fix: update version to 4.8.7 in pyproject.toml, __init__.py, and uv.lock

fix: update pnpm-lock.yaml for rehype-sanitize
Merge commit from fork
2026-06-02 03:55:55 +00:00 · 2026-03-04 18:30:53 +08:00 · 2026-03-01 04:12:27 -05:00 · 2026-03-01 17:01:23 +08:00 · 2026-02-28 19:26:50 +08:00 · 2026-02-28 06:02:41 -05:00
235 changed files with 42027 additions and 11711 deletions
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -19,7 +19,7 @@ body:
  - type: textarea
    attributes:
      label: 复现步骤
-      description: 提供越多信息，我们会越快解决问题，建议多提供配置截图；**如果你不认真填写（只一两句话概括），我们会很生气并且立即关闭 issue 或两年后才回复你**
+      description: 提供越多信息，我们会越快解决问题，建议多提供配置截图；**如果涉及 Dify、n8n、Langflow 等外部平台，请提供应用的导出文件（如 Dify 应用的 DSL），我们将更快回复您。**
    validations:
      required: false
  - type: textarea
--- a/.github/ISSUE_TEMPLATE/bug-report_en.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report_en.yml
@@ -19,7 +19,7 @@ body:
  - type: textarea
    attributes:
      label: Reproduction steps
-      description: How to reproduce this problem, the more detailed the better; the more information you provide, the faster we will solve the problem. 【注意】请务必认真填写此部分，若不提供完整信息（如只有一两句话的概括），我们将不会回复！
+      description: How to reproduce this problem, the more detailed the better; the more information you provide, the faster we will solve the problem.
    validations:
      required: false
  - type: textarea
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,60 @@
+name: Lint
+
+on:
+  push:
+    branches:
+      - main
+      - master
+      - dev
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+
+jobs:
+  ruff:
+    name: Ruff Lint & Format
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Install dependencies
+        run: uv sync --dev
+
+      - name: Run ruff check
+        run: uv run ruff check src
+
+      - name: Run ruff format
+        run: uv run ruff format src --check
+
+  frontend:
+    name: Frontend Lint
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '25'
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Install dependencies
+        working-directory: web
+        run: pnpm install
+
+      - name: Run lint
+        working-directory: web
+        run: pnpm lint
--- a/.gitignore
+++ b/.gitignore
@@ -42,7 +42,6 @@ botpy.log*
 test.py
 /web_ui
 .venv/
-uv.lock
 /test
 plugins.bak
 coverage.xml
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -70,6 +70,7 @@ Plugin Runtime automatically starts each installed plugin and interacts through
    - type: must be a specific type, such as feat (new feature), fix (bug fix), docs (documentation), style (code style), refactor (refactoring), perf (performance optimization), etc.
    - scope: the scope of the commit, such as the package name, the file name, the function name, the class name, the module name, etc.
    - subject: the subject of the commit, such as the description of the commit, the reason for the commit, the impact of the commit, etc.
+- If you changed the definition of database entities, please update the migration file in `src/langbot/pkg/persistence/migrations/` and update the constants.py file in `src/langbot/pkg/utils/constants.py` with the new migration number.

 ## Some Principles

--- a/README.md
+++ b/README.md
@@ -1,47 +1,69 @@
-
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

-<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>使用 LangBot 快速构建、调试、部署即时通信机器人。</h3>
+<h3>Production-grade platform for building agentic IM bots.</h3>
+<h4>Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.</h4>

-[English](README_EN.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+English / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
-[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
-[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
+[![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">项目主页</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">部署文档</a> ｜
-<a href="https://docs.langbot.app/zh/plugin/plugin-intro.html">插件介绍</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">提交插件</a>
+<a href="https://langbot.app">Website</a> ｜
+<a href="https://docs.langbot.app/en/insight/features">Features</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide">Docs</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme">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>

 </div>

 </p>

+---

-## 📦 开始使用
+## What is LangBot?

-#### 快速部署
+LangBot is an **open-source, production-grade platform** for building AI-powered instant messaging bots. It connects Large Language Models (LLMs) to any chat platform, enabling you to create intelligent agents that can converse, execute tasks, and integrate with your existing workflows.

-使用 `uvx` 一键启动（需要先安装 [uv](https://docs.astral.sh/uv/getting-started/installation/)）：
+### Key Capabilities
+
+- **AI Conversations & Agents** — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Universal IM Platform Support** — One codebase for Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Production-Ready** — Access control, rate limiting, sensitive word filtering, comprehensive monitoring, and exception handling. Trusted by enterprises.
+- **Plugin Ecosystem** — Hundreds of plugins, event-driven architecture, component extensions, and [MCP protocol](https://modelcontextprotocol.io/) support.
+- **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)
+
+---
+
+## Quick Start
+
+### ☁️ LangBot Cloud (Recommended)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Zero deployment, ready to use.
+
+### One-Line Launch

 ```bash
 uvx langbot
 ```

-访问 http://localhost:5300 即可开始使用。
+> Requires [uv](https://docs.astral.sh/uv/getting-started/installation/). Visit http://localhost:5300 — done.

-#### Docker Compose 部署
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -49,126 +71,102 @@ cd LangBot/docker
 docker compose up -d
 ```

-访问 http://localhost:5300 即可开始使用。
-
-详细文档[Docker 部署](https://docs.langbot.app/zh/deploy/langbot/docker.html)。
-
-#### 宝塔面板部署
-
-已上架宝塔面板，若您已安装宝塔面板，可以根据[文档](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html)使用。
-
-#### Zeabur 云部署
-
-社区贡献的 Zeabur 模板。
-
-[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
-
-#### Railway 云部署
+### One-Click Cloud Deploy

+[![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)

-#### 手动部署
+**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)

-直接使用发行版运行，查看文档[手动部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
+---

-#### Kubernetes 部署
+## Supported Platforms

-参考 [Kubernetes 部署](./docker/README_K8S.md) 文档。
-
-## 😎 保持更新
-
-点击仓库右上角 Star 和 Watch 按钮，获取最新动态。
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 特性
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-zh-rounded.png" />
-
-
- 💬 大模型对话、Agent：支持多种大模型，适配群聊和私聊；具有多轮对话、工具调用、多模态、流式输出能力，自带 RAG（知识库）实现，并深度适配 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)等 LLMOps 平台。
- 🤖 多平台支持：目前支持 QQ、QQ频道、企业微信、个人微信、飞书、Discord、Telegram、KOOK、Slack、LINE 等平台。
- 🛠️ 高稳定性、功能完备：原生支持访问控制、限速、敏感词过滤等机制；配置简单，支持多种部署方式。支持多流水线配置，不同机器人用于不同应用场景。
- 🧩 插件扩展、活跃社区：高稳定性、高安全性的生产级插件系统，支持事件驱动、组件扩展等插件机制；适配 Anthropic [MCP 协议](https://modelcontextprotocol.io/)；目前已有数百个插件。
- 😻 Web 管理面板：支持通过浏览器管理 LangBot 实例，不再需要手动编写配置文件。
-
-详细规格特性请访问[文档](https://docs.langbot.app/zh/insight/features.html)。
-
-或访问 demo 环境：https://demo.langbot.dev/  
-  - 登录信息：邮箱：`demo@langbot.app` 密码：`langbot123456`
-  - 注意：仅展示 WebUI 效果，公开环境，请不要在其中填入您的任何敏感信息。
-
-### 消息平台
-
-| 平台 | 状态 | 备注 |
-| --- | --- | --- |
-| QQ 个人号 | ✅ | QQ 个人号私聊、群聊 |
-| QQ 官方机器人 | ✅ | QQ 官方机器人，支持频道、私聊、群聊 |
-| 企业微信 | ✅ |  |
-| 企微对外客服 | ✅ |  |
-| 企微智能机器人 | ✅ |  |
-| 个人微信 | ✅ |  |
-| 微信公众号 | ✅ |  |
-| 飞书 | ✅ |  |
-| 钉钉 | ✅ |  |
-| KOOK | ✅ |  |
+| Platform | Status | Notes |
+|----------|--------|-------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
+| QQ | ✅ | Personal & Official API |
+| WeCom | ✅ | Enterprise WeChat, External CS, AI Bot |
+| WeChat | ✅ | Personal & Official Account |
+| Lark | ✅ |  |
+| DingTalk | ✅ |  |
+| KOOK | ✅ |  |
+| Satori | ✅ |  |

-### 大模型能力
+---

-| 模型 | 状态 | 备注 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 可接入任何 OpenAI 接口格式模型 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [智谱AI](https://open.bigmodel.cn/) | ✅ |  |
-| [胜算云](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | 全球大模型都可调用（友情推荐） |
-| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型和 GPU 资源平台 |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型和 GPU 资源平台 |
-| [接口 AI](https://jiekou.ai/) | ✅ | 大模型聚合平台，专注全球大模型接入 |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | 大模型聚合平台 |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOps 平台 |
-| [Ollama](https://ollama.com/) | ✅ | 本地大模型运行平台 |
-| [LMStudio](https://lmstudio.ai/) | ✅ | 本地大模型运行平台 |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | 大模型接口聚合平台 |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
-| [小马算力](https://www.tokenpony.cn/453z1) | ✅ | 大模型聚合平台 |
-| [阿里云百炼](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | 支持通过 MCP 协议获取工具 |
-| [百宝箱Tbox](https://www.tbox.cn/open) | ✅ | 蚂蚁百宝箱智能体平台，每月免费10亿大模型Token |
+## Supported LLMs & Integrations

-### TTS
+| Provider | Type | Status |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | Local LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | Local LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Protocol | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Gateway | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Gateway | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Gateway | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Gateway | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Gateway | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU Platform | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU Platform | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU Platform | ✅ |
+| [接口 AI](https://jiekou.ai/) | Gateway | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Gateway | ✅ |

-| 平台/模型 | 备注 |
-| --- | --- |
-| [FishAudio](https://fish.audio/zh-CN/discovery/) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
-| [海豚 AI](https://www.ttson.cn/?source=thelazy) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
-| [AzureTTS](https://portal.azure.com/) | [插件](https://github.com/Ingnaryk/LangBot_AzureTTS) |
+[→ View all integrations](https://docs.langbot.app/en/insight/features)

-### 文生图
+---

-| 平台/模型 | 备注 |
-| --- | --- |
-| 阿里云百炼 | [插件](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin)
+## Why LangBot?

-## 😘 社区贡献
+| Use Case | How LangBot Helps |
+|----------|-------------------|
+| **Customer Support** | Deploy AI agents to Slack/Discord/Telegram that answer questions using your knowledge base |
+| **Internal Tools** | Connect n8n/Dify workflows to WeCom/DingTalk for automated business processes |
+| **Community Management** | Moderate QQ/Discord groups with AI-powered content filtering and interaction |
+| **Multi-Platform Presence** | One bot, all platforms. Manage from a single dashboard |

-感谢以下[代码贡献者](https://github.com/langbot-app/LangBot/graphs/contributors)和社区里其他成员对 LangBot 的贡献：
+---
+
+## Live Demo
+
+**Try it now:** https://demo.langbot.dev/
+- Email: `demo@langbot.app`
+- Password: `langbot123456`
+
+*Note: Public demo environment. Do not enter sensitive information.*
+
+---
+
+## Community
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Discord Community](https://discord.gg/wdNEHETs87)
+
+---
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Contributors
+
+Thanks to all [contributors](https://github.com/langbot-app/LangBot/graphs/contributors) who have helped make LangBot better:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
 </a>
-
-<!--
-## For Code Agents
-
-If you're a code agent based on LLM, you should read [AGENTS.md](AGENTS.md) to understand the project structure and development process before you start any development.
-->
--- a/README_CN.md
+++ b/README_CN.md
@@ -0,0 +1,197 @@
+<p align="center">
+<a href="https://langbot.app">
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
+</a>
+
+<div align="center">
+
+<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+
+<h3>生产级 AI 即时通信机器人开发平台。</h3>
+<h4>快速构建、调试和部署 AI 机器人到微信、QQ、飞书、Slack、Discord、Telegram 等平台。</h4>
+
+[English](README.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/DxZZcNxM1W)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
+[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
+<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
+[![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://docs.langbot.app/zh/insight/guide.html">文档</a> ｜
+<a href="https://docs.langbot.app/zh/tags/readme.html">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>
+
+</div>
+
+</p>
+
+---
+
+## 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
+
+- **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/)。
+- **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)
+
+---
+
+## 快速开始
+
+### ☁️ LangBot Cloud（推荐）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，开箱即用。
+
+### 一键启动
+
+```bash
+uvx langbot
+```
+
+> 需要安装 [uv](https://docs.astral.sh/uv/getting-started/installation/)。访问 http://localhost:5300 即可使用。
+
+### Docker Compose
+
+```bash
+git clone https://github.com/langbot-app/LangBot
+cd LangBot/docker
+docker compose up -d
+```
+
+### 一键云部署
+
+[![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)
+
+---
+
+## 支持的平台
+
+| 平台 | 状态 | 备注 |
+|------|------|------|
+| QQ | ✅ | 个人号、官方机器人（频道、私聊、群聊） |
+| 微信 | ✅ | 个人微信、微信公众号 |
+| 企业微信 | ✅ | 应用消息、对外客服、智能机器人 |
+| 飞书 | ✅ |  |
+| 钉钉 | ✅ |  |
+| Discord | ✅ |  |
+| Telegram | ✅ |  |
+| Slack | ✅ |  |
+| LINE | ✅ |  |
+| KOOK | ✅ |  |
+
+---
+
+## 支持的大模型与集成
+
+| 提供商 | 类型 | 状态 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [智谱AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 本地 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 本地 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 协议 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 聚合平台 | ✅ |
+| [阿里云百炼](https://bailian.console.aliyun.com/) | 聚合平台 | ✅ |
+| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 聚合平台 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 聚合平台 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 聚合平台 | ✅ |
+| [胜算云](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 平台 | ✅ |
+| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+| [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)
+
+### TTS（语音合成）
+
+| 平台/模型 | 备注 |
+|-----------|------|
+| [FishAudio](https://fish.audio/zh-CN/discovery/) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
+| [海豚 AI](https://www.ttson.cn/?source=thelazy) | [插件](https://github.com/the-lazy-me/NewChatVoice) |
+| [AzureTTS](https://portal.azure.com/) | [插件](https://github.com/Ingnaryk/LangBot_AzureTTS) |
+
+### 文生图
+
+| 平台/模型 | 备注 |
+|-----------|------|
+| 阿里云百炼 | [插件](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |
+
+---
+
+## 为什么选择 LangBot？
+
+| 使用场景 | LangBot 如何帮助 |
+|----------|------------------|
+| **客户服务** | 将 AI Agent 部署到微信/企微/钉钉/飞书，基于知识库自动回答用户问题 |
+| **内部工具** | 将 n8n/Dify 工作流接入企微/钉钉，实现业务流程自动化 |
+| **社群运营** | 在 QQ/Discord 群中使用 AI 驱动的内容审核与智能互动 |
+| **多平台触达** | 一个机器人，覆盖所有平台。通过统一面板集中管理 |
+
+---
+
+## 在线演示
+
+**立即体验：** https://demo.langbot.dev/
+- 邮箱：`demo@langbot.app`
+- 密码：`langbot123456`
+
+*注意：公开演示环境，请不要在其中填入任何敏感信息。*
+
+---
+
+## 社区
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/DxZZcNxM1W)
+
+- [Discord 社区](https://discord.gg/wdNEHETs87)
+- [QQ 社区群](https://qm.qq.com/q/DxZZcNxM1W)
+
+---
+
+## Star 趋势
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 贡献者
+
+感谢所有[贡献者](https://github.com/langbot-app/LangBot/graphs/contributors)对 LangBot 的帮助：
+
+<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
+</a>
+
+<!--
+## For Code Agents
+
+If you're a code agent based on LLM, you should read [AGENTS.md](AGENTS.md) to understand the project structure and development process before you start any development.
+-->
--- a/README_EN.md
+++ b/README_EN.md
@@ -1,148 +0,0 @@
-<p align="center">
-<a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
-</a>
-
-<div align="center">
-
-<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
-
-<h3>Quickly build, debug, and ship IM bots with LangBot.</h3>
-
-English / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
-
-[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
-[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
-
-<a href="https://langbot.app">Home</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide.html">Deployment</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Plugin</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Submit Plugin</a>
-
-</div>
-
-</p>
-
-
-## 📦 Getting Started
-
-#### Quick Start
-
-Use `uvx` to start with one command (need to install [uv](https://docs.astral.sh/uv/getting-started/installation/)):
-
-```bash
-uvx langbot
-```
-
-Visit http://localhost:5300 to start using it.
-
-#### Docker Compose Deployment
-
-```bash
-git clone https://github.com/langbot-app/LangBot
-cd LangBot/docker
-docker compose up -d
-```
-
-Visit http://localhost:5300 to start using it.
-
-Detailed documentation [Docker Deployment](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### One-click Deployment on BTPanel
-
-LangBot has been listed on the BTPanel, if you have installed the BTPanel, you can use the [document](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) to use it.
-
-#### Zeabur Cloud Deployment
-
-Community contributed Zeabur template.
-
-[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Railway Cloud Deployment
-
-[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
-
-#### Other Deployment Methods
-
-Directly use the released version to run, see the [Manual Deployment](https://docs.langbot.app/en/deploy/langbot/manual.html) documentation.
-
-#### Kubernetes Deployment
-
-Refer to the [Kubernetes Deployment](./docker/README_K8S.md) documentation.
-
-## 😎 Stay Ahead
-
-Click the Star and Watch button in the upper right corner of the repository to get the latest updates.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Features
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 Chat with LLM / Agent: Supports multiple LLMs, adapt to group chats and private chats; Supports multi-round conversations, tool calls, multi-modal, and streaming output capabilities. Built-in RAG (knowledge base) implementation, and deeply integrates with [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io) etc. LLMOps platforms.
- 🤖 Multi-platform Support: Currently supports QQ, QQ Channel, WeCom, personal WeChat, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE, etc.
- 🛠️ High Stability, Feature-rich: Native access control, rate limiting, sensitive word filtering, etc. mechanisms; Easy to use, supports multiple deployment methods. Supports multiple pipeline configurations, different bots can be used for different scenarios.
- 🧩 Plugin Extension, Active Community: High stability, high security production-level plugin system; Support event-driven, component extension, etc. plugin mechanisms; Integrate Anthropic [MCP protocol](https://modelcontextprotocol.io/); Currently has hundreds of plugins.
- 😻 Web UI: Support management LangBot instance through the browser. No need to manually write configuration files.
-
-For more detailed specifications, please refer to the [documentation](https://docs.langbot.app/en/insight/features.html).
-
-Or visit the demo environment: https://demo.langbot.dev/
-  - Login information: Email: `demo@langbot.app` Password: `langbot123456`
-  - Note: For WebUI demo only, please do not fill in any sensitive information in the public environment.
-
-### Message Platform
-
-| Platform | Status | Remarks |
-| --- | --- | --- |
-| Discord | ✅ |  |
-| Telegram | ✅ |  |
-| Slack | ✅ |  |
-| LINE | ✅ |  |
-| Personal QQ | ✅ |  |
-| QQ Official API | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| Personal WeChat | ✅ |  |
-| Lark | ✅ |  |
-| DingTalk | ✅ |  |
-| KOOK | ✅ |  |
-
-### LLMs
-
-| LLM | Status | Remarks |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Available for any OpenAI interface format model |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | LLM and GPU resource platform |
-| [Dify](https://dify.ai) | ✅ | LLMOps platform |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | LLM and GPU resource platform |
-| [接口 AI](https://jiekou.ai/) | ✅ | LLM aggregation platform, dedicated to global LLMs |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | LLM and GPU resource platform |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | LLM gateway(MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Ollama](https://ollama.com/) | ✅ | Local LLM running platform |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Local LLM running platform |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | LLM interface gateway(MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | LLM gateway(MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLM gateway(MaaS), LLMOps platform |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLM gateway(MaaS), LLMOps platform |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLM gateway(MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Support tool access through MCP protocol |
-
-## 🤝 Community Contribution
-
-Thank you for the following [code contributors](https://github.com/langbot-app/LangBot/graphs/contributors) and other members in the community for their contributions to LangBot:
-
-<a href="https://github.com/langbot-app/LangBot/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
-</a>
--- a/README_ES.md
+++ b/README_ES.md
@@ -1,44 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>Cree, depure y despliegue bots de mensajería instantánea rápidamente con LangBot.</h3>
+<h3>Plataforma de grado de producción para construir bots de mensajería instantánea con agentes de IA.</h3>
+<h4>Construya, depure y despliegue bots de IA rápidamente en Slack, Discord, Telegram, WeChat y más.</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / Español / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / Español / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/guide.html">Despliegue</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Plugin</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Enviar Plugin</a>
+<a href="https://docs.langbot.app/en/insight/features.html">Características</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">Documentación</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">Mercado de Plugins</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Hoja de Ruta</a>

 </div>

 </p>

+---

-## 📦 Comenzar
+## ¿Qué es LangBot?

-#### Inicio Rápido
+LangBot es una **plataforma de código abierto y grado de producción** para construir bots de mensajería instantánea impulsados por IA. Conecta modelos de lenguaje de gran escala (LLMs) con cualquier plataforma de chat, permitiéndole crear agentes inteligentes que pueden conversar, ejecutar tareas e integrarse con sus flujos de trabajo existentes.

-Use `uvx` para iniciar con un comando (necesita instalar [uv](https://docs.astral.sh/uv/getting-started/installation/)):
+### Capacidades Clave
+
+- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Soporte Universal de Plataformas de MI** — Un solo código base para Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Listo para Producción** — Control de acceso, limitación de velocidad, filtrado de palabras sensibles, monitoreo completo y manejo de excepciones. De confianza para empresas.
+- **Ecosistema de Plugins** — Cientos de plugins, arquitectura basada en eventos, extensiones de componentes y soporte del [protocolo MCP](https://modelcontextprotocol.io/).
+- **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)
+
+---
+
+## Inicio Rápido
+
+### ☁️ LangBot Cloud (Recomendado)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Sin despliegue, listo para usar.
+
+### Lanzamiento en una línea

 ```bash
 uvx langbot
 ```

-Visite http://localhost:5300 para comenzar a usarlo.
+> Requiere [uv](https://docs.astral.sh/uv/getting-started/installation/). Visite http://localhost:5300 — listo.

-#### Despliegue con Docker Compose
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -46,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-Visite http://localhost:5300 para comenzar a usarlo.
-
-Documentación detallada [Despliegue con Docker](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### Despliegue con un clic en BTPanel
-
-LangBot ha sido listado en BTPanel. Si tiene BTPanel instalado, puede usar la [documentación](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) para usarlo.
-
-#### Despliegue en la Nube Zeabur
-
-Plantilla de Zeabur contribuida por la comunidad.
+### Despliegue en la Nube con un Clic

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Despliegue en la Nube Railway
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### Otros Métodos de Despliegue
+**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)

-Use directamente la versión publicada para ejecutar, consulte la documentación de [Despliegue Manual](https://docs.langbot.app/en/deploy/langbot/manual.html).
+---

-#### Despliegue en Kubernetes
+## Plataformas Soportadas

-Consulte la documentación de [Despliegue en Kubernetes](./docker/README_K8S.md).
-
-## 😎 Manténgase Actualizado
-
-Haga clic en los botones Star y Watch en la esquina superior derecha del repositorio para obtener las últimas actualizaciones.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Características
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 Chat con LLM / Agent: Compatible con múltiples LLMs, adaptado para chats grupales y privados; Admite conversaciones de múltiples rondas, llamadas a herramientas, capacidades multimodales y de salida en streaming. Implementación RAG (base de conocimientos) incorporada, e integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io) etc. LLMOps platforms.
- 🤖 Soporte Multiplataforma: Actualmente compatible con QQ, QQ Channel, WeCom, WeChat personal, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE, etc.
- 🛠️ Alta Estabilidad, Rico en Funciones: Control de acceso nativo, limitación de velocidad, filtrado de palabras sensibles, etc.; Fácil de usar, admite múltiples métodos de despliegue. Compatible con múltiples configuraciones de pipeline, diferentes bots para diferentes escenarios.
- 🧩 Extensión de Plugin, Comunidad Activa: Sistema de plugin de alta estabilidad, alta seguridad de nivel de producción; Compatible con mecanismos de plugin impulsados por eventos, extensión de componentes, etc.; Integración del protocolo [MCP](https://modelcontextprotocol.io/) de Anthropic; Actualmente cuenta con cientos de plugins.
- 😻 Interfaz Web: Admite la gestión de instancias de LangBot a través del navegador. No es necesario escribir archivos de configuración manualmente.
-
-Para especificaciones más detalladas, consulte la [documentación](https://docs.langbot.app/en/insight/features.html).
-
-O visite el entorno de demostración: https://demo.langbot.dev/
-  - Información de inicio de sesión: Correo electrónico: `demo@langbot.app` Contraseña: `langbot123456`
-  - Nota: Solo para demostración de WebUI, por favor no ingrese información confidencial en el entorno público.
-
-### Plataformas de Mensajería
-
-| Plataforma | Estado | Observaciones |
-| --- | --- | --- |
+| Plataforma | Estado | Notas |
+|----------|--------|-------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| QQ Personal | ✅ |  |
-| QQ API Oficial | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| WeChat Personal | ✅ |  |
+| QQ | ✅ | Personal y API Oficial |
+| WeCom | ✅ | WeChat Empresarial, CS Externo, AI Bot |
+| WeChat | ✅ | Personal y Cuenta Oficial |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
 | KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | Estado | Observaciones |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Disponible para cualquier modelo con formato de interfaz OpenAI |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | Plataforma de recursos LLM y GPU |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | Plataforma de recursos LLM y GPU |
-| [接口 AI](https://jiekou.ai/) | ✅ | Plataforma de agregación LLM |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | Plataforma de recursos LLM y GPU |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | Gateway LLM (MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | Plataforma LLMOps |
-| [Ollama](https://ollama.com/) | ✅ | Plataforma de ejecución de LLM local |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Plataforma de ejecución de LLM local |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | Gateway de interfaz LLM (MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | Gateway LLM (MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | Gateway LLM (MaaS), plataforma LLMOps |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | Gateway LLM (MaaS), plataforma LLMOps |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | Gateway LLM (MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Compatible con acceso a herramientas a través del protocolo MCP |
+## LLMs e Integraciones Soportadas

-## 🤝 Contribución de la Comunidad
+| Proveedor | Tipo | Estado |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM Local | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM Local | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Protocolo | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Pasarela | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Pasarela | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Pasarela | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Pasarela | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Pasarela | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plataforma GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plataforma GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plataforma GPU | ✅ |
+| [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |

-Gracias a los siguientes [contribuidores de código](https://github.com/langbot-app/LangBot/graphs/contributors) y otros miembros de la comunidad por sus contribuciones a LangBot:
+[→ Ver todas las integraciones](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## ¿Por qué LangBot?
+
+| Caso de Uso | Cómo Ayuda LangBot |
+|----------|-------------------|
+| **Atención al cliente** | Despliegue agentes de IA en Slack/Discord/Telegram que respondan preguntas usando su base de conocimientos |
+| **Herramientas internas** | Conecte flujos de trabajo de n8n/Dify a WeCom/DingTalk para procesos empresariales automatizados |
+| **Gestión de comunidades** | Modere grupos de QQ/Discord con filtrado de contenido e interacción impulsados por IA |
+| **Presencia multiplataforma** | Un solo bot, todas las plataformas. Gestione desde un único panel de control |
+
+---
+
+## Demo en Vivo
+
+**Pruébelo ahora:** https://demo.langbot.dev/
+- Correo electrónico: `demo@langbot.app`
+- Contraseña: `langbot123456`
+
+*Nota: Entorno de demostración público. No ingrese información confidencial.*
+
+---
+
+## Comunidad
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Comunidad de Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Historial de Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Colaboradores
+
+Gracias a todos los [colaboradores](https://github.com/langbot-app/LangBot/graphs/contributors) que han ayudado a mejorar LangBot:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/README_FR.md
+++ b/README_FR.md
@@ -1,43 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>Créez, déboguez et déployez rapidement des bots de messagerie instantanée avec LangBot.</h3>
+<h3>Plateforme de niveau production pour construire des bots de messagerie instantanée avec agents IA.</h3>
+<h4>Créez, déboguez et déployez rapidement des bots IA sur Slack, Discord, Telegram, WeChat et plus.</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / Français / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / Français / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/guide.html">Déploiement</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Plugin</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Soumettre un Plugin</a>
+<a href="https://docs.langbot.app/en/insight/features.html">Fonctionnalités</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">Documentation</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">Marché des Plugins</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Feuille de Route</a>

 </div>

 </p>

-## 📦 Commencer
+---

-#### Démarrage Rapide
+## Qu'est-ce que LangBot ?

-Utilisez `uvx` pour démarrer avec une commande (besoin d'installer [uv](https://docs.astral.sh/uv/getting-started/installation/)) :
+LangBot est une **plateforme open-source de niveau production** pour créer des bots de messagerie instantanée alimentés par l'IA. Elle connecte les grands modèles de langage (LLMs) à n'importe quelle plateforme de chat, vous permettant de créer des agents intelligents capables de converser, d'exécuter des tâches et de s'intégrer à vos workflows existants.
+
+### Capacités Clés
+
+- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Support Universel des Plateformes de MI** — Un seul code pour Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Prêt pour la Production** — Contrôle d'accès, limitation de débit, filtrage de mots sensibles, surveillance complète et gestion des exceptions. Approuvé par les entreprises.
+- **Écosystème de Plugins** — Des centaines de plugins, architecture événementielle, extensions de composants, et support du [protocole MCP](https://modelcontextprotocol.io/).
+- **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)
+
+---
+
+## Démarrage Rapide
+
+### ☁️ LangBot Cloud (Recommandé)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Sans déploiement, prêt à utiliser.
+
+### Lancement en une ligne

 ```bash
 uvx langbot
 ```

-Visitez http://localhost:5300 pour commencer à l'utiliser.
+> Nécessite [uv](https://docs.astral.sh/uv/getting-started/installation/). Visitez http://localhost:5300 — c'est prêt.

-#### Déploiement avec Docker Compose
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-Visitez http://localhost:5300 pour commencer à l'utiliser.
-
-Documentation détaillée [Déploiement Docker](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### Déploiement en un clic sur BTPanel
-
-LangBot a été répertorié sur BTPanel. Si vous avez installé BTPanel, vous pouvez utiliser la [documentation](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) pour l'utiliser.
-
-#### Déploiement Cloud Zeabur
-
-Modèle Zeabur contribué par la communauté.
+### Déploiement Cloud en un Clic

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Déploiement Cloud Railway
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### Autres Méthodes de Déploiement
+**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)

-Utilisez directement la version publiée pour exécuter, consultez la documentation de [Déploiement Manuel](https://docs.langbot.app/en/deploy/langbot/manual.html).
+---

-#### Déploiement Kubernetes
+## Plateformes Supportées

-Consultez la documentation de [Déploiement Kubernetes](./docker/README_K8S.md).
-
-## 😎 Restez à Jour
-
-Cliquez sur les boutons Star et Watch dans le coin supérieur droit du dépôt pour obtenir les dernières mises à jour.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Fonctionnalités
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 Chat avec LLM / Agent : Prend en charge plusieurs LLM, adapté aux chats de groupe et privés ; Prend en charge les conversations multi-tours, les appels d'outils, les capacités multimodales et de sortie en streaming. Implémentation RAG (base de connaissances) intégrée, et intégration profonde avec [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io) etc. LLMOps platforms.
- 🤖 Support Multi-plateforme : Actuellement compatible avec QQ, QQ Channel, WeCom, WeChat personnel, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE, etc.
- 🛠️ Haute Stabilité, Riche en Fonctionnalités : Contrôle d'accès natif, limitation de débit, filtrage de mots sensibles, etc. ; Facile à utiliser, prend en charge plusieurs méthodes de déploiement. Prend en charge plusieurs configurations de pipeline, différents bots pour différents scénarios.
- 🧩 Extension de Plugin, Communauté Active : Système de plugin de haute stabilité, haute sécurité de niveau production; Prend en charge les mécanismes de plugin pilotés par événements, l'extension de composants, etc. ; Intégration du protocole [MCP](https://modelcontextprotocol.io/) d'Anthropic ; Dispose actuellement de centaines de plugins.
- 😻 Interface Web : Prend en charge la gestion des instances LangBot via le navigateur. Pas besoin d'écrire manuellement les fichiers de configuration.
-
-Pour des spécifications plus détaillées, veuillez consulter la [documentation](https://docs.langbot.app/en/insight/features.html).
-
-Ou visitez l'environnement de démonstration : https://demo.langbot.dev/
-  - Informations de connexion : Email : `demo@langbot.app` Mot de passe : `langbot123456`
-  - Note : Pour la démonstration WebUI uniquement, veuillez ne pas entrer d'informations sensibles dans l'environnement public.
-
-### Plateformes de Messagerie
-
-| Plateforme | Statut | Remarques |
-| --- | --- | --- |
+| Plateforme | Statut | Notes |
+|----------|--------|-------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| QQ Personnel | ✅ |  |
-| API Officielle QQ | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| WeChat Personnel | ✅ |  |
+| QQ | ✅ | Personnel & API Officielle |
+| WeCom | ✅ | WeChat Entreprise, CS Externe, AI Bot |
+| WeChat | ✅ | Personnel & Compte Officiel |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
 | KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | Statut | Remarques |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Disponible pour tout modèle au format d'interface OpenAI |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | Plateforme de ressources LLM et GPU |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | Plateforme de ressources LLM et GPU |
-| [接口 AI](https://jiekou.ai/) | ✅ | Plateforme d'agrégation LLM |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | Plateforme de ressources LLM et GPU |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | Passerelle LLM (MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | Plateforme LLMOps |
-| [Ollama](https://ollama.com/) | ✅ | Plateforme d'exécution LLM locale |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Plateforme d'exécution LLM locale |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | Passerelle d'interface LLM (MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | Passerelle LLM (MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | Passerelle LLM (MaaS), plateforme LLMOps |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | Passerelle LLM (MaaS), plateforme LLMOps |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | Passerelle LLM (MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Prend en charge l'accès aux outils via le protocole MCP |
+## LLMs et Intégrations Supportés

-## 🤝 Contribution de la Communauté
+| Fournisseur | Type | Statut |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM Local | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM Local | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Protocole | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Passerelle | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Passerelle | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Passerelle | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Passerelle | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Passerelle | ✅ |
+| [接口 AI](https://jiekou.ai/) | Passerelle | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Passerelle | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plateforme GPU | ✅ |
+| [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 | ✅ |

-Merci aux [contributeurs de code](https://github.com/langbot-app/LangBot/graphs/contributors) suivants et aux autres membres de la communauté pour leurs contributions à LangBot :
+[→ Voir toutes les intégrations](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## Pourquoi LangBot ?
+
+| Cas d'Usage | Comment LangBot Aide |
+|----------|-------------------|
+| **Support Client** | Déployez des agents IA sur Slack/Discord/Telegram qui répondent aux questions en utilisant votre base de connaissances |
+| **Outils Internes** | Connectez les workflows n8n/Dify à WeCom/DingTalk pour automatiser vos processus métier |
+| **Gestion de Communauté** | Modérez les groupes QQ/Discord avec un filtrage de contenu et des interactions alimentés par l'IA |
+| **Présence Multi-plateforme** | Un seul bot, toutes les plateformes. Gérez tout depuis un tableau de bord unique |
+
+---
+
+## Démo en Ligne
+
+**Essayez maintenant :** https://demo.langbot.dev/
+- Email : `demo@langbot.app`
+- Mot de passe : `langbot123456`
+
+*Note : Environnement de démonstration public. Ne saisissez pas d'informations sensibles.*
+
+---
+
+## Communauté
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Communauté Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Historique des Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Contributeurs
+
+Merci à tous les [contributeurs](https://github.com/langbot-app/LangBot/graphs/contributors) qui ont aidé à améliorer LangBot :

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/README_JP.md
+++ b/README_JP.md
@@ -1,43 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>LangBotでIMボットを素早く構築、デバッグ、デプロイ。</h3>
+<h3>AIエージェント搭載IMボットを構築するための本番グレードプラットフォーム。</h3>
+<h4>Slack、Discord、Telegram、WeChat などに AI ボットを素早く構築、デバッグ、デプロイ。</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / 日本語 / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / 日本語 / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/en/insight/guide.html">デプロイ</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">プラグイン</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">プラグインの提出</a>
+<a href="https://docs.langbot.app/ja/insight/features.html">機能</a> ｜
+<a href="https://docs.langbot.app/ja/insight/guide.html">ドキュメント</a> ｜
+<a href="https://docs.langbot.app/ja/tags/readme.html">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>

 </div>

 </p>

-## 📦 始め方
+---

-#### クイックスタート
+## LangBot とは？

-`uvx` を使用した迅速なデプロイ（[uv](https://docs.astral.sh/uv/getting-started/installation/) が必要です）：
+LangBot は、AI搭載のインスタントメッセージングボットを構築するための**オープンソースの本番グレードプラットフォーム**です。大規模言語モデル（LLM）をあらゆるチャットプラットフォームに接続し、会話、タスク実行、既存のワークフローとの統合が可能なインテリジェントエージェントを作成できます。
+
+### 主な機能
+
+- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) と深く統合。
+- **ユニバーサルIMプラットフォーム対応** — 単一のコードベースで Discord、Telegram、Slack、LINE、QQ、WeChat、WeCom、Lark、DingTalk、KOOK に対応。
+- **本番環境対応** — アクセス制御、レート制限、センシティブワードフィルタリング、包括的な監視、例外処理を搭載。エンタープライズの信頼に応える品質。
+- **プラグインエコシステム** — 数百のプラグイン、イベント駆動アーキテクチャ、コンポーネント拡張、[MCPプロトコル](https://modelcontextprotocol.io/)対応。
+- **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)
+
+---
+
+## クイックスタート
+
+### ☁️ LangBot Cloud（推奨）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — デプロイ不要、すぐに使えます。
+
+### ワンライン起動

