feat: release 2.0.7

- Add Gemini, Seedream (Volcengine Ark), Qwen (DashScope), MiniMax
  providers to image-generation skill with universal sequential
  fallback: OpenAI → Gemini → Seedream → Qwen → MiniMax → LinkAI
- Each provider filters unsupported size tiers to valid values
  (e.g. Seedream 1K→2K, Qwen 3K→2K, Gemini 3K→2K)
- Pinned model only tries its native provider; auto-routing uses
  each provider's default model
- Support skill-namespaced config (config.skill.image-generation.model
  → SKILL_IMAGE_GENERATION_MODEL env var)
- Add image lightbox (click-to-enlarge) in web console
- Add  docs for built-in skills (skill-creator, knowledge-wiki,
  image-generation) under docs/skills/

.github/ISSUE_TEMPLATE/1.bug.yml

@@ -79,8 +79,6 @@ body:
       description: |
         请确保你正确配置了该`channel`所需的配置项，所有可选的配置项都写在了[该文件中](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py)，请将所需配置项填写在根目录下的`config.json`文件中。
       options:
-        - wx(个人微信, itchat)
-        - wxy(个人微信, wechaty)
         - wechatmp(公众号, 订阅号)
         - wechatmp_service(公众号, 服务号)
         - terminal

.github/workflows/deploy-image-arm.yml

@@ -19,7 +19,7 @@ env:
 
 jobs:
   build-and-push-image:
-    if: github.repository == 'zhayujie/chatgpt-on-wechat'
+    if: github.repository == 'zhayujie/CowAgent'
     runs-on: ubuntu-latest
     permissions:
       contents: read
@@ -51,7 +51,12 @@ jobs:
         uses: docker/metadata-action@v4
         with:
           images: |
-            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+            ${{ env.REGISTRY }}/zhayujie/chatgpt-on-wechat
+            ${{ env.REGISTRY }}/zhayujie/cowagent
+          tags: |
+            type=raw,value=latest-arm64,enable={{is_default_branch}}
+            type=ref,event=branch,suffix=-arm64
+            type=ref,event=tag,suffix=-arm64
 
       - name: Build and push Docker image
         uses: docker/build-push-action@v3
@@ -60,7 +65,7 @@ jobs:
           push: true
           file: ./docker/Dockerfile.latest
           platforms: linux/arm64
-          tags: ${{ steps.meta.outputs.tags }}-arm64
+          tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
 
       - uses: actions/delete-package-versions@v4

.github/workflows/deploy-image.yml

@@ -16,10 +16,11 @@ on:
 env:
   REGISTRY: ghcr.io
   IMAGE_NAME: ${{ github.repository }}
+  DOCKERHUB_IMAGE: zhayujie/chatgpt-on-wechat
 
 jobs:
   build-and-push-image:
-    if: github.repository == 'zhayujie/chatgpt-on-wechat'
+    if: github.repository == 'zhayujie/CowAgent'
     runs-on: ubuntu-latest
     permissions:
       contents: read
@@ -47,8 +48,14 @@ jobs:
         uses: docker/metadata-action@v4
         with:
           images: |
-            ${{ env.IMAGE_NAME }}
-            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+            zhayujie/chatgpt-on-wechat
+            zhayujie/cowagent
+            ${{ env.REGISTRY }}/zhayujie/chatgpt-on-wechat
+            ${{ env.REGISTRY }}/zhayujie/cowagent
+          tags: |
+            type=raw,value=latest,enable={{is_default_branch}}
+            type=ref,event=branch
+            type=ref,event=tag
 
       - name: Build and push Docker image
         uses: docker/build-push-action@v3

.gitignore

@@ -3,16 +3,15 @@
 .vscode
 .venv
 .vs
-.wechaty/
 __pycache__/
 venv*
 *.pyc
+python
 config.json
 QR.png
 nohup.out
 tmp
 plugins.json
-itchat.pkl
 *.log
 logs/
 workspace
@@ -34,7 +33,16 @@ plugins/banwords/lib/__pycache__
 !plugins/keyword
 !plugins/linkai
 !plugins/agent
+!plugins/cow_cli
 client_config.json
 ref/
+**/.dev.vars
 .cursor/
 local/
+node_modules/
+
+# cow cli
+dist/
+build/
+*.egg-info/
+.cow.pid

README.md

@@ -1,37 +1,50 @@
-<p align="center"><img src= "https://github.com/user-attachments/assets/eca9a9ec-8534-4615-9e0f-96c5ac1d10a3" alt="Chatgpt-on-Wechat" width="550" /></p>
+<p align="center"><img src= "https://github.com/user-attachments/assets/eca9a9ec-8534-4615-9e0f-96c5ac1d10a3" alt="CowAgent" width="550" /></p>
 
