: 蚂蚁百宝箱平台 API
+          - name: dashscope-app-api
+            label:
+              en_US: Aliyun Dashscope App API
+              zh_Hans: 阿里云百炼平台 API
+          - name: langflow-api
+            label:
+              en_US: Langflow API
+              zh_Hans: Langflow API
   - name: local-agent
     label:
       en_US: Local Agent
@@ -59,8 +59,11 @@ stages:
         label:
           en_US: Model
           zh_Hans: 模型
-        type: llm-model-selector
+        type: model-fallback-selector
         required: true
+        default:
+          primary: ''
+          fallbacks: []
       - name: max-round
         label:
           en_US: Max Round
@@ -71,6 +74,10 @@ stages:
         type: integer
         required: true
         default: 10
+        show_if:
+          field: __system.is_wizard
+          operator: neq
+          value: true
       - name: prompt
         label:
           en_US: Prompt
@@ -80,6 +87,9 @@ stages:
           zh_Hans: 除非您了解消息结构，否则请只使用 system 单提示词
         type: prompt-editor
         required: true
+        default:
+          - role: system
+            content: "You are a helpful assistant."
       - name: knowledge-bases
         label:
           en_US: Knowledge Bases
@@ -90,26 +100,10 @@ stages:
         type: knowledge-base-multi-selector
         required: false
         default: []
-  - name: tbox-app-api
+        show_if:
-    label:
+          field: __system.is_wizard
-      en_US: Tbox App API
+          operator: neq
-      zh_Hans: 蚂蚁百宝箱平台 API
+          value: true
-    description:
-      en_US: Configure the Tbox App API of the pipeline
-      zh_Hans: 配置蚂蚁百宝箱平台 API
-    config:
-      - name: api-key
-        label:
-          en_US: API Key
-          zh_Hans: API 密钥
-        type: string
-        required: true
-      - name: app-id
-        label:
-          en_US: App ID
-          zh_Hans: 应用 ID
-        type: string
-        required: true
   - name: dify-service-api
     label:
       en_US: Dify Service API
@@ -124,6 +118,12 @@ stages:
           zh_Hans: 基础 URL
         type: string
         required: true
+        options:
+          - name: 'https://api.dify.ai/v1'
+            label:
+              en_US: Dify Cloud
+              zh_Hans: Dify 云服务
+        default: 'https://api.dify.ai/v1'
       - name: base-prompt
         label:
           en_US: Base PROMPT
@@ -160,52 +160,7 @@ stages:
           zh_Hans: API 密钥
         type: string
         required: true
-  - name: dashscope-app-api
+        default: 'your-api-key'
-    label:
-      en_US: Aliyun Dashscope App API
-      zh_Hans: 阿里云百炼平台 API
-    description:
-      en_US: Configure the Aliyun Dashscope App API of the pipeline
-      zh_Hans: 配置阿里云百炼平台 API
-    config:
-      - name: app-type
-        label:
-          en_US: App Type
-          zh_Hans: 应用类型
-        type: select
-        required: true
-        default: agent
-        options:
-          - name: agent
-            label:
-              en_US: Agent
-              zh_Hans: Agent
-          - name: workflow
-            label:
-              en_US: Workflow
-              zh_Hans: 工作流
-      - name: api-key
-        label:
-          en_US: API Key
-          zh_Hans: API 密钥
-        type: string
-        required: true
-      - name: app-id
-        label:
-          en_US: App ID
-          zh_Hans: 应用 ID
-        type: string
-        required: true
-      - name: references_quote
-        label:
-          en_US: References Quote
-          zh_Hans: 引用文本
-        description:
-          en_US: The text prompt when the references are included
-          zh_Hans: 包含引用资料时的文本提示
-        type: string
-        required: false
-        default: '参考资料来自:'
   - name: n8n-service-api
     label:
       en_US: n8n Workflow API
@@ -223,6 +178,7 @@ stages:
           zh_Hans: n8n 工作流的 webhook URL
         type: string
         required: true
+        default: 'http://your-n8n-webhook-url'
       - name: auth-type
         label:
           en_US: Authentication Type
@@ -260,6 +216,10 @@ stages:
         type: string
         required: false
         default: ''
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'basic'
       - name: basic-password
         label:
           en_US: Password
@@ -270,6 +230,10 @@ stages:
         type: string
         required: false
         default: ''
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'basic'
       - name: jwt-secret
         label:
           en_US: Secret
@@ -280,6 +244,10 @@ stages:
         type: string