 ```bash
 uvx langbot
 ```

-http://localhost:5300 にアクセスして使用を開始します。
+> [uv](https://docs.astral.sh/uv/getting-started/installation/) が必要です。http://localhost:5300 にアクセスして完了。

-#### Docker Compose デプロイ
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-http://localhost:5300 にアクセスして使用を開始します。
-
-詳細なドキュメントは[Dockerデプロイ](https://docs.langbot.app/en/deploy/langbot/docker.html)を参照してください。
-
-#### Panelでのワンクリックデプロイ
-
-LangBotはBTPanelにリストされています。BTPanelをインストールしている場合は、[ドキュメント](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html)を使用して使用できます。
-
-#### Zeaburクラウドデプロイ
-
-コミュニティが提供するZeaburテンプレート。
+### ワンクリッククラウドデプロイ

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Railwayクラウドデプロイ
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### その他のデプロイ方法
+**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)

-リリースバージョンを直接使用して実行します。[手動デプロイ](https://docs.langbot.app/en/deploy/langbot/manual.html)のドキュメントを参照してください。
+---

-#### Kubernetes デプロイ
-
-[Kubernetes デプロイ](./docker/README_K8S.md) ドキュメントを参照してください。
-
-## 😎 最新情報を入手
-
-リポジトリの右上にある Star と Watch ボタンをクリックして、最新の更新を取得してください。
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 機能
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 LLM / エージェントとのチャット: 複数のLLMをサポートし、グループチャットとプライベートチャットに対応。マルチラウンドの会話、ツールの呼び出し、マルチモーダル、ストリーミング出力機能をサポート、RAG（知識ベース）を組み込み、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io) などの LLMOps プラットフォームと深く統合。
- 🤖 多プラットフォーム対応: 現在、QQ、QQ チャンネル、WeChat、個人 WeChat、Lark、DingTalk、Discord、Telegram、KOOK、Slack、LINE など、複数のプラットフォームをサポートしています。
- 🛠️ 高い安定性、豊富な機能: ネイティブのアクセス制御、レート制限、敏感な単語のフィルタリングなどのメカニズムをサポート。使いやすく、複数のデプロイ方法をサポート。複数のパイプライン設定をサポートし、異なるボットを異なる用途に使用できます。
- 🧩 プラグイン拡張、活発なコミュニティ: 高い安定性、高いセキュリティの生産レベルのプラグインシステム；イベント駆動、コンポーネント拡張などのプラグインメカニズムをサポート。適配 Anthropic [MCP プロトコル](https://modelcontextprotocol.io/)；豊富なエコシステム、現在数百のプラグインが存在。
- 😻 Web UI: ブラウザを通じてLangBotインスタンスを管理することをサポート。
-
-詳細な仕様については、[ドキュメント](https://docs.langbot.app/en/insight/features.html)を参照してください。
-
-または、デモ環境にアクセスしてください: https://demo.langbot.dev/
-  - ログイン情報: メール: `demo@langbot.app` パスワード: `langbot123456`
-  - 注意: WebUI のデモンストレーションのみの場合、公開環境では機密情報を入力しないでください。
-
-### メッセージプラットフォーム
+## 対応プラットフォーム

 | プラットフォーム | ステータス | 備考 |
-| --- | --- | --- |
+|----------|--------|-------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| 個人QQ | ✅ |  |
-| QQ公式API | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| 個人WeChat | ✅ | |
+| QQ | ✅ | 個人 & 公式API |
+| WeCom | ✅ | 企業WeChat、外部CS、AIボット |
+| WeChat | ✅ | 個人 & 公式アカウント |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
 | KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | ステータス | 備考 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 任意のOpenAIインターフェース形式モデルに対応 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型とGPUリソースプラットフォーム |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型とGPUリソースプラットフォーム |
-| [接口 AI](https://jiekou.ai/) | ✅ | LLMゲートウェイ(MaaS) |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | LLMとGPUリソースプラットフォーム |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | LLMゲートウェイ(MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOpsプラットフォーム |
-| [Ollama](https://ollama.com/) | ✅ | ローカルLLM実行プラットフォーム |
-| [LMStudio](https://lmstudio.ai/) | ✅ | ローカルLLM実行プラットフォーム |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | LLMインターフェースゲートウェイ(MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | LLMゲートウェイ(MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLMゲートウェイ(MaaS), LLMOpsプラットフォーム |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLMゲートウェイ(MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | MCPプロトコルをサポート |
+## 対応LLMと統合

-## 🤝 コミュニティ貢献
+| プロバイダー | タイプ | ステータス |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | ローカルLLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | ローカルLLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | プロトコル | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | ゲートウェイ | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ゲートウェイ | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ゲートウェイ | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ゲートウェイ | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | ゲートウェイ | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPUプラットフォーム | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPUプラットフォーム | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPUプラットフォーム | ✅ |
+| [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 | ✅ |

-LangBot への貢献に対して、以下の [コード貢献者](https://github.com/langbot-app/LangBot/graphs/contributors) とコミュニティの他のメンバーに感謝します。
+[→ すべての統合を表示](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## なぜ LangBot？
+
+| ユースケース | LangBot の活用方法 |
+|----------|-------------------|
+| **カスタマーサポート** | ナレッジベースを活用して質問に回答するAIエージェントをSlack/Discord/Telegramにデプロイ |
+| **社内ツール** | n8n/Difyのワークフローを WeCom/DingTalk に接続し、業務プロセスを自動化 |
+| **コミュニティ管理** | AI搭載のコンテンツフィルタリングとインタラクションでQQ/Discordグループをモデレーション |
+| **マルチプラットフォーム展開** | 1つのボットで全プラットフォームに対応。単一のダッシュボードから管理 |
+
+---
+
+## ライブデモ
+
+**今すぐ試す:** https://demo.langbot.dev/
+- メール: `demo@langbot.app`
+- パスワード: `langbot123456`
+
+*注意: 公開デモ環境です。機密情報を入力しないでください。*
+
+---
+
+## コミュニティ
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Discord コミュニティ](https://discord.gg/wdNEHETs87)
+
+---
+
+## Star 推移
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## コントリビューター
+
+LangBot をより良くするために貢献してくださったすべての[コントリビューター](https://github.com/langbot-app/LangBot/graphs/contributors)に感謝します:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/README_KO.md
+++ b/README_KO.md
@@ -1,43 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>LangBot으로 IM 봇을 빠르게 구축, 디버그 및 배포하세요.</h3>
+<h3>AI 에이전트 IM 봇 구축을 위한 프로덕션 등급 플랫폼.</h3>
+<h4>Slack, Discord, Telegram, WeChat 등에 AI 봇을 빠르게 구축, 디버그 및 배포.</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / 한국어 / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / 한국어 / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/guide.html">배포</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">플러그인</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">플러그인 제출</a>
+<a href="https://docs.langbot.app/en/insight/features.html">기능</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">문서</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">플러그인 마켓</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">로드맵</a>

 </div>

 </p>

-## 📦 시작하기
+---

-#### 빠른 시작
+## LangBot이란?

-`uvx`를 사용하여 한 명령으로 시작하세요 ([uv](https://docs.astral.sh/uv/getting-started/installation/) 설치 필요):
+LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈소스 프로덕션 등급 플랫폼**입니다. 대규모 언어 모델(LLM)을 모든 채팅 플랫폼에 연결하여 대화, 작업 실행, 기존 워크플로우와의 통합이 가능한 지능형 에이전트를 만들 수 있습니다.
+
+### 핵심 기능
+
+- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org) 심층 통합.
+- **유니버설 IM 플랫폼 지원** — 단일 코드베이스로 Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK 지원.
+- **프로덕션 레디** — 접근 제어, 속도 제한, 민감어 필터링, 종합 모니터링 및 예외 처리. 기업 환경에서 검증됨.
+- **플러그인 생태계** — 수백 개의 플러그인, 이벤트 기반 아키텍처, 컴포넌트 확장, [MCP 프로토콜](https://modelcontextprotocol.io/) 지원.
+- **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)
+
+---
+
+## 빠른 시작
+
+### ☁️ LangBot Cloud (추천)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 배포 없이 바로 사용.
+
+### 원라인 실행

 ```bash
 uvx langbot
 ```

-http://localhost:5300을 방문하여 사용을 시작하세요.
+> [uv](https://docs.astral.sh/uv/getting-started/installation/) 설치 필요. http://localhost:5300 방문 — 완료.

-#### Docker Compose 배포
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-http://localhost:5300을 방문하여 사용을 시작하세요.
-
-자세한 문서는 [Docker 배포](https://docs.langbot.app/en/deploy/langbot/docker.html)를 참조하세요.
-
-#### BTPanel 원클릭 배포
-
-LangBot은 BTPanel에 등록되어 있습니다. BTPanel을 설치한 경우 [문서](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html)를 사용하여 사용할 수 있습니다.
-
-#### Zeabur 클라우드 배포
-
-커뮤니티에서 제공하는 Zeabur 템플릿입니다.
+### 원클릭 클라우드 배포

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Railway 클라우드 배포
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### 기타 배포 방법
+**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)

-릴리스 버전을 직접 사용하여 실행하려면 [수동 배포](https://docs.langbot.app/en/deploy/langbot/manual.html) 문서를 참조하세요.
+---

-#### Kubernetes 배포
-
-[Kubernetes 배포](./docker/README_K8S.md) 문서를 참조하세요.
-
-## 😎 최신 정보 받기
-
-리포지토리 오른쪽 상단의 Star 및 Watch 버튼을 클릭하여 최신 업데이트를 받으세요.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 기능
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 LLM / Agent와 채팅: 여러 LLM을 지원하며 그룹 채팅 및 개인 채팅에 적응; 멀티 라운드 대화, 도구 호출, 멀티모달, 스트리밍 출력 기능을 지원합니다. 내장된 RAG(지식 베이스) 구현 및 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io) 등의 LLMOps 플랫폼과 깊이 통합됩니다.
- 🤖 다중 플랫폼 지원: 현재 QQ, QQ Channel, WeCom, 개인 WeChat, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE 등을 지원합니다.
- 🛠️ 높은 안정성, 풍부한 기능: 네이티브 액세스 제어, 속도 제한, 민감한 단어 필터링 등의 메커니즘; 사용하기 쉽고 여러 배포 방법을 지원합니다. 여러 파이프라인 구성을 지원하며 다양한 시나리오에 대해 다른 봇을 사용할 수 있습니다.
- 🧩 플러그인 확장, 활발한 커뮤니티: 고안정성, 고보안 생산 수준의 플러그인 시스템; 이벤트 기반, 컴포넌트 확장 등의 플러그인 메커니즘을 지원; Anthropic [MCP 프로토콜](https://modelcontextprotocol.io/) 통합; 현재 수백 개의 플러그인이 있습니다.
- 😻 웹 UI: 브라우저를 통해 LangBot 인스턴스 관리를 지원합니다. 구성 파일을 수동으로 작성할 필요가 없습니다.
-
-더 자세한 사양은 [문서](https://docs.langbot.app/en/insight/features.html)를 참조하세요.
-
-또는 데모 환경을 방문하세요: https://demo.langbot.dev/
-  - 로그인 정보: 이메일: `demo@langbot.app` 비밀번호: `langbot123456`
-  - 참고: WebUI 데모 전용이므로 공개 환경에서는 민감한 정보를 입력하지 마세요.
-
-### 메시징 플랫폼
+## 지원 플랫폼

 | 플랫폼 | 상태 | 비고 |
-| --- | --- | --- |
+|--------|------|------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| 개인 QQ | ✅ |  |
-| QQ 공식 API | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| 개인 WeChat | ✅ |  |
-| KOOK | ✅ |  |
+| QQ | ✅ | 개인 및 공식 API |
+| WeCom | ✅ | 기업 WeChat, 외부 CS, AI Bot |
+| WeChat | ✅ | 개인 및 공식 계정 |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
+| KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | 상태 | 비고 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 모든 OpenAI 인터페이스 형식 모델에 사용 가능 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | LLM 및 GPU 리소스 플랫폼 |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | LLM 및 GPU 리소스 플랫폼 |
-| [接口 AI](https://jiekou.ai/) | ✅ | LLM 집계 플랫폼 |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | LLM 및 GPU 리소스 플랫폼 |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | LLM 게이트웨이(MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOps 플랫폼 |
-| [Ollama](https://ollama.com/) | ✅ | 로컬 LLM 실행 플랫폼 |
-| [LMStudio](https://lmstudio.ai/) | ✅ | 로컬 LLM 실행 플랫폼 |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | LLM 인터페이스 게이트웨이(MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | LLM 게이트웨이(MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | LLM 게이트웨이(MaaS), LLMOps 플랫폼 |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | LLM 게이트웨이(MaaS), LLMOps 플랫폼 |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | LLM 게이트웨이(MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | MCP 프로토콜을 통한 도구 액세스 지원 |
+## 지원 LLM 및 통합

-## 🤝 커뮤니티 기여
+| 제공자 | 유형 | 상태 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 로컬 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 로컬 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 프로토콜 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 게이트웨이 | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | 게이트웨이 | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 게이트웨이 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 게이트웨이 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 게이트웨이 | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 플랫폼 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 플랫폼 | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 플랫폼 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |

-다음 [코드 기여자](https://github.com/langbot-app/LangBot/graphs/contributors) 및 커뮤니티의 다른 구성원들의 LangBot 기여에 감사드립니다:
+[→ 모든 통합 보기](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## 왜 LangBot인가?
+
+| 사용 사례 | LangBot 활용 방법 |
+|-----------|-------------------|
+| **고객 지원** | 지식 베이스를 활용하여 질문에 답변하는 AI 에이전트를 Slack/Discord/Telegram에 배포 |
+| **내부 도구** | n8n/Dify 워크플로우를 WeCom/DingTalk에 연결하여 비즈니스 프로세스 자동화 |
+| **커뮤니티 관리** | AI 기반 콘텐츠 필터링 및 상호작용으로 QQ/Discord 그룹 관리 |
+| **멀티 플랫폼** | 하나의 봇으로 모든 플랫폼 지원. 단일 대시보드에서 관리 |
+
+---
+
+## 라이브 데모
+
+**지금 체험:** https://demo.langbot.dev/
+- 이메일: `demo@langbot.app`
+- 비밀번호: `langbot123456`
+
+*참고: 공개 데모 환경입니다. 민감한 정보를 입력하지 마세요.*
+
+---
+
+## 커뮤니티
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Discord 커뮤니티](https://discord.gg/wdNEHETs87)
+
+---
+
+## Star 추이
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 기여자
+
+LangBot을 더 나은 프로젝트로 만들어 주신 모든 [기여자](https://github.com/langbot-app/LangBot/graphs/contributors)분들께 감사드립니다:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/README_RU.md
+++ b/README_RU.md
@@ -1,43 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>Быстро создавайте, отлаживайте и развертывайте IM-ботов с LangBot.</h3>
+<h3>Платформа производственного уровня для создания агентных IM-ботов.</h3>
+<h4>Быстро создавайте, отлаживайте и развертывайте ИИ-ботов в Slack, Discord, Telegram, WeChat и других платформах.</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / Русский / [Tiếng Việt](README_VI.md)
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / Русский / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/guide.html">Развертывание</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Плагин</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Отправить плагин</a>
+<a href="https://docs.langbot.app/en/insight/features.html">Возможности</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">Документация</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">Магазин плагинов</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Дорожная карта</a>

 </div>

 </p>

-## 📦 Начало работы
+---

-#### Быстрый старт
+## Что такое LangBot?

-Используйте `uvx` для запуска одной командой (требуется установка [uv](https://docs.astral.sh/uv/getting-started/installation/)):
+LangBot — это **платформа с открытым исходным кодом производственного уровня** для создания ИИ-ботов в мессенджерах. Она связывает большие языковые модели (LLM) с любой чат-платформой, позволяя создавать интеллектуальных агентов, которые могут вести диалоги, выполнять задачи и интегрироваться с вашими существующими рабочими процессами.
+
+### Ключевые возможности
+
+- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Универсальная поддержка IM-платформ** — Единая кодовая база для Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Готовность к продакшену** — Контроль доступа, ограничение скорости, фильтрация чувствительных слов, комплексный мониторинг и обработка исключений. Проверено в корпоративной среде.
+- **Экосистема плагинов** — Сотни плагинов, событийно-ориентированная архитектура, расширения компонентов и поддержка [протокола MCP](https://modelcontextprotocol.io/).
+- **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)
+
+---
+
+## Быстрый старт
+
+### ☁️ LangBot Cloud (Рекомендуется)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Без развёртывания, готово к использованию.
+
+### Запуск одной командой

 ```bash
 uvx langbot
 ```

-Посетите http://localhost:5300, чтобы начать использование.
+> Требуется [uv](https://docs.astral.sh/uv/getting-started/installation/). Откройте http://localhost:5300 — готово.

-#### Развертывание с Docker Compose
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-Посетите http://localhost:5300, чтобы начать использование.
-
-Подробная документация [Развертывание Docker](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### Развертывание одним кликом на BTPanel
-
-LangBot добавлен в BTPanel. Если у вас установлен BTPanel, вы можете использовать [документацию](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) для его использования.
-
-#### Облачное развертывание Zeabur
-
-Шаблон Zeabur, предоставленный сообществом.
+### Облачное развертывание одним кликом

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Облачное развертывание Railway
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### Другие методы развертывания
+**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)

-Используйте выпущенную версию напрямую для запуска, см. документацию [Ручное развертывание](https://docs.langbot.app/en/deploy/langbot/manual.html).
+---

-#### Развертывание Kubernetes
-
-См. документацию [Развертывание Kubernetes](./docker/README_K8S.md).
-
-## 😎 Оставайтесь в курсе
-
-Нажмите кнопки Star и Watch в правом верхнем углу репозитория, чтобы получать последние обновления.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Функции
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 Чат с LLM / Agent: Поддержка нескольких LLM, адаптация к групповым и личным чатам; Поддержка многораундовых разговоров, вызовов инструментов, мультимодальных возможностей и потоковой передачи. Встроенная реализация RAG (база знаний) и глубокая интеграция с [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io) 등의 LLMOps 플랫포트폼과 깊이 통합됩니다.
- 🤖 Многоплатформенная поддержка: В настоящее время поддерживает QQ, QQ Channel, WeCom, личный WeChat, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE и т.д.
- 🛠️ Высокая стабильность, богатство функций: Нативный контроль доступа, ограничение скорости, фильтрация чувствительных слов и т.д.; Простота в использовании, поддержка нескольких методов развертывания. Поддержка нескольких конфигураций конвейера, разные боты для разных сценариев.
- 🧩 Расширение плагинов, активное сообщество: Высокая стабильность, высокая безопасность уровня производства; Поддержка механизмов плагинов, управляемых событиями, расширения компонентов и т.д.; Интеграция протокола [MCP](https://modelcontextprotocol.io/) от Anthropic; В настоящее время сотни плагинов.
- 😻 Веб-интерфейс: Поддержка управления экземплярами LangBot через браузер. Нет необходимости вручную писать конфигурационные файлы.
-
-Для более подробных спецификаций обратитесь к [документации](https://docs.langbot.app/en/insight/features.html).
-
-Или посетите демонстрационную среду: https://demo.langbot.dev/
-  - Информация для входа: Email: `demo@langbot.app` Пароль: `langbot123456`
-  - Примечание: Только для демонстрации WebUI, пожалуйста, не вводите конфиденциальную информацию в общедоступной среде.
-
-### Платформы обмена сообщениями
+## Поддерживаемые платформы

 | Платформа | Статус | Примечания |
-| --- | --- | --- |
+|-----------|--------|------------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| Личный QQ | ✅ |  |
-| Официальный API QQ | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| Личный WeChat | ✅ |  |
-| KOOK | ✅ |  |
+| QQ | ✅ | Личный и официальный API |
+| WeCom | ✅ | Корпоративный WeChat, внешний CS, AI-бот |
+| WeChat | ✅ | Личный и официальный аккаунт |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
+| KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | Статус | Примечания |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Доступна для любой модели формата интерфейса OpenAI |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | Платформа ресурсов LLM и GPU |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | Платформа ресурсов LLM и GPU |
-| [接口 AI](https://jiekou.ai/) | ✅ | Платформа агрегации LLM |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | Платформа ресурсов LLM и GPU |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | Шлюз LLM (MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | Платформа LLMOps |
-| [Ollama](https://ollama.com/) | ✅ | Платформа локального запуска LLM |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Платформа локального запуска LLM |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | Шлюз интерфейса LLM (MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | Шлюз LLM (MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | Шлюз LLM (MaaS), платформа LLMOps |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | Шлюз LLM (MaaS), платформа LLMOps |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | Шлюз LLM (MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Поддержка доступа к инструментам через протокол MCP |
+## Поддерживаемые LLM и интеграции

-## 🤝 Вклад сообщества
+| Провайдер | Тип | Статус |
+|-----------|-----|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | Локальный LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | Локальный LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Протокол | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Шлюз | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Шлюз | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Шлюз | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Шлюз | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Шлюз | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Шлюз | ✅ |
+| [接口 AI](https://jiekou.ai/) | Шлюз | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Платформа GPU | ✅ |
+| [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 | ✅ |

-Спасибо следующим [контрибьюторам кода](https://github.com/langbot-app/LangBot/graphs/contributors) и другим членам сообщества за их вклад в LangBot:
+[→ Смотреть все интеграции](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## Почему LangBot?
+
+| Сценарий использования | Как помогает LangBot |
+|------------------------|----------------------|
+| **Поддержка клиентов** | Разверните ИИ-агентов в Slack/Discord/Telegram, которые отвечают на вопросы, используя вашу базу знаний |
+| **Внутренние инструменты** | Подключите рабочие процессы n8n/Dify к WeCom/DingTalk для автоматизации бизнес-процессов |
+| **Управление сообществом** | Модерируйте группы QQ/Discord с помощью ИИ-фильтрации контента и взаимодействия |
+| **Мультиплатформенное присутствие** | Один бот — все платформы. Управляйте из единой панели |
+
+---
+
+## Демо
+
+**Попробуйте прямо сейчас:** https://demo.langbot.dev/
+- Email: `demo@langbot.app`
+- Пароль: `langbot123456`
+
+*Примечание: Публичная демо-среда. Не вводите конфиденциальную информацию.*
+
+---
+
+## Сообщество
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Сообщество Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## История Stars
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Участники
+
+Спасибо всем [участникам](https://github.com/langbot-app/LangBot/graphs/contributors), которые помогли сделать LangBot лучше:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/README_TW.md
+++ b/README_TW.md
@@ -1,43 +1,70 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

-<div align="center"><a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
+<div align="center">

-<h3>使用 LangBot 快速建構、除錯和部署 IM 機器人。</h3>
+<a href="https://hellogithub.com/repository/langbot-app/LangBot" target="_blank"><img src="https://abroad.hellogithub.com/v1/widgets/recommend.svg?rid=5ce8ae2aa4f74316bf393b57b952433c&claim_uid=gtmc6YWjMZkT21R" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-[English](README_EN.md) / [简体中文](README.md) / 繁體中文 / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
+<h3>生產級 AI 即時通訊機器人開發平台。</h3>
+<h4>快速建構、除錯和部署 AI 機器人到微信、QQ、飛書、Slack、Discord、Telegram 等平台。</h4>
+
+[English](README.md) / [简体中文](README_CN.md) / 繁體中文 / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 [![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)

-<a href="https://langbot.app">主頁</a> ｜
-<a href="https://docs.langbot.app/zh/insight/guide.html">部署文件</a> ｜
-<a href="https://docs.langbot.app/zh/plugin/plugin-intro.html">外掛介紹</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">提交外掛</a>
+<a href="https://langbot.app">官網</a> ｜
+<a href="https://docs.langbot.app/zh/insight/features.html">特性</a> ｜
+<a href="https://docs.langbot.app/zh/insight/guide.html">文件</a> ｜
+<a href="https://docs.langbot.app/zh/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">外掛市場</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">路線圖</a>

 </div>

 </p>

-## 📦 開始使用
+---

-#### 快速部署
+## 什麼是 LangBot？

-使用 `uvx` 一鍵啟動（需要先安裝 [uv](https://docs.astral.sh/uv/getting-started/installation/) ）：
+LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時通訊機器人。它將大語言模型（LLM）連接到各種聊天平台，幫助你創建能夠對話、執行任務、並整合到現有工作流程中的智能 Agent。
+
+### 核心能力
+
+- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **全平台支援** — 一套程式碼，覆蓋 QQ、微信、企業微信、飛書、釘釘、Discord、Telegram、Slack、LINE、KOOK 等平台。
+- **生產就緒** — 存取控制、限速、敏感詞過濾、全面監控與異常處理，已被多家企業採用。
+- **外掛生態** — 數百個外掛，事件驅動架構，組件擴展，適配 [MCP 協議](https://modelcontextprotocol.io/)。
+- **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)
+
+---
+
+## 快速開始
+
+### ☁️ LangBot Cloud（推薦）
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，開箱即用。
+
+### 一鍵啟動

 ```bash
 uvx langbot
 ```

-訪問 http://localhost:5300 即可開始使用。
+> 需要安裝 [uv](https://docs.astral.sh/uv/getting-started/installation/)。訪問 http://localhost:5300 即可使用。

-#### Docker Compose 部署
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,103 +72,63 @@ cd LangBot/docker
 docker compose up -d
 ```

-訪問 http://localhost:5300 即可開始使用。
-
-詳細文件[Docker 部署](https://docs.langbot.app/zh/deploy/langbot/docker.html)。
-
-#### 寶塔面板部署
-
-已上架寶塔面板，若您已安裝寶塔面板，可以根據[文件](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html)使用。
-
-#### Zeabur 雲端部署
-
-社群貢獻的 Zeabur 模板。
+### 一鍵雲端部署

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
-
-#### Railway 雲端部署
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### 手動部署
+**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)

-直接使用發行版運行，查看文件[手動部署](https://docs.langbot.app/zh/deploy/langbot/manual.html)。
+---

-#### Kubernetes 部署
-
-參考 [Kubernetes 部署](./docker/README_K8S.md) 文件。
-
-## 😎 保持更新
-
-點擊倉庫右上角 Star 和 Watch 按鈕，獲取最新動態。
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ 特性
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 大模型對話、Agent：支援多種大模型，適配群聊和私聊；具有多輪對話、工具調用、多模態、流式輸出能力，自帶 RAG（知識庫）實現，並深度適配 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io) 等 LLMOps 平台。
- 🤖 多平台支援：目前支援 QQ、QQ頻道、企業微信、個人微信、飛書、Discord、Telegram、KOOK、Slack、LINE 等平台。
- 🛠️ 高穩定性、功能完備：原生支援訪問控制、限速、敏感詞過濾等機制；配置簡單，支援多種部署方式。支援多流水線配置，不同機器人用於不同應用場景。
- 🧩 外掛擴展、活躍社群：高穩定性、高安全性的生產級外掛系統；支援事件驅動、組件擴展等外掛機制；適配 Anthropic [MCP 協議](https://modelcontextprotocol.io/)；目前已有數百個外掛。
- 😻 Web 管理面板：支援通過瀏覽器管理 LangBot 實例，不再需要手動編寫配置文件。
-
-詳細規格特性請訪問[文件](https://docs.langbot.app/zh/insight/features.html)。
-
-或訪問 demo 環境：https://demo.langbot.dev/  
-  - 登入資訊：郵箱：`demo@langbot.app` 密碼：`langbot123456`
-  - 注意：僅展示 WebUI 效果，公開環境，請不要在其中填入您的任何敏感資訊。
-
-### 訊息平台
+## 支援的平台

 | 平台 | 狀態 | 備註 |
-| --- | --- | --- |
+|------|------|------|
+| QQ | ✅ | 個人號、官方機器人（頻道、私聊、群聊） |
+| 微信 | ✅ | 個人微信、微信公眾號 |
+| 企業微信 | ✅ | 應用訊息、對外客服、智能機器人 |
+| 飛書 | ✅ |  |
+| 釘釘 | ✅ |  |
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| QQ 個人號 | ✅ | QQ 個人號私聊、群聊 |
-| QQ 官方機器人 | ✅ | QQ 官方機器人，支援頻道、私聊、群聊 |
-| 微信 | ✅ |  |
-| 企微對外客服 | ✅ |  |
-| 企微智能機器人 | ✅ |  |
-| 微信公眾號 | ✅ |  |
 | KOOK | ✅ |  |
-| Lark | ✅ |  |
-| DingTalk | ✅ |  |
+| Satori | ✅ |  |

-### 大模型能力
+---

-| 模型 | 狀態 | 備註 |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | 可接入任何 OpenAI 介面格式模型 |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [智譜AI](https://open.bigmodel.cn/) | ✅ |  |
-| [勝算雲](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | 大模型和 GPU 資源平台 |
-| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | 大模型和 GPU 資源平台 |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | 大模型和 GPU 資源平台 |
-| [接口 AI](https://jiekou.ai/) | ✅ | 大模型聚合平台，專注全球大模型接入 |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | 大模型聚合平台 |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | LLMOps 平台 |
-| [Ollama](https://ollama.com/) | ✅ | 本地大模型運行平台 |
-| [LMStudio](https://lmstudio.ai/) | ✅ | 本地大模型運行平台 |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | 大模型介面聚合平台 |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | 大模型聚合平台 |
-| [阿里雲百煉](https://bailian.console.aliyun.com/) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | 大模型聚合平台, LLMOps 平台 |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | 大模型聚合平台 |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | 支援通過 MCP 協議獲取工具 |
+## 支援的大模型與整合

-### TTS
+| 提供商 | 類型 | 狀態 |
+|--------|------|------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [智譜AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | 本地 LLM | ✅ |
+| [LM Studio](https://lmstudio.ai/) | 本地 LLM | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | 協議 | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | 聚合平台 | ✅ |
+| [阿里雲百煉](https://bailian.console.aliyun.com/) | 聚合平台 | ✅ |
+| [火山方舟](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | 聚合平台 | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | 聚合平台 | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | 聚合平台 | ✅ |
+| [勝算雲](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 平台 | ✅ |
+| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
+| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
+
+### TTS（語音合成）

 | 平台/模型 | 備註 |
-| --- | --- |
+|-----------|------|
 | [FishAudio](https://fish.audio/zh-CN/discovery/) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
 | [海豚 AI](https://www.ttson.cn/?source=thelazy) | [外掛](https://github.com/the-lazy-me/NewChatVoice) |
 | [AzureTTS](https://portal.azure.com/) | [外掛](https://github.com/Ingnaryk/LangBot_AzureTTS) |
@@ -149,13 +136,54 @@ docker compose up -d
 ### 文生圖

 | 平台/模型 | 備註 |
-| --- | --- |
-| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin)
+|-----------|------|
+| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |

-## 😘 社群貢獻
+[→ 查看完整整合列表](https://docs.langbot.app/zh/insight/features.html)

-感謝以下[程式碼貢獻者](https://github.com/langbot-app/LangBot/graphs/contributors)和社群裡其他成員對 LangBot 的貢獻：
+---
+
+## 為什麼選擇 LangBot？
+
+| 使用場景 | LangBot 如何幫助 |
+|----------|------------------|
+| **客戶服務** | 將 AI Agent 部署到微信/企微/釘釘/飛書，基於知識庫自動回答使用者問題 |
+| **內部工具** | 將 n8n/Dify 工作流接入企微/釘釘，實現業務流程自動化 |
+| **社群運營** | 在 QQ/Discord 群中使用 AI 驅動的內容審核與智能互動 |
+| **多平台觸達** | 一個機器人，覆蓋所有平台。透過統一面板集中管理 |
+
+---
+
+## 線上演示
+
+**立即體驗：** https://demo.langbot.dev/
+- 信箱：`demo@langbot.app`
+- 密碼：`langbot123456`
+
+*注意：公開演示環境，請不要在其中填入任何敏感資訊。*
+
+---
+
+## 社群
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-966235608-blue)](https://qm.qq.com/q/JLi38whHum)
+
+- [Discord 社群](https://discord.gg/wdNEHETs87)
+- [QQ 社群群](https://qm.qq.com/q/JLi38whHum)
+
+---
+
+## Star 趨勢
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## 貢獻者
+
+感謝所有[貢獻者](https://github.com/langbot-app/LangBot/graphs/contributors)對 LangBot 的幫助：

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
-</a> 
+</a>
--- a/README_VI.md
+++ b/README_VI.md
@@ -1,43 +1,68 @@
 <p align="center">
 <a href="https://langbot.app">
-<img width="130" src="https://docs.langbot.app/langbot-logo.png" alt="LangBot"/>
+<img width="130" src="res/logo-blue.png" alt="LangBot"/>
 </a>

 <div align="center">

 <a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>

-<h3>Xây dựng, gỡ lỗi và triển khai bot IM nhanh chóng với LangBot.</h3>
+<h3>Nền tảng cấp sản xuất để xây dựng bot IM với AI agent.</h3>
+<h4>Xây dựng, gỡ lỗi và triển khai bot AI nhanh chóng trên Slack, Discord, Telegram, WeChat và nhiều nền tảng khác.</h4>

-[English](README_EN.md) / [简体中文](README.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / Tiếng Việt
+[English](README.md) / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / Tiếng Việt

 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
+[![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/guide.html">Triển khai</a> ｜
-<a href="https://docs.langbot.app/en/plugin/plugin-intro.html">Plugin</a> ｜
-<a href="https://github.com/langbot-app/LangBot/issues/new?assignees=&labels=%E7%8B%AC%E7%AB%8B%E6%8F%92%E4%BB%B6&projects=&template=submit-plugin.yml&title=%5BPlugin%5D%3A+%E8%AF%B7%E6%B1%82%E7%99%BB%E8%AE%B0%E6%96%B0%E6%8F%92%E4%BB%B6">Gửi Plugin</a>
+<a href="https://docs.langbot.app/en/insight/features.html">Tính năng</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">Tài liệu</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
+<a href="https://space.langbot.app">Chợ Plugin</a> ｜
+<a href="https://langbot.featurebase.app/roadmap">Lộ trình</a>

 </div>

 </p>

-## 📦 Bắt đầu
+---

-#### Khởi động Nhanh
+## LangBot là gì?

-Sử dụng `uvx` để khởi động bằng một lệnh (cần cài đặt [uv](https://docs.astral.sh/uv/getting-started/installation/)):
+LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để xây dựng bot nhắn tin tức thời được hỗ trợ bởi AI. Nó kết nối các Mô hình Ngôn ngữ Lớn (LLM) với bất kỳ nền tảng chat nào, cho phép bạn tạo các agent thông minh có thể trò chuyện, thực hiện tác vụ và tích hợp với quy trình làm việc hiện có của bạn.
+
+### Khả năng chính
+
+- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Hỗ trợ đa nền tảng IM** — Một mã nguồn cho Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
+- **Sẵn sàng cho sản xuất** — Kiểm soát truy cập, giới hạn tốc độ, lọc từ nhạy cảm, giám sát toàn diện và xử lý ngoại lệ. Được doanh nghiệp tin dùng.
+- **Hệ sinh thái Plugin** — Hàng trăm plugin, kiến trúc hướng sự kiện, mở rộng thành phần, và hỗ trợ [giao thức MCP](https://modelcontextprotocol.io/).
+- **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)
+
+---
+
+## Bắt đầu nhanh
+
+### ☁️ LangBot Cloud (Khuyên dùng)
+
+**[LangBot Cloud](https://space.langbot.app/cloud)** — Không cần triển khai, sẵn sàng sử dụng.
+
+### Khởi chạy một dòng

 ```bash
 uvx langbot
 ```

-Truy cập http://localhost:5300 để bắt đầu sử dụng.
+> Yêu cầu [uv](https://docs.astral.sh/uv/getting-started/installation/). Truy cập http://localhost:5300 — xong.