 <p align="center">
-   <a href="https://github.com/zhayujie/chatgpt-on-wechat/releases/latest"><img src="https://img.shields.io/github/v/release/zhayujie/chatgpt-on-wechat" alt="Latest release"></a>
-  <a href="https://github.com/zhayujie/chatgpt-on-wechat/blob/master/LICENSE"><img src="https://img.shields.io/github/license/zhayujie/chatgpt-on-wechat" alt="License: MIT"></a>
-  <a href="https://github.com/zhayujie/chatgpt-on-wechat"><img src="https://img.shields.io/github/stars/zhayujie/chatgpt-on-wechat?style=flat-square" alt="Stars"></a> <br/>
+  <a href="https://github.com/zhayujie/CowAgent/releases/latest"><img src="https://img.shields.io/github/v/release/zhayujie/CowAgent" alt="Latest release"></a>
+  <a href="https://github.com/zhayujie/CowAgent/blob/master/LICENSE"><img src="https://img.shields.io/github/license/zhayujie/CowAgent" alt="License: MIT"></a>
+  <a href="https://github.com/zhayujie/CowAgent"><img src="https://img.shields.io/github/stars/zhayujie/CowAgent?style=flat-square" alt="Stars"></a> <br/>
+  [中文] | [<a href="docs/en/README.md">English</a>] | [<a href="docs/ja/README.md">日本語</a>]
 </p>
 
-**CowAgent** 是基于大模型的超级AI助理，能够主动思考和任务规划、操作计算机和外部资源、创造和执行Skills、拥有长期记忆并不断成长。CowAgent 支持灵活切换多种模型，能处理文本、语音、图片、文件等多模态消息，可接入网页、飞书、钉钉、企业微信应用、微信公众号中使用，7*24小时运行于你的个人电脑或服务器中。
+**CowAgent** 是基于大模型的超级 AI 助理，能够主动思考和任务规划、操作计算机和外部资源、创造和执行 Skills、拥有长期记忆和知识库并不断成长，比 OpenClaw 更轻量和便捷。CowAgent 支持灵活切换多种模型，能处理文本、语音、图片、文件等多模态消息，可接入微信、飞书、钉钉、企微智能机器人、QQ、企微自建应用、微信公众号、网页中使用，7*24小时运行于你的个人电脑或服务器中。
+
+<p align="center">
+  <a href="https://cowagent.ai/">🌐 官网</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/">📖 文档中心</a> &nbsp;·&nbsp;
+  <a href="https://docs.cowagent.ai/guide/quick-start">🚀 快速开始</a> &nbsp;·&nbsp;
+  <a href="https://skills.cowagent.ai/">🧩 技能广场</a> &nbsp;·&nbsp;
+  <a href="https://link-ai.tech/cowagent/create">☁️ 在线体验</a>
+</p>
 
-📖能力介绍：[CowAgent 2.0](/docs/agent.md)
 
 # 简介
 
-> 该项目既是一个可以开箱即用的超级AI助理，也是一个支持高扩展的Agent框架，可以通过为项目扩展大模型接口、接入渠道、内置工具、Skills系统来灵活实现各种定制需求。核心能力如下：
+> 该项目既是一个可以开箱即用的超级 AI 助理，也是一个支持高扩展的 Agent 框架，可以通过为项目扩展大模型接口、接入渠道、内置工具、Skills 系统来灵活实现各种定制需求。核心能力如下：
 