         required: false
         default: ''
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'jwt'
       - name: jwt-algorithm
         label:
           en_US: Algorithm
@@ -290,6 +258,10 @@ stages:
         type: string
         required: false
         default: 'HS256'
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'jwt'
       - name: header-name
         label:
           en_US: Header Name
@@ -300,6 +272,10 @@ stages:
         type: string
         required: false
         default: ''
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'header'
       - name: header-value
         label:
           en_US: Header Value
@@ -310,6 +286,10 @@ stages:
         type: string
         required: false
         default: ''
+        show_if:
+          field: auth-type
+          operator: eq
+          value: 'header'
       - name: timeout
         label:
           en_US: Timeout
@@ -330,6 +310,140 @@ stages:
         type: string
         required: false
         default: 'response'
+  - name: coze-api
+    label:
+      en_US: coze API
+      zh_Hans: 扣子 API
+    description:
+      en_US: Configure the Coze API of the pipeline
+      zh_Hans: 配置Coze API
+    config:
+      - name: api-key
+        label:
+          en_US: API Key
+          zh_Hans: API 密钥
+        description:
+          en_US: The API key for the Coze server
+          zh_Hans: Coze服务器的 API 密钥
+        type: string
+        required: true
+        default: ''
+      - name: bot-id
+        label:
+          en_US: Bot ID
+          zh_Hans: 机器人 ID
+        description:
+          en_US: The ID of the bot to run
+          zh_Hans: 要运行的机器人 ID
+        type: string
+        required: true
+        default: ''
+      - name: api-base
+        label:
+          en_US: API Base URL
+          zh_Hans: API 基础 URL
+        description:
+          en_US: The base URL for the Coze API, please use https://api.coze.com for global Coze edition(coze.com).
+          zh_Hans: Coze API 的基础 URL，请使用 https://api.coze.com 用于全球 Coze 版（coze.com）
+        type: string
+        options:
+          - name: 'https://api.coze.cn'
+            label:
+              en_US: Coze China
+              zh_Hans: Coze 中国版
+          - name: 'https://api.coze.com'
+            label:
+              en_US: Coze Global
+              zh_Hans: Coze 全球版
+        default: "https://api.coze.cn"
+      - name: auto-save-history
+        label:
+          en_US: Auto Save History
+          zh_Hans: 自动保存历史
+        description:
+          en_US: Whether to automatically save conversation history
+          zh_Hans: 是否自动保存对话历史
+        type: boolean
+        default: true
+      - name: timeout
+        label:
+          en_US: Request Timeout
+          zh_Hans: 请求超时
+        description:
+          en_US: Timeout in seconds for API requests
+          zh_Hans: API 请求超时时间（秒）
+        type: number
+        default: 120
+  - name: tbox-app-api
+    label:
+      en_US: Tbox App API
+      zh_Hans: 蚂蚁百宝箱平台 API
+    description:
+      en_US: Configure the Tbox App API of the pipeline
+      zh_Hans: 配置蚂蚁百宝箱平台 API
+    config:
+      - name: api-key
+        label:
+          en_US: API Key
+          zh_Hans: API 密钥
+        type: string
+        required: true
+        default: ''
+      - name: app-id
+        label:
+          en_US: App ID
+          zh_Hans: 应用 ID
+        type: string
+        required: true
+        default: ''
+  - name: dashscope-app-api
+    label:
+      en_US: Aliyun Dashscope App API
+      zh_Hans: 阿里云百炼平台 API
+    description:
+      en_US: Configure the Aliyun Dashscope App API of the pipeline
+      zh_Hans: 配置阿里云百炼平台 API
+    config:
+      - name: app-type
+        label:
+          en_US: App Type
+          zh_Hans: 应用类型
+        type: select
+        required: true
+        default: agent
+        options:
+          - name: agent
+            label:
+              en_US: Agent
+              zh_Hans: Agent
+          - name: workflow
+            label:
+              en_US: Workflow
+              zh_Hans: 工作流
+      - name: api-key
+        label:
+          en_US: API Key
+          zh_Hans: API 密钥
+        type: string
+        required: true
+        default: 'your-api-key'
+      - name: app-id
+        label:
+          en_US: App ID
+          zh_Hans: 应用 ID
+        type: string
+        required: true
+        default: 'your-app-id'
+      - name: references_quote
+        label:
+          en_US: References Quote
+          zh_Hans: 引用文本