-#### Triển khai Docker Compose
+### Docker Compose

 ```bash
 git clone https://github.com/langbot-app/LangBot
@@ -45,102 +70,101 @@ cd LangBot/docker
 docker compose up -d
 ```

-Truy cập http://localhost:5300 để bắt đầu sử dụng.
-
-Tài liệu chi tiết [Triển khai Docker](https://docs.langbot.app/en/deploy/langbot/docker.html).
-
-#### Triển khai Một cú nhấp chuột trên BTPanel
-
-LangBot đã được liệt kê trên BTPanel. Nếu bạn đã cài đặt BTPanel, bạn có thể sử dụng [tài liệu](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) để sử dụng nó.
-
-#### Triển khai Cloud Zeabur
-
-Mẫu Zeabur được đóng góp bởi cộng đồng.
+### Triển khai đám mây một cú nhấp

 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
-
-#### Triển khai Cloud Railway
-
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)

-#### Các Phương pháp Triển khai Khác
+**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)

-Sử dụng trực tiếp phiên bản phát hành để chạy, xem tài liệu [Triển khai Thủ công](https://docs.langbot.app/en/deploy/langbot/manual.html).
+---

-#### Triển khai Kubernetes
-
-Tham khảo tài liệu [Triển khai Kubernetes](./docker/README_K8S.md).
-
-## 😎 Cập nhật Mới nhất
-
-Nhấp vào các nút Star và Watch ở góc trên bên phải của kho lưu trữ để nhận các bản cập nhật mới nhất.
-
-![star gif](https://docs.langbot.app/star.gif)
-
-## ✨ Tính năng
-
-<img width="500" src="https://docs.langbot.app/ui/bot-page-en-rounded.png" />
-
-
- 💬 Chat với LLM / Agent: Hỗ trợ nhiều LLM, thích ứng với chat nhóm và chat riêng tư; Hỗ trợ các cuộc trò chuyện nhiều vòng, gọi công cụ, khả năng đa phương thức và đầu ra streaming. Triển khai RAG (cơ sở kiến thức) tích hợp sẵn và tích hợp sâu với [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io) v.v. LLMOps platforms.
- 🤖 Hỗ trợ Đa nền tảng: Hiện hỗ trợ QQ, QQ Channel, WeCom, WeChat cá nhân, Lark, DingTalk, Discord, Telegram, KOOK, Slack, LINE, v.v.
- 🛠️ Độ ổn định Cao, Tính năng Phong phú: Kiểm soát truy cập gốc, giới hạn tốc độ, lọc từ nhạy cảm, v.v.; Dễ sử dụng, hỗ trợ nhiều phương pháp triển khai. Hỗ trợ nhiều cấu hình pipeline, các bot khác nhau cho các kịch bản khác nhau.
- 🧩 Mở rộng Plugin, Cộng đồng Hoạt động: Hỗ trợ các cơ chế plugin hướng sự kiện, mở rộng thành phần, v.v.; Tích hợp giao thức [MCP](https://modelcontextprotocol.io/) của Anthropic; Hiện có hàng trăng plugin.
- 😻 Giao diện Web: Hỗ trợ quản lý các phiên bản LangBot thông qua trình duyệt. Không cần viết tệp cấu hình thủ công.
-
-Để biết thêm thông số kỹ thuật chi tiết, vui lòng tham khảo [tài liệu](https://docs.langbot.app/en/insight/features.html).
-
-Hoặc truy cập môi trường demo: https://demo.langbot.dev/
-  - Thông tin đăng nhập: Email: `demo@langbot.app` Mật khẩu: `langbot123456`
-  - Lưu ý: Chỉ dành cho demo WebUI, vui lòng không nhập bất kỳ thông tin nhạy cảm nào trong môi trường công cộng.
-
-### Nền tảng Nhắn tin
+## Nền tảng được hỗ trợ

 | Nền tảng | Trạng thái | Ghi chú |
-| --- | --- | --- |
+|----------|--------|-------|
 | Discord | ✅ |  |
 | Telegram | ✅ |  |
 | Slack | ✅ |  |
 | LINE | ✅ |  |
-| QQ Cá nhân | ✅ |  |
-| QQ API Chính thức | ✅ |  |
-| WeCom | ✅ |  |
-| WeComCS | ✅ |  |
-| WeCom AI Bot | ✅ |  |
-| WeChat Cá nhân | ✅ |  |
-| KOOK | ✅ |  |
+| QQ | ✅ | Cá nhân & API chính thức |
+| WeCom | ✅ | WeChat doanh nghiệp, CS bên ngoài, AI Bot |
+| WeChat | ✅ | Cá nhân & Tài khoản công khai |
 | Lark | ✅ |  |
 | DingTalk | ✅ |  |
+| KOOK | ✅ |  |
+| Satori | ✅ |  |

-### LLMs
+---

-| LLM | Trạng thái | Ghi chú |
-| --- | --- | --- |
-| [OpenAI](https://platform.openai.com/) | ✅ | Có sẵn cho bất kỳ mô hình định dạng giao diện OpenAI nào |
-| [DeepSeek](https://www.deepseek.com/) | ✅ |  |
-| [Moonshot](https://www.moonshot.cn/) | ✅ |  |
-| [Anthropic](https://www.anthropic.com/) | ✅ |  |
-| [xAI](https://x.ai/) | ✅ |  |
-| [Zhipu AI](https://open.bigmodel.cn/) | ✅ |  |
-| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | ✅ | Nền tảng tài nguyên LLM và GPU |
-| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | ✅ | Nền tảng tài nguyên LLM và GPU |
-| [接口 AI](https://jiekou.ai/) | ✅ | Nền tảng tổng hợp LLM |
-| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | ✅ | Nền tảng tài nguyên LLM và GPU |
-| [302.AI](https://share.302.ai/SuTG99) | ✅ | Cổng LLM (MaaS) |
-| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | ✅ | |
-| [Dify](https://dify.ai) | ✅ | Nền tảng LLMOps |
-| [Ollama](https://ollama.com/) | ✅ | Nền tảng chạy LLM cục bộ |
-| [LMStudio](https://lmstudio.ai/) | ✅ | Nền tảng chạy LLM cục bộ |
-| [GiteeAI](https://ai.gitee.com/) | ✅ | Cổng giao diện LLM (MaaS) |
-| [SiliconFlow](https://siliconflow.cn/) | ✅ | Cổng LLM (MaaS) |
-| [Aliyun Bailian](https://bailian.console.aliyun.com/) | ✅ | Cổng LLM (MaaS), nền tảng LLMOps |
-| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | ✅ | Cổng LLM (MaaS), nền tảng LLMOps |
-| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | ✅ | Cổng LLM (MaaS) |
-| [MCP](https://modelcontextprotocol.io/) | ✅ | Hỗ trợ truy cập công cụ qua giao thức MCP |
+## LLM và tích hợp được hỗ trợ

-## 🤝 Đóng góp Cộng đồng
+| Nhà cung cấp | Loại | Trạng thái |
+|----------|------|--------|
+| [OpenAI](https://platform.openai.com/) | LLM | ✅ |
+| [Anthropic](https://www.anthropic.com/) | LLM | ✅ |
+| [DeepSeek](https://www.deepseek.com/) | LLM | ✅ |
+| [Google Gemini](https://aistudio.google.com/prompts/new_chat) | LLM | ✅ |
+| [xAI](https://x.ai/) | LLM | ✅ |
+| [Moonshot](https://www.moonshot.cn/) | LLM | ✅ |
+| [Zhipu AI](https://open.bigmodel.cn/) | LLM | ✅ |
+| [Ollama](https://ollama.com/) | LLM cục bộ | ✅ |
+| [LM Studio](https://lmstudio.ai/) | LLM cục bộ | ✅ |
+| [Dify](https://dify.ai) | LLMOps | ✅ |
+| [MCP](https://modelcontextprotocol.io/) | Giao thức | ✅ |
+| [SiliconFlow](https://siliconflow.cn/) | Cổng | ✅ |
+| [Aliyun Bailian](https://bailian.console.aliyun.com/) | Cổng | ✅ |
+| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Cổng | ✅ |
+| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Cổng | ✅ |
+| [GiteeAI](https://ai.gitee.com/) | Cổng | ✅ |
+| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Nền tảng GPU | ✅ |
+| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Nền tảng GPU | ✅ |
+| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Nền tảng GPU | ✅ |
+| [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
+| [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |

-Cảm ơn các [người đóng góp mã](https://github.com/langbot-app/LangBot/graphs/contributors) sau đây và các thành viên khác trong cộng đồng vì những đóng góp của họ cho LangBot:
+[→ Xem tất cả tích hợp](https://docs.langbot.app/en/insight/features.html)
+
+---
+
+## Tại sao chọn LangBot?
+
+| Trường hợp sử dụng | LangBot giúp như thế nào |
+|----------|-------------------|
+| **Hỗ trợ khách hàng** | Triển khai agent AI trên Slack/Discord/Telegram để trả lời câu hỏi bằng cơ sở kiến thức của bạn |
+| **Công cụ nội bộ** | Kết nối quy trình n8n/Dify với WeCom/DingTalk để tự động hóa quy trình kinh doanh |
+| **Quản lý cộng đồng** | Quản lý nhóm QQ/Discord với tính năng lọc nội dung và tương tác được hỗ trợ bởi AI |
+| **Đa nền tảng** | Một bot, tất cả nền tảng. Quản lý từ một bảng điều khiển duy nhất |
+
+---
+
+## Demo trực tuyến
+
+**Thử ngay:** https://demo.langbot.dev/
+- Email: `demo@langbot.app`
+- Mật khẩu: `langbot123456`
+
+*Lưu ý: Môi trường demo công khai. Không nhập thông tin nhạy cảm.*
+
+---
+
+## Cộng đồng
+
+[![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&label=Discord)](https://discord.gg/wdNEHETs87)
+
+- [Cộng đồng Discord](https://discord.gg/wdNEHETs87)
+
+---
+
+## Lịch sử Star
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langbot-app/LangBot&type=Date)](https://star-history.com/#langbot-app/LangBot&Date)
+
+---
+
+## Người đóng góp
+
+Cảm ơn tất cả [người đóng góp](https://github.com/langbot-app/LangBot/graphs/contributors) đã giúp LangBot trở nên tốt hơn:

 <a href="https://github.com/langbot-app/LangBot/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=langbot-app/LangBot" />
--- a/docker/docker-compose.yaml
+++ b/docker/docker-compose.yaml
@@ -14,7 +14,7 @@ services:
    restart: on-failure
    environment:
      - TZ=Asia/Shanghai
-    command: ["uv", "run", "-m", "langbot_plugin.cli.__init__", "rt"]
+    command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "rt"]
    networks:
      - langbot_network

--- a/docs/service-api-openapi.json
+++ b/docs/service-api-openapi.json
@@ -9,7 +9,7 @@
      "url": "https://langbot.app"
    },
    "license": {
-      "name": "AGPL-3.0",
+      "name": "Apache-2.0",
      "url": "https://github.com/langbot-app/LangBot/blob/master/LICENSE"
    }
  },
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "langbot"
-version = "4.6.5"
-description = "Easy-to-use global IM bot platform designed for LLM era"
+version = "4.8.7"
+description = "Production-grade platform for building agentic IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]
 requires-python = ">=3.11,<4.0"
@@ -17,13 +17,13 @@ dependencies = [
    "certifi>=2025.4.26",
    "colorlog~=6.6.0",
    "cryptography>=44.0.3",
-    "dashscope>=1.23.2",
+    "dashscope>=1.25.10",
    "dingtalk-stream>=0.24.0",
    "discord-py>=2.5.2",
    "pynacl>=1.5.0", # Required for Discord voice support
    "gewechat-client>=0.1.5",
    "lark-oapi>=1.4.15",
-    "mcp>=1.8.1",
+    "mcp>=1.25.0",
    "nakuru-project-idk>=0.0.2.1",
    "ollama>=0.4.8",
    "openai>1.0.0",
@@ -63,14 +63,15 @@ dependencies = [
    "langchain-text-splitters>=0.0.1",
    "chromadb>=0.4.24",
    "qdrant-client (>=1.15.1,<2.0.0)",
-    "pyseekdb>=0.1.0",
-    "langbot-plugin==0.2.4",
+    "pyseekdb==1.0.0b7",
+    "langbot-plugin==0.2.7",
    "asyncpg>=0.30.0",
    "line-bot-sdk>=3.19.0",
    "tboxsdk>=0.0.10",
    "boto3>=1.35.0",
    "pymilvus>=2.6.4",
    "pgvector>=0.4.1",
+    "botocore>=1.42.39",
 ]
 keywords = [
    "bot",
--- a/res/logo-blue.png
+++ b/res/logo-blue.png
--- a/src/langbot/init.py
+++ b/src/langbot/init.py
@@ -1,3 +1,3 @@
-"""LangBot - Easy-to-use global IM bot platform designed for LLM era"""
+"""LangBot - Production-grade platform for building agentic IM bots"""

-__version__ = '4.6.5'
+__version__ = '4.8.7'
--- a/src/langbot/libs/dingtalk_api/api.py
+++ b/src/langbot/libs/dingtalk_api/api.py
@@ -347,10 +347,15 @@ class DingTalkClient:
            raise Exception(f'failed to send proactive massage to group: {traceback.format_exc()}')

    async def create_and_card(
-        self, temp_card_id: str, incoming_message: dingtalk_stream.ChatbotMessage, quote_origin: bool = False
+        self,
+        temp_card_id: str,
+        incoming_message: dingtalk_stream.ChatbotMessage,
+        quote_origin: bool = False,
+        card_auto_layout: bool = False,
    ):
-        content_key = 'content'
-        card_data = {content_key: ''}
+        card_data = {}
+        card_data['config'] = json.dumps({'autoLayout': card_auto_layout})
+        card_data['content'] = ''

        card_instance = dingtalk_stream.AICardReplier(self.client, incoming_message)
        # print(card_instance)
--- a/src/langbot/libs/official_account_api/api.py
+++ b/src/langbot/libs/official_account_api/api.py
@@ -23,12 +23,21 @@ xml_template = """


 class OAClient:
-    def __init__(self, token: str, EncodingAESKey: str, AppID: str, Appsecret: str, logger: None, unified_mode: bool = False):
+    def __init__(
+        self,
+        token: str,
+        EncodingAESKey: str,
+        AppID: str,
+        Appsecret: str,
+        logger: None,
+        unified_mode: bool = False,
+        api_base_url: str = 'https://api.weixin.qq.com',
+    ):
        self.token = token
        self.aes = EncodingAESKey
        self.appid = AppID
        self.appsecret = Appsecret
-        self.base_url = 'https://api.weixin.qq.com'
+        self.base_url = api_base_url
        self.access_token = ''
        self.unified_mode = unified_mode
        self.app = Quart(__name__)
@@ -208,12 +217,13 @@ class OAClientForLongerResponse:
        LoadingMessage: str,
        logger: None,
        unified_mode: bool = False,
+        api_base_url: str = 'https://api.weixin.qq.com',
    ):
        self.token = token
        self.aes = EncodingAESKey
        self.appid = AppID
        self.appsecret = Appsecret
-        self.base_url = 'https://api.weixin.qq.com'
+        self.base_url = api_base_url
        self.access_token = ''
        self.unified_mode = unified_mode
        self.app = Quart(__name__)
--- a/src/langbot/libs/qq_official_api/api.py
+++ b/src/langbot/libs/qq_official_api/api.py
@@ -85,7 +85,6 @@ class QQOfficialClient:
            req: Quart Request 对象
        """
        try:
-            
            body = await req.get_data()

            print(f'[QQ Official] Received request, body length: {len(body)}')
@@ -96,7 +95,6 @@ class QQOfficialClient:

            payload = json.loads(body)

-            
            if payload.get('op') == 13:
                validation_data = payload.get('d')
                if not validation_data:
@@ -276,21 +274,21 @@ class QQOfficialClient:
        seed = bot_secret
        while len(seed) < target_size:
            seed *= 2
-        return seed[:target_size].encode("utf-8")
+        return seed[:target_size].encode('utf-8')

    async def verify(self, validation_payload: dict):
        seed = await self.repeat_seed(self.secret)
        private_key = ed25519.Ed25519PrivateKey.from_private_bytes(seed)

-        event_ts = validation_payload.get("event_ts", "")
-        plain_token = validation_payload.get("plain_token", "")
+        event_ts = validation_payload.get('event_ts', '')
+        plain_token = validation_payload.get('plain_token', '')
        msg = event_ts + plain_token

        # sign
        signature = private_key.sign(msg.encode()).hex()

        response = {
-            "plain_token": plain_token,
-            "signature": signature,
+            'plain_token': plain_token,
+            'signature': signature,
        }
        return response
--- a/src/langbot/libs/wechatpad_api/util/http_util.py
+++ b/src/langbot/libs/wechatpad_api/util/http_util.py
@@ -1,5 +1,5 @@
 import requests
-import aiohttp
+from langbot.pkg.utils import httpclient


 def post_json(base_url, token, data=None):
@@ -63,16 +63,16 @@ async def async_request(
    """
    headers = {'Content-Type': 'application/json'}
    url = f'{base_url}?key={token_key}'
-    async with aiohttp.ClientSession() as session:
-        async with session.request(
-            method=method, url=url, params=params, headers=headers, data=data, json=json
-        ) as response:
-            response.raise_for_status()  # 如果状态码不是200，抛出异常
-            result = await response.json()
-            # print(result)
-            return result
-            # if result.get('Code') == 200:
-            #
-            #     return await result
-            # else:
-            #     raise RuntimeError("请求失败",response.text)
+    session = httpclient.get_session()
+    async with session.request(
+        method=method, url=url, params=params, headers=headers, data=data, json=json
+    ) as response:
+        response.raise_for_status()  # 如果状态码不是200，抛出异常
+        result = await response.json()
+        # print(result)
+        return result
+        # if result.get('Code') == 200:
+        #
+        #     return await result
+        # else:
+        #     raise RuntimeError("请求失败",response.text)
--- a/src/langbot/libs/wecom_ai_bot_api/wecombotevent.py
+++ b/src/langbot/libs/wecom_ai_bot_api/wecombotevent.py
@@ -36,7 +36,12 @@ class WecomBotEvent(dict):
        """
        用户名称
        """
-        return self.get('username', '') or self.get('from', {}).get('alias', '') or self.get('from', {}).get('name', '') or self.userid
+        return (
+            self.get('username', '')
+            or self.get('from', {}).get('alias', '')
+            or self.get('from', {}).get('name', '')
+            or self.userid
+        )

    @property
    def chatname(self) -> str:
@@ -121,7 +126,7 @@ class WecomBotEvent(dict):
        消息id
        """
        return self.get('msgid', '')
-    
+
    @property
    def ai_bot_id(self) -> str:
        """
--- a/src/langbot/libs/wecom_api/api.py
+++ b/src/langbot/libs/wecom_api/api.py
@@ -22,13 +22,14 @@ class WecomClient:
        contacts_secret: str,
        logger: None,
        unified_mode: bool = False,
+        api_base_url: str = 'https://qyapi.weixin.qq.com/cgi-bin',
    ):
        self.corpid = corpid
        self.secret = secret
        self.access_token_for_contacts = ''
        self.token = token
        self.aes = EncodingAESKey
-        self.base_url = 'https://qyapi.weixin.qq.com/cgi-bin'
+        self.base_url = api_base_url
        self.access_token = ''
        self.secret_for_contacts = contacts_secret
        self.logger = logger
@@ -56,7 +57,7 @@ class WecomClient:
        return bool(self.access_token_for_contacts and self.access_token_for_contacts.strip())

    async def get_access_token(self, secret):
-        url = f'https://qyapi.weixin.qq.com/cgi-bin/gettoken?corpid={self.corpid}&corpsecret={secret}'
+        url = f'{self.base_url}/gettoken?corpid={self.corpid}&corpsecret={secret}'
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            data = response.json()
@@ -196,7 +197,7 @@ class WecomClient:
            self.access_token = await self.get_access_token(self.secret)

        url = self.base_url + '/message/send?access_token=' + self.access_token
-        async with httpx.AsyncClient() as client:
+        async with httpx.AsyncClient(timeout=None) as client:
            params = {
                'touser': user_id,
                'msgtype': 'text',
--- a/src/langbot/libs/wecom_customer_service_api/api.py
+++ b/src/langbot/libs/wecom_customer_service_api/api.py
@@ -13,13 +13,22 @@ import aiofiles


 class WecomCSClient:
-    def __init__(self, corpid: str, secret: str, token: str, EncodingAESKey: str, logger: None, unified_mode: bool = False):
+    def __init__(
+        self,
+        corpid: str,
+        secret: str,
+        token: str,
+        EncodingAESKey: str,
+        logger: None,
+        unified_mode: bool = False,
+        api_base_url: str = 'https://qyapi.weixin.qq.com/cgi-bin',
+    ):
        self.corpid = corpid
        self.secret = secret
        self.access_token_for_contacts = ''
        self.token = token
        self.aes = EncodingAESKey
-        self.base_url = 'https://qyapi.weixin.qq.com/cgi-bin'
+        self.base_url = api_base_url
        self.access_token = ''
        self.logger = logger
        self.unified_mode = unified_mode
@@ -66,7 +75,7 @@ class WecomCSClient:
        return bool(self.access_token_for_contacts and self.access_token_for_contacts.strip())

    async def get_access_token(self, secret):
-        url = f'https://qyapi.weixin.qq.com/cgi-bin/gettoken?corpid={self.corpid}&corpsecret={secret}'
+        url = f'{self.base_url}/gettoken?corpid={self.corpid}&corpsecret={secret}'
        async with httpx.AsyncClient() as client:
            response = await client.get(url)
            data = response.json()
@@ -172,7 +181,7 @@ class WecomCSClient:
        if not await self.check_access_token():
            self.access_token = await self.get_access_token(self.secret)

-        url = f'https://qyapi.weixin.qq.com/cgi-bin/kf/send_msg?access_token={self.access_token}'
+        url = f'{self.base_url}/kf/send_msg?access_token={self.access_token}'

        payload = {
            'touser': external_userid,
--- a/src/langbot/pkg/api/http/controller/groups/monitoring.py
+++ b/src/langbot/pkg/api/http/controller/groups/monitoring.py
@@ -0,0 +1,488 @@
+from __future__ import annotations
+
+import datetime
+import quart
+
+from .. import group
+
+
+def parse_iso_datetime(datetime_str: str | None) -> datetime.datetime | None:
+    """Parse ISO 8601 datetime string, handling 'Z' suffix for UTC timezone"""
+    if not datetime_str:
+        return None
+    # Replace 'Z' with '+00:00' for Python 3.10 compatibility
+    if datetime_str.endswith('Z'):
+        datetime_str = datetime_str[:-1] + '+00:00'
+    dt = datetime.datetime.fromisoformat(datetime_str)
+    # Convert to UTC and remove timezone info to match database storage (which stores UTC as naive datetime)
+    if dt.tzinfo is not None:
+        # Convert to UTC and remove timezone info
+        dt = dt.astimezone(datetime.timezone.utc).replace(tzinfo=None)
+    return dt
+
+
+@group.group_class('monitoring', '/api/v1/monitoring')
+class MonitoringRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('/overview', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_overview() -> str:
+            """Get overview metrics"""
+            # 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)
+
+            metrics = await self.ap.monitoring_service.get_overview_metrics(
+                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=metrics)
+
+        @self.route('/messages', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_messages() -> str:
+            """Get message logs"""
+            # Parse query parameters
+            bot_ids = quart.request.args.getlist('botId')
+            pipeline_ids = quart.request.args.getlist('pipelineId')
+            session_ids = quart.request.args.getlist('sessionId')
+            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)
+
+            messages, total = await self.ap.monitoring_service.get_messages(
+                bot_ids=bot_ids if bot_ids else None,
+                pipeline_ids=pipeline_ids if pipeline_ids else None,
+                session_ids=session_ids if session_ids else None,
+                start_time=start_time,
+                end_time=end_time,
+                limit=limit,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'messages': messages,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )
+
+        @self.route('/llm-calls', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_llm_calls() -> str:
+            """Get LLM call records"""
+            # 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')
+            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)
+
+            llm_calls, total = await self.ap.monitoring_service.get_llm_calls(
+                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,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'llm_calls': llm_calls,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )
+
+        @self.route('/embedding-calls', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_embedding_calls() -> str:
+            """Get embedding call records"""
+            # Parse query parameters
+            start_time_str = quart.request.args.get('startTime')
+            end_time_str = quart.request.args.get('endTime')
+            knowledge_base_id = quart.request.args.get('knowledgeBaseId')
+            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)
+
+            embedding_calls, total = await self.ap.monitoring_service.get_embedding_calls(
+                start_time=start_time,
+                end_time=end_time,
+                knowledge_base_id=knowledge_base_id if knowledge_base_id else None,
+                limit=limit,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'embedding_calls': embedding_calls,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )
+
+        @self.route('/sessions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_sessions() -> str:
+            """Get session information"""
+            # 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')
+            is_active_str = quart.request.args.get('isActive')
+            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 is_active
+            is_active = None
+            if is_active_str:
+                is_active = is_active_str.lower() == 'true'
+
+            sessions, total = await self.ap.monitoring_service.get_sessions(
+                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,
+                is_active=is_active,
+                limit=limit,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'sessions': sessions,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )
+
+        @self.route('/errors', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_errors() -> str:
+            """Get error logs"""
+            # 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')
+            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)
+
+            errors, total = await self.ap.monitoring_service.get_errors(
+                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,
+                offset=offset,
+            )
+
+            return self.success(
+                data={
+                    'errors': errors,
+                    'total': total,
+                    'limit': limit,
+                    'offset': offset,
+                }
+            )
+
+        @self.route('/data', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_all_data() -> str:
+            """Get all monitoring data in a single request"""
+            # 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')
+            limit = int(quart.request.args.get('limit', 50))
+
+            # Parse datetime
+            start_time = parse_iso_datetime(start_time_str)
+            end_time = parse_iso_datetime(end_time_str)
+
+            # Get overview metrics
+            overview = await self.ap.monitoring_service.get_overview_metrics(
+                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,
+            )
+
+            # Get messages
+            messages, messages_total = await self.ap.monitoring_service.get_messages(
+                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,
+                offset=0,
+            )
+
+            # Get LLM calls
+            llm_calls, llm_calls_total = await self.ap.monitoring_service.get_llm_calls(
+                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,
+                offset=0,
+            )
+
+            # Get sessions
+            sessions, sessions_total = await self.ap.monitoring_service.get_sessions(
+                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,
+                is_active=None,
+                limit=limit,
+                offset=0,
+            )
+
+            # Get errors
+            errors, errors_total = await self.ap.monitoring_service.get_errors(
+                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,
+                offset=0,
+            )
+
+            # Get embedding calls
+            embedding_calls, embedding_calls_total = await self.ap.monitoring_service.get_embedding_calls(
+                start_time=start_time,
+                end_time=end_time,
+                limit=limit,
+                offset=0,
+            )
+
+            return self.success(
+                data={
+                    'overview': overview,
+                    'messages': messages,
+                    'llmCalls': llm_calls,
+                    'embeddingCalls': embedding_calls,
+                    'sessions': sessions,
+                    'errors': errors,
+                    'totalCount': {
+                        'messages': messages_total,
+                        'llmCalls': llm_calls_total,
+                        'embeddingCalls': embedding_calls_total,
+                        'sessions': sessions_total,
+                        'errors': errors_total,
+                    },
+                }
+            )
+
+        @self.route('/sessions/<session_id>/analysis', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_session_analysis(session_id: str) -> str:
+            """Get detailed analysis for a specific session"""
+            analysis = await self.ap.monitoring_service.get_session_analysis(session_id)
+
+            # Always return success with the analysis data
+            # The frontend will handle the 'found: false' case
+            return self.success(data=analysis)
+
+        @self.route('/messages/<message_id>/details', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def get_message_details(message_id: str) -> str:
+            """Get detailed information for a specific message"""
+            details = await self.ap.monitoring_service.get_message_details(message_id)
+
+            if not details.get('found'):
+                return self.error(message=f'Message {message_id} not found', code=404)
+
+            return self.success(data=details)
+
+        @self.route('/export', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def export_data() -> tuple[str, int]:
+            """Export monitoring data as CSV"""
+            # Parse query parameters
+            export_type = quart.request.args.get('type', 'messages')
+            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')
+            limit = int(quart.request.args.get('limit', 100000))
+
+            # Parse datetime
+            start_time = parse_iso_datetime(start_time_str)
+            end_time = parse_iso_datetime(end_time_str)
+
+            # Get data based on export type
+            if export_type == 'messages':
+                data = await self.ap.monitoring_service.export_messages(
+                    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',
+                    'bot_id',
+                    'bot_name',
+                    'pipeline_id',
+                    'pipeline_name',
+                    'runner_name',
+                    'message_content',
+                    'message_text',
+                    'session_id',
+                    'status',
+                    'level',
+                    'platform',
+                    'user_id',
+                ]
+            elif export_type == 'llm-calls':
+                data = await self.ap.monitoring_service.export_llm_calls(
+                    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',
+                    'model_name',
+                    'input_tokens',
+                    'output_tokens',
+                    'total_tokens',
+                    'duration_ms',
+                    'cost',
+                    'status',
+                    'bot_id',
+                    'bot_name',
+                    'pipeline_id',
+                    'pipeline_name',
+                    'session_id',
+                    'message_id',
+                    'error_message',
+                ]
+            elif export_type == 'embedding-calls':
+                data = await self.ap.monitoring_service.export_embedding_calls(
+                    start_time=start_time,
+                    end_time=end_time,
+                    limit=limit,
+                )
+                headers = [
+                    'id',
+                    'timestamp',
+                    'model_name',
+                    'prompt_tokens',
+                    'total_tokens',
+                    'duration_ms',
+                    'input_count',
+                    'status',
+                    'error_message',
+                    'knowledge_base_id',
+                    'query_text',
+                    'session_id',
+                    'message_id',
+                    'call_type',
+                ]
+            elif export_type == 'errors':
+                data = await self.ap.monitoring_service.export_errors(
+                    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',
+                    'error_type',
+                    'error_message',
+                    'bot_id',
+                    'bot_name',
+                    'pipeline_id',
+                    'pipeline_name',
+                    'session_id',
+                    'message_id',
+                    'stack_trace',
+                ]
+            elif export_type == 'sessions':
+                data = await self.ap.monitoring_service.export_sessions(
+                    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 = [
+                    'session_id',
+                    'bot_id',
+                    'bot_name',
+                    'pipeline_id',
+                    'pipeline_name',
+                    'message_count',
+                    'start_time',
+                    'last_activity',
+                    'is_active',
+                    'platform',
+                    'user_id',
+                ]
+            else:
+                return self.error(message=f'Invalid export type: {export_type}', code=400)
+
+            # Generate CSV content with UTF-8 BOM for Excel compatibility
+            import io
+
+            output = io.StringIO()
+            # Write UTF-8 BOM for Excel
+            output.write('\ufeff')
+            # Write header
+            output.write(','.join(headers) + '\n')
+
+            # Escape and write each row
+            for row in data:
+                escaped_values = []
+                for header in headers:
+                    value = row.get(header, '')
+                    escaped_values.append(self.ap.monitoring_service._escape_csv_field(value))
+                output.write(','.join(escaped_values) + '\n')
+
+            csv_content = output.getvalue()
+
+            # Return as file download
+            response = await quart.make_response(csv_content)
+            response.headers['Content-Type'] = 'text/csv; charset=utf-8'
+            response.headers['Content-Disposition'] = (
+                f'attachment; filename="monitoring-{export_type}-{int(datetime.datetime.now().timestamp())}.csv"'
+            )
+
+            return response, 200
--- a/src/langbot/pkg/api/http/controller/groups/plugins.py
+++ b/src/langbot/pkg/api/http/controller/groups/plugins.py
@@ -14,6 +14,18 @@ from langbot_plugin.runtime.plugin.mgr import PluginInstallSource

@group.group_class('plugins', '/api/v1/plugins')
 class PluginsRouterGroup(group.RouterGroup):
+    async def _check_extensions_limit(self) -> str | None:
+        """Check if extensions limit is reached. Returns error response if limit exceeded, None otherwise."""
+        limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
+        max_extensions = limitation.get('max_extensions', -1)
+        if max_extensions >= 0:
+            plugins = await self.ap.plugin_connector.list_plugins()
+            mcp_servers = await self.ap.mcp_service.get_mcp_servers()
+            total_extensions = len(plugins) + len(mcp_servers)
+            if total_extensions >= max_extensions:
+                return self.http_status(400, -1, f'Maximum number of extensions ({max_extensions}) reached')
+        return None
+
    async def initialize(self) -> None:
        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
        async def _() -> str:
@@ -239,6 +251,10 @@ class PluginsRouterGroup(group.RouterGroup):
        @self.route('/install/github', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
        async def _() -> str:
            """Install plugin from GitHub release asset"""
+            limit_error = await self._check_extensions_limit()
+            if limit_error is not None:
+                return limit_error
+
            data = await quart.request.json
            asset_url = data.get('asset_url', '')
            owner = data.get('owner', '')
@@ -273,6 +289,10 @@ class PluginsRouterGroup(group.RouterGroup):
            auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
        )
        async def _() -> str:
+            limit_error = await self._check_extensions_limit()
+            if limit_error is not None:
+                return limit_error
+
            data = await quart.request.json

            ctx = taskmgr.TaskContext.new()
@@ -288,6 +308,10 @@ class PluginsRouterGroup(group.RouterGroup):

        @self.route('/install/local', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
        async def _() -> str:
+            limit_error = await self._check_extensions_limit()
+            if limit_error is not None:
+                return limit_error
+
            file = (await quart.request.files).get('file')
            if file is None:
                return self.http_status(400, -1, 'file is required')
--- a/src/langbot/pkg/api/http/controller/groups/provider/models.py
+++ b/src/langbot/pkg/api/http/controller/groups/provider/models.py
@@ -9,12 +9,15 @@ class LLMModelsRouterGroup(group.RouterGroup):
        @self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
        async def _() -> str:
            if quart.request.method == 'GET':
+                provider_uuid = quart.request.args.get('provider_uuid')
+                if provider_uuid:
+                    return self.success(
+                        data={'models': await self.ap.llm_model_service.get_llm_models_by_provider(provider_uuid)}
+                    )
                return self.success(data={'models': await self.ap.llm_model_service.get_llm_models()})
            elif quart.request.method == 'POST':
                json_data = await quart.request.json
-
                model_uuid = await self.ap.llm_model_service.create_llm_model(json_data)
-
                return self.success(data={'uuid': model_uuid})

        @self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
@@ -52,12 +55,19 @@ class EmbeddingModelsRouterGroup(group.RouterGroup):
        @self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
        async def _() -> str:
            if quart.request.method == 'GET':
+                provider_uuid = quart.request.args.get('provider_uuid')
+                if provider_uuid:
+                    return self.success(
+                        data={
+                            'models': await self.ap.embedding_models_service.get_embedding_models_by_provider(
+                                provider_uuid
+                            )
+                        }
+                    )
                return self.success(data={'models': await self.ap.embedding_models_service.get_embedding_models()})
            elif quart.request.method == 'POST':
                json_data = await quart.request.json
-
                model_uuid = await self.ap.embedding_models_service.create_embedding_model(json_data)
-
                return self.success(data={'uuid': model_uuid})

        @self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
--- a/src/langbot/pkg/api/http/controller/groups/provider/providers.py
+++ b/src/langbot/pkg/api/http/controller/groups/provider/providers.py
@@ -0,0 +1,45 @@
+import quart
+
+from ... import group
+
+
+@group.group_class('models/providers', '/api/v1/provider/providers')
+class ModelProvidersRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def _() -> str:
+            if quart.request.method == 'GET':
+                providers = await self.ap.provider_service.get_providers()
+                # Add model counts
+                for provider in providers:
+                    counts = await self.ap.provider_service.get_provider_model_counts(provider['uuid'])
+                    provider['llm_count'] = counts['llm_count']
+                    provider['embedding_count'] = counts['embedding_count']
+                return self.success(data={'providers': providers})
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                provider_uuid = await self.ap.provider_service.create_provider(json_data)
+                return self.success(data={'uuid': provider_uuid})
+
+        @self.route(
+            '/<provider_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
+        )
+        async def _(provider_uuid: str) -> str:
+            if quart.request.method == 'GET':
+                provider = await self.ap.provider_service.get_provider(provider_uuid)
+                if provider is None:
+                    return self.http_status(404, -1, 'provider not found')
+                counts = await self.ap.provider_service.get_provider_model_counts(provider_uuid)
+                provider['llm_count'] = counts['llm_count']
+                provider['embedding_count'] = counts['embedding_count']
+                return self.success(data={'provider': provider})
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+                await self.ap.provider_service.update_provider(provider_uuid, json_data)
+                return self.success()
+            elif quart.request.method == 'DELETE':
+                try:
+                    await self.ap.provider_service.delete_provider(provider_uuid)
+                    return self.success()
+                except ValueError as e:
+                    return self.http_status(400, -1, str(e))
--- a/src/langbot/pkg/api/http/controller/groups/survey.py
+++ b/src/langbot/pkg/api/http/controller/groups/survey.py
@@ -0,0 +1,47 @@
+import quart
+
+from .. import group
+
+
+@group.group_class('survey', '/api/v1/survey')
+class SurveyRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('/pending', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _get_pending() -> str:
+            """Get pending survey for the frontend to display."""
+            survey = self.ap.survey.get_pending_survey() if self.ap.survey else None
+            return self.success(data={'survey': survey})
+
+        @self.route('/respond', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _respond() -> str:
+            """Submit survey response."""
+            json_data = await quart.request.json
+            survey_id = json_data.get('survey_id')
+            answers = json_data.get('answers', {})
+            completed = json_data.get('completed', True)
+
+            if not survey_id:
+                return self.fail(1, 'survey_id required')
+
+            if self.ap.survey:
+                ok = await self.ap.survey.submit_response(survey_id, answers, completed)
+                if ok:
+                    return self.success()
+                return self.fail(2, 'Failed to submit response')
+            return self.fail(3, 'Survey not available')
+
+        @self.route('/dismiss', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _dismiss() -> str:
+            """Dismiss survey."""
+            json_data = await quart.request.json
+            survey_id = json_data.get('survey_id')
+
+            if not survey_id:
+                return self.fail(1, 'survey_id required')
+
+            if self.ap.survey:
+                ok = await self.ap.survey.dismiss_survey(survey_id)
+                if ok:
+                    return self.success()
+                return self.fail(2, 'Failed to dismiss')
+            return self.fail(3, 'Survey not available')
--- a/src/langbot/pkg/api/http/controller/groups/system.py
+++ b/src/langbot/pkg/api/http/controller/groups/system.py
@@ -13,19 +13,20 @@ class SystemRouterGroup(group.RouterGroup):
                data={
                    'version': constants.semantic_version,
                    'debug': constants.debug_mode,
+                    'edition': constants.edition,
                    'enable_marketplace': self.ap.instance_config.data.get('plugin', {}).get(
                        'enable_marketplace', True
                    ),
                    'cloud_service_url': (
-                        self.ap.instance_config.data.get('plugin', {}).get(
-                            'cloud_service_url', 'https://space.langbot.app'
-                        )
-                        if 'cloud_service_url' in self.ap.instance_config.data.get('plugin', {})
-                        else 'https://space.langbot.app'
+                        self.ap.instance_config.data.get('space', {}).get('url', 'https://space.langbot.app')
                    ),
-                    'allow_change_password': self.ap.instance_config.data.get('system', {}).get(
-                        'allow_change_password', True
+                    'allow_modify_login_info': self.ap.instance_config.data.get('system', {}).get(
+                        'allow_modify_login_info', True
                    ),
+                    'disable_models_service': self.ap.instance_config.data.get('space', {}).get(
+                        'disable_models_service', False
+                    ),
+                    'limitation': self.ap.instance_config.data.get('system', {}).get('limitation', {}),
                }
            )

--- a/src/langbot/pkg/api/http/controller/groups/user.py
+++ b/src/langbot/pkg/api/http/controller/groups/user.py
@@ -1,8 +1,10 @@
 import quart
 import argon2
 import asyncio
+import traceback

 from .. import group
+from .....entity.errors import account as account_errors


@group.group_class('user', '/api/v1/user')
@@ -33,6 +35,8 @@ class UserRouterGroup(group.RouterGroup):
                token = await self.ap.user_service.authenticate(json_data['user'], json_data['password'])
            except argon2.exceptions.VerifyMismatchError:
                return self.fail(1, 'Invalid username or password')
+            except ValueError as e:
+                return self.fail(1, str(e))

            return self.success(data={'token': token})

@@ -71,11 +75,11 @@ class UserRouterGroup(group.RouterGroup):
        @self.route('/change-password', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
        async def _(user_email: str) -> str:
            # Check if password change is allowed
-            allow_change_password = self.ap.instance_config.data.get('system', {}).get(
-                'allow_change_password', True
+            allow_modify_login_info = self.ap.instance_config.data.get('system', {}).get(
+                'allow_modify_login_info', True
            )
-            if not allow_change_password:
-                return self.http_status(403, -1, 'Password change is disabled')
+            if not allow_modify_login_info:
+                return self.http_status(403, -1, 'Modifying login info is disabled')

            json_data = await quart.request.json

@@ -90,3 +94,169 @@ class UserRouterGroup(group.RouterGroup):
                return self.http_status(400, -1, str(e))

            return self.success(data={'user': user_email})
+
+        # Space OAuth endpoints (redirect flow)
+
+        @self.route('/space/authorize-url', methods=['GET'], auth_type=group.AuthType.NONE)
+        async def _() -> str:
+            """Get Space OAuth authorization URL for redirect"""
+            redirect_uri = quart.request.args.get('redirect_uri', '')
+            state = quart.request.args.get('state', '')
+
+            if not redirect_uri:
+                return self.fail(1, 'Missing redirect_uri parameter')
+
+            try:
+                authorize_url = self.ap.space_service.get_oauth_authorize_url(redirect_uri, state)
+                return self.success(data={'authorize_url': authorize_url})
+            except Exception as e:
+                return self.fail(1, str(e))
+
+        @self.route('/space/callback', methods=['POST'], auth_type=group.AuthType.NONE)
+        async def _() -> str:
+            """Handle OAuth callback - exchange code for tokens and authenticate"""
+            json_data = await quart.request.json
+            code = json_data.get('code')
+
+            if not code:
+                return self.fail(1, 'Missing authorization code')
+
+            try:
+                # Exchange code for tokens
+                token_data = await self.ap.space_service.exchange_oauth_code(code)
+                access_token = token_data.get('access_token')
+                refresh_token = token_data.get('refresh_token')
+                expires_in = token_data.get('expires_in', 0)
+
+                if not access_token:
+                    return self.fail(1, 'Failed to get access token from Space')
+
+                # Authenticate and create/update local user
+                jwt_token, user_obj = await self.ap.user_service.authenticate_space_user(
+                    access_token, refresh_token, expires_in
+                )
+
+                return self.success(
+                    data={
+                        'token': jwt_token,
+                        'user': user_obj.user,
+                    }
+                )
+            except account_errors.AccountEmailMismatchError as e:
+                return self.fail(3, str(e))
+            except ValueError as e:
+                traceback.print_exc()
+                return self.fail(1, str(e))
+            except Exception as e:
+                traceback.print_exc()
+                return self.fail(2, f'OAuth callback failed: {str(e)}')
+
+        @self.route('/info', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(user_email: str) -> str:
+            """Get current user information including account type"""
+            user_obj = await self.ap.user_service.get_user_by_email(user_email)
+
+            if user_obj is None:
+                return self.http_status(404, -1, 'User not found')
+
+            return self.success(
+                data={
+                    'user': user_obj.user,
+                    'account_type': user_obj.account_type,
+                    'has_password': bool(user_obj.password and user_obj.password.strip()),
+                }
+            )
+
+        @self.route('/space-credits', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(user_email: str) -> str:
+            """Get Space credits balance for current user"""
+            credits = await self.ap.space_service.get_credits(user_email)
+            return self.success(data={'credits': credits})
+
+        @self.route('/account-info', methods=['GET'], auth_type=group.AuthType.NONE)
+        async def _() -> str:
+            """Get account info for login page (account type and has_password)"""
+            if not await self.ap.user_service.is_initialized():
+                return self.success(data={'initialized': False})
+
+            user_obj = await self.ap.user_service.get_first_user()
+            if user_obj is None:
+                return self.success(data={'initialized': False})
+
+            return self.success(
+                data={
+                    'initialized': True,
+                    'account_type': user_obj.account_type,
+                    'has_password': bool(user_obj.password and user_obj.password.strip()),
+                }
+            )
+
+        @self.route('/set-password', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
+        async def _(user_email: str) -> str:
+            """Set password for Space account (first time) or change password"""
+            json_data = await quart.request.json
+            new_password = json_data.get('new_password')
+            current_password = json_data.get('current_password')
+
+            if not new_password:
+                return self.http_status(400, -1, 'New password is required')
+
+            user_obj = await self.ap.user_service.get_user_by_email(user_email)
+            if user_obj is None:
+                return self.http_status(404, -1, 'User not found')
+
+            try:
+                await self.ap.user_service.set_password(user_email, new_password, current_password)
+                return self.success(data={'user': user_email})
+            except ValueError as e:
+                return self.http_status(400, -1, str(e))
+            except argon2.exceptions.VerifyMismatchError:
+                return self.http_status(400, -1, 'Current password is incorrect')
+
+        @self.route('/bind-space', methods=['POST'], auth_type=group.AuthType.NONE)
+        async def _() -> str:
+            """Bind Space account to existing local account"""
+            # Check if modifying login info is allowed
+            allow_modify_login_info = self.ap.instance_config.data.get('system', {}).get(
+                'allow_modify_login_info', True
+            )
+            if not allow_modify_login_info:
+                return self.http_status(403, -1, 'Modifying login info is disabled')
+
+            json_data = await quart.request.json
+            code = json_data.get('code')
+            state = json_data.get('state')  # JWT token passed as state
+
+            if not code:
+                return self.http_status(400, -1, 'Missing authorization code')
+
+            if not state:
+                return self.http_status(400, -1, 'Missing state parameter')
+
+            # Verify state is a valid JWT token
+            try:
+                user_email = await self.ap.user_service.verify_jwt_token(state)
+            except Exception:
+                return self.http_status(401, -1, 'Invalid or expired state')
+
+            user_obj = await self.ap.user_service.get_user_by_email(user_email)
+            if user_obj is None:
+                return self.http_status(404, -1, 'User not found')
+
+            if user_obj.account_type != 'local':
+                return self.http_status(400, -1, 'Only local accounts can bind to Space')
+
+            try:
+                updated_user = await self.ap.user_service.bind_space_account(user_email, code)
+                jwt_token = await self.ap.user_service.generate_jwt_token(updated_user.user)
+                return self.success(
+                    data={
+                        'token': jwt_token,
+                        'user': updated_user.user,
+                        'account_type': updated_user.account_type,
+                    }
+                )
+            except ValueError as e:
+                return self.http_status(400, -1, str(e))
+            except Exception as e:
+                return self.http_status(500, -1, f'Failed to bind Space account: {str(e)}')
--- a/src/langbot/pkg/api/http/controller/groups/webhooks.py
+++ b/src/langbot/pkg/api/http/controller/groups/webhooks.py
@@ -30,7 +30,6 @@ class WebhookRouterGroup(group.RouterGroup):
            适配器返回的响应
        """
        try:
-            
            runtime_bot = await self.ap.platform_mgr.get_bot_by_uuid(bot_uuid)

            if not runtime_bot:
@@ -39,11 +38,9 @@ class WebhookRouterGroup(group.RouterGroup):
            if not runtime_bot.enable:
                return quart.jsonify({'error': 'Bot is disabled'}), 403

-            
            if not hasattr(runtime_bot.adapter, 'handle_unified_webhook'):
                return quart.jsonify({'error': 'Adapter does not support unified webhook'}), 501

-            
            response = await runtime_bot.adapter.handle_unified_webhook(
                bot_uuid=bot_uuid,
                path=path,
--- a/src/langbot/pkg/api/http/service/bot.py
+++ b/src/langbot/pkg/api/http/service/bot.py
@@ -59,7 +59,16 @@ class BotService:
            adapter_runtime_values['bot_account_id'] = runtime_bot.adapter.bot_account_id

        # Webhook URL for unified webhook adapters (independent of bot running state)
-        if persistence_bot['adapter'] in ['wecom', 'wecombot', 'officialaccount', 'qqofficial', 'slack', 'wecomcs', 'LINE', 'lark']:
+        if persistence_bot['adapter'] in [
+            'wecom',
+            'wecombot',
+            'officialaccount',
+            'qqofficial',
+            'slack',
+            'wecomcs',
+            'LINE',
+            'lark',
+        ]:
            webhook_prefix = self.ap.instance_config.data['api'].get('webhook_prefix', 'http://127.0.0.1:5300')
            webhook_url = f'/bots/{bot_uuid}'
            adapter_runtime_values['webhook_url'] = webhook_url
@@ -74,6 +83,14 @@ class BotService:

    async def create_bot(self, bot_data: dict) -> str:
        """Create bot"""
+        # Check limitation
+        limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
+        max_bots = limitation.get('max_bots', -1)
+        if max_bots >= 0:
+            existing_bots = await self.get_bots()
+            if len(existing_bots) >= max_bots:
+                raise ValueError(f'Maximum number of bots ({max_bots}) reached')
+
        # TODO: 检查配置信息格式
        bot_data['uuid'] = str(uuid.uuid4())

--- a/src/langbot/pkg/api/http/service/mcp.py
+++ b/src/langbot/pkg/api/http/service/mcp.py
@@ -38,6 +38,16 @@ class MCPService:
        return serialized_servers

    async def create_mcp_server(self, server_data: dict) -> str:
+        # Check limitation (extensions = MCP servers + plugins)
+        limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
+        max_extensions = limitation.get('max_extensions', -1)
+        if max_extensions >= 0:
+            existing_mcp_servers = await self.get_mcp_servers()
+            plugins = await self.ap.plugin_connector.list_plugins()
+            total_extensions = len(existing_mcp_servers) + len(plugins)
+            if total_extensions >= max_extensions:
+                raise ValueError(f'Maximum number of extensions ({max_extensions}) reached')
+
        server_data['uuid'] = str(uuid.uuid4())
        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_mcp.MCPServer).values(server_data))

--- a/src/langbot/pkg/api/http/service/model.py
+++ b/src/langbot/pkg/api/http/service/model.py
@@ -11,6 +11,18 @@ from ....entity.persistence import pipeline as persistence_pipeline
 from ....provider.modelmgr import requester as model_requester


+def _parse_provider_api_keys(provider_dict: dict) -> dict:
+    """Parse api_keys if it's a JSON string"""
+    if isinstance(provider_dict.get('api_keys'), str):
+        import json
+
+        try:
+            provider_dict['api_keys'] = json.loads(provider_dict['api_keys'])
+        except Exception:
+            provider_dict['api_keys'] = []
+    return provider_dict
+
+
 class LLMModelsService:
    ap: app.Application

@@ -18,59 +30,131 @@ class LLMModelsService:
        self.ap = ap

    async def get_llm_models(self, include_secret: bool = True) -> list[dict]:
+        """Get all LLM models with provider info"""
        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
-
        models = result.all()

-        masked_columns = []
-        if not include_secret:
-            masked_columns = ['api_keys']
+        # Get all providers for lookup
+        providers_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider)
+        )
+        providers = {p.uuid: p for p in providers_result.all()}

-        return [
-            self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model, masked_columns)
-            for model in models
-        ]
+        models_list = []
+        for model in models:
+            model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model)
+            provider = providers.get(model.provider_uuid)
+            if provider:
+                provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+                provider_dict = _parse_provider_api_keys(provider_dict)
+                if not include_secret:
+                    provider_dict['api_keys'] = ['***'] * len(provider_dict.get('api_keys', []))
+                model_dict['provider'] = provider_dict
+            models_list.append(model_dict)

-    async def create_llm_model(self, model_data: dict) -> str:
-        model_data['uuid'] = str(uuid.uuid4())
+        return models_list
+
+    async def get_llm_models_by_provider(self, provider_uuid: str) -> list[dict]:
+        """Get LLM models by provider UUID"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.LLMModel).where(
+                persistence_model.LLMModel.provider_uuid == provider_uuid
+            )
+        )
+        models = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, m) for m in models]
+
+    async def create_llm_model(
+        self, model_data: dict, preserve_uuid: bool = False, auto_set_to_default_pipeline: bool = True
+    ) -> str:
+        """Create a new LLM model"""
+        if not preserve_uuid:
+            model_data['uuid'] = str(uuid.uuid4())
+
+        # Handle provider creation if needed
+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                # Create new provider
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid

        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_model.LLMModel).values(**model_data))

-        llm_model = await self.get_llm_model(model_data['uuid'])
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')

-        await self.ap.model_mgr.load_llm_model(llm_model)
-
-        # check if default pipeline has no model bound
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
-                persistence_pipeline.LegacyPipeline.is_default == True
-            )
+        runtime_llm_model = await self.ap.model_mgr.load_llm_model_with_provider(
+            persistence_model.LLMModel(**model_data),
+            runtime_provider,
        )
-        pipeline = result.first()
-        if pipeline is not None and pipeline.config['ai']['local-agent']['model'] == '':
-            pipeline_config = pipeline.config
-            pipeline_config['ai']['local-agent']['model'] = model_data['uuid']
-            pipeline_data = {'config': pipeline_config}
-            await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
+        self.ap.model_mgr.llm_models.append(runtime_llm_model)
+
+        if auto_set_to_default_pipeline:
+            # set the default pipeline model to this model
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
+                    persistence_pipeline.LegacyPipeline.is_default == True
+                )
+            )
+            pipeline = result.first()
+            if pipeline is not None and pipeline.config['ai']['local-agent']['model'] == '':
+                pipeline_config = pipeline.config
+                pipeline_config['ai']['local-agent']['model'] = model_data['uuid']
+                pipeline_data = {'config': pipeline_config}
+                await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)

        return model_data['uuid']

    async def get_llm_model(self, model_uuid: str) -> dict | None:
+        """Get a single LLM model with provider info"""
        result = await self.ap.persistence_mgr.execute_async(
            sqlalchemy.select(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
        )
-
        model = result.first()
-
        if model is None:
            return None

-        return self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model)
+        model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.LLMModel, model)
+
+        # Get provider
+        provider_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == model.provider_uuid
+            )
+        )
+        provider = provider_result.first()
+        if provider:
+            provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+            model_dict['provider'] = _parse_provider_api_keys(provider_dict)
+
+        return model_dict

    async def update_llm_model(self, model_uuid: str, model_data: dict) -> None:
+        """Update an existing LLM model"""
        if 'uuid' in model_data:
            del model_data['uuid']

+        # Handle provider update if needed
+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid
+
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.update(persistence_model.LLMModel)
            .where(persistence_model.LLMModel.uuid == model_uuid)
@@ -79,18 +163,25 @@ class LLMModelsService:

        await self.ap.model_mgr.remove_llm_model(model_uuid)

-        llm_model = await self.get_llm_model(model_uuid)
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')

-        await self.ap.model_mgr.load_llm_model(llm_model)
+        runtime_llm_model = await self.ap.model_mgr.load_llm_model_with_provider(
+            persistence_model.LLMModel(**model_data),
+            runtime_provider,
+        )
+        self.ap.model_mgr.llm_models.append(runtime_llm_model)

    async def delete_llm_model(self, model_uuid: str) -> None:
+        """Delete an LLM model"""
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.delete(persistence_model.LLMModel).where(persistence_model.LLMModel.uuid == model_uuid)
        )
-
        await self.ap.model_mgr.remove_llm_model(model_uuid)

    async def test_llm_model(self, model_uuid: str, model_data: dict) -> None:
+        """Test an LLM model"""
        runtime_llm_model: model_requester.RuntimeLLMModel | None = None

        if model_uuid != '_':
@@ -98,25 +189,18 @@ class LLMModelsService:
                if model.model_entity.uuid == model_uuid:
                    runtime_llm_model = model
                    break
-
            if runtime_llm_model is None:
                raise Exception('model not found')
-
        else:
-            runtime_llm_model = await self.ap.model_mgr.init_runtime_llm_model(model_data)
+            runtime_llm_model = await self.ap.model_mgr.init_temporary_runtime_llm_model(model_data)

-        # Mon Nov 10 2025: Commented for some providers may not support thinking parameter
-        # # 有些模型厂商默认开启了思考功能，测试容易延迟
-        # extra_args = model_data.get('extra_args', {})
-        # if not extra_args or 'thinking' not in extra_args:
-        #     extra_args['thinking'] = {'type': 'disabled'}
-
-        await runtime_llm_model.requester.invoke_llm(
+        extra_args = model_data.get('extra_args', {})
+        await runtime_llm_model.provider.invoke_llm(
            query=None,
            model=runtime_llm_model,
            messages=[provider_message.Message(role='user', content='Hello, world! Please just reply a "Hello".')],
            funcs=[],
-            # extra_args=extra_args,
+            extra_args=extra_args,
        )


@@ -127,42 +211,111 @@ class EmbeddingModelsService:
        self.ap = ap

    async def get_embedding_models(self) -> list[dict]:
+        """Get all embedding models with provider info"""
        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
-
        models = result.all()
-        return [self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model) for model in models]

-    async def create_embedding_model(self, model_data: dict) -> str:
-        model_data['uuid'] = str(uuid.uuid4())
+        providers_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider)
+        )
+        providers = {p.uuid: p for p in providers_result.all()}
+
+        models_list = []
+        for model in models:
+            model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
+            provider = providers.get(model.provider_uuid)
+            if provider:
+                provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+                model_dict['provider'] = _parse_provider_api_keys(provider_dict)
+            models_list.append(model_dict)
+
+        return models_list
+
+    async def get_embedding_models_by_provider(self, provider_uuid: str) -> list[dict]:
+        """Get embedding models by provider UUID"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.EmbeddingModel).where(
+                persistence_model.EmbeddingModel.provider_uuid == provider_uuid
+            )
+        )
+        models = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, m) for m in models]
+
+    async def create_embedding_model(self, model_data: dict, preserve_uuid: bool = False) -> str:
+        """Create a new embedding model"""
+        if not preserve_uuid:
+            model_data['uuid'] = str(uuid.uuid4())
+
+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid

        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
        )

-        embedding_model = await self.get_embedding_model(model_data['uuid'])
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')

-        await self.ap.model_mgr.load_embedding_model(embedding_model)
+        runtime_embedding_model = await self.ap.model_mgr.load_embedding_model_with_provider(
+            persistence_model.EmbeddingModel(**model_data),
+            runtime_provider,
+        )
+        self.ap.model_mgr.embedding_models.append(runtime_embedding_model)

        return model_data['uuid']

    async def get_embedding_model(self, model_uuid: str) -> dict | None:
+        """Get a single embedding model with provider info"""
        result = await self.ap.persistence_mgr.execute_async(
            sqlalchemy.select(persistence_model.EmbeddingModel).where(
                persistence_model.EmbeddingModel.uuid == model_uuid
            )
        )
-
        model = result.first()
-
        if model is None:
            return None

-        return self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
+        model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.EmbeddingModel, model)
+
+        provider_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == model.provider_uuid
+            )
+        )
+        provider = provider_result.first()
+        if provider:
+            provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+            model_dict['provider'] = _parse_provider_api_keys(provider_dict)
+
+        return model_dict

    async def update_embedding_model(self, model_uuid: str, model_data: dict) -> None:
+        """Update an existing embedding model"""
        if 'uuid' in model_data:
            del model_data['uuid']

+        if 'provider' in model_data:
+            provider_data = model_data.pop('provider')
+            if provider_data.get('uuid'):
+                model_data['provider_uuid'] = provider_data['uuid']
+            else:
+                provider_uuid = await self.ap.provider_service.find_or_create_provider(
+                    requester=provider_data.get('requester', ''),
+                    base_url=provider_data.get('base_url', ''),
+                    api_keys=provider_data.get('api_keys', []),
+                )
+                model_data['provider_uuid'] = provider_uuid
+
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.update(persistence_model.EmbeddingModel)
            .where(persistence_model.EmbeddingModel.uuid == model_uuid)
@@ -171,20 +324,27 @@ class EmbeddingModelsService:

        await self.ap.model_mgr.remove_embedding_model(model_uuid)

-        embedding_model = await self.get_embedding_model(model_uuid)
+        runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
+        if runtime_provider is None:
+            raise Exception('provider not found')

-        await self.ap.model_mgr.load_embedding_model(embedding_model)
+        runtime_embedding_model = await self.ap.model_mgr.load_embedding_model_with_provider(
+            persistence_model.EmbeddingModel(**model_data),
+            runtime_provider,
+        )
+        self.ap.model_mgr.embedding_models.append(runtime_embedding_model)

    async def delete_embedding_model(self, model_uuid: str) -> None:
+        """Delete an embedding model"""
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.delete(persistence_model.EmbeddingModel).where(
                persistence_model.EmbeddingModel.uuid == model_uuid
            )
        )
-
        await self.ap.model_mgr.remove_embedding_model(model_uuid)

    async def test_embedding_model(self, model_uuid: str, model_data: dict) -> None:
+        """Test an embedding model"""
        runtime_embedding_model: model_requester.RuntimeEmbeddingModel | None = None

        if model_uuid != '_':
@@ -192,14 +352,12 @@ class EmbeddingModelsService:
                if model.model_entity.uuid == model_uuid:
                    runtime_embedding_model = model
                    break
-
            if runtime_embedding_model is None:
                raise Exception('model not found')
-
        else:
-            runtime_embedding_model = await self.ap.model_mgr.init_runtime_embedding_model(model_data)
+            runtime_embedding_model = await self.ap.model_mgr.init_temporary_runtime_embedding_model(model_data)

-        await runtime_embedding_model.requester.invoke_embedding(
+        await runtime_embedding_model.provider.invoke_embedding(
            model=runtime_embedding_model,
            input_text=['Hello, world!'],
            extra_args={},
--- a/src/langbot/pkg/api/http/service/monitoring.py
+++ b/src/langbot/pkg/api/http/service/monitoring.py
--- a/src/langbot/pkg/api/http/service/pipeline.py
+++ b/src/langbot/pkg/api/http/service/pipeline.py
@@ -76,6 +76,14 @@ class PipelineService:
    async def create_pipeline(self, pipeline_data: dict, default: bool = False) -> str:
        from ....utils import paths as path_utils

+        # Check limitation
+        limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
+        max_pipelines = limitation.get('max_pipelines', -1)
+        if max_pipelines >= 0:
+            existing_pipelines = await self.get_pipelines()
+            if len(existing_pipelines) >= max_pipelines:
+                raise ValueError(f'Maximum number of pipelines ({max_pipelines}) reached')
+
        pipeline_data['uuid'] = str(uuid.uuid4())
        pipeline_data['for_version'] = self.ap.ver_mgr.get_current_version()
        pipeline_data['stages'] = default_stage_order.copy()
@@ -153,6 +161,14 @@ class PipelineService:

    async def copy_pipeline(self, pipeline_uuid: str) -> str:
        """Copy a pipeline with all its configurations"""
+        # Check limitation
+        limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
+        max_pipelines = limitation.get('max_pipelines', -1)
+        if max_pipelines >= 0:
+            existing_pipelines = await self.get_pipelines()
+            if len(existing_pipelines) >= max_pipelines:
+                raise ValueError(f'Maximum number of pipelines ({max_pipelines}) reached')
+
        # Get the original pipeline
        result = await self.ap.persistence_mgr.execute_async(
            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
--- a/src/langbot/pkg/api/http/service/provider.py
+++ b/src/langbot/pkg/api/http/service/provider.py
@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+import uuid
+
+import sqlalchemy
+
+from ....core import app
+from ....entity.persistence import model as persistence_model
+
+
+class ModelProviderService:
+    """Service for managing model providers"""
+
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    async def get_providers(self) -> list[dict]:
+        """Get all providers"""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.ModelProvider))
+        providers = result.all()
+        providers_list = []
+        for p in providers:
+            provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, p)
+            # Parse api_keys if it's a JSON string
+            if isinstance(provider_dict.get('api_keys'), str):
+                import json
+
+                try:
+                    provider_dict['api_keys'] = json.loads(provider_dict['api_keys'])
+                except Exception:
+                    provider_dict['api_keys'] = []
+            providers_list.append(provider_dict)
+        return providers_list
+
+    async def get_provider(self, provider_uuid: str) -> dict | None:
+        """Get a single provider by UUID"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == provider_uuid
+            )
+        )
+        provider = result.first()
+        if provider is None:
+            return None
+        provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
+        # Parse api_keys if it's a JSON string
+        if isinstance(provider_dict.get('api_keys'), str):
+            import json
+
+            try:
+                provider_dict['api_keys'] = json.loads(provider_dict['api_keys'])
+            except Exception:
+                provider_dict['api_keys'] = []
+        return provider_dict
+
+    async def create_provider(self, provider_data: dict) -> str:
+        """Create a new provider"""
+        provider_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_model.ModelProvider).values(**provider_data)
+        )
+
+        # load to runtime
+        runtime_provider = await self.ap.model_mgr.load_provider(provider_data)
+        self.ap.model_mgr.provider_dict[runtime_provider.provider_entity.uuid] = runtime_provider
+        return provider_data['uuid']
+
+    async def update_provider(self, provider_uuid: str, provider_data: dict) -> None:
+        """Update an existing provider"""
+        if 'uuid' in provider_data:
+            del provider_data['uuid']
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_model.ModelProvider)
+            .where(persistence_model.ModelProvider.uuid == provider_uuid)
+            .values(**provider_data)
+        )
+        await self.ap.model_mgr.reload_provider(provider_uuid)
+
+    async def delete_provider(self, provider_uuid: str) -> None:
+        """Delete a provider (only if no models reference it)"""
+        # Check if any models use this provider
+        llm_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.LLMModel).where(
+                persistence_model.LLMModel.provider_uuid == provider_uuid
+            )
+        )
+        if llm_result.first() is not None:
+            raise ValueError('Cannot delete provider: LLM models still reference it')
+
+        embedding_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.EmbeddingModel).where(
+                persistence_model.EmbeddingModel.provider_uuid == provider_uuid
+            )
+        )
+        if embedding_result.first() is not None:
+            raise ValueError('Cannot delete provider: Embedding models still reference it')
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == provider_uuid
+            )
+        )
+
+        await self.ap.model_mgr.remove_provider(provider_uuid)
+
+    async def get_provider_model_counts(self, provider_uuid: str) -> dict:
+        """Get count of models using this provider"""
+        llm_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(sqlalchemy.func.count())
+            .select_from(persistence_model.LLMModel)
+            .where(persistence_model.LLMModel.provider_uuid == provider_uuid)
+        )
+        llm_count = llm_result.scalar() or 0
+
+        embedding_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(sqlalchemy.func.count())
+            .select_from(persistence_model.EmbeddingModel)
+            .where(persistence_model.EmbeddingModel.provider_uuid == provider_uuid)
+        )
+        embedding_count = embedding_result.scalar() or 0
+
+        return {'llm_count': llm_count, 'embedding_count': embedding_count}
+
+    async def find_or_create_provider(self, requester: str, base_url: str, api_keys: list) -> str:
+        """Find existing provider or create new one"""
+        # Try to find existing provider with same config
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.requester == requester,
+                persistence_model.ModelProvider.base_url == base_url,
+            )
+        )
+        for provider in result.all():
+            if sorted(provider.api_keys or []) == sorted(api_keys or []):
+                return provider.uuid
+
+        # Create new provider
+        provider_name = requester
+        if base_url:
+            try:
+                from urllib.parse import urlparse
+
+                parsed = urlparse(base_url)
+                provider_name = parsed.netloc or requester
+            except Exception:
+                pass
+
+        return await self.create_provider(
+            {
+                'name': provider_name,
+                'requester': requester,
+                'base_url': base_url,
+                'api_keys': api_keys or [],
+            }
+        )
+
+    async def update_space_model_provider_api_keys(self, api_key: str) -> None:
+        """Update Space model provider API keys"""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_model.ModelProvider)
+            .where(persistence_model.ModelProvider.uuid == '00000000-0000-0000-0000-000000000000')
+            .values(api_keys=[api_key])
+        )
+        await self.ap.model_mgr.reload_provider('00000000-0000-0000-0000-000000000000')
--- a/src/langbot/pkg/api/http/service/space.py
+++ b/src/langbot/pkg/api/http/service/space.py
@@ -0,0 +1,189 @@
+from __future__ import annotations
+
+from langbot.pkg.utils import httpclient
+import typing
+import datetime
+import time
+import sqlalchemy
+
+from ....core import app
+from ....entity.persistence import user
+from ....entity.dto.space_model import SpaceModel
+
+
+class SpaceService:
+    """Service for interacting with LangBot Space API"""
+
+    ap: app.Application
+    _credits_cache: typing.Dict[str, typing.Tuple[int, float]]  # {user_email: (credits, timestamp)}
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+        self._credits_cache = {}
+
+    def _get_space_config(self) -> typing.Dict[str, str]:
+        """Get Space configuration from config file"""
+        space_config = self.ap.instance_config.data.get('space', {})
+        return {
+            'url': space_config.get('url', 'https://space.langbot.app'),
+            'oauth_authorize_url': space_config.get('oauth_authorize_url', 'https://space.langbot.app/auth/authorize'),
+        }
+
+    async def _get_user_by_email(self, user_email: str) -> user.User | None:
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(user.User).where(user.User.user == user_email)
+        )
+        result_list = result.all()
+        return result_list[0] if result_list else None
+
+    async def _ensure_valid_token(self, user_email: str) -> str | None:
+        """Ensure access token is valid, refresh if expired. Returns valid access_token or None."""
+        user_obj = await self._get_user_by_email(user_email)
+        if not user_obj or user_obj.account_type != 'space':
+            return None
+
+        if not user_obj.space_access_token:
+            return None
+
+        # Check if token is expired (with 60s buffer)
+        if user_obj.space_access_token_expires_at:
+            if datetime.datetime.now() >= user_obj.space_access_token_expires_at - datetime.timedelta(seconds=60):
+                # Token expired, try to refresh
+                if user_obj.space_refresh_token:
+                    try:
+                        new_token = await self._refresh_and_save_token(user_obj)
+                        return new_token
+                    except Exception:
+                        return None
+                return None
+
+        return user_obj.space_access_token
+
+    async def _refresh_and_save_token(self, user_obj: user.User) -> str:
+        """Refresh token and save to database"""
+        token_data = await self.refresh_token(user_obj.space_refresh_token)
+        access_token = token_data.get('access_token')
+        expires_in = token_data.get('expires_in', 0)
+
+        if not access_token:
+            raise ValueError('Failed to refresh token')
+
+        expires_at = datetime.datetime.now() + datetime.timedelta(seconds=expires_in) if expires_in > 0 else None
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(user.User)
+            .where(user.User.user == user_obj.user)
+            .values(
+                space_access_token=access_token,
+                space_access_token_expires_at=expires_at,
+            )
+        )
+
+        return access_token
+
+    # === Raw API calls (no token validation) ===
+
+    def get_oauth_authorize_url(self, redirect_uri: str, state: str = '') -> str:
+        """Get the Space OAuth authorization URL for redirect"""
+        space_config = self._get_space_config()
+        authorize_url = space_config['oauth_authorize_url']
+        params = f'redirect_uri={redirect_uri}'
+        if state:
+            params += f'&state={state}'
+        return f'{authorize_url}?{params}'
+
+    async def exchange_oauth_code(self, code: str) -> typing.Dict:
+        """Exchange OAuth authorization code for tokens"""
+        from langbot.pkg.utils import constants
+
+        space_config = self._get_space_config()
+        space_url = space_config['url']
+
+        session = httpclient.get_session()
+        async with session.post(
+            f'{space_url}/api/v1/accounts/oauth/token',
+            json={'code': code, 'instance_id': constants.instance_id},
+        ) as response:
+            if response.status != 200:
+                raise ValueError(f'Failed to exchange OAuth code: {await response.text()}')
+            data = await response.json()
+            if data.get('code') != 0:
+                raise ValueError(f'Failed to exchange OAuth code: {data.get("msg")}')
+            return data.get('data', {})
+
+    async def refresh_token(self, refresh_token: str) -> typing.Dict:
+        """Refresh Space access token"""
+        space_config = self._get_space_config()
+        space_url = space_config['url']
+
+        session = httpclient.get_session()
+        async with session.post(
+            f'{space_url}/api/v1/accounts/token/refresh', json={'refresh_token': refresh_token}
+        ) as response:
+            if response.status != 200:
+                raise ValueError(f'Failed to refresh token: {await response.text()}')
+            data = await response.json()
+            if data.get('code') != 0:
+                raise ValueError(f'Failed to refresh token: {data.get("msg")}')
+            return data.get('data', {})
+
+    async def get_user_info_raw(self, access_token: str) -> typing.Dict:
+        """Get user info from Space using access token (no validation)"""
+        space_config = self._get_space_config()
+        space_url = space_config['url']
+
+        session = httpclient.get_session()
+        async with session.get(
+            f'{space_url}/api/v1/accounts/me', headers={'Authorization': f'Bearer {access_token}'}
+        ) as response:
+            if response.status != 200:
+                raise ValueError(f'Failed to get user info: {await response.text()}')
+            data = await response.json()
+            if data.get('code') != 0:
+                raise ValueError(f'Failed to get user info: {data.get("msg")}')
+            return data.get('data', {})
+
+    # === API calls with token validation ===
+
+    async def get_user_info(self, user_email: str) -> typing.Dict | None:
+        """Get user info from Space (with token validation)"""
+        access_token = await self._ensure_valid_token(user_email)
+        if not access_token:
+            return None
+        return await self.get_user_info_raw(access_token)
+
+    async def get_credits(self, user_email: str, force_refresh: bool = False) -> int | None:
+        """Get Space credits for user with caching (60s TTL)"""
+        cache_ttl = 60
+
+        if not force_refresh and user_email in self._credits_cache:
+            credits, ts = self._credits_cache[user_email]
+            if time.time() - ts < cache_ttl:
+                return credits
+
+        try:
+            info = await self.get_user_info(user_email)
+            if info is None:
+                return None
+            credits = info.get('credits')
+            if credits is not None:
+                self._credits_cache[user_email] = (credits, time.time())
+            return credits
+        except Exception:
+            return self._credits_cache.get(user_email, (None, 0))[0]
+
+    async def get_models(self) -> typing.List[SpaceModel]:
+        """Get models from Space"""
+
+        space_config = self._get_space_config()
+        space_url = space_config['url']
+
+        session = httpclient.get_session()
+        async with session.get(f'{space_url}/api/v1/models') as response:
+            if response.status != 200:
+                raise ValueError(f'Failed to get models: {await response.text()}')
+            data = await response.json()
+            if data.get('code') != 0:
+                raise ValueError(f'Failed to get models: {data.get("msg")}')
+            models_data = data.get('data', {}).get('models', [])
+            return [SpaceModel.model_validate(model_dict) for model_dict in models_data]
--- a/src/langbot/pkg/api/http/service/user.py
+++ b/src/langbot/pkg/api/http/service/user.py
@@ -4,17 +4,22 @@ import sqlalchemy
 import argon2
 import jwt
 import datetime
+import typing
+import asyncio

 from ....core import app
 from ....entity.persistence import user
 from ....utils import constants
+from ....entity.errors import account as account_errors


 class UserService:
    ap: app.Application
+    _create_user_lock: asyncio.Lock

    def __init__(self, ap: app.Application) -> None:
        self.ap = ap
+        self._create_user_lock = asyncio.Lock()

    async def is_initialized(self) -> bool:
        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(user.User).limit(1))
@@ -28,7 +33,7 @@ class UserService:
        hashed_password = ph.hash(password)

        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.insert(user.User).values(user=user_email, password=hashed_password)
+            sqlalchemy.insert(user.User).values(user=user_email, password=hashed_password, account_type='local')
        )

    async def get_user_by_email(self, user_email: str) -> user.User | None:
@@ -39,6 +44,15 @@ class UserService:
        result_list = result.all()
        return result_list[0] if result_list is not None and len(result_list) > 0 else None

+    async def get_user_by_space_account_uuid(self, space_account_uuid: str) -> user.User | None:
+        """Get user by Space account UUID"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(user.User).where(user.User.space_account_uuid == space_account_uuid)
+        )
+
+        result_list = result.all()
+        return result_list[0] if result_list is not None and len(result_list) > 0 else None
+
    async def authenticate(self, user_email: str, password: str) -> str | None:
        result = await self.ap.persistence_mgr.execute_async(
            sqlalchemy.select(user.User).where(user.User.user == user_email)
@@ -51,6 +65,10 @@ class UserService:

        user_obj = result_list[0]

+        # Check if this is a Space account
+        if user_obj.account_type == 'space':
+            raise ValueError('请使用 Space 账户登录')
+
        ph = argon2.PasswordHasher()

        ph.verify(user_obj.password, password)
@@ -90,6 +108,10 @@ class UserService:
        if user_obj is None:
            raise ValueError('User not found')