+        description:
+          en_US: The text prompt when the references are included
+          zh_Hans: 包含引用资料时的文本提示
+        type: string
+        required: false
+        default: '参考资料来自:'
   - name: langflow-api
     label:
       en_US: Langflow API
@@ -347,6 +461,7 @@ stages:
           zh_Hans: Langflow 服务器的基础 URL
         type: string
         required: true
+        default: 'http://localhost:7860'
       - name: api-key
         label:
           en_US: API Key
@@ -356,6 +471,7 @@ stages:
           zh_Hans: Langflow 服务器的 API 密钥
         type: string
         required: true
+        default: 'your-api-key'
       - name: flow-id
         label:
           en_US: Flow ID
@@ -365,6 +481,7 @@ stages:
           zh_Hans: 要运行的流程 ID
         type: string
         required: true
+        default: 'your-flow-id'
       - name: input-type
         label:
           en_US: Input Type
@@ -394,57 +511,4 @@ stages:
           zh_Hans: 可选的流程调整参数
         type: json
         required: false
-        default: '{}'
+        default: '{}'
-  - name: coze-api
-    label:
-      en_US: coze API
-      zh_Hans: 扣子 API
-    description:
-      en_US: Configure the Coze API of the pipeline
-      zh_Hans: 配置Coze API
-    config:
-      - name: api-key
-        label:
-          en_US: API Key
-          zh_Hans: API 密钥
-        description:
-          en_US: The API key for the Coze server
-          zh_Hans: Coze服务器的 API 密钥
-        type: string
-        required: true
-      - name: bot-id
-        label:
-          en_US: Bot ID
-          zh_Hans: 机器人 ID
-        description:
-          en_US: The ID of the bot to run
-          zh_Hans: 要运行的机器人 ID
-        type: string
-        required: true
-      - name: api-base
-        label:
-          en_US: API Base URL
-          zh_Hans: API 基础 URL
-        description:
-          en_US: The base URL for the Coze API, please use https://api.coze.com for global Coze edition(coze.com).
-          zh_Hans: Coze API 的基础 URL，请使用 https://api.coze.com 用于全球 Coze 版（coze.com）
-        type: string
-        default: "https://api.coze.cn"
-      - name: auto-save-history
-        label:
-          en_US: Auto Save History
-          zh_Hans: 自动保存历史
-        description:
-          en_US: Whether to automatically save conversation history
-          zh_Hans: 是否自动保存对话历史
-        type: boolean
-        default: true
-      - name: timeout
-        label:
-          en_US: Request Timeout
-          zh_Hans: 请求超时
-        description:
-          en_US: Timeout in seconds for API requests
-          zh_Hans: API 请求超时时间（秒）
-        type: number
-        default: 120

src/langbot/templates/metadata/pipeline/output.yaml

@@ -37,10 +37,6 @@ stages:
             label:
               en_US: Convert to Image
               zh_Hans: 转换为图片
-          - name: split
-            label:
-              en_US: Split into Multiple Messages
-              zh_Hans: 分割为多条消息发送
           - name: none
             label:
               en_US: None
@@ -82,13 +78,39 @@ stages:
       en_US: Misc
       zh_Hans: 杂项
     config:
-      - name: hide-exception
+      - name: exception-handling
         label:
-          en_US: Hide Exception
+          en_US: Exception Handling Strategy
-          zh_Hans: 不输出异常信息给用户
+          zh_Hans: 异常处理策略
-        type: boolean
+        description:
+          en_US: Controls how error messages are displayed to the user when an AI request fails
+          zh_Hans: 控制 AI 请求失败时向用户展示错误信息的方式
+        type: select
         required: true
-        default: true
+        default: show-hint
+        options:
+          - name: show-error
+            label:
+              en_US: Show Full Error
+              zh_Hans: 显示完整报错信息
+          - name: show-hint
+            label:
+              en_US: Show Failure Hint
+              zh_Hans: 仅文字提示
+          - name: hide
+            label:
+              en_US: Hide All
+              zh_Hans: 不显示任何异常信息
+      - name: failure-hint
+        label:
+          en_US: Failure Hint Text
+          zh_Hans: 失败提示文本
+        description:
+          en_US: The text to display when a request fails. Only effective when Exception Handling Strategy is set to "Show Failure Hint"
+          zh_Hans: 请求失败时显示的提示文本，仅在异常处理策略设置为"仅文字提示"时生效
+        type: string
+        required: false
+        default: 'Request failed.'
       - name: at-sender
         label:
           en_US: At Sender
@@ -123,3 +145,4 @@ stages:
         type: boolean
         required: true
         default: false
+