+        # Space accounts cannot change password locally
+        if user_obj.account_type == 'space':
+            raise ValueError('Space account cannot change password locally')
+
        ph.verify(user_obj.password, current_password)

        hashed_password = ph.hash(new_password)
@@ -97,3 +119,183 @@ class UserService:
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
        )
+
+    # Space user management
+
+    async def create_or_update_space_user(
+        self,
+        space_account_uuid: str,
+        email: str,
+        access_token: str,
+        refresh_token: str,
+        api_key: str,
+        expires_in: int = 0,
+    ) -> user.User:
+        """Create or update a Space user account (only if system not initialized or user exists)"""
+        expires_at = datetime.datetime.now() + datetime.timedelta(seconds=expires_in) if expires_in > 0 else None
+
+        async with self._create_user_lock:
+            # Check if user with this Space UUID already exists
+            existing_user = await self.get_user_by_space_account_uuid(space_account_uuid)
+
+            if existing_user:
+                # Update existing user's tokens
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.update(user.User)
+                    .where(user.User.space_account_uuid == space_account_uuid)
+                    .values(
+                        space_access_token=access_token,
+                        space_refresh_token=refresh_token,
+                        space_api_key=api_key,
+                        space_access_token_expires_at=expires_at,
+                    )
+                )
+                await self.ap.provider_service.update_space_model_provider_api_keys(api_key)
+                return await self.get_user_by_space_account_uuid(space_account_uuid)
+
+            # Check if user with same email exists
+            existing_email_user = await self.get_user_by_email(email)
+            if existing_email_user:
+                # Update existing user to link with Space account
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.update(user.User)
+                    .where(user.User.user == email)
+                    .values(
+                        account_type='space',
+                        space_account_uuid=space_account_uuid,
+                        space_access_token=access_token,
+                        space_refresh_token=refresh_token,
+                        space_api_key=api_key,
+                        space_access_token_expires_at=expires_at,
+                    )
+                )
+                await self.ap.provider_service.update_space_model_provider_api_keys(api_key)
+                return await self.get_user_by_email(email)
+
+            # Check if system is already initialized
+            is_initialized = await self.is_initialized()
+            if is_initialized:
+                raise account_errors.AccountEmailMismatchError()
+
+            # Create new Space user (first time initialization)
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.insert(user.User).values(
+                    user=email,
+                    password='',  # Space users don't have local password
+                    account_type='space',
+                    space_account_uuid=space_account_uuid,
+                    space_access_token=access_token,
+                    space_refresh_token=refresh_token,
+                    space_api_key=api_key,
+                    space_access_token_expires_at=expires_at,
+                )
+            )
+            await self.ap.provider_service.update_space_model_provider_api_keys(api_key)
+
+            return await self.get_user_by_space_account_uuid(space_account_uuid)
+
+    async def authenticate_space_user(
+        self, access_token: str, refresh_token: str, expires_in: int = 0
+    ) -> typing.Tuple[str, user.User]:
+        """Authenticate with Space and return JWT token"""
+        # Get user info from Space using raw API (token just obtained, no need to validate)
+        user_info = await self.ap.space_service.get_user_info_raw(access_token)
+
+        account = user_info.get('account', {})
+        api_key = user_info.get('api_key', '')
+
+        space_account_uuid = account.get('uuid')
+        email = account.get('email')
+
+        if not space_account_uuid or not email:
+            raise ValueError('Invalid Space user info')
+
+        # Create or update Space user in local database
+        user_obj = await self.create_or_update_space_user(
+            space_account_uuid=space_account_uuid,
+            email=email,
+            access_token=access_token,
+            refresh_token=refresh_token,
+            api_key=api_key,
+            expires_in=expires_in,
+        )
+
+        # Generate JWT token
+        jwt_token = await self.generate_jwt_token(email)
+
+        return jwt_token, user_obj
+
+    async def get_first_user(self) -> user.User | None:
+        """Get the first user (for single-user mode)"""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(user.User).limit(1))
+        result_list = result.all()
+        return result_list[0] if result_list else None
+
+    async def set_password(self, user_email: str, new_password: str, current_password: str | None = None) -> None:
+        """Set or change password for a user"""
+        ph = argon2.PasswordHasher()
+        user_obj = await self.get_user_by_email(user_email)
+
+        if user_obj is None:
+            raise ValueError('User not found')
+
+        # If user already has a password, verify current password
+        has_password = bool(user_obj.password and user_obj.password.strip())
+        if has_password:
+            if not current_password:
+                raise ValueError('Current password is required')
+            ph.verify(user_obj.password, current_password)
+
+        hashed_password = ph.hash(new_password)
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
+        )
+
+    async def bind_space_account(self, user_email: str, code: str) -> user.User:
+        """Bind Space account to existing local account"""
+        # Exchange code for tokens
+        token_data = await self.ap.space_service.exchange_oauth_code(code)
+        access_token = token_data.get('access_token')
+        refresh_token = token_data.get('refresh_token')
+        expires_in = token_data.get('expires_in', 0)
+
+        if not access_token:
+            raise ValueError('Failed to get access token from Space')
+
+        expires_at = datetime.datetime.now() + datetime.timedelta(seconds=expires_in) if expires_in > 0 else None
+
+        # Get Space user info (token just obtained, use raw API)
+        user_info = await self.ap.space_service.get_user_info_raw(access_token)
+        account = user_info.get('account', {})
+        api_key = user_info.get('api_key', '')
+
+        space_account_uuid = account.get('uuid')
+        space_email = account.get('email')
+
+        if not space_account_uuid or not space_email:
+            raise ValueError('Invalid Space user info')
+
+        # Check if this Space account is already bound to another user
+        existing_space_user = await self.get_user_by_space_account_uuid(space_account_uuid)
+        if existing_space_user and existing_space_user.user != user_email:
+            raise ValueError('This Space account is already bound to another user')
+
+        # Update local account to Space account
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(user.User)
+            .where(user.User.user == user_email)
+            .values(
+                user=space_email,  # Update email to Space email
+                account_type='space',
+                space_account_uuid=space_account_uuid,
+                space_access_token=access_token,
+                space_refresh_token=refresh_token,
+                space_api_key=api_key,
+                space_access_token_expires_at=expires_at,
+            )
+        )
+
+        # Update Space model provider API keys
+        await self.ap.provider_service.update_space_model_provider_api_keys(api_key)
+
+        return await self.get_user_by_email(space_email)
--- a/src/langbot/pkg/core/app.py
+++ b/src/langbot/pkg/core/app.py
@@ -15,11 +15,14 @@ from ..command import cmdmgr
 from ..plugin import connector as plugin_connector
 from ..pipeline import pool
 from ..pipeline import controller, pipelinemgr
+from ..pipeline import aggregator as message_aggregator
 from ..utils import version as version_mgr, proxy as proxy_mgr
 from ..persistence import mgr as persistencemgr
 from ..api.http.controller import main as http_controller
 from ..api.http.service import user as user_service
+from ..api.http.service import space as space_service
 from ..api.http.service import model as model_service
+from ..api.http.service import provider as provider_service
 from ..api.http.service import pipeline as pipeline_service
 from ..api.http.service import bot as bot_service
 from ..api.http.service import knowledge as knowledge_service
@@ -27,6 +30,7 @@ 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 external_kb as external_kb_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
@@ -34,6 +38,8 @@ from . import taskmgr
 from . import entities as core_entities
 from ..rag.knowledge import kbmgr as rag_mgr
 from ..vector import mgr as vectordb_mgr
+from ..telemetry import telemetry as telemetry_module
+from ..survey import manager as survey_module


 class Application:
@@ -75,6 +81,8 @@ class Application:

    instance_config: config_mgr.ConfigManager = None

+    instance_id: config_mgr.ConfigManager = None  # used to identify the instance
+
    # ======= Metadata config manager =======

    sensitive_meta: config_mgr.ConfigManager = None
@@ -90,6 +98,8 @@ class Application:

    query_pool: pool.QueryPool = None

+    msg_aggregator: message_aggregator.MessageAggregator = None
+
    ctrl: controller.Controller = None

    pipeline_mgr: pipelinemgr.PipelineManager = None
@@ -114,10 +124,14 @@ class Application:

    user_service: user_service.UserService = None

+    space_service: space_service.SpaceService = None
+
    llm_model_service: model_service.LLMModelsService = None

    embedding_models_service: model_service.EmbeddingModelsService = None

+    provider_service: provider_service.ModelProviderService = None
+
    pipeline_service: pipeline_service.PipelineService = None

    bot_service: bot_service.BotService = None
@@ -132,6 +146,12 @@ class Application:

    webhook_service: webhook_service.WebhookService = None

+    telemetry: telemetry_module.TelemetryManager = None
+
+    survey: survey_module.SurveyManager = None
+
+    monitoring_service: monitoring_service.MonitoringService = None
+
    def __init__(self):
        pass

--- a/src/langbot/pkg/core/bootutils/deps.py
+++ b/src/langbot/pkg/core/bootutils/deps.py
@@ -1,3 +1,4 @@
+import importlib.util
 import pip
 import os
 from ...utils import pkgmgr
@@ -49,9 +50,10 @@ async def check_deps() -> list[str]:

    missing_deps = []
    for dep in required_deps:
-        try:
-            __import__(dep)
-        except ImportError:
+        # Use find_spec instead of __import__ to avoid actually loading
+        # all modules into memory. find_spec only checks if the module
+        # can be found, without executing module-level code.
+        if importlib.util.find_spec(dep) is None:
            missing_deps.append(dep)
    return missing_deps

--- a/src/langbot/pkg/core/migrations/m041_dingtalk_card_autolayout_config.py
+++ b/src/langbot/pkg/core/migrations/m041_dingtalk_card_autolayout_config.py
@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('dingtalk_card_auto_layout', 41)
+class DingTalkCardAutoLayoutMigration(migration.Migration):
+    """迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return True
+
+    async def run(self):
+        """执行迁移"""
+        self.ap.platform_cfg.data['platform-adapters']['app']['dingtalk']['card_auto_layout'] = False
+        await self.ap.platform_cfg.dump_config()
--- a/src/langbot/pkg/core/stages/build_app.py
+++ b/src/langbot/pkg/core/stages/build_app.py
@@ -5,6 +5,7 @@ import asyncio
 from .. import stage, app
 from ...utils import version, proxy
 from ...pipeline import pool, controller, pipelinemgr
+from ...pipeline import aggregator as message_aggregator
 from ...plugin import connector as plugin_connector
 from ...command import cmdmgr
 from ...provider.session import sessionmgr as llm_session_mgr
@@ -16,7 +17,9 @@ from ...platform.webhook_pusher import WebhookPusher
 from ...persistence import mgr as persistencemgr
 from ...api.http.controller import main as http_controller
 from ...api.http.service import user as user_service
+from ...api.http.service import space as space_service
 from ...api.http.service import model as model_service
+from ...api.http.service import provider as provider_service
 from ...api.http.service import pipeline as pipeline_service
 from ...api.http.service import bot as bot_service
 from ...api.http.service import knowledge as knowledge_service
@@ -24,11 +27,14 @@ 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 external_kb as external_kb_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
 from ...vector import mgr as vectordb_mgr
 from .. import taskmgr
+from ...telemetry import telemetry as telemetry_module
+from ...survey import manager as survey_module


@stage.stage_class('BuildAppStage')
@@ -43,77 +49,21 @@ class BuildAppStage(stage.BootingStage):
        discover.discover_blueprint('templates/components.yaml')
        ap.discover = discover

-        proxy_mgr = proxy.ProxyManager(ap)
-        await proxy_mgr.initialize()
-        ap.proxy_mgr = proxy_mgr
-
-        ver_mgr = version.VersionManager(ap)
-        await ver_mgr.initialize()
-        ap.ver_mgr = ver_mgr
-
-        ap.query_pool = pool.QueryPool()
-
-        log_cache = logcache.LogCache()
-        ap.log_cache = log_cache
-
-        storage_mgr_inst = storagemgr.StorageMgr(ap)
-        await storage_mgr_inst.initialize()
-        ap.storage_mgr = storage_mgr_inst
-
-        persistence_mgr_inst = persistencemgr.PersistenceManager(ap)
-        ap.persistence_mgr = persistence_mgr_inst
-        await persistence_mgr_inst.initialize()
-
-        cmd_mgr_inst = cmdmgr.CommandManager(ap)
-        await cmd_mgr_inst.initialize()
-        ap.cmd_mgr = cmd_mgr_inst
-
-        llm_model_mgr_inst = llm_model_mgr.ModelManager(ap)
-        await llm_model_mgr_inst.initialize()
-        ap.model_mgr = llm_model_mgr_inst
-
-        llm_session_mgr_inst = llm_session_mgr.SessionManager(ap)
-        await llm_session_mgr_inst.initialize()
-        ap.sess_mgr = llm_session_mgr_inst
-
-        llm_tool_mgr_inst = llm_tool_mgr.ToolManager(ap)
-        await llm_tool_mgr_inst.initialize()
-        ap.tool_mgr = llm_tool_mgr_inst
-
-        im_mgr_inst = im_mgr.PlatformManager(ap=ap)
-        await im_mgr_inst.initialize()
-        ap.platform_mgr = im_mgr_inst
-
-        # Initialize webhook pusher
-        webhook_pusher_inst = WebhookPusher(ap)
-        ap.webhook_pusher = webhook_pusher_inst
-
-        pipeline_mgr = pipelinemgr.PipelineManager(ap)
-        await pipeline_mgr.initialize()
-        ap.pipeline_mgr = pipeline_mgr
-
-        rag_mgr_inst = rag_mgr.RAGManager(ap)
-        await rag_mgr_inst.initialize()
-        ap.rag_mgr = rag_mgr_inst
-
-        # 初始化向量数据库管理器
-        vectordb_mgr_inst = vectordb_mgr.VectorDBManager(ap)
-        await vectordb_mgr_inst.initialize()
-        ap.vector_db_mgr = vectordb_mgr_inst
-
-        http_ctrl = http_controller.HTTPController(ap)
-        await http_ctrl.initialize()
-        ap.http_ctrl = http_ctrl
-
        user_service_inst = user_service.UserService(ap)
        ap.user_service = user_service_inst

+        space_service_inst = space_service.SpaceService(ap)
+        ap.space_service = space_service_inst
+
        llm_model_service_inst = model_service.LLMModelsService(ap)
        ap.llm_model_service = llm_model_service_inst

        embedding_models_service_inst = model_service.EmbeddingModelsService(ap)
        ap.embedding_models_service = embedding_models_service_inst

+        provider_service_inst = provider_service.ModelProviderService(ap)
+        ap.provider_service = provider_service_inst
+
        pipeline_service_inst = pipeline_service.PipelineService(ap)
        ap.pipeline_service = pipeline_service_inst

@@ -135,6 +85,85 @@ class BuildAppStage(stage.BootingStage):
        webhook_service_inst = webhook_service.WebhookService(ap)
        ap.webhook_service = webhook_service_inst

+        proxy_mgr = proxy.ProxyManager(ap)
+        await proxy_mgr.initialize()
+        ap.proxy_mgr = proxy_mgr
+
+        ver_mgr = version.VersionManager(ap)
+        await ver_mgr.initialize()
+        ap.ver_mgr = ver_mgr
+
+        ap.query_pool = pool.QueryPool()
+
+        log_cache = logcache.LogCache()
+        ap.log_cache = log_cache
+
+        storage_mgr_inst = storagemgr.StorageMgr(ap)
+        await storage_mgr_inst.initialize()
+        ap.storage_mgr = storage_mgr_inst
+
+        persistence_mgr_inst = persistencemgr.PersistenceManager(ap)
+        ap.persistence_mgr = persistence_mgr_inst
+        await persistence_mgr_inst.initialize()
+
+        # Telemetry manager: attach to app so other components can call via self.ap.telemetry
+        telemetry_inst = telemetry_module.TelemetryManager(ap)
+        await telemetry_inst.initialize()
+        ap.telemetry = telemetry_inst
+
+        # Survey manager
+        survey_inst = survey_module.SurveyManager(ap)
+        await survey_inst.initialize()
+        ap.survey = survey_inst
+
+        cmd_mgr_inst = cmdmgr.CommandManager(ap)
+        await cmd_mgr_inst.initialize()
+        ap.cmd_mgr = cmd_mgr_inst
+
+        llm_model_mgr_inst = llm_model_mgr.ModelManager(ap)
+        ap.model_mgr = llm_model_mgr_inst
+        await llm_model_mgr_inst.initialize()
+
+        llm_session_mgr_inst = llm_session_mgr.SessionManager(ap)
+        await llm_session_mgr_inst.initialize()
+        ap.sess_mgr = llm_session_mgr_inst
+
+        llm_tool_mgr_inst = llm_tool_mgr.ToolManager(ap)
+        await llm_tool_mgr_inst.initialize()
+        ap.tool_mgr = llm_tool_mgr_inst
+
+        im_mgr_inst = im_mgr.PlatformManager(ap=ap)
+        await im_mgr_inst.initialize()
+        ap.platform_mgr = im_mgr_inst
+
+        # Initialize webhook pusher
+        webhook_pusher_inst = WebhookPusher(ap)
+        ap.webhook_pusher = webhook_pusher_inst
+
+        pipeline_mgr = pipelinemgr.PipelineManager(ap)
+        await pipeline_mgr.initialize()
+        ap.pipeline_mgr = pipeline_mgr
+
+        # Initialize message aggregator (after pipeline_mgr, as it needs pipeline config)
+        msg_aggregator_inst = message_aggregator.MessageAggregator(ap)
+        ap.msg_aggregator = msg_aggregator_inst
+
+        rag_mgr_inst = rag_mgr.RAGManager(ap)
+        await rag_mgr_inst.initialize()
+        ap.rag_mgr = rag_mgr_inst
+
+        # 初始化向量数据库管理器
+        vectordb_mgr_inst = vectordb_mgr.VectorDBManager(ap)
+        await vectordb_mgr_inst.initialize()
+        ap.vector_db_mgr = vectordb_mgr_inst
+
+        http_ctrl = http_controller.HTTPController(ap)
+        await http_ctrl.initialize()
+        ap.http_ctrl = http_ctrl
+
+        monitoring_service_inst = monitoring_service.MonitoringService(ap)
+        ap.monitoring_service = monitoring_service_inst
+
        async def runtime_disconnect_callback(connector: plugin_connector.PluginRuntimeConnector) -> None:
            await asyncio.sleep(3)
            await plugin_connector_inst.initialize()
--- a/src/langbot/pkg/core/stages/load_config.py
+++ b/src/langbot/pkg/core/stages/load_config.py
@@ -2,8 +2,11 @@ from __future__ import annotations

 import os
 from typing import Any
+from langbot.pkg.utils import constants
 import yaml
 import importlib.resources as resources
+import uuid
+import time

 from .. import stage, app
 from ..bootutils import config
@@ -142,6 +145,24 @@ class LoadConfigStage(stage.BootingStage):

        await ap.instance_config.dump_config()

+        # load or generate instance id
+        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,
+        )
+
+        constants.instance_id = ap.instance_id.data['instance_id']
+        constants.edition = ap.instance_config.data.get('system', {}).get('edition', 'community')
+
+        print(f'LangBot instance id: {constants.instance_id}')
+        print(f'LangBot edition: {constants.edition}')
+
+        await ap.instance_id.dump_config()
+
        ap.sensitive_meta = await config.load_json_config(
            'data/metadata/sensitive-words.json',
            'metadata/sensitive-words.json',
--- a/src/langbot/pkg/entity/dto/init.py
+++ b/src/langbot/pkg/entity/dto/init.py
--- a/src/langbot/pkg/entity/dto/space_model.py
+++ b/src/langbot/pkg/entity/dto/space_model.py
@@ -0,0 +1,49 @@
+# [
+#   {
+#     "uuid": "7652ebdb-54dc-412c-a830-e9268ac88471",
+#     "model_id": "claude-opus-4-5-20251101",
+#     "display_name": {
+#       "en_US": "claude-opus-4-5-20251101",
+#       "zh_Hans": "claude-opus-4-5-20251101"
+#     },
+#     "description": {},
+#     "provider": "anthropic",
+#     "category": "chat",
+#     "icon_url": "Claude.Color",
+#     "tags": {},
+#     "is_featured": true,
+#     "featured_order": 999,
+#     "model_ratio": 2.5,
+#     "completion_ratio": 5,
+#     "quota_type": 0,
+#     "model_price": 0,
+#     "input_credits": 500,
+#     "output_credits": 2500,
+#     "vendor_id": 1,
+#     "vendor_name": "Anthropic",
+#     "vendor_icon": "Claude.Color",
+#     "supported_endpoints": [
+#       "anthropic",
+#       "openai"
+#     ],
+#     "status": "active",
+#     "metadata": null,
+#     "created_at": "2025-12-30T22:23:38.337207+08:00",
+#     "updated_at": "2025-12-30T22:23:38.337207+08:00"
+#   }
+# ]
+
+import pydantic
+
+
+class SpaceModel(pydantic.BaseModel):
+    uuid: str
+    model_id: str
+    provider: str
+    category: str  # chat / embedding
+    llm_abilities: list[str] | None = None
+    is_featured: bool = False
+    featured_order: int = 0
+    status: str
+    created_at: str | None = None
+    updated_at: str | None = None
--- a/src/langbot/pkg/entity/errors/account.py
+++ b/src/langbot/pkg/entity/errors/account.py
@@ -0,0 +1,6 @@
+from __future__ import annotations
+
+
+class AccountEmailMismatchError(Exception):
+    def __str__(self):
+        return 'Account email mismatch'
--- a/src/langbot/pkg/entity/errors/provider.py
+++ b/src/langbot/pkg/entity/errors/provider.py
@@ -7,3 +7,11 @@ class RequesterNotFoundError(Exception):

    def __str__(self):
        return f'Requester {self.requester_name} not found'
+
+
+class ProviderNotFoundError(Exception):
+    def __init__(self, provider_name: str):
+        self.provider_name = provider_name
+
+    def __str__(self):
+        return f'Provider {self.provider_name} not found'
--- a/src/langbot/pkg/entity/persistence/mcp.py
+++ b/src/langbot/pkg/entity/persistence/mcp.py
@@ -9,7 +9,7 @@ class MCPServer(Base):
    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
    enable = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=False)
-    mode = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)  # stdio, sse
+    mode = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)  # stdio, sse, http
    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(
--- a/src/langbot/pkg/entity/persistence/model.py
+++ b/src/langbot/pkg/entity/persistence/model.py
@@ -3,6 +3,25 @@ import sqlalchemy
 from .base import Base


+class ModelProvider(Base):
+    """Model provider"""
+
+    __tablename__ = 'model_providers'
+
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    base_url = sqlalchemy.Column(sqlalchemy.String(512), nullable=False)
+    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default=[])
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
+    updated_at = sqlalchemy.Column(
+        sqlalchemy.DateTime,
+        nullable=False,
+        server_default=sqlalchemy.func.now(),
+        onupdate=sqlalchemy.func.now(),
+    )
+
+
 class LLMModel(Base):
    """LLM model"""

@@ -10,12 +29,10 @@ class LLMModel(Base):

    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
+    provider_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
    abilities = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default=[])
    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    prefered_ranking = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(
        sqlalchemy.DateTime,
@@ -26,17 +43,15 @@ class LLMModel(Base):


 class EmbeddingModel(Base):
-    """Embedding 模型"""
+    """Embedding model"""

    __tablename__ = 'embedding_models'

    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    requester_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
-    api_keys = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
+    provider_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
    extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    prefered_ranking = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(
        sqlalchemy.DateTime,
--- a/src/langbot/pkg/entity/persistence/monitoring.py
+++ b/src/langbot/pkg/entity/persistence/monitoring.py
@@ -0,0 +1,106 @@
+import sqlalchemy
+
+from .base import Base
+
+
+class MonitoringMessage(Base):
+    """Monitoring message records"""
+
+    __tablename__ = 'monitoring_messages'
+
+    id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    message_content = sqlalchemy.Column(sqlalchemy.Text, nullable=False)
+    session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # success, error, pending
+    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)
+    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
+
+
+class MonitoringLLMCall(Base):
+    """LLM call records"""
+
+    __tablename__ = 'monitoring_llm_calls'
+
+    id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    input_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    output_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)  # milliseconds
+    cost = sqlalchemy.Column(sqlalchemy.Float, nullable=True)
+    status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # success, error
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)  # Associated message ID
+
+
+class MonitoringSession(Base):
+    """Session tracking records"""
+
+    __tablename__ = 'monitoring_sessions'
+
+    session_id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    message_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
+    start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    last_activity = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    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)
+
+
+class MonitoringError(Base):
+    """Error log records"""
+
+    __tablename__ = 'monitoring_errors'
+
+    id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    error_type = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=False)
+    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
+    pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    stack_trace = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)  # Associated message ID
+
+
+class MonitoringEmbeddingCall(Base):
+    """Embedding call records"""
+
+    __tablename__ = 'monitoring_embedding_calls'
+
+    id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
+    timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
+    model_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    prompt_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    total_tokens = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
+    duration = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)  # milliseconds
+    input_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)  # Number of input texts
+    status = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # success, error
+    error_message = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    # Optional context fields
+    knowledge_base_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
+    query_text = sqlalchemy.Column(sqlalchemy.Text, nullable=True)  # For retrieval calls
+    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
--- a/src/langbot/pkg/entity/persistence/pipeline.py
+++ b/src/langbot/pkg/entity/persistence/pipeline.py
@@ -11,6 +11,7 @@ class LegacyPipeline(Base):
    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
    description = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+    emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='⚙️')
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(
        sqlalchemy.DateTime,
--- a/src/langbot/pkg/entity/persistence/rag.py
+++ b/src/langbot/pkg/entity/persistence/rag.py
@@ -7,6 +7,7 @@ class KnowledgeBase(Base):
    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String, index=True)
    description = sqlalchemy.Column(sqlalchemy.Text)
+    emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='📚')
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now(), onupdate=sqlalchemy.func.now())
    embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
@@ -35,6 +36,7 @@ class ExternalKnowledgeBase(Base):
    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
    name = sqlalchemy.Column(sqlalchemy.String, index=True)
    description = sqlalchemy.Column(sqlalchemy.Text)
+    emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔗')
    plugin_author = sqlalchemy.Column(sqlalchemy.String, nullable=False)
    plugin_name = sqlalchemy.Column(sqlalchemy.String, nullable=False)
    retriever_name = sqlalchemy.Column(sqlalchemy.String, nullable=False)
--- a/src/langbot/pkg/entity/persistence/user.py
+++ b/src/langbot/pkg/entity/persistence/user.py
@@ -9,6 +9,17 @@ class User(Base):
    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True)
    user = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
    password = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
+
+    # Account type: 'local' (default) or 'space'
+    account_type = sqlalchemy.Column(sqlalchemy.String(32), nullable=False, server_default='local')
+
+    # Space account fields (nullable, only used when account_type='space')
+    space_account_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    space_access_token = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    space_refresh_token = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
+    space_access_token_expires_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
+    space_api_key = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+
    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
    updated_at = sqlalchemy.Column(
        sqlalchemy.DateTime,
--- a/src/langbot/pkg/persistence/mgr.py
+++ b/src/langbot/pkg/persistence/mgr.py
@@ -9,7 +9,7 @@ import sqlalchemy.ext.asyncio as sqlalchemy_asyncio
 import sqlalchemy

 from . import database, migration
-from ..entity.persistence import base, pipeline, metadata
+from ..entity.persistence import base, pipeline, metadata, model as persistence_model
 from ..entity import persistence
 from ..core import app
 from ..utils import constants, importutil
@@ -79,6 +79,7 @@ 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):
        # create tables
@@ -123,7 +124,42 @@ class PersistenceManager:

            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'
+        )
+
+        # write space model providers
+        result = await self.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.requester == 'space-chat-completions'
+            )
+        )
+        exists_space_chat_completions_model_provider = result.first()
+
+        # api keys will be set/updated when the oauth callback
+        if exists_space_chat_completions_model_provider is None:
+            self.ap.logger.info('Creating space model providers...')
+            space_chat_completions_model_provider = {
+                'uuid': '00000000-0000-0000-0000-000000000000',
+                'name': 'LangBot Models',
+                'requester': 'space-chat-completions',
+                'base_url': space_models_gateway_api_url,
+                'api_keys': [],
+            }
+
+            await self.execute_async(
+                sqlalchemy.insert(persistence_model.ModelProvider).values(space_chat_completions_model_provider)
+            )
+        else:
+            if exists_space_chat_completions_model_provider.base_url != space_models_gateway_api_url:
+                await self.execute_async(
+                    sqlalchemy.update(persistence_model.ModelProvider)
+                    .where(persistence_model.ModelProvider.uuid == exists_space_chat_completions_model_provider.uuid)
+                    .values({'base_url': space_models_gateway_api_url})
+                )
+
+    # =================================

    async def execute_async(self, *args, **kwargs) -> sqlalchemy.engine.cursor.CursorResult:
        async with self.get_db_engine().connect() as conn:
--- a/src/langbot/pkg/persistence/migrations/dbm014_space_account_support.py
+++ b/src/langbot/pkg/persistence/migrations/dbm014_space_account_support.py
@@ -0,0 +1,94 @@
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(14)
+class DBMigrateSpaceAccountSupport(migration.DBMigration):
+    """Add Space account support fields to users table"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        # Get all column names from the users table
+        columns = []
+
+        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 = 'users';")
+            )
+            all_result = result.fetchall()
+            columns = [row[0] for row in all_result]
+        else:
+            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text('PRAGMA table_info(users);'))
+            all_result = result.fetchall()
+            columns = [row[1] for row in all_result]
+
+        # Add account_type column
+        if 'account_type' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text("ALTER TABLE users ADD COLUMN account_type VARCHAR(32) DEFAULT 'local' NOT NULL")
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text("ALTER TABLE users ADD COLUMN account_type VARCHAR(32) DEFAULT 'local' NOT NULL")
+                )
+
+        # Add space_account_uuid column
+        if 'space_account_uuid' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_account_uuid VARCHAR(255)')
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_account_uuid VARCHAR(255)')
+                )
+
+        # Add space_access_token column
+        if 'space_access_token' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_access_token TEXT')
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_access_token TEXT')
+                )
+
+        # Add space_refresh_token column
+        if 'space_refresh_token' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_refresh_token TEXT')
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_refresh_token TEXT')
+                )
+
+        # Add space_access_token_expires_at column
+        if 'space_access_token_expires_at' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_access_token_expires_at TIMESTAMP')
+                )
+
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_access_token_expires_at DATETIME')
+                )
+
+        # Add space_api_key column
+        if 'space_api_key' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_api_key VARCHAR(255)')
+                )
+            else:
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text('ALTER TABLE users ADD COLUMN space_api_key VARCHAR(255)')
+                )
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass
--- a/src/langbot/pkg/persistence/migrations/dbm015_model_source_tracking.py
+++ b/src/langbot/pkg/persistence/migrations/dbm015_model_source_tracking.py
@@ -0,0 +1,15 @@
+from .. import migration
+
+
+# this is a deprecated migration
+@migration.migration_class(15)
+class DBMigrateModelSourceTracking(migration.DBMigration):
+    """Add source tracking fields to models tables for Space integration"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        pass
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass
--- a/src/langbot/pkg/persistence/migrations/dbm016_model_provider_refactor.py
+++ b/src/langbot/pkg/persistence/migrations/dbm016_model_provider_refactor.py
@@ -0,0 +1,305 @@
+import uuid as uuid_lib
+
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(16)
+class DBMigrateModelProviderRefactor(migration.DBMigration):
+    """Refactor model structure: create providers from existing models and update references"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        # Step 1: Create model_providers table if not exists
+        await self._create_providers_table()
+
+        # Step 2: Migrate existing models to use providers
+        await self._migrate_llm_models()
+        await self._migrate_embedding_models()
+
+        # Step 3: Remove deprecated columns
+        await self._cleanup_columns()
+
+    async def _create_providers_table(self):
+        """Create model_providers table"""
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("""
+                    CREATE TABLE IF NOT EXISTS model_providers (
+                        uuid VARCHAR(255) PRIMARY KEY,
+                        name VARCHAR(255) NOT NULL,
+                        requester VARCHAR(255) NOT NULL,
+                        base_url VARCHAR(512) NOT NULL,
+                        api_keys JSONB NOT NULL DEFAULT '[]',
+                        created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+                        updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+            )
+        else:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("""
+                    CREATE TABLE IF NOT EXISTS model_providers (
+                        uuid VARCHAR(255) PRIMARY KEY,
+                        name VARCHAR(255) NOT NULL,
+                        requester VARCHAR(255) NOT NULL,
+                        base_url VARCHAR(512) NOT NULL,
+                        api_keys JSON NOT NULL DEFAULT '[]',
+                        created_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
+                        updated_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+            )
+
+    async def _migrate_llm_models(self):
+        """Migrate LLM models to use providers"""
+        llm_columns = await self._get_columns('llm_models')
+
+        # Add provider_uuid column if not exists
+        if 'provider_uuid' not in llm_columns:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('ALTER TABLE llm_models ADD COLUMN provider_uuid VARCHAR(255)')
+            )
+
+        # Add prefered_ranking column if not exists
+        if 'prefered_ranking' not in llm_columns:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('ALTER TABLE llm_models ADD COLUMN prefered_ranking INTEGER NOT NULL DEFAULT 0')
+            )
+
+        # Only migrate if old columns exist
+        if 'requester' not in llm_columns:
+            return
+
+        # Get all LLM models with old structure
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, name, requester, requester_config, api_keys FROM llm_models')
+        )
+        models = result.fetchall()
+
+        # Create providers and update models
+        provider_cache = {}  # (requester, base_url, api_keys_str) -> provider_uuid
+
+        for model in models:
+            model_uuid, model_name, requester, requester_config, api_keys = model
+
+            # Extract base_url from requester_config
+            base_url = ''
+            if requester_config:
+                if isinstance(requester_config, str):
+                    import json
+
+                    requester_config = json.loads(requester_config)
+                base_url = requester_config.get('base_url', '') or requester_config.get('base-url', '')
+
+            # Parse api_keys if it's a string
+            if isinstance(api_keys, str):
+                import json
+
+                try:
+                    api_keys = json.loads(api_keys)
+                except Exception:
+                    api_keys = []
+            if not api_keys:
+                api_keys = []
+
+            # Create cache key
+            api_keys_str = str(sorted(api_keys)) if api_keys else '[]'
+            cache_key = (requester, base_url, api_keys_str)
+
+            if cache_key in provider_cache:
+                provider_uuid = provider_cache[cache_key]
+            else:
+                # Create new provider
+                provider_uuid = str(uuid_lib.uuid4())
+                provider_name = f'{requester}'
+                if base_url:
+                    # Extract domain for name
+                    try:
+                        from urllib.parse import urlparse
+
+                        parsed = urlparse(base_url)
+                        provider_name = parsed.netloc or requester
+                    except Exception:
+                        pass
+
+                import json
+
+                api_keys_json = json.dumps(api_keys) if api_keys else '[]'
+
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text("""
+                        INSERT INTO model_providers (uuid, name, requester, base_url, api_keys)
+                        VALUES (:uuid, :name, :requester, :base_url, :api_keys)
+                    """),
+                    {
+                        'uuid': provider_uuid,
+                        'name': provider_name,
+                        'requester': requester,
+                        'base_url': base_url,
+                        'api_keys': api_keys_json,
+                    },
+                )
+                provider_cache[cache_key] = provider_uuid
+
+            # Update model with provider_uuid
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('UPDATE llm_models SET provider_uuid = :provider_uuid WHERE uuid = :uuid'),
+                {'provider_uuid': provider_uuid, 'uuid': model_uuid},
+            )
+
+    async def _migrate_embedding_models(self):
+        """Migrate embedding models to use providers"""
+        embedding_columns = await self._get_columns('embedding_models')
+
+        # Add provider_uuid column if not exists
+        if 'provider_uuid' not in embedding_columns:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('ALTER TABLE embedding_models ADD COLUMN provider_uuid VARCHAR(255)')
+            )
+
+        # Add prefered_ranking column if not exists
+        if 'prefered_ranking' not in embedding_columns:
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('ALTER TABLE embedding_models ADD COLUMN prefered_ranking INTEGER NOT NULL DEFAULT 0')
+            )
+
+        # Only migrate if old columns exist
+        if 'requester' not in embedding_columns:
+            return
+
+        # Get all embedding models with old structure
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, name, requester, requester_config, api_keys FROM embedding_models')
+        )
+        models = result.fetchall()
+
+        # Get existing providers
+        provider_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text('SELECT uuid, requester, base_url, api_keys FROM model_providers')
+        )
+        existing_providers = provider_result.fetchall()
+
+        provider_cache = {}
+        for p in existing_providers:
+            p_uuid, p_requester, p_base_url, p_api_keys = p
+            api_keys_str = str(sorted(p_api_keys)) if p_api_keys else '[]'
+            provider_cache[(p_requester, p_base_url, api_keys_str)] = p_uuid
+
+        for model in models:
+            model_uuid, model_name, requester, requester_config, api_keys = model
+
+            base_url = ''
+            if requester_config:
+                if isinstance(requester_config, str):
+                    import json
+
+                    requester_config = json.loads(requester_config)
+                base_url = requester_config.get('base_url', '') or requester_config.get('base-url', '')
+
+            # Parse api_keys if it's a string
+            if isinstance(api_keys, str):
+                import json
+
+                try:
+                    api_keys = json.loads(api_keys)
+                except Exception:
+                    api_keys = []
+            if not api_keys:
+                api_keys = []
+
+            api_keys_str = str(sorted(api_keys)) if api_keys else '[]'
+            cache_key = (requester, base_url, api_keys_str)
+
+            if cache_key in provider_cache:
+                provider_uuid = provider_cache[cache_key]
+            else:
+                provider_uuid = str(uuid_lib.uuid4())
+                provider_name = f'{requester}'
+                if base_url:
+                    try:
+                        from urllib.parse import urlparse
+
+                        parsed = urlparse(base_url)
+                        provider_name = parsed.netloc or requester
+                    except Exception:
+                        pass
+
+                import json
+
+                api_keys_json = json.dumps(api_keys) if api_keys else '[]'
+
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text("""
+                        INSERT INTO model_providers (uuid, name, requester, base_url, api_keys)
+                        VALUES (:uuid, :name, :requester, :base_url, :api_keys)
+                    """),
+                    {
+                        'uuid': provider_uuid,
+                        'name': provider_name,
+                        'requester': requester,
+                        'base_url': base_url,
+                        'api_keys': api_keys_json,
+                    },
+                )
+                provider_cache[cache_key] = provider_uuid
+
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text('UPDATE embedding_models SET provider_uuid = :provider_uuid WHERE uuid = :uuid'),
+                {'provider_uuid': provider_uuid, 'uuid': model_uuid},
+            )
+
+    async def _cleanup_columns(self):
+        """Remove deprecated columns from model tables"""
+
+        llm_columns = await self._get_columns('llm_models')
+        deprecated_llm_cols = ['requester', 'requester_config', 'api_keys', 'description', 'source', 'space_model_id']
+        for col in deprecated_llm_cols:
+            if col in llm_columns:
+                if self.ap.persistence_mgr.db.name == 'postgresql':
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(f'ALTER TABLE llm_models DROP COLUMN IF EXISTS {col}')
+                    )
+                else:
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(f'ALTER TABLE llm_models DROP COLUMN {col}')
+                    )
+
+        embedding_columns = await self._get_columns('embedding_models')
+        deprecated_embedding_cols = [
+            'requester',
+            'requester_config',
+            'api_keys',
+            'description',
+            'source',
+            'space_model_id',
+        ]
+        for col in deprecated_embedding_cols:
+            if col in embedding_columns:
+                if self.ap.persistence_mgr.db.name == 'postgresql':
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(f'ALTER TABLE embedding_models DROP COLUMN IF EXISTS {col}')
+                    )
+                else:
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(f'ALTER TABLE embedding_models DROP COLUMN {col}')
+                    )
+
+    async def _get_columns(self, table_name: str) -> list:
+        """Get column names for a table"""
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    f"SELECT column_name FROM information_schema.columns WHERE table_name = '{table_name}';"
+                )
+            )
+            all_result = result.fetchall()
+            return [row[0] for row in all_result]
+        else:
+            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});'))
+            all_result = result.fetchall()
+            return [row[1] for row in all_result]
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass
--- a/src/langbot/pkg/persistence/migrations/dbm017_move_cloud_service_url.py
+++ b/src/langbot/pkg/persistence/migrations/dbm017_move_cloud_service_url.py
@@ -0,0 +1,25 @@
+from .. import migration
+
+
+@migration.migration_class(17)
+class MoveCloudServiceUrl(migration.DBMigration):
+    """迁移云服务 URL 配置"""
+
+    async def upgrade(self):
+        """升级"""
+        if 'space' not in self.ap.instance_config.data:
+            self.ap.instance_config.data['space'] = {
+                'url': 'https://space.langbot.app',
+                'models_gateway_api_url': 'https://api.langbot.cloud/v1',
+                'oauth_authorize_url': 'https://space.langbot.app/auth/authorize',
+                'disable_models_service': False,
+            }
+
+        if 'plugin' in self.ap.instance_config.data:
+            self.ap.instance_config.data['plugin'].pop('cloud_service_url', None)
+
+        await self.ap.instance_config.dump_config()
+
+    async def downgrade(self):
+        """降级"""
+        pass
--- a/src/langbot/pkg/persistence/migrations/dbm018_add_emoji_support.py
+++ b/src/langbot/pkg/persistence/migrations/dbm018_add_emoji_support.py
@@ -0,0 +1,58 @@
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(18)
+class DBMigrateAddEmojiSupport(migration.DBMigration):
+    """Add emoji field to knowledge_bases, external_knowledge_bases and legacy_pipelines tables"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        # Add emoji field to knowledge_bases
+        await self._add_emoji_to_table('knowledge_bases', '📚')
+
+        # Add emoji field to external_knowledge_bases
+        await self._add_emoji_to_table('external_knowledge_bases', '🔗')
+
+        # Add emoji field to legacy_pipelines
+        await self._add_emoji_to_table('legacy_pipelines', '⚙️')
+
+    async def _add_emoji_to_table(self, table_name: str, default_emoji: str):
+        """Add emoji column to specified table if it doesn't exist"""
+        # Get all column names from the table
+        columns = []
+
+        if self.ap.persistence_mgr.db.name == 'postgresql':
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    f"SELECT column_name FROM information_schema.columns WHERE table_name = '{table_name}';"
+                )
+            )
+            all_result = result.fetchall()
+            columns = [row[0] for row in all_result]
+        else:
+            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});'))
+            all_result = result.fetchall()
+            columns = [row[1] for row in all_result]
+
+        # Check and add emoji column
+        if 'emoji' not in columns:
+            if self.ap.persistence_mgr.db.name == 'postgresql':
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(f"ALTER TABLE {table_name} ADD COLUMN emoji VARCHAR(10) DEFAULT '{default_emoji}'")
+                )
+            else:
+                # SQLite doesn't support DEFAULT with emoji directly in ALTER TABLE
+                # Add column without default first
+                await self.ap.persistence_mgr.execute_async(
+                    sqlalchemy.text(f'ALTER TABLE {table_name} ADD COLUMN emoji VARCHAR(10)')
+                )
+
+            # Set default emoji value for existing records
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(f"UPDATE {table_name} SET emoji = '{default_emoji}' WHERE emoji IS NULL")
+            )
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass
--- a/src/langbot/pkg/persistence/migrations/dbm019_monitoring_message_role.py
+++ b/src/langbot/pkg/persistence/migrations/dbm019_monitoring_message_role.py
@@ -0,0 +1,24 @@
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(19)
+class DBMigrateMonitoringMessageRole(migration.DBMigration):
+    """Add role column to monitoring_messages table"""
+
+    async def upgrade(self):
+        """Upgrade"""
+        try:
+            sql_text = sqlalchemy.text("ALTER TABLE monitoring_messages ADD COLUMN role VARCHAR(50) DEFAULT 'user'")
+            await self.ap.persistence_mgr.execute_async(sql_text)
+        except Exception:
+            # Column may already exist
+            pass
+
+    async def downgrade(self):
+        """Downgrade"""
+        try:
+            sql_text = sqlalchemy.text('ALTER TABLE monitoring_messages DROP COLUMN role')
+            await self.ap.persistence_mgr.execute_async(sql_text)
+        except Exception:
+            pass
--- a/src/langbot/pkg/pipeline/aggregator.py
+++ b/src/langbot/pkg/pipeline/aggregator.py
@@ -0,0 +1,289 @@
+"""Message Aggregator Module
+
+This module provides message aggregation/debounce functionality.
+When users send multiple messages consecutively, the aggregator will wait
+for a configurable delay period and merge them into a single message
+before processing.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import time
+import typing
+from dataclasses import dataclass, field
+
+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.builtin.provider.session as provider_session
+import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
+
+if typing.TYPE_CHECKING:
+    from ..core import app
+
+# Maximum number of messages to buffer before forcing a flush
+MAX_BUFFER_MESSAGES = 10
+
+
+@dataclass
+class PendingMessage:
+    """A pending message waiting to be aggregated"""
+
+    bot_uuid: str
+    launcher_type: provider_session.LauncherTypes
+    launcher_id: typing.Union[int, str]
+    sender_id: typing.Union[int, str]
+    message_event: platform_events.MessageEvent
+    message_chain: platform_message.MessageChain
+    adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter
+    pipeline_uuid: typing.Optional[str]
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class SessionBuffer:
+    """Buffer for a single session's pending messages"""
+
+    session_id: str
+    messages: list[PendingMessage] = field(default_factory=list)
+    timer_task: typing.Optional[asyncio.Task] = None
+    last_message_time: float = field(default_factory=time.time)
+
+
+class MessageAggregator:
+    """Message aggregator that buffers and merges consecutive messages
+
+    This class implements a debounce mechanism for incoming messages.
+    When a message arrives, it starts a timer. If more messages arrive
+    before the timer expires, they are buffered. When the timer expires,
+    all buffered messages are merged and sent to the query pool.
+    """
+
+    ap: app.Application
+
+    buffers: dict[str, SessionBuffer]
+    """Session ID -> SessionBuffer mapping"""
+
+    lock: asyncio.Lock
+    """Lock for thread-safe buffer operations"""
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+        self.buffers = {}
+        self.lock = asyncio.Lock()
+
+    def _get_session_id(
+        self,
+        bot_uuid: str,
+        launcher_type: provider_session.LauncherTypes,
+        launcher_id: typing.Union[int, str],
+    ) -> str:
+        """Generate a unique session ID"""
+        return f'{bot_uuid}:{launcher_type.value}:{launcher_id}'
+
+    async def _get_aggregation_config(self, pipeline_uuid: typing.Optional[str]) -> tuple[bool, float]:
+        """Get aggregation configuration for a pipeline
+
+        Returns:
+            tuple: (enabled, delay_seconds)
+        """
+        default_enabled = False
+        default_delay = 1.5
+
+        if pipeline_uuid is None:
+            return default_enabled, default_delay
+
+        # Get pipeline from pipeline manager
+        pipeline = await self.ap.pipeline_mgr.get_pipeline_by_uuid(pipeline_uuid)
+        if pipeline is None:
+            return default_enabled, default_delay
+
+        config = pipeline.pipeline_entity.config or {}
+        trigger_config = config.get('trigger', {})
+        aggregation_config = trigger_config.get('message-aggregation', {})
+
+        enabled = aggregation_config.get('enabled', default_enabled)
+
+        delay_raw = aggregation_config.get('delay', default_delay)
+        try:
+            delay = float(delay_raw)
+        except (TypeError, ValueError):
+            delay = default_delay
+
+        # Clamp delay to valid range
+        delay = max(1.0, min(10.0, delay))
+
+        return enabled, delay
+
+    async def add_message(
+        self,
+        bot_uuid: str,
+        launcher_type: provider_session.LauncherTypes,
+        launcher_id: typing.Union[int, str],
+        sender_id: typing.Union[int, str],
+        message_event: platform_events.MessageEvent,
+        message_chain: platform_message.MessageChain,
+        adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
+        pipeline_uuid: typing.Optional[str] = None,
+    ) -> None:
+        """Add a message to the aggregation buffer
+
+        If aggregation is disabled for the pipeline, the message is sent
+        directly to the query pool. Otherwise, it's buffered and will be
+        merged with other messages from the same session.
+        """
+        enabled, delay = await self._get_aggregation_config(pipeline_uuid)
+
+        if not enabled:
+            # Aggregation disabled, send directly to query pool
+            await self.ap.query_pool.add_query(
+                bot_uuid=bot_uuid,
+                launcher_type=launcher_type,
+                launcher_id=launcher_id,
+                sender_id=sender_id,
+                message_event=message_event,
+                message_chain=message_chain,
+                adapter=adapter,
+                pipeline_uuid=pipeline_uuid,
+            )
+            return
+
+        session_id = self._get_session_id(bot_uuid, launcher_type, launcher_id)
+
+        pending_msg = PendingMessage(
+            bot_uuid=bot_uuid,
+            launcher_type=launcher_type,
+            launcher_id=launcher_id,
+            sender_id=sender_id,
+            message_event=message_event,
+            message_chain=message_chain,
+            adapter=adapter,
+            pipeline_uuid=pipeline_uuid,
+        )
+
+        force_flush = False
+        async with self.lock:
+            if session_id in self.buffers:
+                buffer = self.buffers[session_id]
+                # Cancel existing timer (just cancel, don't await inside lock)
+                if buffer.timer_task and not buffer.timer_task.done():
+                    buffer.timer_task.cancel()
+                buffer.messages.append(pending_msg)
+            else:
+                buffer = SessionBuffer(
+                    session_id=session_id,
+                    messages=[pending_msg],
+                )
+                self.buffers[session_id] = buffer
+
+            buffer.last_message_time = time.time()
+
+            # Check if buffer reached max capacity
+            if len(buffer.messages) >= MAX_BUFFER_MESSAGES:
+                force_flush = True
+            else:
+                # Start new timer
+                buffer.timer_task = asyncio.create_task(self._delayed_flush(session_id, delay))
+
+        if force_flush:
+            await self._flush_buffer(session_id)
+
+    async def _delayed_flush(self, session_id: str, delay: float) -> None:
+        """Wait for delay then flush the buffer"""
+        try:
+            await asyncio.sleep(delay)
+            await self._flush_buffer(session_id)
+        except asyncio.CancelledError:
+            # Timer was cancelled, new message arrived
+            pass
+
+    async def _flush_buffer(self, session_id: str) -> None:
+        """Flush the buffer for a session, merging all messages"""
+        async with self.lock:
+            buffer = self.buffers.pop(session_id, None)
+
+        if buffer is None or not buffer.messages:
+            return
+
+        if len(buffer.messages) == 1:
+            # Only one message, no need to merge
+            msg = buffer.messages[0]
+            await self.ap.query_pool.add_query(
+                bot_uuid=msg.bot_uuid,
+                launcher_type=msg.launcher_type,
+                launcher_id=msg.launcher_id,
+                sender_id=msg.sender_id,
+                message_event=msg.message_event,
+                message_chain=msg.message_chain,
+                adapter=msg.adapter,
+                pipeline_uuid=msg.pipeline_uuid,
+            )
+            return
+
+        # Merge multiple messages
+        merged_msg = self._merge_messages(buffer.messages)
+        await self.ap.query_pool.add_query(
+            bot_uuid=merged_msg.bot_uuid,
+            launcher_type=merged_msg.launcher_type,
+            launcher_id=merged_msg.launcher_id,
+            sender_id=merged_msg.sender_id,
+            message_event=merged_msg.message_event,
+            message_chain=merged_msg.message_chain,
+            adapter=merged_msg.adapter,
+            pipeline_uuid=merged_msg.pipeline_uuid,
+        )
+
+    def _merge_messages(self, messages: list[PendingMessage]) -> PendingMessage:
+        """Merge multiple messages into one
+
+        The merged message uses the first message as base and combines
+        all message chains with newline separators.
+        The original message_event is kept unmodified to preserve
+        message metadata (message_id, etc.) for reply/quote.
+        """
+        if len(messages) == 1:
+            return messages[0]
+
+        base_msg = messages[0]
+
+        # Build merged message chain
+        merged_chain = platform_message.MessageChain([])
+
+        for i, msg in enumerate(messages):
+            if i > 0:
+                # Add newline separator between messages
+                merged_chain.append(platform_message.Plain(text='\n'))
+
+            # Copy all components from this message
+            for component in msg.message_chain:
+                merged_chain.append(component)
+
+        # Keep message_event unmodified (preserves original message_id and
+        # metadata for reply/quote), only pass merged chain separately
+        return PendingMessage(
+            bot_uuid=base_msg.bot_uuid,
+            launcher_type=base_msg.launcher_type,
+            launcher_id=base_msg.launcher_id,
+            sender_id=base_msg.sender_id,
+            message_event=base_msg.message_event,
+            message_chain=merged_chain,
+            adapter=base_msg.adapter,
+            pipeline_uuid=base_msg.pipeline_uuid,
+        )
+
+    async def flush_all(self) -> None:
+        """Flush all pending buffers immediately
+
+        This is useful during shutdown to ensure no messages are lost.
+        """
+        # Snapshot session IDs and cancel all timers under lock
+        async with self.lock:
+            session_ids = list(self.buffers.keys())
+            for sid in session_ids:
+                buffer = self.buffers.get(sid)
+                if buffer and buffer.timer_task and not buffer.timer_task.done():
+                    buffer.timer_task.cancel()
+
+        # Flush each buffer outside the lock
+        for session_id in session_ids:
+            await self._flush_buffer(session_id)
--- a/src/langbot/pkg/pipeline/cntfilter/filters/baiduexamine.py
+++ b/src/langbot/pkg/pipeline/cntfilter/filters/baiduexamine.py
@@ -1,10 +1,9 @@
 from __future__ import annotations

-import aiohttp
-
 from .. import entities
 from .. import filter as filter_model
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+from langbot.pkg.utils import httpclient

 BAIDU_EXAMINE_URL = 'https://aip.baidubce.com/rest/2.0/solution/v1/text_censor/v2/user_defined?access_token={}'
 BAIDU_EXAMINE_TOKEN_URL = 'https://aip.baidubce.com/oauth/2.0/token'
@@ -15,50 +14,50 @@ class BaiduCloudExamine(filter_model.ContentFilter):
    """百度云内容审核"""

    async def _get_token(self) -> str:
-        async with aiohttp.ClientSession() as session:
-            async with session.post(
-                BAIDU_EXAMINE_TOKEN_URL,
-                params={
-                    'grant_type': 'client_credentials',
-                    'client_id': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-key'],
-                    'client_secret': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-secret'],
-                },
-            ) as resp:
-                return (await resp.json())['access_token']
+        session = httpclient.get_session()
+        async with session.post(
+            BAIDU_EXAMINE_TOKEN_URL,
+            params={
+                'grant_type': 'client_credentials',
+                'client_id': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-key'],
+                'client_secret': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-secret'],
+            },
+        ) as resp:
+            return (await resp.json())['access_token']

    async def process(self, query: pipeline_query.Query, message: str) -> entities.FilterResult:
-        async with aiohttp.ClientSession() as session:
-            async with session.post(
-                BAIDU_EXAMINE_URL.format(await self._get_token()),
-                headers={
-                    'Content-Type': 'application/x-www-form-urlencoded',
-                    'Accept': 'application/json',
-                },
-                data=f'text={message}'.encode('utf-8'),
-            ) as resp:
-                result = await resp.json()
+        session = httpclient.get_session()
+        async with session.post(
+            BAIDU_EXAMINE_URL.format(await self._get_token()),
+            headers={
+                'Content-Type': 'application/x-www-form-urlencoded',
+                'Accept': 'application/json',
+            },
+            data=f'text={message}'.encode('utf-8'),
+        ) as resp:
+            result = await resp.json()

-                if 'error_code' in result:
+            if 'error_code' in result:
+                return entities.FilterResult(
+                    level=entities.ResultLevel.BLOCK,
+                    replacement=message,
+                    user_notice='',
+                    console_notice=f'百度云判定出错，错误信息：{result["error_msg"]}',
+                )
+            else:
+                conclusion = result['conclusion']
+
+                if conclusion in ('合规'):
+                    return entities.FilterResult(
+                        level=entities.ResultLevel.PASS,
+                        replacement=message,
+                        user_notice='',
+                        console_notice=f'百度云判定结果：{conclusion}',
+                    )
+                else:
                    return entities.FilterResult(
                        level=entities.ResultLevel.BLOCK,
                        replacement=message,
-                        user_notice='',
-                        console_notice=f'百度云判定出错，错误信息：{result["error_msg"]}',
+                        user_notice='消息中存在不合适的内容, 请修改',
+                        console_notice=f'百度云判定结果：{conclusion}',
                    )
-                else:
-                    conclusion = result['conclusion']
-
-                    if conclusion in ('合规'):
-                        return entities.FilterResult(
-                            level=entities.ResultLevel.PASS,
-                            replacement=message,
-                            user_notice='',
-                            console_notice=f'百度云判定结果：{conclusion}',
-                        )
-                    else:
-                        return entities.FilterResult(
-                            level=entities.ResultLevel.BLOCK,
-                            replacement=message,
-                            user_notice='消息中存在不合适的内容, 请修改',
-                            console_notice=f'百度云判定结果：{conclusion}',
-                        )
--- a/src/langbot/pkg/pipeline/monitoring_helper.py
+++ b/src/langbot/pkg/pipeline/monitoring_helper.py
@@ -0,0 +1,324 @@
+"""
+Monitoring helper for recording events during pipeline execution.
+This module provides convenient methods to record monitoring data
+without cluttering the main pipeline code.
+"""
+
+from __future__ import annotations
+
+import traceback
+import typing
+import time
+import json
+
+if typing.TYPE_CHECKING:
+    from ..core import app
+    import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+
+
+class MonitoringHelper:
+    """Helper class for monitoring operations"""
+
+    @staticmethod
+    async def record_query_start(
+        ap: app.Application,
+        query: pipeline_query.Query,
+        bot_id: str,
+        bot_name: str,
+        pipeline_id: str,
+        pipeline_name: str,
+        runner_name: str | None = None,
+    ) -> str:
+        """Record the start of query processing, returns message_id"""
+        try:
+            # Check if session exists, if not, record session start
+            session_id = f'{query.launcher_type}_{query.launcher_id}'
+
+            # 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'):
+                message_content = json.dumps(query.message_chain.model_dump(), ensure_ascii=False)
+            else:
+                message_content = str(query)
+
+            # Variables will be updated in record_query_success after preproc stage sets them
+            # Here we just record None, the full variables will be set when query completes
+
+            message_id = await ap.monitoring_service.record_message(
+                bot_id=bot_id,
+                bot_name=bot_name,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+                message_content=message_content,
+                session_id=session_id,
+                status='pending',
+                level='info',
+                platform=query.launcher_type.value
+                if hasattr(query.launcher_type, 'value')
+                else str(query.launcher_type),
+                user_id=query.sender_id,
+                runner_name=runner_name,
+                variables=None,  # Will be updated in record_query_success
+            )
+
+            # Update session activity or create new session if it doesn't exist
+            # Always pass pipeline info to handle pipeline switches
+            session_updated = await ap.monitoring_service.update_session_activity(
+                session_id,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+            )
+            if not session_updated:
+                # Session doesn't exist, create it
+                await ap.monitoring_service.record_session_start(
+                    session_id=session_id,
+                    bot_id=bot_id,
+                    bot_name=bot_name,
+                    pipeline_id=pipeline_id,
+                    pipeline_name=pipeline_name,
+                    platform=query.launcher_type.value
+                    if hasattr(query.launcher_type, 'value')
+                    else str(query.launcher_type),
+                    user_id=query.sender_id,
+                )
+
+            return message_id
+        except Exception as e:
+            ap.logger.error(f'Failed to record query start: {e}')
+            return ''
+
+    @staticmethod
+    async def record_query_success(
+        ap: app.Application,
+        message_id: str,
+        query: pipeline_query.Query | None = None,
+    ):
+        """Record successful query processing by updating message status and variables"""
+        try:
+            if message_id:
+                # Serialize query.variables (filtering out internal variables)
+                query_variables_str = None
+                if query and hasattr(query, 'variables') and query.variables:
+                    filtered_vars = {k: v for k, v in query.variables.items() if not k.startswith('_')}
+                    if filtered_vars:
+                        try:
+                            query_variables_str = json.dumps(filtered_vars, ensure_ascii=False, default=str)
+                        except Exception:
+                            pass
+
+                await ap.monitoring_service.update_message_status(
+                    message_id=message_id,
+                    status='success',
+                    variables=query_variables_str,
+                )
+        except Exception as e:
+            ap.logger.error(f'Failed to record query success: {e}')
+
+    @staticmethod
+    async def record_query_response(
+        ap: app.Application,
+        query: pipeline_query.Query,
+        bot_id: str,
+        bot_name: str,
+        pipeline_id: str,
+        pipeline_name: str,
+        runner_name: str | None = None,
+    ):
+        """Record bot response message to monitoring"""
+        try:
+            session_id = f'{query.launcher_type}_{query.launcher_id}'
+
+            # Extract response content from resp_message_chain
+            if hasattr(query, 'resp_message_chain') and query.resp_message_chain:
+                # Serialize the last response message chain
+                last_resp = query.resp_message_chain[-1]
+                if hasattr(last_resp, 'model_dump'):
+                    message_content = json.dumps(last_resp.model_dump(), ensure_ascii=False)
+                else:
+                    message_content = str(last_resp)
+            elif hasattr(query, 'resp_messages') and query.resp_messages:
+                last_resp = query.resp_messages[-1]
+                if hasattr(last_resp, 'get_content_platform_message_chain'):
+                    chain = last_resp.get_content_platform_message_chain()
+                    if hasattr(chain, 'model_dump'):
+                        message_content = json.dumps(chain.model_dump(), ensure_ascii=False)
+                    else:
+                        message_content = str(chain)
+                else:
+                    message_content = str(last_resp)
+            else:
+                return  # No response to record
+
+            await ap.monitoring_service.record_message(
+                bot_id=bot_id,
+                bot_name=bot_name,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+                message_content=message_content,
+                session_id=session_id,
+                status='success',
+                level='info',
+                platform=query.launcher_type.value
+                if hasattr(query.launcher_type, 'value')
+                else str(query.launcher_type),
+                user_id=query.sender_id,
+                runner_name=runner_name,
+                role='assistant',
+            )
+        except Exception as e:
+            ap.logger.error(f'Failed to record query response: {e}')
+
+    @staticmethod
+    async def record_query_error(
+        ap: app.Application,
+        query: pipeline_query.Query,
+        bot_id: str,
+        bot_name: str,
+        pipeline_id: str,
+        pipeline_name: str,
+        error: Exception,
+        runner_name: str | None = None,
+    ) -> str:
+        """Record query processing error, returns message_id"""
+        try:
+            session_id = f'{query.launcher_type}_{query.launcher_id}'
+
+            # Record error message
+            message_id = await ap.monitoring_service.record_message(
+                bot_id=bot_id,
+                bot_name=bot_name,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+                message_content=f'Error: {str(error)}',
+                session_id=session_id,
+                status='error',
+                level='error',
+                platform=query.launcher_type.value
+                if hasattr(query.launcher_type, 'value')
+                else str(query.launcher_type),
+                user_id=query.sender_id,
+                runner_name=runner_name,
+            )
+
+            # Record error log
+            await ap.monitoring_service.record_error(
+                bot_id=bot_id,
+                bot_name=bot_name,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+                error_type=type(error).__name__,
+                error_message=str(error),
+                session_id=session_id,
+                stack_trace=traceback.format_exc(),
+                message_id=message_id,
+            )
+
+            return message_id
+        except Exception as e:
+            ap.logger.error(f'Failed to record query error: {e}')
+            return ''
+
+    @staticmethod
+    async def record_llm_call(
+        ap: app.Application,
+        query: pipeline_query.Query,
+        bot_id: str,
+        bot_name: str,
+        pipeline_id: str,
+        pipeline_name: str,
+        model_name: str,
+        input_tokens: int,
+        output_tokens: int,
+        duration_ms: int,
+        status: str = 'success',
+        cost: float | None = None,
+        error_message: str | None = None,
+        message_id: str | None = None,
+    ):
+        """Record LLM call"""
+        try:
+            session_id = f'{query.launcher_type}_{query.launcher_id}'
+
+            await ap.monitoring_service.record_llm_call(
+                bot_id=bot_id,
+                bot_name=bot_name,
+                pipeline_id=pipeline_id,
+                pipeline_name=pipeline_name,
+                session_id=session_id,
+                model_name=model_name,
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                duration=duration_ms,
+                status=status,
+                cost=cost,
+                error_message=error_message,
+                message_id=message_id,
+            )
+        except Exception as e:
+            ap.logger.error(f'Failed to record LLM call: {e}')
+
+
+class LLMCallMonitor:
+    """Context manager for monitoring LLM calls"""
+
+    def __init__(
+        self,
+        ap: app.Application,
+        query: pipeline_query.Query,
+        bot_id: str,
+        bot_name: str,
+        pipeline_id: str,
+        pipeline_name: str,
+        model_name: str,
+    ):
+        self.ap = ap
+        self.query = query
+        self.bot_id = bot_id
+        self.bot_name = bot_name
+        self.pipeline_id = pipeline_id
+        self.pipeline_name = pipeline_name
+        self.model_name = model_name
+        self.start_time = None
+        self.input_tokens = 0
+        self.output_tokens = 0
+
+    async def __aenter__(self):
+        self.start_time = time.time()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        duration_ms = int((time.time() - self.start_time) * 1000)
+
+        if exc_type is not None:
+            # Error occurred
+            await MonitoringHelper.record_llm_call(
+                ap=self.ap,
+                query=self.query,
+                bot_id=self.bot_id,
+                bot_name=self.bot_name,
+                pipeline_id=self.pipeline_id,
+                pipeline_name=self.pipeline_name,
+                model_name=self.model_name,
+                input_tokens=self.input_tokens,
+                output_tokens=self.output_tokens,
+                duration_ms=duration_ms,
+                status='error',
+                error_message=str(exc_val) if exc_val else None,
+            )
+        else:
+            # Success
+            await MonitoringHelper.record_llm_call(
+                ap=self.ap,
+                query=self.query,
+                bot_id=self.bot_id,
+                bot_name=self.bot_name,
+                pipeline_id=self.pipeline_id,
+                pipeline_name=self.pipeline_name,
+                model_name=self.model_name,
+                input_tokens=self.input_tokens,
+                output_tokens=self.output_tokens,
+                duration_ms=duration_ms,
+                status='success',
+            )
+
+        return False  # Don't suppress exceptions
--- a/src/langbot/pkg/pipeline/pipelinemgr.py
+++ b/src/langbot/pkg/pipeline/pipelinemgr.py
@@ -115,6 +115,25 @@ class RuntimePipeline:
        # Store bound plugins and MCP servers in query for filtering
        query.variables['_pipeline_bound_plugins'] = self.bound_plugins
        query.variables['_pipeline_bound_mcp_servers'] = self.bound_mcp_servers
+
+        # Record query start for monitoring
+        try:
+            # Get bot name from bot_uuid
+            bot_name = 'WebChat'
+            if query.bot_uuid:
+                try:
+                    bot = await self.ap.bot_service.get_bot(query.bot_uuid, include_secret=False)
+                    if bot:
+                        bot_name = bot.get('name', 'Unknown')
+                except Exception:
+                    pass
+
+            # Store for later use in process_query
+            query.variables['_monitoring_bot_name'] = bot_name
+            query.variables['_monitoring_pipeline_name'] = self.pipeline_entity.name
+        except Exception as e:
+            self.ap.logger.error(f'Failed to prepare monitoring data: {e}')
+
        await self.process_query(query)

    async def _check_output(self, query: pipeline_query.Query, result: pipeline_entities.StageProcessResult):
@@ -131,7 +150,7 @@ class RuntimePipeline:
                query.message_event, platform_events.GroupMessage
            ):
                result.user_notice.insert(0, platform_message.At(target=query.message_event.sender.id))
-            if await query.adapter.is_stream_output_supported():
+            if await query.adapter.is_stream_output_supported() and query.resp_messages:
                await query.adapter.reply_message_chunk(
                    message_source=query.message_event,
                    bot_message=query.resp_messages[-1],
@@ -151,6 +170,37 @@ class RuntimePipeline:
            self.ap.logger.info(result.console_notice)
        if result.error_notice:
            self.ap.logger.error(result.error_notice)
+            # Mark query as having error
+            query.variables['_monitoring_has_error'] = True
+            # Record error to monitoring system
+            try:
+                bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
+                pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
+                message_id = query.variables.get('_monitoring_message_id', '')
+                session_id = f'{query.launcher_type}_{query.launcher_id}'
+
+                # Update message status to error
+                if message_id:
+                    await self.ap.monitoring_service.update_message_status(
+                        message_id=message_id,
+                        status='error',
+                        level='error',
+                    )
+
+                # Record error log
+                await self.ap.monitoring_service.record_error(
+                    bot_id=query.bot_uuid or 'unknown',
+                    bot_name=bot_name,
+                    pipeline_id=self.pipeline_entity.uuid,
+                    pipeline_name=pipeline_name,
+                    error_type='PipelineError',
+                    error_message=result.error_notice,
+                    session_id=session_id,
+                    stack_trace=result.debug_notice if result.debug_notice else None,
+                    message_id=message_id,
+                )
+            except Exception as e:
+                self.ap.logger.error(f'Failed to record error to monitoring: {e}')

    async def _execute_from_stage(
        self,
@@ -221,6 +271,34 @@ class RuntimePipeline:

    async def process_query(self, query: pipeline_query.Query):
        """处理请求"""
+        # Get monitoring metadata
+        bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
+        pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
+
+        # Get runner name from pipeline config
+        runner_name = None
+        if query.pipeline_config and 'ai' in query.pipeline_config and 'runner' in query.pipeline_config['ai']:
+            runner_name = query.pipeline_config['ai']['runner'].get('runner')
+
+        # Record query start and store message_id
+        message_id = ''
+        try:
+            from . import monitoring_helper
+
+            message_id = await monitoring_helper.MonitoringHelper.record_query_start(
+                ap=self.ap,
+                query=query,
+                bot_id=query.bot_uuid or 'unknown',
+                bot_name=bot_name,
+                pipeline_id=self.pipeline_entity.uuid,
+                pipeline_name=pipeline_name,
+                runner_name=runner_name,
+            )
+            # Store message_id in query variables for LLM call monitoring
+            query.variables['_monitoring_message_id'] = message_id
+        except Exception as e:
+            self.ap.logger.error(f'Failed to record query start: {e}')
+
        try:
            # Get bound plugins for this pipeline
            bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
@@ -249,10 +327,54 @@ class RuntimePipeline:
            self.ap.logger.debug(f'Processing query {query.query_id}')

            await self._execute_from_stage(0, query)
+
+            # Record query success only if no error occurred during processing
+            if not query.variables.get('_monitoring_has_error', False):
+                try:
+                    await monitoring_helper.MonitoringHelper.record_query_success(
+                        ap=self.ap,
+                        message_id=message_id,
+                        query=query,
+                    )
+                except Exception as e:
+                    self.ap.logger.error(f'Failed to record query success: {e}')
+
+                # Record bot response message
+                try:
+                    await monitoring_helper.MonitoringHelper.record_query_response(
+                        ap=self.ap,
+                        query=query,
+                        bot_id=query.bot_uuid or 'unknown',
+                        bot_name=bot_name,
+                        pipeline_id=self.pipeline_entity.uuid,
+                        pipeline_name=pipeline_name,
+                        runner_name=runner_name,
+                    )
+                except Exception as e:
+                    self.ap.logger.error(f'Failed to record query response: {e}')
+
        except Exception as e:
            inst_name = query.current_stage_name if query.current_stage_name else 'unknown'
            self.ap.logger.error(f'Error processing query {query.query_id} stage={inst_name} : {e}')
            self.ap.logger.error(f'Traceback: {traceback.format_exc()}')
+
+            # Record query error
+            try:
+                from . import monitoring_helper
+
+                await monitoring_helper.MonitoringHelper.record_query_error(
+                    ap=self.ap,
+                    query=query,
+                    bot_id=query.bot_uuid or 'unknown',
+                    bot_name=bot_name,
+                    pipeline_id=self.pipeline_entity.uuid,
+                    pipeline_name=pipeline_name,
+                    error=e,
+                    runner_name=runner_name,
+                )
+            except Exception as me:
+                self.ap.logger.error(f'Failed to record query error: {me}')
+
        finally:
            self.ap.logger.debug(f'Query {query.query_id} processed')
            del self.ap.query_pool.cached_queries[query.query_id]
@@ -261,8 +383,6 @@ class RuntimePipeline:
 class PipelineManager:
    """流水线管理器"""

-    # ====== 4.0 ======
-
    ap: app.Application

    pipelines: list[RuntimePipeline]
--- a/src/langbot/pkg/pipeline/process/handlers/chat.py
+++ b/src/langbot/pkg/pipeline/process/handlers/chat.py
@@ -3,6 +3,8 @@ from __future__ import annotations
 import uuid
 import typing
 import traceback
+import time
+from datetime import datetime


 from .. import handler
@@ -10,7 +12,7 @@ from ... import entities
 from ....provider import runner as runner_module

 import langbot_plugin.api.entities.events as events
-from ....utils import importutil
+from ....utils import importutil, constants
 from ....provider import runners
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -84,6 +86,9 @@ class ChatMessageHandler(handler.MessageHandler):
                        break
                else:
                    raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
+                # Mark start time for telemetry
+                start_ts = time.time()
+
                if is_stream:
                    resp_message_id = uuid.uuid4()
                    chunk_count = 0  # Track streaming chunks to reduce excessive logging
@@ -140,7 +145,8 @@ class ChatMessageHandler(handler.MessageHandler):

                query.session.using_conversation.messages.extend(query.resp_messages)
            except Exception as e:
-                self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {type(e).__name__} {str(e)}')
+                error_info = f'{traceback.format_exc()}'
+                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']
@@ -153,5 +159,52 @@ class ChatMessageHandler(handler.MessageHandler):
                    debug_notice=traceback.format_exc(),
                )
            finally:
-                # TODO statistics
-                pass
+                # Telemetry reporting: collect minimal per-query execution info and send asynchronously
+                try:
+                    end_ts = time.time()
+                    duration_ms = None
+                    if 'start_ts' in locals():
+                        duration_ms = int((end_ts - start_ts) * 1000)
+
+                    adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
+                    runner_name = (
+                        query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
+                        if query.pipeline_config
+                        else None
+                    )
+
+                    # Model name if using localagent
+                    model_name = None
+                    try:
+                        if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
+                            m = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
+                            if m and getattr(m, 'model_entity', None):
+                                model_name = getattr(m.model_entity, 'name', None)
+                    except Exception:
+                        model_name = None
+
+                    pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
+
+                    payload = {
+                        'query_id': query.query_id,
+                        'adapter': adapter_name,
+                        'runner': runner_name,
+                        'duration_ms': duration_ms,
+                        'model_name': model_name,
+                        'version': constants.semantic_version,
+                        'instance_id': constants.instance_id,
+                        'pipeline_plugins': pipeline_plugins,
+                        'error': locals().get('error_info', None),
+                        'timestamp': datetime.utcnow().isoformat(),
+                    }
+
+                    # Send telemetry asynchronously and do not block pipeline via app's telemetry manager
+                    await self.ap.telemetry.start_send_task(payload)
+
+                    # Trigger survey event on first successful non-WebSocket response
+                    if not locals().get('error_info') and adapter_name and 'WebSocket' not in adapter_name:
+                        if self.ap.survey:
+                            await self.ap.survey.trigger_event('first_bot_response_success')
+                except Exception as ex:
+                    # Ensure telemetry issues do not affect normal flow
+                    self.ap.logger.warning(f'Failed to send telemetry: {ex}')
--- a/src/langbot/pkg/platform/botmgr.py
+++ b/src/langbot/pkg/platform/botmgr.py
@@ -75,10 +75,17 @@ class RuntimeBot:

            # Only add to query pool if no webhook requested to skip pipeline
            if not skip_pipeline:
-                await self.ap.query_pool.add_query(
+                launcher_id = event.sender.id
+
+                if hasattr(adapter, 'get_launcher_id'):
+                    custom_launcher_id = adapter.get_launcher_id(event)
+                    if custom_launcher_id:
+                        launcher_id = custom_launcher_id
+
+                await self.ap.msg_aggregator.add_message(
                    bot_uuid=self.bot_entity.uuid,
                    launcher_type=provider_session.LauncherTypes.PERSON,
-                    launcher_id=event.sender.id,
+                    launcher_id=launcher_id,
                    sender_id=event.sender.id,
                    message_event=event,
                    message_chain=event.message_chain,
@@ -86,7 +93,7 @@ class RuntimeBot:
                    pipeline_uuid=self.bot_entity.use_pipeline_uuid,
                )
            else:
-                await self.logger.info(f'Pipeline skipped for person message due to webhook response')
+                await self.logger.info('Pipeline skipped for person message due to webhook response')

        async def on_group_message(
            event: platform_events.GroupMessage,
@@ -111,10 +118,17 @@ class RuntimeBot:

            # Only add to query pool if no webhook requested to skip pipeline
            if not skip_pipeline:
-                await self.ap.query_pool.add_query(
+                launcher_id = event.group.id
+
+                if hasattr(adapter, 'get_launcher_id'):
+                    custom_launcher_id = adapter.get_launcher_id(event)
+                    if custom_launcher_id:
+                        launcher_id = custom_launcher_id
+
+                await self.ap.msg_aggregator.add_message(
                    bot_uuid=self.bot_entity.uuid,
                    launcher_type=provider_session.LauncherTypes.GROUP,
-                    launcher_id=event.group.id,
+                    launcher_id=launcher_id,
                    sender_id=event.sender.id,
                    message_event=event,
                    message_chain=event.message_chain,
@@ -122,7 +136,7 @@ class RuntimeBot:
                    pipeline_uuid=self.bot_entity.use_pipeline_uuid,
                )
            else:
-                await self.logger.info(f'Pipeline skipped for group message due to webhook response')
+                await self.logger.info('Pipeline skipped for group message due to webhook response')

        self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
        self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
--- a/src/langbot/pkg/platform/sources/aiocqhttp.py
+++ b/src/langbot/pkg/platform/sources/aiocqhttp.py
@@ -375,6 +375,18 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
            self.bot = aiocqhttp.CQHttp()

    async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
+        # Check if message contains a Forward component
+        forward_msg = message.get_first(platform_message.Forward)
+        if forward_msg:
+            if target_type == 'group':
+                # Send as merged forward message via OneBot API
+                await self._send_forward_message(int(target_id), forward_msg)
+                return
+            else:
+                await self.logger.warning(
+                    f'Forward message is only supported for group targets, got target_type={target_type}. Falling through to normal send.'
+                )
+
        aiocq_msg = (await AiocqhttpMessageConverter.yiri2target(message))[0]

        if target_type == 'group':
@@ -382,6 +394,90 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
        elif target_type == 'person':
            await self.bot.send_private_msg(user_id=int(target_id), message=aiocq_msg)

+    async def _send_forward_message(self, group_id: int, forward: platform_message.Forward):
+        """Send a merged forward message to a group using NapCat extended API."""
+        messages = []
+
+        for node in forward.node_list:
+            # Build content for each node
+            content = []
+            if node.message_chain:
+                for component in node.message_chain:
+                    if isinstance(component, platform_message.Plain):
+                        if component.text:
+                            content.append({'type': 'text', 'data': {'text': component.text}})
+                    elif isinstance(component, platform_message.Image):
+                        img_data = {}
+                        if component.base64:
+                            b64 = component.base64
+                            if b64.startswith('data:'):
+                                b64 = b64.split(',', 1)[-1] if ',' in b64 else b64
+                            img_data['file'] = f'base64://{b64}'
+                        elif component.url:
+                            img_data['file'] = component.url
+                        elif component.path:
+                            img_data['file'] = str(component.path)
+
+                        if img_data:
+                            content.append({'type': 'image', 'data': img_data})
+
+            if not content:
+                continue
+
+            # Build node data - use user_id and nickname format for NapCat
+            user_id = str(node.sender_id) if node.sender_id else str(self.bot_account_id or '10000')
+            node_data = {
+                'type': 'node',
+                'data': {
+                    'user_id': user_id,
+                    'nickname': node.sender_name or '未知',
+                    'content': content,
+                },
+            }
+
+            messages.append(node_data)
+
+        if not messages:
+            return
+
+        # Build the full message payload for NapCat's send_forward_msg API
+        # This matches the format used by GiveMeSetuPlugin
+        bot_id = str(self.bot_account_id) if self.bot_account_id else '10000'
+        payload = {
+            'group_id': group_id,
+            'user_id': bot_id,  # Required by NapCat for display
+            'messages': messages,
+        }
+
+        # Add display settings if available
+        if forward.display:
+            if forward.display.title:
+                payload['news'] = [{'text': forward.display.title}]
+            if forward.display.brief:
+                payload['prompt'] = forward.display.brief
+            if forward.display.summary:
+                payload['summary'] = forward.display.summary
+            if forward.display.source:
+                payload['source'] = forward.display.source
+
+        try:
+            # Use send_forward_msg (NapCat extended API) instead of send_group_forward_msg
+            await self.logger.info(
+                f'Sending forward message to group {group_id} with {len(messages)} nodes, payload keys: {list(payload.keys())}'
+            )
+            result = await self.bot.call_action('send_forward_msg', **payload)
+            await self.logger.info(f'Forward message sent to group {group_id}, result: {result}')
+        except Exception as e:
+            await self.logger.error(f'Failed to send forward message to group {group_id}: {e}')
+            # Fallback: try standard OneBot API with integer group_id
+            try:
+                await self.logger.info('Trying fallback API send_group_forward_msg')
+                await self.bot.call_action('send_group_forward_msg', group_id=group_id, messages=messages)
+                await self.logger.info(f'Forward message sent via fallback API to group {group_id}')
+            except Exception as e2:
+                await self.logger.error(f'Fallback also failed: {e2}')
+                raise
+
    async def reply_message(
        self,
        message_source: platform_events.MessageEvent,
--- a/src/langbot/pkg/platform/sources/dingtalk.py
+++ b/src/langbot/pkg/platform/sources/dingtalk.py
@@ -231,7 +231,10 @@ class DingTalkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
        card_template_id = self.config['card_template_id']
        incoming_message = event.source_platform_object.incoming_message
        # message_id = incoming_message.message_id
-        card_instance, card_instance_id = await self.bot.create_and_card(card_template_id, incoming_message)
+        card_auto_layout = self.config.get('card_ auto_layout', False)
+        card_instance, card_instance_id = await self.bot.create_and_card(
+            card_template_id, incoming_message, card_auto_layout=card_auto_layout
+        )
        self.card_instance_id_dict[message_id] = (card_instance, card_instance_id)
        return True

--- a/src/langbot/pkg/platform/sources/dingtalk.yaml
+++ b/src/langbot/pkg/platform/sources/dingtalk.yaml
@@ -56,6 +56,13 @@ spec:
      type: boolean
      required: true
      default: false
+    - name: card_auto_layout
+      label:
+        en_US: Card Auto Layout
+        zh_Hans: 卡片宽屏自动布局
+      type: boolean
+      required: false
+      default: false
    - name: card_template_id
      label:
        en_US: card template id
--- a/src/langbot/pkg/platform/sources/discord.py
+++ b/src/langbot/pkg/platform/sources/discord.py
@@ -14,7 +14,7 @@ import io
 import asyncio
 from enum import Enum

-import aiohttp
+from langbot.pkg.utils import httpclient
 import pydantic

 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
@@ -622,23 +622,23 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                    image_bytes = base64.b64decode(base64_data)
                elif ele.url:
                    # 从URL下载图片
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(ele.url) as response:
-                            image_bytes = await response.read()
-                            # 从URL或Content-Type推断文件类型
-                            content_type = response.headers.get('Content-Type', '')
-                            if 'jpeg' in content_type or 'jpg' in content_type:
-                                filename = f'{uuid.uuid4()}.jpg'
-                            elif 'gif' in content_type:
-                                filename = f'{uuid.uuid4()}.gif'
-                            elif 'webp' in content_type:
-                                filename = f'{uuid.uuid4()}.webp'
-                            elif ele.url.lower().endswith(('.jpg', '.jpeg')):
-                                filename = f'{uuid.uuid4()}.jpg'
-                            elif ele.url.lower().endswith('.gif'):
-                                filename = f'{uuid.uuid4()}.gif'
-                            elif ele.url.lower().endswith('.webp'):
-                                filename = f'{uuid.uuid4()}.webp'
+                    session = httpclient.get_session()
+                    async with session.get(ele.url) as response:
+                        image_bytes = await response.read()
+                        # 从URL或Content-Type推断文件类型
+                        content_type = response.headers.get('Content-Type', '')
+                        if 'jpeg' in content_type or 'jpg' in content_type:
+                            filename = f'{uuid.uuid4()}.jpg'
+                        elif 'gif' in content_type:
+                            filename = f'{uuid.uuid4()}.gif'
+                        elif 'webp' in content_type:
+                            filename = f'{uuid.uuid4()}.webp'
+                        elif ele.url.lower().endswith(('.jpg', '.jpeg')):
+                            filename = f'{uuid.uuid4()}.jpg'
+                        elif ele.url.lower().endswith('.gif'):
+                            filename = f'{uuid.uuid4()}.gif'
+                        elif ele.url.lower().endswith('.webp'):
+                            filename = f'{uuid.uuid4()}.webp'
                elif ele.path:
                    # 从文件路径读取图片
                    # 确保路径没有空字节
@@ -702,9 +702,9 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                    file_base64 = ele.base64.split(',')[-1]
                    file_bytes = base64.b64decode(file_base64)
                elif ele.url:
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(ele.url) as response:
-                            file_bytes = await response.read()
+                    session = httpclient.get_session()
+                    async with session.get(ele.url) as response:
+                        file_bytes = await response.read()
                if file_bytes:
                    files.append(discord.File(fp=io.BytesIO(file_bytes), filename=filename))
            elif isinstance(ele, platform_message.File):
@@ -717,9 +717,9 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                    else:
                        file_bytes = base64.b64decode(ele.base64)
                elif ele.url:
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(ele.url) as response:
-                            file_bytes = await response.read()
+                    session = httpclient.get_session()
+                    async with session.get(ele.url) as response:
+                        file_bytes = await response.read()
                if file_bytes:
                    files.append(discord.File(fp=io.BytesIO(file_bytes), filename=filename))
            elif isinstance(ele, platform_message.Forward):
@@ -775,12 +775,12 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter

        # attachments
        for attachment in message.attachments:
-            async with aiohttp.ClientSession(trust_env=True) as session:
-                async with session.get(attachment.url) as response:
-                    image_data = await response.read()
-                    image_base64 = base64.b64encode(image_data).decode('utf-8')
-                    image_format = response.headers['Content-Type']
-                    element_list.append(platform_message.Image(base64=f'data:{image_format};base64,{image_base64}'))
+            session = httpclient.get_session(trust_env=True)
+            async with session.get(attachment.url) as response:
+                image_data = await response.read()
+                image_base64 = base64.b64encode(image_data).decode('utf-8')
+                image_format = response.headers['Content-Type']
+                element_list.append(platform_message.Image(base64=f'data:{image_format};base64,{image_base64}'))

        return platform_message.MessageChain(element_list)

--- a/src/langbot/pkg/platform/sources/kook.py
+++ b/src/langbot/pkg/platform/sources/kook.py
@@ -9,6 +9,8 @@ import traceback
 import time

 import aiohttp
+
+from langbot.pkg.utils import httpclient
 import websockets
 import pydantic

@@ -120,16 +122,16 @@ class KookMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
            if content:
                # Download image and convert to base64
                try:
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(content) as response:
-                            if response.status == 200:
-                                image_bytes = await response.read()
-                                image_base64 = base64.b64encode(image_bytes).decode('utf-8')
-                                # Detect image format
-                                content_type = response.headers.get('Content-Type', 'image/png')
-                                components.append(
-                                    platform_message.Image(base64=f'data:{content_type};base64,{image_base64}')
-                                )
+                    session = httpclient.get_session()
+                    async with session.get(content) as response:
+                        if response.status == 200:
+                            image_bytes = await response.read()
+                            image_base64 = base64.b64encode(image_bytes).decode('utf-8')
+                            # Detect image format
+                            content_type = response.headers.get('Content-Type', 'image/png')
+                            components.append(
+                                platform_message.Image(base64=f'data:{content_type};base64,{image_base64}')
+                            )
                except Exception:
                    # If download fails, just add as plain text
                    components.append(platform_message.Plain(text=f'[Image: {content}]'))
@@ -295,17 +297,17 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
            'Authorization': f'Bot {self.config["token"]}',
        }

-        async with aiohttp.ClientSession() as session:
-            async with session.get(base_url, params=params, headers=headers) as response:
-                if response.status == 200:
-                    data = await response.json()
-                    if data.get('code') == 0:
-                        gateway_url = data['data']['url']
-                        return gateway_url
-                    else:
-                        raise Exception(f'Failed to get gateway URL: {data.get("message")}')
+        session = httpclient.get_session()
+        async with session.get(base_url, params=params, headers=headers) as response:
+            if response.status == 200:
+                data = await response.json()
+                if data.get('code') == 0:
+                    gateway_url = data['data']['url']
+                    return gateway_url
                else:
-                    raise Exception(f'Failed to get gateway URL: HTTP {response.status}')
+                    raise Exception(f'Failed to get gateway URL: {data.get("message")}')
+            else:
+                raise Exception(f'Failed to get gateway URL: HTTP {response.status}')

    async def _get_bot_user_info(self) -> dict:
        """Get bot's own user information from KOOK API"""
@@ -315,17 +317,17 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
            'Authorization': f'Bot {self.config["token"]}',
        }

-        async with aiohttp.ClientSession() as session:
-            async with session.get(base_url, headers=headers) as response:
-                if response.status == 200:
-                    data = await response.json()
-                    if data.get('code') == 0:
-                        user_info = data['data']
-                        return user_info
-                    else:
-                        raise Exception(f'Failed to get bot user info: {data.get("message")}')
+        session = httpclient.get_session()
+        async with session.get(base_url, headers=headers) as response:
+            if response.status == 200:
+                data = await response.json()
+                if data.get('code') == 0:
+                    user_info = data['data']
+                    return user_info
                else:
-                    raise Exception(f'Failed to get bot user info: HTTP {response.status}')
+                    raise Exception(f'Failed to get bot user info: {data.get("message")}')
+            else:
+                raise Exception(f'Failed to get bot user info: HTTP {response.status}')

    async def _handle_hello(self, data: dict):
        """Handle HELLO signal (signal 1)"""
@@ -510,7 +512,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):

        try:
            if not self.http_session:
-                self.http_session = aiohttp.ClientSession()
+                self.http_session = httpclient.get_session()

            async with self.http_session.post(url, json=payload, headers=headers) as response:
                if response.status == 200:
@@ -576,7 +578,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):

        try:
            if not self.http_session:
-                self.http_session = aiohttp.ClientSession()
+                self.http_session = httpclient.get_session()

            async with self.http_session.post(url, json=payload, headers=headers) as response:
                if response.status == 200:
@@ -624,7 +626,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):

        try:
            # Create HTTP session
-            self.http_session = aiohttp.ClientSession()
+            self.http_session = httpclient.get_session()

            await self.logger.info('Starting KOOK adapter')

--- a/src/langbot/pkg/platform/sources/lark.py
+++ b/src/langbot/pkg/platform/sources/lark.py
@@ -1,7 +1,7 @@
 from __future__ import annotations

 import lark_oapi
-from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody
+from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody, CreateFileRequest, CreateFileRequestBody
 import traceback
 import typing
 import asyncio
@@ -17,7 +17,7 @@ import tempfile
 import os
 import mimetypes

-import aiohttp
+from langbot.pkg.utils import httpclient
 import lark_oapi.ws.exception
 import quart
 from lark_oapi.api.im.v1 import *
@@ -78,13 +78,13 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                return None
        elif msg.url:
            try:
-                async with aiohttp.ClientSession() as session:
-                    async with session.get(msg.url) as response:
-                        if response.status == 200:
-                            image_bytes = await response.read()
-                        else:
-                            print(f'Failed to download image from {msg.url}: HTTP {response.status}')
-                            return None
+                session = httpclient.get_session()
+                async with session.get(msg.url) as response:
+                    if response.status == 200:
+                        image_bytes = await response.read()
+                    else:
+                        print(f'Failed to download image from {msg.url}: HTTP {response.status}')
+                        return None
            except Exception as e:
                print(f'Failed to download image from {msg.url}: {e}')
                traceback.print_exc()
@@ -141,6 +141,88 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
            traceback.print_exc()
            return None

+    @staticmethod
+    async def upload_file_to_lark(
+        file_bytes: bytes,
+        api_client: lark_oapi.Client,
+        file_type: str,
+        file_name: str = 'file',
+        duration: typing.Optional[int] = None,
+    ) -> typing.Optional[str]:
+        """Upload a file to Lark and return the file_key, or None if upload fails.
+
+        Args:
+            file_bytes: Raw file bytes.
+            api_client: Lark API client.
+            file_type: Lark file type, e.g. 'opus', 'mp4', 'pdf', 'doc', etc.
+            file_name: Display name for the file.
+            duration: Duration in milliseconds (for audio files).
+        """
+        try:
+            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+                temp_file.write(file_bytes)
+                temp_file_path = temp_file.name
+
+            try:
+                body_builder = (
+                    CreateFileRequestBody.builder()
+                    .file_type(file_type)
+                    .file_name(file_name)
+                    .file(open(temp_file_path, 'rb'))
+                )
+                if duration is not None:
+                    body_builder = body_builder.duration(duration)
+
+                request = CreateFileRequest.builder().request_body(body_builder.build()).build()
+
+                response = await api_client.im.v1.file.acreate(request)
+
+                if not response.success():
+                    print(
+                        f'client.im.v1.file.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}'
+                    )
+                    return None
+
+                return response.data.file_key
+            finally:
+                os.unlink(temp_file_path)
+        except Exception as e:
+            print(f'Failed to upload file to Lark: {e}')
+            traceback.print_exc()
+            return None
+
+    @staticmethod
+    async def _get_media_bytes(
+        msg: typing.Union[platform_message.Voice, platform_message.File],
+    ) -> typing.Optional[bytes]:
+        """Get bytes from a Voice or File message (base64, url, or path)."""
+        data = None
+
+        if msg.base64:
+            try:
+                base64_str = msg.base64
+                if ',' in base64_str:
+                    base64_str = base64_str.split(',', 1)[1]
+                data = base64.b64decode(base64_str)
+            except Exception:
+                pass
+        elif msg.url:
+            try:
+                session = httpclient.get_session()
+                async with session.get(msg.url) as resp:
+                    if resp.status == 200:
+                        data = await resp.read()
+            except Exception:
+                pass
+        elif msg.path:
+            try:
+                with open(msg.path, 'rb') as f:
+                    data = f.read()
+            except Exception:
+                pass
+
+        return data
+
    @staticmethod
    async def yiri2target(
        message_chain: platform_message.MessageChain, api_client: lark_oapi.Client
@@ -150,10 +232,10 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
        Returns:
            Tuple of (text_elements, image_keys):
            - text_elements: List of paragraphs for post message format
-            - image_keys: List of image_key strings for separate image messages
+            - media_items: List of dicts with 'msg_type' and 'content' for separate media messages
        """
        message_elements = []
-        image_keys = []
+        media_items = []
        pending_paragraph = []

        # Regex pattern to match Markdown image syntax: ![alt](url)
@@ -196,40 +278,77 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                # Check for and extract Markdown images from text
                cleaned_text, extracted_urls = await process_text_with_images(text)

-                # Add cleaned text if not empty
+                # Split by blank lines to create separate paragraphs for Lark post format.
+                # Lark truncates md elements at the first \n\n, so we must use the
+                # post format's native paragraph structure instead.
                if cleaned_text:
-                    pending_paragraph.append({'tag': 'md', 'text': cleaned_text})
+                    segments = re.split(r'\n\s*\n', cleaned_text)
+                    for i, segment in enumerate(segments):
+                        segment = segment.strip()
+                        if not segment:
+                            continue
+                        if i > 0 and pending_paragraph:
+                            message_elements.append(pending_paragraph)
+                            pending_paragraph = []
+                        pending_paragraph.append({'tag': 'md', 'text': segment})

                # Process extracted image URLs
                for url in extracted_urls:
-                    # Create a temporary Image message to upload
                    temp_image = platform_message.Image(url=url)
                    image_key = await LarkMessageConverter.upload_image_to_lark(temp_image, api_client)
                    if image_key:
-                        image_keys.append(image_key)
+                        media_items.append({'msg_type': 'image', 'content': {'image_key': image_key}})

            elif isinstance(msg, platform_message.At):
                pending_paragraph.append({'tag': 'at', 'user_id': msg.target, 'style': []})
            elif isinstance(msg, platform_message.AtAll):
                pending_paragraph.append({'tag': 'at', 'user_id': 'all', 'style': []})
            elif isinstance(msg, platform_message.Image):
-                # Upload image and get image_key
                image_key = await LarkMessageConverter.upload_image_to_lark(msg, api_client)
                if image_key:
-                    # Store image_key for separate image message
-                    image_keys.append(image_key)
+                    media_items.append({'msg_type': 'image', 'content': {'image_key': image_key}})
+            elif isinstance(msg, platform_message.Voice):
+                data = await LarkMessageConverter._get_media_bytes(msg)
+                if data:
+                    duration = int(msg.length * 1000) if msg.length else None
+                    file_key = await LarkMessageConverter.upload_file_to_lark(
+                        data, api_client, file_type='opus', file_name='voice.opus', duration=duration
+                    )
+                    if file_key:
+                        media_items.append({'msg_type': 'audio', 'content': {'file_key': file_key}})
+            elif isinstance(msg, platform_message.File):
+                data = await LarkMessageConverter._get_media_bytes(msg)
+                if data:
+                    file_name = msg.name or 'file'
+                    # Guess file_type from extension
+                    ext = os.path.splitext(file_name)[1].lstrip('.').lower() if file_name else ''
+                    file_type_map = {
+                        'opus': 'opus',
+                        'mp4': 'mp4',
+                        'pdf': 'pdf',
+                        'doc': 'doc',
+                        'docx': 'doc',
+                        'xls': 'xls',
+                        'xlsx': 'xls',
+                        'ppt': 'ppt',
+                        'pptx': 'ppt',
+                    }
+                    file_type = file_type_map.get(ext, 'stream')
+                    file_key = await LarkMessageConverter.upload_file_to_lark(
+                        data, api_client, file_type=file_type, file_name=file_name
+                    )
+                    if file_key:
+                        media_items.append({'msg_type': 'file', 'content': {'file_key': file_key}})
            elif isinstance(msg, platform_message.Forward):
                for node in msg.node_list:
-                    sub_elements, sub_image_keys = await LarkMessageConverter.yiri2target(
-                        node.message_chain, api_client
-                    )
+                    sub_elements, sub_media = await LarkMessageConverter.yiri2target(node.message_chain, api_client)
                    message_elements.extend(sub_elements)
-                    image_keys.extend(sub_image_keys)
+                    media_items.extend(sub_media)

        if pending_paragraph:
            message_elements.append(pending_paragraph)

-        return message_elements, image_keys
+        return message_elements, media_items

    @staticmethod
    async def target2yiri(
@@ -244,7 +363,6 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):

        lb_msg_list.append(platform_message.Source(id=message.message_id, time=msg_create_time))

-
        if message.message_type == 'text':
            element_list = []

@@ -310,7 +428,11 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
            ]
        elif message.message_type == 'audio':
            message_content['content'] = [
-                {'tag': 'audio', 'file_key': message_content['file_key'], "duration": message_content.get('duration',0)}
+                {
+                    'tag': 'audio',
+                    'file_key': message_content['file_key'],
+                    'duration': message_content.get('duration', 0),
+                }
            ]

        for ele in message_content['content']:
@@ -367,12 +489,9 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                    audio_bytes = response.file.read()
                    audio_base64 = base64.b64encode(audio_bytes).decode()

-
                    # Get content type from response headers
                    content_type = response.raw.headers.get('content-type', 'audio/mpeg')

-
-
                    mime_main = content_type.split(';')[0].strip()
                    ext = mimetypes.guess_extension(mime_main) or '.bin'
                    temp_dir = tempfile.gettempdir()
@@ -418,7 +537,6 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                file_bytes = response.file.read()
                file_base64 = base64.b64encode(file_bytes).decode()

-
                file_format = response.raw.headers['content-type']

                file_size = len(file_bytes)
@@ -453,7 +571,6 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                    )
                )

-
        return platform_message.MessageChain(lb_msg_list)


@@ -919,23 +1036,40 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
    ):
        # 不再需要了，因为message_id已经被包含到message_chain中
        # lark_event = await self.event_converter.yiri2target(message_source)
-        text_elements, image_keys = await self.message_converter.yiri2target(message, self.api_client)
+        text_elements, media_items = await self.message_converter.yiri2target(message, self.api_client)

        # Send text message if there are text elements
        if text_elements:
-            final_content = {
-                'zh_Hans': {
-                    'title': '',
-                    'content': text_elements,
-                },
-            }
+            # Determine msg_type based on content: use 'post' if at mentions
+            # are present (requires post paragraph structure), otherwise 'text'
+            needs_post = any(ele['tag'] == 'at' for paragraph in text_elements for ele in paragraph)
+
+            if needs_post:
+                msg_type = 'post'
+                final_content = json.dumps(
+                    {
+                        'zh_Hans': {
+                            'title': '',
+                            'content': text_elements,
+                        },
+                    }
+                )
+            else:
+                msg_type = 'text'
+                parts = []
+                for paragraph in text_elements:
+                    para_text = ''.join(ele.get('text', '') for ele in paragraph)
+                    if para_text:
+                        parts.append(para_text)
+                final_content = json.dumps({'text': '\n\n'.join(parts)})
+
            request: ReplyMessageRequest = (
                ReplyMessageRequest.builder()
                .message_id(message_source.message_chain.message_id)
                .request_body(
                    ReplyMessageRequestBody.builder()
-                    .content(json.dumps(final_content))
-                    .msg_type('post')
+                    .content(final_content)
+                    .msg_type(msg_type)
                    .reply_in_thread(False)
                    .uuid(str(uuid.uuid4()))
                    .build()
@@ -965,17 +1099,15 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                    f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
                )

-        # Send image messages separately using msg_type='image'
-        for image_key in image_keys:
-            image_content = json.dumps({'image_key': image_key})
-
+        # Send media messages separately (image, audio, file, etc.)
+        for media in media_items:
            request: ReplyMessageRequest = (
                ReplyMessageRequest.builder()
                .message_id(message_source.message_chain.message_id)
                .request_body(
                    ReplyMessageRequestBody.builder()
-                    .content(image_content)
-                    .msg_type('image')
+                    .content(json.dumps(media['content']))
+                    .msg_type(media['msg_type'])
                    .reply_in_thread(False)
                    .uuid(str(uuid.uuid4()))
                    .build()
@@ -1002,7 +1134,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):

            if not response.success():
                raise Exception(
-                    f'client.im.v1.message.reply (image) failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
+                    f'client.im.v1.message.reply ({media["msg_type"]}) failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
                )

    async def reply_message_chunk(
@@ -1020,15 +1152,16 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
        message_id = bot_message.resp_message_id
        msg_seq = bot_message.msg_sequence
        if msg_seq % 8 == 0 or is_final:
-            text_elements, image_keys = await self.message_converter.yiri2target(message, self.api_client)
+            text_elements, media_items = await self.message_converter.yiri2target(message, self.api_client)

            text_message = ''
            if text_elements:
-                for ele in text_elements[0]:
-                    if ele['tag'] == 'text':
-                        text_message += ele['text']
-                    elif ele['tag'] == 'md':
-                        text_message += ele['text']
+                parts = []
+                for paragraph in text_elements:
+                    para_text = ''.join(ele['text'] for ele in paragraph if ele['tag'] in ('text', 'md'))
+                    if para_text:
+                        parts.append(para_text)
+                text_message = '\n\n'.join(parts)

            # content = {
            #     'type': 'card_json',
@@ -1078,6 +1211,30 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                )
                return

+            # Send media messages when streaming is done
+            if is_final and media_items:
+                for media in media_items:
+                    media_request: ReplyMessageRequest = (
+                        ReplyMessageRequest.builder()
+                        .message_id(message_source.message_chain.message_id)
+                        .request_body(
+                            ReplyMessageRequestBody.builder()
+                            .content(json.dumps(media['content']))
+                            .msg_type(media['msg_type'])
+                            .reply_in_thread(False)
+                            .uuid(str(uuid.uuid4()))
+                            .build()
+                        )
+                        .build()
+                    )
+                    media_response: ReplyMessageResponse = await self.api_client.im.v1.message.areply(
+                        media_request, req_opt
+                    )
+                    if not media_response.success():
+                        raise Exception(
+                            f'client.im.v1.message.reply ({media["msg_type"]}) failed, code: {media_response.code}, msg: {media_response.msg}, log_id: {media_response.get_log_id()}'
+                        )
+
    async def is_muted(self, group_id: int) -> bool:
        return False

--- a/src/langbot/pkg/platform/sources/legacy/gewechat.py
+++ b/src/langbot/pkg/platform/sources/legacy/gewechat.py
@@ -9,7 +9,7 @@ import copy
 import threading

 import quart
-import aiohttp
+from langbot.pkg.utils import httpclient

 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
 from ....core import app
@@ -639,14 +639,14 @@ class GeWeChatAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):

    async def run_async(self):
        if not self.config['token']:
-            async with aiohttp.ClientSession() as session:
-                async with session.post(
-                    f'{self.config["gewechat_url"]}/v2/api/tools/getTokenId',
-                    json={'app_id': self.config['app_id']},
-                ) as response:
-                    if response.status != 200:
-                        raise Exception(f'获取gewechat token失败: {await response.text()}')
-                    self.config['token'] = (await response.json())['data']
+            session = httpclient.get_session()
+            async with session.post(
+                f'{self.config["gewechat_url"]}/v2/api/tools/getTokenId',
+                json={'app_id': self.config['app_id']},
+            ) as response:
+                if response.status != 200:
+                    raise Exception(f'获取gewechat token失败: {await response.text()}')
+                self.config['token'] = (await response.json())['data']

        self.bot = gewechat_client.GewechatClient(f'{self.config["gewechat_url"]}/v2/api', self.config['token'])

--- a/src/langbot/pkg/platform/sources/officialaccount.py
+++ b/src/langbot/pkg/platform/sources/officialaccount.py
@@ -76,6 +76,7 @@ class OfficialAccountAdapter(abstract_platform_adapter.AbstractMessagePlatformAd
                AppID=config['AppID'],
                logger=logger,
                unified_mode=True,
+                api_base_url=config.get('api_base_url', 'https://api.weixin.qq.com'),
            )
        elif config['Mode'] == 'passive':
            bot = OAClientForLongerResponse(
@@ -86,6 +87,7 @@ class OfficialAccountAdapter(abstract_platform_adapter.AbstractMessagePlatformAd
                LoadingMessage=config.get('LoadingMessage', ''),
                logger=logger,
                unified_mode=True,
+                api_base_url=config.get('api_base_url', 'https://api.weixin.qq.com'),
            )
        else:
            raise KeyError('请设置微信公众号通信模式')
--- a/src/langbot/pkg/platform/sources/officialaccount.yaml
+++ b/src/langbot/pkg/platform/sources/officialaccount.yaml
@@ -53,6 +53,16 @@ spec:
      type: string
      required: true
      default: "AI正在思考中，请发送任意内容获取回复。"
+    - name: api_base_url
+      label:
+        en_US: API Base URL
+        zh_Hans: API 基础 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
+      type: string
+      required: false
+      default: "https://api.weixin.qq.com"
 execution:
  python:
    path: ./officialaccount.py
--- a/src/langbot/pkg/platform/sources/satori.png
+++ b/src/langbot/pkg/platform/sources/satori.png
--- a/src/langbot/pkg/platform/sources/satori.py
+++ b/src/langbot/pkg/platform/sources/satori.py
--- a/src/langbot/pkg/platform/sources/satori.yaml
+++ b/src/langbot/pkg/platform/sources/satori.yaml
@@ -0,0 +1,65 @@
+apiVersion: v1
+kind: MessagePlatformAdapter
+metadata:
+  name: satori
+  label:
+    en_US: Satori
+    zh_Hans: Satori
+  description:
+    en_US: SatoriAdapter
+    zh_Hans: 古明地觉协议适配器
+  icon: satori.png
+spec:
+  config:
+    - name: platform
+      label:
+        en_US: Platform
+        zh_Hans: 平台名称
+      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）
+    - name: host
+      label:
+        en_US: Host
+        zh_Hans: 主机地址
+      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）
+    - name: port
+      label:
+        en_US: Port
+        zh_Hans: 监听端口
+      type: integer
+      required: true
+      default: 5600
+    - name: satori_api_base_url
+      label:
+        en_US: Satori API Endpoint
+        zh_Hans: Satori API 终结点
+      type: string
+      required: true
+      default: "http://localhost:5600/v1"
+    - name: satori_endpoint
+      label:
+        en_US: Satori WebSocket Endpoint
+        zh_Hans: Satori WebSocket 终结点
+      type: string
+      required: true
+      default: "ws://localhost:5600/v1/events"
+    - name: token
+      label:
+        en_US: Token
+        zh_Hans: 令牌
+      type: string
+      required: true
+      default: ""
+execution:
+  python:
+    path: ./satori.py
+    attr: SatoriAdapter
--- a/src/langbot/pkg/platform/sources/telegram.py
+++ b/src/langbot/pkg/platform/sources/telegram.py
@@ -9,9 +9,9 @@ import telegramify_markdown
 import typing
 import traceback
 import base64
-import aiohttp
 import pydantic

+from langbot.pkg.utils import httpclient
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
@@ -33,9 +33,9 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
                if component.base64:
                    photo_bytes = base64.b64decode(component.base64)
                elif component.url:
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(component.url) as response:
-                            photo_bytes = await response.read()
+                    session = httpclient.get_session()
+                    async with session.get(component.url) as response:
+                        photo_bytes = await response.read()
                elif component.path:
                    with open(component.path, 'rb') as f:
                        photo_bytes = f.read()
@@ -74,10 +74,9 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
            file_bytes = None
            file_format = ''

-            async with aiohttp.ClientSession(trust_env=True) as session:
-                async with session.get(file.file_path) as response:
-                    file_bytes = await response.read()
-                    file_format = 'image/jpeg'
+            async with httpclient.get_session(trust_env=True).get(file.file_path) as response:
+                file_bytes = await response.read()
+                file_format = 'image/jpeg'

            message_components.append(
                platform_message.Image(
@@ -85,6 +84,25 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
                )
            )

+        if message.voice:
+            if message.caption:
+                message_components.extend(parse_message_text(message.caption))
+
+            file = await message.voice.get_file()
+
+            file_bytes = None
+            file_format = message.voice.mime_type or 'audio/ogg'
+
+            async with httpclient.get_session(trust_env=True).get(file.file_path) as response:
+                file_bytes = await response.read()
+
+            message_components.append(
+                platform_message.Voice(
+                    base64=f'data:{file_format};base64,{base64.b64encode(file_bytes).decode("utf-8")}',
+                    length=message.voice.duration,
+                )
+            )
+
        return platform_message.MessageChain(message_components)


@@ -159,7 +177,9 @@ 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, telegram_callback))
+        application.add_handler(
+            MessageHandler(filters.TEXT | (filters.COMMAND) | filters.PHOTO | filters.VOICE, telegram_callback)
+        )
        super().__init__(
            config=config,
            logger=logger,
@@ -172,7 +192,31 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
        )

    async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
-        pass
+        components = await TelegramMessageConverter.yiri2target(message, self.bot)
+
+        chat_id_str, _, thread_id_str = str(target_id).partition('#')
+        chat_id: int | str = int(chat_id_str) if chat_id_str.lstrip('-').isdigit() else chat_id_str
+        message_thread_id = int(thread_id_str) if thread_id_str and thread_id_str.isdigit() else None
+
+        for component in components:
+            component_type = component.get('type')
+            args = {'chat_id': chat_id}
+            if message_thread_id is not None:
+                args['message_thread_id'] = message_thread_id
+
+            if component_type == 'text':
+                text = component.get('text', '')
+                if self.config['markdown_card'] is True:
+                    text = telegramify_markdown.markdownify(content=text)
+                    args['parse_mode'] = 'MarkdownV2'
+                args['text'] = text
+                await self.bot.send_message(**args)
+            elif component_type == 'photo':
+                photo = component.get('photo')
+                if photo is None:
+                    continue
+                args['photo'] = telegram.InputFile(photo)
+                await self.bot.send_photo(**args)

    async def reply_message(
        self,
@@ -197,6 +241,10 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                }
                if self.config['markdown_card'] is True:
                    args['parse_mode'] = 'MarkdownV2'
+
+        if message_source.source_platform_object.message.message_thread_id:
+            args['message_thread_id'] = message_source.source_platform_object.message.message_thread_id
+
        if quote_origin:
            args['reply_to_message_id'] = message_source.source_platform_object.message.id

@@ -216,8 +264,6 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
            components = await TelegramMessageConverter.yiri2target(message, self.bot)
            args = {}
            message_id = message_source.source_platform_object.message.id
-            if quote_origin:
-                args['reply_to_message_id'] = message_source.source_platform_object.message.id

            component = components[0]
            if message_id not in self.msg_stream_id:  # 当消息回复第一次时，发送新消息
@@ -233,6 +279,12 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                        'chat_id': message_source.source_platform_object.effective_chat.id,
                        'text': content,
                    }
+                    if message_source.source_platform_object.message.message_thread_id:
+                        args['message_thread_id'] = message_source.source_platform_object.message.message_thread_id
+
+                    if quote_origin:
+                        args['reply_to_message_id'] = message_source.source_platform_object.message.id
+
                    if self.config['markdown_card'] is True:
                        args['parse_mode'] = 'MarkdownV2'

@@ -260,6 +312,24 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                # self.seq = 1  # 消息回复结束之后重置seq
                self.msg_stream_id.pop(message_id)  # 消息回复结束之后删除流式消息id

+    def get_launcher_id(self, event: platform_events.MessageEvent) -> str | None:
+        if not isinstance(event.source_platform_object, Update):
+            return None
+
+        message = event.source_platform_object.message
+        if not message:
+            return None
+
+        # specifically handle telegram forum topic and private thread(not supported by official client yet but supported by bot api)
+        if message.message_thread_id:
+            # check if it is a group
+            if isinstance(event, platform_events.GroupMessage):
+                return f'{event.group.id}#{message.message_thread_id}'
+            elif isinstance(event, platform_events.FriendMessage):
+                return f'{event.sender.id}#{message.message_thread_id}'
+
+        return None
+
    async def is_stream_output_supported(self) -> bool:
        is_stream = False
        if self.config.get('enable-stream-reply', None):
--- a/src/langbot/pkg/platform/sources/wecom.py
+++ b/src/langbot/pkg/platform/sources/wecom.py
@@ -15,6 +15,58 @@ import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.builtin.platform.entities as platform_entities


+def split_string_by_bytes(text, limit=2048, encoding='utf-8'):
+    """
+    Splits a string into a list of strings, where each part is at most 'limit' bytes.
+
+    Args:
+        text (str): The original string to split.
+        limit (int): The maximum byte size for each split part.
+        encoding (str): The encoding to use (default is 'utf-8').
+
+    Returns:
+        list: A list of split strings.
+    """
+    # 1. Encode the entire string into bytes
+    bytes_data = text.encode(encoding)
+    total_len = len(bytes_data)
+
+    parts = []
+    start = 0
+
+    while start < total_len:
+        # 2. Determine the end index for the current chunk
+        # It shouldn't exceed the total length
+        end = min(start + limit, total_len)
+
+        # 3. Slice the byte array
+        chunk = bytes_data[start:end]
+
+        # 4. Attempt to decode the chunk
+        # Use errors='ignore' to drop any partial bytes at the end of the chunk
+        # (e.g., if a 3-byte character was cut after the 2nd byte)
+        part_str = chunk.decode(encoding, errors='ignore')
+
+        # 5. Calculate the actual byte length of the successfully decoded string
+        # This tells us exactly where the valid character boundary ended
+        part_bytes = part_str.encode(encoding)
+        part_len = len(part_bytes)
+
+        # Safety check: Prevent infinite loop if limit is too small (e.g., limit=1 for a Chinese char)
+        if part_len == 0 and end < total_len:
+            # Force advance by 1 byte to consume the un-decodable byte or raise error
+            # Here we just treat it as a part to avoid stuck loops, though it might be invalid
+            start += 1
+            continue
+
+        parts.append(part_str)
+
+        # 6. Move the start pointer by the actual length consumed
+        start += part_len
+
+    return parts
+
+
 class WecomMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
    @staticmethod
    async def yiri2target(message_chain: platform_message.MessageChain, bot: WecomClient):
@@ -22,11 +74,15 @@ class WecomMessageConverter(abstract_platform_adapter.AbstractMessageConverter):

        for msg in message_chain:
            if type(msg) is platform_message.Plain:
-                content_list.append(
-                    {
-                        'type': 'text',
-                        'content': msg.text,
-                    }
+                chunks = split_string_by_bytes(msg.text)
+                content_list.extend(
+                    [
+                        {
+                            'type': 'text',
+                            'content': chunk,
+                        }
+                        for chunk in chunks
+                    ]
                )
            elif type(msg) is platform_message.Image:
                content_list.append(
@@ -170,6 +226,7 @@ class WecomAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
            contacts_secret=config['contacts_secret'],
            logger=logger,
            unified_mode=True,
+            api_base_url=config.get('api_base_url', 'https://qyapi.weixin.qq.com/cgi-bin'),
        )

        super().__init__(
--- a/src/langbot/pkg/platform/sources/wecom.yaml
+++ b/src/langbot/pkg/platform/sources/wecom.yaml
@@ -46,6 +46,16 @@ spec:
      type: string
      required: true
      default: ""
+    - name: api_base_url
+      label:
+        en_US: API Base URL
+        zh_Hans: API 基础 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
+      type: string
+      required: false
+      default: "https://qyapi.weixin.qq.com/cgi-bin"
 execution:
  python:
    path: ./wecom.py
--- a/src/langbot/pkg/platform/sources/wecomcs.py
+++ b/src/langbot/pkg/platform/sources/wecomcs.py
@@ -141,6 +141,7 @@ class WecomCSAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
            EncodingAESKey=config['EncodingAESKey'],
            logger=logger,
            unified_mode=True,
+            api_base_url=config.get('api_base_url', 'https://qyapi.weixin.qq.com/cgi-bin'),
        )

        super().__init__(
--- a/src/langbot/pkg/platform/sources/wecomcs.yaml
+++ b/src/langbot/pkg/platform/sources/wecomcs.yaml
@@ -39,6 +39,16 @@ spec:
      type: string
      required: true
      default: ""
+    - name: api_base_url
+      label:
+        en_US: API Base URL
+        zh_Hans: API 基础 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
+      type: string
+      required: false
+      default: "https://qyapi.weixin.qq.com/cgi-bin"
 execution:
  python:
    path: ./wecomcs.py
--- a/src/langbot/pkg/platform/webhook_pusher.py
+++ b/src/langbot/pkg/platform/webhook_pusher.py
@@ -3,6 +3,8 @@ from __future__ import annotations
 import asyncio
 import logging
 import aiohttp
+
+from langbot.pkg.utils import httpclient
 import uuid
 from typing import TYPE_CHECKING

@@ -56,7 +58,7 @@ class WebhookPusher:
            # Check if any webhook responded with skip_pipeline=true
            for result in results:
                if isinstance(result, dict) and result.get('skip_pipeline') is True:
-                    self.logger.info(f'Webhook responded with skip_pipeline=true, skipping pipeline for person message')
+                    self.logger.info('Webhook responded with skip_pipeline=true, skipping pipeline for person message')
                    return True

            return False
@@ -103,7 +105,7 @@ class WebhookPusher:
            # Check if any webhook responded with skip_pipeline=true
            for result in results:
                if isinstance(result, dict) and result.get('skip_pipeline') is True:
-                    self.logger.info(f'Webhook responded with skip_pipeline=true, skipping pipeline for group message')
+                    self.logger.info('Webhook responded with skip_pipeline=true, skipping pipeline for group message')
                    return True

            return False
@@ -119,23 +121,23 @@ class WebhookPusher:
            dict | None: The response JSON if successful, None otherwise
        """
        try:
-            async with aiohttp.ClientSession() as session:
-                async with session.post(
-                    url,
-                    json=payload,
-                    headers={'Content-Type': 'application/json'},
-                    timeout=aiohttp.ClientTimeout(total=15),
-                ) as response:
-                    if response.status >= 400:
-                        self.logger.warning(f'Webhook {url} returned status {response.status}')
+            session = httpclient.get_session()
+            async with session.post(
+                url,
+                json=payload,
+                headers={'Content-Type': 'application/json'},
+                timeout=aiohttp.ClientTimeout(total=15),
+            ) as response:
+                if response.status >= 400:
+                    self.logger.warning(f'Webhook {url} returned status {response.status}')
+                    return None
+                else:
+                    self.logger.debug(f'Successfully pushed to webhook {url}')
+                    try:
+                        return await response.json()
+                    except Exception as json_error:
+                        self.logger.debug(f'Failed to parse JSON response from webhook {url}: {json_error}')
                        return None
-                    else:
-                        self.logger.debug(f'Successfully pushed to webhook {url}')
-                        try:
-                            return await response.json()
-                        except Exception as json_error:
-                            self.logger.debug(f'Failed to parse JSON response from webhook {url}: {json_error}')
-                            return None
        except asyncio.TimeoutError:
            self.logger.warning(f'Timeout pushing to webhook {url}')
            return None
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -279,6 +279,7 @@ class RuntimeConnectionHandler(handler.Handler):
            target_id = data['target_id']
            message_chain = data['message_chain']

+            # Use custom deserializer that properly handles Forward messages
            message_chain_obj = platform_message.MessageChain.model_validate(message_chain)

            bot = await self.ap.platform_mgr.get_bot_by_uuid(bot_uuid)
@@ -324,7 +325,7 @@ 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]

-            result = await llm_model.requester.invoke_llm(
+            result = await llm_model.provider.invoke_llm(
                query=None,
                model=llm_model,
                messages=messages_obj,
--- a/src/langbot/pkg/provider/modelmgr/modelmgr.py
+++ b/src/langbot/pkg/provider/modelmgr/modelmgr.py
@@ -9,22 +9,24 @@ from ...discover import engine
 from . import token
 from ...entity.persistence import model as persistence_model
 from ...entity.errors import provider as provider_errors
-
-FETCH_MODEL_LIST_URL = 'https://api.qchatgpt.rockchin.top/api/v2/fetch/model_list'
+from async_lru import alru_cache


 class ModelManager:
-    """模型管理器"""
+    """Model manager"""

    ap: app.Application

+    provider_dict: dict[str, requester.RuntimeProvider]
+    """运行时模型提供商字典, uuid -> RuntimeProvider"""
+
    llm_models: list[requester.RuntimeLLMModel]

    embedding_models: list[requester.RuntimeEmbeddingModel]

    requester_components: list[engine.Component]

-    requester_dict: dict[str, type[requester.ProviderAPIRequester]]  # cache
+    requester_dict: dict[str, type[requester.ProviderAPIRequester]]

    def __init__(self, ap: app.Application):
        self.ap = ap
@@ -36,7 +38,6 @@ class ModelManager:
    async def initialize(self):
        self.requester_components = self.ap.discover.get_components_by_kind('LLMAPIRequester')

-        # forge requester class dict
        requester_dict: dict[str, type[requester.ProviderAPIRequester]] = {}
        for component in self.requester_components:
            requester_dict[component.metadata.name] = component.get_python_component_class()
@@ -45,139 +46,343 @@ class ModelManager:

        await self.load_models_from_db()

+        # Check if space models service is disabled
+        space_config = self.ap.instance_config.data.get('space', {})
+        if space_config.get('disable_models_service', False):
+            self.ap.logger.info('LangBot Space Models service is disabled, skipping sync.')
+            return
+
+        try:
+            await self.sync_new_models_from_space()
+        except Exception as e:
+            self.ap.logger.warning('Failed to sync new models from LangBot Space, model list may not be updated.')
+            self.ap.logger.warning(f'  - Error: {e}')
+
    async def load_models_from_db(self):
-        """从数据库加载模型"""
+        """Load models from database"""
        self.ap.logger.info('Loading models from db...')

        self.llm_models = []
        self.embedding_models = []

-        # llm models
+        # Load all providers first
+        self.provider_dict = {}
+        providers_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider)
+        )
+        for provider in providers_result.all():
+            try:
+                runtime_provider = await self.load_provider(provider)
+                self.provider_dict[provider.uuid] = runtime_provider
+            except provider_errors.RequesterNotFoundError as e:
+                self.ap.logger.warning(f'Requester {e.requester_name} not found, skipping provider {provider.uuid}')
+                continue
+            except Exception as e:
+                self.ap.logger.error(f'Failed to load provider {provider.uuid}: {e}\n{traceback.format_exc()}')
+
+        # Load LLM models
        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
        llm_models = result.all()
        for llm_model in llm_models:
            try:
-                await self.load_llm_model(llm_model)
-            except provider_errors.RequesterNotFoundError as e:
-                self.ap.logger.warning(f'Requester {e.requester_name} not found, skipping llm model {llm_model.uuid}')
+                provider = self.provider_dict.get(llm_model.provider_uuid)
+                if provider is None:
+                    self.ap.logger.warning(f'Provider {llm_model.provider_uuid} not found for model {llm_model.uuid}')
+                    continue
+                runtime_llm_model = await self.load_llm_model_with_provider(llm_model, provider)
+                self.llm_models.append(runtime_llm_model)
            except Exception as e:
                self.ap.logger.error(f'Failed to load model {llm_model.uuid}: {e}\n{traceback.format_exc()}')

-        # embedding models
+        # Load embedding models
        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
        embedding_models = result.all()
        for embedding_model in embedding_models:
            try:
-                await self.load_embedding_model(embedding_model)
-            except provider_errors.RequesterNotFoundError as e:
-                self.ap.logger.warning(
-                    f'Requester {e.requester_name} not found, skipping embedding model {embedding_model.uuid}'
-                )
+                provider = self.provider_dict.get(embedding_model.provider_uuid)
+                if provider is None:
+                    self.ap.logger.warning(
+                        f'Provider {embedding_model.provider_uuid} not found for model {embedding_model.uuid}'
+                    )
+                    continue
+                runtime_embedding_model = await self.load_embedding_model_with_provider(embedding_model, provider)
+                self.embedding_models.append(runtime_embedding_model)
            except Exception as e:
                self.ap.logger.error(f'Failed to load model {embedding_model.uuid}: {e}\n{traceback.format_exc()}')

-    async def init_runtime_llm_model(
+    async def sync_new_models_from_space(self):
+        """Sync models from Space"""
+        space_model_provider = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.requester == 'space-chat-completions'
+            )
+        )
+        result = space_model_provider.first()
+        if result is None:
+            raise provider_errors.ProviderNotFoundError('LangBot Models')
+
+        space_model_provider = result
+
+        # get the latest models from space
+        space_models = await self.ap.space_service.get_models()
+
+        exists_llm_models_uuids = [m['uuid'] for m in await self.ap.llm_model_service.get_llm_models()]
+        exists_embedding_models_uuids = [
+            m['uuid'] for m in await self.ap.embedding_models_service.get_embedding_models()
+        ]
+
+        for space_model in space_models:
+            if space_model.category == 'chat':
+                uuid = space_model.uuid
+
+                if uuid in exists_llm_models_uuids:
+                    continue
+
+                # model will be automatically loaded
+                await self.ap.llm_model_service.create_llm_model(
+                    {
+                        'uuid': space_model.uuid,
+                        'name': space_model.model_id,
+                        'provider_uuid': space_model_provider.uuid,
+                        'abilities': space_model.llm_abilities or [],
+                        'extra_args': {},
+                        'prefered_ranking': space_model.featured_order,
+                    },
+                    preserve_uuid=True,
+                    auto_set_to_default_pipeline=False,
+                )
+
+            elif space_model.category == 'embedding':
+                uuid = space_model.uuid
+
+                if uuid in exists_embedding_models_uuids:
+                    continue
+
+                # model will be automatically loaded
+                await self.ap.embedding_models_service.create_embedding_model(
+                    {
+                        'uuid': space_model.uuid,
+                        'name': space_model.model_id,
+                        'provider_uuid': space_model_provider.uuid,
+                        'extra_args': {},
+                        'prefered_ranking': space_model.featured_order,
+                    },
+                    preserve_uuid=True,
+                )
+
+    async def init_temporary_runtime_llm_model(
        self,
-        model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
-    ):
-        """初始化运行时 LLM 模型"""
-        if isinstance(model_info, sqlalchemy.Row):
-            model_info = persistence_model.LLMModel(**model_info._mapping)
-        elif isinstance(model_info, dict):
-            model_info = persistence_model.LLMModel(**model_info)
+        model_info: dict,
+    ) -> requester.RuntimeLLMModel:
+        """Initialize runtime LLM model from dict (for testing)"""
+        provider_info = model_info.get('provider', {})

-        if model_info.requester not in self.requester_dict:
-            raise provider_errors.RequesterNotFoundError(model_info.requester)
-
-        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
-
-        await requester_inst.initialize()
+        runtime_provider = await self.load_provider(provider_info)

        runtime_llm_model = requester.RuntimeLLMModel(
-            model_entity=model_info,
-            token_mgr=token.TokenManager(
-                name=model_info.uuid,
-                tokens=model_info.api_keys,
+            model_entity=persistence_model.LLMModel(
+                uuid=model_info.get('uuid', ''),
+                name=model_info.get('name', ''),
+                provider_uuid='',
+                abilities=model_info.get('abilities', []),
+                extra_args=model_info.get('extra_args', {}),
            ),
-            requester=requester_inst,
+            provider=runtime_provider,
        )

        return runtime_llm_model

-    async def init_runtime_embedding_model(
+    async def init_temporary_runtime_embedding_model(
        self,
-        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
-    ):
-        """初始化运行时 Embedding 模型"""
-        if isinstance(model_info, sqlalchemy.Row):
-            model_info = persistence_model.EmbeddingModel(**model_info._mapping)
-        elif isinstance(model_info, dict):
-            model_info = persistence_model.EmbeddingModel(**model_info)
-
-        if model_info.requester not in self.requester_dict:
-            raise provider_errors.RequesterNotFoundError(model_info.requester)
-
-        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
-
-        await requester_inst.initialize()
+        model_info: dict,
+    ) -> requester.RuntimeEmbeddingModel:
+        """Initialize runtime embedding model from dict (for testing)"""
+        provider_info = model_info.get('provider', {})
+        runtime_provider = await self.load_provider(provider_info)

        runtime_embedding_model = requester.RuntimeEmbeddingModel(
-            model_entity=model_info,
-            token_mgr=token.TokenManager(
-                name=model_info.uuid,
-                tokens=model_info.api_keys,
+            model_entity=persistence_model.EmbeddingModel(
+                uuid=model_info.get('uuid', ''),
+                name=model_info.get('name', ''),
+                provider_uuid='',
+                extra_args=model_info.get('extra_args', {}),
            ),
-            requester=requester_inst,
+            provider=runtime_provider,
        )

        return runtime_embedding_model

-    async def load_llm_model(
-        self,
-        model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
-    ):
-        """加载 LLM 模型"""
-        runtime_llm_model = await self.init_runtime_llm_model(model_info)
-        self.llm_models.append(runtime_llm_model)
+    async def load_provider(
+        self, provider_info: persistence_model.ModelProvider | sqlalchemy.Row | dict
+    ) -> requester.RuntimeProvider:
+        """Load provider from dict"""
+        if isinstance(provider_info, sqlalchemy.Row):
+            provider_entity = persistence_model.ModelProvider(**provider_info._mapping)
+        elif isinstance(provider_info, dict):
+            provider_entity = persistence_model.ModelProvider(**provider_info)
+        else:
+            provider_entity = provider_info

-    async def load_embedding_model(
-        self,
-        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
-    ):
-        """加载 Embedding 模型"""
-        runtime_embedding_model = await self.init_runtime_embedding_model(model_info)
-        self.embedding_models.append(runtime_embedding_model)
+        if provider_entity.requester not in self.requester_dict:
+            raise provider_errors.RequesterNotFoundError(provider_entity.requester)

+        requester_inst = self.requester_dict[provider_entity.requester](
+            ap=self.ap, config={'base_url': provider_entity.base_url}
+        )
+        await requester_inst.initialize()
+
+        token_mgr = token.TokenManager(name=provider_entity.uuid, tokens=provider_entity.api_keys or [])
+
+        provider = requester.RuntimeProvider(
+            provider_entity=provider_entity,
+            token_mgr=token_mgr,
+            requester=requester_inst,
+        )
+        return provider
+
+    async def remove_provider(self, provider_uuid: str):
+        """Remove provider
+
+        This method will not consider the models using this provider,
+        because the models should be removed by the caller.
+        """
+        del self.provider_dict[provider_uuid]
+
+    async def reload_provider(self, provider_uuid: str):
+        """Reload provider"""
+        provider_entity = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_model.ModelProvider).where(
+                persistence_model.ModelProvider.uuid == provider_uuid
+            )
+        )
+        provider_entity = provider_entity.first()
+        if provider_entity is None:
+            raise provider_errors.ProviderNotFoundError(provider_uuid)
+
+        new_runtime_provider = await self.load_provider(provider_entity)
+
+        # update refs in runtime models
+        for model in self.llm_models:
+            if model.provider.provider_entity.uuid == provider_uuid:
+                model.provider = new_runtime_provider
+        for model in self.embedding_models:
+            if model.provider.provider_entity.uuid == provider_uuid:
+                model.provider = new_runtime_provider
+
+        # update ref in provider dict
+        self.provider_dict[provider_uuid] = new_runtime_provider
+
+    async def load_llm_model_with_provider(
+        self,
+        model_info: persistence_model.LLMModel | sqlalchemy.Row,
+        provider: requester.RuntimeProvider,
+    ) -> requester.RuntimeLLMModel:
+        """Load LLM model with provider info"""
+        if isinstance(model_info, sqlalchemy.Row):
+            model_info = persistence_model.LLMModel(**model_info._mapping)
+
+        runtime_llm_model = requester.RuntimeLLMModel(
+            model_entity=model_info,
+            provider=provider,
+        )
+
+        return runtime_llm_model
+
+    async def load_embedding_model_with_provider(
+        self,
+        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row,
+        provider: requester.RuntimeProvider,
+    ) -> requester.RuntimeEmbeddingModel:
+        """Load embedding model with provider info"""
+        if isinstance(model_info, sqlalchemy.Row):
+            model_info = persistence_model.EmbeddingModel(**model_info._mapping)
+
+        runtime_embedding_model = requester.RuntimeEmbeddingModel(
+            model_entity=model_info,
+            provider=provider,
+        )
+
+        return runtime_embedding_model
+
+    async def load_llm_model(self, model_info: dict):
+        """Load LLM model from dict (with provider info)"""
+        provider_info = model_info.get('provider', {})
+        if not provider_info:
+            raise ValueError('Provider info is required')
+
+        model_entity = persistence_model.LLMModel(
+            uuid=model_info.get('uuid', ''),
+            name=model_info.get('name', ''),
+            provider_uuid=model_info.get('provider_uuid', ''),
+            abilities=model_info.get('abilities', []),
+            extra_args=model_info.get('extra_args', {}),
+        )
+
+        provider_entity = persistence_model.ModelProvider(
+            uuid=provider_info.get('uuid', ''),
+            name=provider_info.get('name', ''),
+            requester=provider_info.get('requester', ''),
+            base_url=provider_info.get('base_url', ''),
+            api_keys=provider_info.get('api_keys', []),
+        )
+
+        await self.load_llm_model_with_provider(model_entity, provider_entity)
+
+    async def load_embedding_model(self, model_info: dict):
+        """Load embedding model from dict (with provider info)"""
+        provider_info = model_info.get('provider', {})
+        if not provider_info:
+            raise ValueError('Provider info is required')
+
+        model_entity = persistence_model.EmbeddingModel(
+            uuid=model_info.get('uuid', ''),
+            name=model_info.get('name', ''),
+            provider_uuid=model_info.get('provider_uuid', ''),
+            extra_args=model_info.get('extra_args', {}),
+        )
+
+        provider_entity = persistence_model.ModelProvider(
+            uuid=provider_info.get('uuid', ''),
+            name=provider_info.get('name', ''),
+            requester=provider_info.get('requester', ''),
+            base_url=provider_info.get('base_url', ''),
+            api_keys=provider_info.get('api_keys', []),
+        )
+
+        await self.load_embedding_model_with_provider(model_entity, provider_entity)
+
+    @alru_cache(ttl=60 * 5)
    async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
-        """通过uuid获取 LLM 模型"""
+        """Get LLM model by uuid"""
        for model in self.llm_models:
            if model.model_entity.uuid == uuid:
                return model
        raise ValueError(f'LLM model {uuid} not found')

+    @alru_cache(ttl=60 * 5)
    async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
-        """通过uuid获取 Embedding 模型"""
+        """Get embedding model by uuid"""
        for model in self.embedding_models:
            if model.model_entity.uuid == uuid:
                return model
        raise ValueError(f'Embedding model {uuid} not found')

    async def remove_llm_model(self, model_uuid: str):
-        """移除 LLM 模型"""
+        """Remove LLM model"""
        for model in self.llm_models:
            if model.model_entity.uuid == model_uuid:
                self.llm_models.remove(model)
                return

    async def remove_embedding_model(self, model_uuid: str):
-        """移除 Embedding 模型"""
+        """Remove embedding model"""
        for model in self.embedding_models:
            if model.model_entity.uuid == model_uuid:
                self.embedding_models.remove(model)
                return

    def get_available_requesters_info(self, model_type: str) -> list[dict]:
-        """获取所有可用的请求器"""
+        """Get all available requesters"""
        if model_type != '':
            return [
                component.to_plain_dict()
@@ -188,14 +393,14 @@ class ModelManager:
            return [component.to_plain_dict() for component in self.requester_components]

    def get_available_requester_info_by_name(self, name: str) -> dict | None:
-        """通过名称获取请求器信息"""
+        """Get requester info by name"""
        for component in self.requester_components:
            if component.metadata.name == name:
                return component.to_plain_dict()
        return None

    def get_available_requester_manifest_by_name(self, name: str) -> engine.Component | None:
-        """通过名称获取请求器清单"""
+        """Get requester manifest by name"""
        for component in self.requester_components:
            if component.metadata.name == name:
                return component
--- a/src/langbot/pkg/provider/modelmgr/requester.py
+++ b/src/langbot/pkg/provider/modelmgr/requester.py
@@ -2,6 +2,7 @@ from __future__ import annotations

 import abc
 import typing
+import time

 from ...core import app
 from ...entity.persistence import model as persistence_model
@@ -11,11 +12,11 @@ import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message


-class RuntimeLLMModel:
-    """运行时模型"""
+class RuntimeProvider:
+    """运行时模型提供商"""

-    model_entity: persistence_model.LLMModel
-    """模型数据"""
+    provider_entity: persistence_model.ModelProvider
+    """提供商数据"""

    token_mgr: token.TokenManager
    """api key管理器"""
@@ -25,14 +26,245 @@ class RuntimeLLMModel:

    def __init__(
        self,
-        model_entity: persistence_model.LLMModel,
+        provider_entity: persistence_model.ModelProvider,
        token_mgr: token.TokenManager,
        requester: ProviderAPIRequester,
    ):
-        self.model_entity = model_entity
+        self.provider_entity = provider_entity
        self.token_mgr = token_mgr
        self.requester = requester

+    async def invoke_llm(
+        self,
+        query: pipeline_query.Query,
+        model: RuntimeLLMModel,
+        messages: typing.List[provider_message.Message],
+        funcs: typing.List[resource_tool.LLMTool] = None,
+        extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
+    ) -> provider_message.Message:
+        """Bridge method for invoking LLM with monitoring"""
+        # Start timing for monitoring
+        start_time = time.time()
+        input_tokens = 0
+        output_tokens = 0
+        status = 'success'
+        error_message = None
+
+        try:
+            # Call the underlying requester
+            result = await self.requester.invoke_llm(
+                query=query,
+                model=model,
+                messages=messages,
+                funcs=funcs,
+                extra_args=extra_args,
+                remove_think=remove_think,
+            )
+
+            # Try to extract token usage if the requester returns it
+            # For requesters that return tuple (message, usage_info)
+            if isinstance(result, tuple):
+                msg, usage_info = result
+                if usage_info:
+                    input_tokens = usage_info.get('input_tokens', 0)
+                    output_tokens = usage_info.get('output_tokens', 0)
+                return msg
+            else:
+                return result
+
+        except Exception as e:
+            status = 'error'
+            error_message = str(e)
+            raise
+        finally:
+            # Record LLM call monitoring data (only if query is provided)
+            if query is not None:
+                duration_ms = int((time.time() - start_time) * 1000)
+
+                # Import monitoring helper
+                try:
+                    from ...pipeline import monitoring_helper
+
+                    # Get monitoring metadata from query variables
+                    if query.variables:
+                        bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
+                        pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
+                        message_id = query.variables.get('_monitoring_message_id')
+                    else:
+                        bot_name = 'Unknown'
+                        pipeline_name = 'Unknown'
+                        message_id = None
+
+                    await monitoring_helper.MonitoringHelper.record_llm_call(
+                        ap=self.requester.ap,
+                        query=query,
+                        bot_id=query.bot_uuid or 'unknown',
+                        bot_name=bot_name,
+                        pipeline_id=query.pipeline_uuid or 'unknown',
+                        pipeline_name=pipeline_name,
+                        model_name=model.model_entity.name,
+                        input_tokens=input_tokens,
+                        output_tokens=output_tokens,
+                        duration_ms=duration_ms,
+                        status=status,
+                        error_message=error_message,
+                        message_id=message_id,
+                    )
+                except Exception as monitor_err:
+                    self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM call: {monitor_err}')
+
+    async def invoke_llm_stream(
+        self,
+        query: pipeline_query.Query,
+        model: RuntimeLLMModel,
+        messages: typing.List[provider_message.Message],
+        funcs: typing.List[resource_tool.LLMTool] = None,
+        extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
+    ) -> provider_message.MessageChunk:
+        """Bridge method for invoking LLM stream with monitoring"""
+        # Start timing for monitoring
+        start_time = time.time()
+        status = 'success'
+        error_message = None
+        # Note: Stream doesn't easily provide token counts, set to 0
+        input_tokens = 0
+        output_tokens = 0
+
+        try:
+            # Stream the response
+            async for chunk in self.requester.invoke_llm_stream(
+                query=query,
+                model=model,
+                messages=messages,
+                funcs=funcs,
+                extra_args=extra_args,
+                remove_think=remove_think,
+            ):
+                yield chunk
+        except Exception as e:
+            status = 'error'
+            error_message = str(e)
+            raise
+        finally:
+            # Record LLM call monitoring data (only if query is provided)
+            if query is not None:
+                duration_ms = int((time.time() - start_time) * 1000)
+
+                # Import monitoring helper
+                try:
+                    from ...pipeline import monitoring_helper
+
+                    # Get monitoring metadata from query variables
+                    if query.variables:
+                        bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
+                        pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
+                        message_id = query.variables.get('_monitoring_message_id')
+                    else:
+                        bot_name = 'Unknown'
+                        pipeline_name = 'Unknown'
+                        message_id = None
+
+                    await monitoring_helper.MonitoringHelper.record_llm_call(
+                        ap=self.requester.ap,
+                        query=query,
+                        bot_id=query.bot_uuid or 'unknown',
+                        bot_name=bot_name,
+                        pipeline_id=query.pipeline_uuid or 'unknown',
+                        pipeline_name=pipeline_name,
+                        model_name=model.model_entity.name,
+                        input_tokens=input_tokens,
+                        output_tokens=output_tokens,
+                        duration_ms=duration_ms,
+                        status=status,
+                        error_message=error_message,
+                        message_id=message_id,
+                    )
+                except Exception as monitor_err:
+                    self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM stream call: {monitor_err}')
+
+    async def invoke_embedding(
+        self,
+        model: RuntimeEmbeddingModel,
+        input_text: typing.List[str],
+        extra_args: dict[str, typing.Any] = {},
+        knowledge_base_id: str | None = None,
+        query_text: str | None = None,
+        session_id: str | None = None,
+        message_id: str | None = None,
+        call_type: str | None = None,
+    ) -> typing.List[typing.List[float]]:
+        """Bridge method for invoking embedding with monitoring"""
+        # Start timing for monitoring
+        start_time = time.time()
+        prompt_tokens = 0
+        total_tokens = 0
+        status = 'success'
+        error_message = None
+
+        try:
+            # Call the underlying requester
+            result = await self.requester.invoke_embedding(
+                model=model,
+                input_text=input_text,
+                extra_args=extra_args,
+            )
+
+            # Handle both old format (list only) and new format (tuple with usage)
+            if isinstance(result, tuple):
+                embeddings, usage_info = result
+                if usage_info:
+                    prompt_tokens = usage_info.get('prompt_tokens', 0)
+                    total_tokens = usage_info.get('total_tokens', 0)
+                return embeddings
+            else:
+                return result
+
+        except Exception as e:
+            status = 'error'
+            error_message = str(e)
+            raise
+        finally:
+            # Record embedding call monitoring data
+            duration_ms = int((time.time() - start_time) * 1000)
+
+            try:
+                await self.requester.ap.monitoring_service.record_embedding_call(
+                    model_name=model.model_entity.name,
+                    prompt_tokens=prompt_tokens,
+                    total_tokens=total_tokens,
+                    duration=duration_ms,
+                    input_count=len(input_text),
+                    status=status,
+                    error_message=error_message,
+                    knowledge_base_id=knowledge_base_id,
+                    query_text=query_text,
+                    session_id=session_id,
+                    message_id=message_id,
+                    call_type=call_type,
+                )
+            except Exception as monitor_err:
+                self.requester.ap.logger.error(f'[Monitoring] Failed to record embedding call: {monitor_err}')
+
+
+class RuntimeLLMModel:
+    """运行时模型"""
+
+    model_entity: persistence_model.LLMModel
+    """模型数据"""
+
+    provider: RuntimeProvider
+    """提供商实例"""
+
+    def __init__(
+        self,
+        model_entity: persistence_model.LLMModel,
+        provider: RuntimeProvider,
+    ):
+        self.model_entity = model_entity
+        self.provider = provider
+

 class RuntimeEmbeddingModel:
    """运行时 Embedding 模型"""
@@ -40,21 +272,16 @@ class RuntimeEmbeddingModel:
    model_entity: persistence_model.EmbeddingModel
    """模型数据"""

-    token_mgr: token.TokenManager
-    """api key管理器"""
-
-    requester: ProviderAPIRequester
-    """请求器实例"""
+    provider: RuntimeProvider
+    """提供商实例"""

    def __init__(
        self,
        model_entity: persistence_model.EmbeddingModel,
-        token_mgr: token.TokenManager,
-        requester: ProviderAPIRequester,
+        provider: RuntimeProvider,
    ):
        self.model_entity = model_entity
-        self.token_mgr = token_mgr
-        self.requester = requester
+        self.provider = provider


 class ProviderAPIRequester(metaclass=abc.ABCMeta):
@@ -128,7 +355,7 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
        model: RuntimeEmbeddingModel,
        input_text: typing.List[str],
        extra_args: dict[str, typing.Any] = {},
-    ) -> typing.List[typing.List[float]]:
+    ) -> typing.Union[typing.List[typing.List[float]], tuple[typing.List[typing.List[float]], dict]]:
        """调用 Embedding API

        Args:
@@ -138,5 +365,6 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):

        Returns:
            typing.List[typing.List[float]]: 返回的 embedding 向量
+            或者 tuple[typing.List[typing.List[float]], dict]: 返回 (embedding 向量, usage_info)
        """
        pass
--- a/src/langbot/pkg/provider/modelmgr/requesters/anthropicmsgs.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/anthropicmsgs.py
@@ -56,7 +56,7 @@ class AnthropicMessages(requester.ProviderAPIRequester):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.Message:
-        self.client.api_key = model.token_mgr.get_token()
+        self.client.api_key = model.provider.token_mgr.get_token()

        args = extra_args.copy()
        args['model'] = model.model_entity.name
@@ -190,7 +190,7 @@ class AnthropicMessages(requester.ProviderAPIRequester):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.Message:
-        self.client.api_key = model.token_mgr.get_token()
+        self.client.api_key = model.provider.token_mgr.get_token()

        args = extra_args.copy()
        args['model'] = model.model_entity.name
--- a/src/langbot/pkg/provider/modelmgr/requesters/bailianchatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/bailianchatcmpl.py
@@ -30,7 +30,7 @@ class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        self.client.api_key = use_model.token_mgr.get_token()
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -117,7 +117,7 @@ class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
        if is_use_dashscope_call:
            response = dashscope.MultiModalConversation.call(
                # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx"
-                api_key=use_model.token_mgr.get_token(),
+                api_key=use_model.provider.token_mgr.get_token(),
                model=use_model.model_entity.name,
                messages=messages,
                result_format='message',
--- a/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py
@@ -4,7 +4,7 @@ import asyncio
 import typing

 import openai
-import openai.types.chat.chat_completion as chat_completion
+import openai.types.chat.chat_completion as chat_completion_module
 import httpx

 from .. import errors, requester
@@ -35,7 +35,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        self,
        args: dict,
        extra_body: dict = {},
-    ) -> chat_completion.ChatCompletion:
+    ) -> chat_completion_module.ChatCompletion:
        return await self.client.chat.completions.create(**args, extra_body=extra_body)

    async def _req_stream(
@@ -48,9 +48,12 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):

    async def _make_msg(
        self,
-        chat_completion: chat_completion.ChatCompletion,
+        chat_completion: chat_completion_module.ChatCompletion,
        remove_think: bool = False,
    ) -> provider_message.Message:
+        if not isinstance(chat_completion, chat_completion_module.ChatCompletion):
+            raise TypeError(f'Expected ChatCompletion, got {type(chat_completion).__name__}: {chat_completion[:16]}')
+
        chatcmpl_message = chat_completion.choices[0].message.model_dump()

        # 确保 role 字段存在且不为 None
@@ -130,7 +133,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.MessageChunk:
-        self.client.api_key = use_model.token_mgr.get_token()
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -250,8 +253,8 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        use_funcs: list[resource_tool.LLMTool] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
-    ) -> provider_message.Message:
-        self.client.api_key = use_model.token_mgr.get_token()
+    ) -> tuple[provider_message.Message, dict]:
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -282,7 +285,14 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        # 处理请求结果
        message = await self._make_msg(resp, remove_think)

-        return message
+        # Extract token usage from response
+        usage_info = {}
+        if hasattr(resp, 'usage') and resp.usage:
+            usage_info['input_tokens'] = resp.usage.prompt_tokens or 0
+            usage_info['output_tokens'] = resp.usage.completion_tokens or 0
+            usage_info['total_tokens'] = resp.usage.total_tokens or 0
+
+        return message, usage_info

    async def invoke_llm(
        self,
@@ -292,7 +302,8 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        funcs: typing.List[resource_tool.LLMTool] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
-    ) -> provider_message.Message:
+    ) -> tuple[provider_message.Message, dict]:
+        """Invoke LLM and return message with usage info"""
        req_messages = []  # req_messages 仅用于类内，外部同步由 query.messages 进行
        for m in messages:
            msg_dict = m.dict(exclude_none=True)
@@ -305,7 +316,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
            req_messages.append(msg_dict)

        try:
-            msg = await self._closure(
+            msg, usage_info = await self._closure(
                query=query,
                req_messages=req_messages,
                use_model=model,
@@ -313,31 +324,39 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
                extra_args=extra_args,
                remove_think=remove_think,
            )
-            return msg
+            return msg, usage_info
        except asyncio.TimeoutError:
            raise errors.RequesterError('请求超时')
        except openai.BadRequestError as e:
-            if 'context_length_exceeded' in e.message:
-                raise errors.RequesterError(f'上文过长，请重置会话: {e.message}')
+            error_message = str(e.message) if hasattr(e, 'message') else str(e)
+            if 'context_length_exceeded' in str(e):
+                raise errors.RequesterError(f'上文过长，请重置会话: {error_message}')
            else:
-                raise errors.RequesterError(f'请求参数错误: {e.message}')
+                raise errors.RequesterError(f'请求参数错误: {error_message}')
        except openai.AuthenticationError as e:
-            raise errors.RequesterError(f'无效的 api-key: {e.message}')
+            error_message = str(e.message) if hasattr(e, 'message') else str(e)
+            raise errors.RequesterError(f'无效的 api-key: {error_message}')
        except openai.NotFoundError as e:
-            raise errors.RequesterError(f'请求路径错误: {e.message}')
+            error_message = str(e.message) if hasattr(e, 'message') else str(e)
+            raise errors.RequesterError(f'请求路径错误: {error_message}')
        except openai.RateLimitError as e:
-            raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
+            error_message = str(e.message) if hasattr(e, 'message') else str(e)
+            raise errors.RequesterError(f'请求过于频繁或余额不足: {error_message}')
+        except openai.APIConnectionError as e:
+            error_message = f'连接错误: {str(e)}'
+            raise errors.RequesterError(error_message)
        except openai.APIError as e:
-            raise errors.RequesterError(f'请求错误: {e.message}')
+            error_message = str(e.message) if hasattr(e, 'message') else str(e)
+            raise errors.RequesterError(f'请求错误: {error_message}')

    async def invoke_embedding(
        self,
        model: requester.RuntimeEmbeddingModel,
        input_text: list[str],
        extra_args: dict[str, typing.Any] = {},
-    ) -> list[list[float]]:
-        """调用 Embedding API"""
-        self.client.api_key = model.token_mgr.get_token()
+    ) -> tuple[list[list[float]], dict]:
+        """调用 Embedding API, returns (embeddings, usage_info)"""
+        self.client.api_key = model.provider.token_mgr.get_token()

        args = {
            'model': model.model_entity.name,
@@ -352,7 +371,13 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
        try:
            resp = await self.client.embeddings.create(**args)

-            return [d.embedding for d in resp.data]
+            # Extract usage info
+            usage_info = {}
+            if hasattr(resp, 'usage') and resp.usage:
+                usage_info['prompt_tokens'] = resp.usage.prompt_tokens or 0
+                usage_info['total_tokens'] = resp.usage.total_tokens or 0
+
+            return [d.embedding for d in resp.data], usage_info
        except asyncio.TimeoutError:
            raise errors.RequesterError('请求超时')
        except openai.BadRequestError as e:
--- a/src/langbot/pkg/provider/modelmgr/requesters/deepseekchatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/deepseekchatcmpl.py
@@ -25,8 +25,8 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
        use_funcs: list[resource_tool.LLMTool] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
-    ) -> provider_message.Message:
-        self.client.api_key = use_model.token_mgr.get_token()
+    ) -> tuple[provider_message.Message, dict]:
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -43,7 +43,7 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
        # deepseek 不支持多模态，把content都转换成纯文字
        for m in messages:
            if 'content' in m and isinstance(m['content'], list):
-                m['content'] = ' '.join([c['text'] for c in m['content']])
+                m['content'] = ' '.join([c['text'] for c in m['content'] if 'text' in c])

        args['messages'] = messages

@@ -57,4 +57,11 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
        # 处理请求结果
        message = await self._make_msg(resp, remove_think)

-        return message
+        # Extract token usage from response
+        usage_info = {}
+        if hasattr(resp, 'usage') and resp.usage:
+            usage_info['input_tokens'] = resp.usage.prompt_tokens or 0
+            usage_info['output_tokens'] = resp.usage.completion_tokens or 0
+            usage_info['total_tokens'] = resp.usage.total_tokens or 0
+
+        return message, usage_info
--- a/src/langbot/pkg/provider/modelmgr/requesters/geminichatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/geminichatcmpl.py
@@ -29,7 +29,7 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.MessageChunk:
-        self.client.api_key = use_model.token_mgr.get_token()
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
--- a/src/langbot/pkg/provider/modelmgr/requesters/jiekouaichatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/jiekouaichatcmpl.py
@@ -109,7 +109,7 @@ class JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        self.client.api_key = use_model.token_mgr.get_token()
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
--- a/src/langbot/pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/modelscopechatcmpl.py
@@ -130,8 +130,8 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
        use_funcs: list[resource_tool.LLMTool] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
-    ) -> provider_message.Message:
-        self.client.api_key = use_model.token_mgr.get_token()
+    ) -> tuple[provider_message.Message, dict]:
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -162,7 +162,10 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
        # 处理请求结果
        message = await self._make_msg(resp)

-        return message
+        # ModelScope uses streaming, usage info not available
+        usage_info = {}
+
+        return message, usage_info

    async def _req_stream(
        self,
@@ -181,7 +184,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
    ) -> provider_message.Message | typing.AsyncGenerator[provider_message.MessageChunk, None]:
-        self.client.api_key = use_model.token_mgr.get_token()
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
--- a/src/langbot/pkg/provider/modelmgr/requesters/moonshotchatcmpl.py
+++ b/src/langbot/pkg/provider/modelmgr/requesters/moonshotchatcmpl.py
@@ -26,8 +26,8 @@ class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
        use_funcs: list[resource_tool.LLMTool] = None,
        extra_args: dict[str, typing.Any] = {},
        remove_think: bool = False,
-    ) -> provider_message.Message:
-        self.client.api_key = use_model.token_mgr.get_token()
+    ) -> tuple[provider_message.Message, dict]:
+        self.client.api_key = use_model.provider.token_mgr.get_token()

        args = {}
        args['model'] = use_model.model_entity.name
@@ -57,4 +57,11 @@ class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
        # 处理请求结果
        message = await self._make_msg(resp, remove_think)

-        return message
+        # Extract token usage from response
+        usage_info = {}
+        if hasattr(resp, 'usage') and resp.usage:
+            usage_info['input_tokens'] = resp.usage.prompt_tokens or 0
+            usage_info['output_tokens'] = resp.usage.completion_tokens or 0
+            usage_info['total_tokens'] = resp.usage.total_tokens or 0
+
+        return message, usage_info
--- a/Show More
+++ b/Show More