--  ✅  **复杂任务规划**：能够理解复杂任务并自主规划执行，持续思考和调用工具直到完成目标，支持通过工具操作访问文件、终端、浏览器、定时任务等系统资源
--  ✅  **长期记忆：** 自动将对话记忆持久化至本地文件和数据库中，包括全局记忆和天级记忆，支持关键词及向量检索
--  ✅  **技能系统：** 实现了Skills创建和运行的引擎，内置多种技能，并支持通过自然语言对话完成自定义Skills开发
+-  ✅  **自主任务规划**：能够理解复杂任务并自主规划执行，持续思考和调用工具直到完成目标
+-  ✅  **长期记忆：** 自动将对话记忆持久化至本地文件和数据库中，包括核心记忆、日级记忆和梦境蒸馏，支持关键词及向量检索
+-  ✅  **个人知识库：** 自动整理结构化知识，通过交叉引用构建知识图谱，支持通过对话管理和可视化浏览知识库
+-  ✅  **技能系统：** Skills 安装和运行的引擎，支持从 [Skill Hub](https://skills.cowagent.ai/)、GitHub 等一键安装技能，或通过对话创造 Skills
+-  ✅  **工具系统：** 内置文件读写、终端执行、浏览器操作、定时任务等工具，Agent 自主调用以完成复杂任务
+-  ✅  **CLI系统：** 提供终端命令和对话命令，支持进程管理、技能安装、配置修改等操作
 -  ✅  **多模态消息：** 支持对文本、图片、语音、文件等多类型消息进行解析、处理、生成、发送等操作
--  ✅  **多模型接入：** 支持OpenAI, Claude, Gemini, DeepSeek, MiniMax、GLM、Qwen、Kimi等国内外主流模型厂商
--  ✅  **多端部署：** 支持运行在本地计算机或服务器，可集成到网页、飞书、钉钉、微信公众号、企业微信应用中使用
--  ✅  **知识库：** 集成企业知识库能力，让Agent成为专属数字员工，基于[LinkAI](https://link-ai.tech)平台实现
+-  ✅  **多模型支持：** 支持 OpenAI, Claude, Gemini, DeepSeek, MiniMax、GLM、Qwen、Kimi、Doubao 等国内外主流模型厂商
+-  ✅  **多通道接入：** 支持运行在本地计算机或服务器，可集成到微信、飞书、钉钉、企业微信、QQ、微信公众号、网页中使用
 
 ## 声明
 
-1. 本项目遵循 [MIT开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任
-2. 成本与安全：Agent模式下Token使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本
+1. 本项目遵循 [MIT 开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任。
+2. 成本与安全：Agent 模式下 Token 使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent 具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本。
+3. CowAgent 项目专注于开源技术开发，不会参与、授权或发行任何加密货币。
 
 ## 演示
 
-使用说明(Agent模式)：[CowAgent介绍](/docs/agent.md)
+- 使用说明( Agent 模式)：[CowAgent 介绍](https://docs.cowagent.ai/intro/features)
 
-DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
+- 免部署在线体验：[CowAgent](https://link-ai.tech/cowagent/create)
+
+- DEMO 视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
 
 ## 社区
 
@@ -43,11 +56,11 @@ DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
 
 # 企业服务
 
-<a href="https://link-ai.tech" target="_blank"><img width="720" src="https://cdn.link-ai.tech/image/link-ai-intro.jpg"></a>
+<a href="https://link-ai.tech" target="_blank"><img width="650" src="https://cdn.link-ai.tech/image/link-ai-intro.jpg"></a>
 
-> [LinkAI](https://link-ai.tech/) 是面向企业和开发者的一站式AI智能体平台，聚合多模态大模型、知识库、Agent 插件、工作流等能力，支持一键接入主流平台并进行管理，支持SaaS、私有化部署等多种模式。
+> [LinkAI](https://link-ai.tech/) 是面向企业和个人的一站式 AI 智能体平台，聚合多模态大模型、知识库、技能、工作流等能力，支持一键接入主流平台并管理，支持 SaaS、私有化部署等多种模式，可免部署在线运行[CowAgent 助理](https://link-ai.tech/cowagent/create)。
 >
-> LinkAI 目前已在智能客服、私域运营、企业效率助手等场景积累了丰富的AI解决方案，在消费、健康、文教、科技制造等各行业沉淀了大模型落地应用的最佳实践，致力于帮助更多企业和开发者拥抱 AI 生产力。
+> LinkAI 目前已在智能客服、私域运营、企业效率助手等场景积累了丰富的 AI 解决方案，在消费、健康、文教、科技制造等各行业沉淀了大模型落地应用的最佳实践，致力于帮助更多企业和开发者拥抱 AI 生产力。
 
 **产品咨询和企业服务** 可联系产品客服：
 
@@ -57,17 +70,23 @@ DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
 
 # 🏷 更新日志
 
->**2026.02.03：** [2.0.0版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/2.0.0)，正式升级为超级Agent助理，支持多轮任务决策、具备长期记忆、实现多种系统工具、支持Skills框架，新增多种模型并优化了接入渠道。
+>**2026.04.22：** [2.0.7版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.7)，图像生成内置技能（GPT Image 2、Nano Banana 等）、新模型支持（Kimi K2.6、Claude Opus 4.7、GLM 5.1）、知识库和记忆增强、Web 控制台优化
 
->**2025.05.23：** [1.7.6版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.6) 优化web网页channel、新增 [AgentMesh](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/plugins/agent/README.md)多智能体插件、百度语音合成优化、企微应用`access_token`获取优化、支持`claude-4-sonnet`和`claude-4-opus`模型
+>**2026.04.14：** [2.0.6版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.6)，知识库系统、梦境记忆模块、上下文智能压缩、Web 控制台多会话及多项优化。
 
->**2025.04.11：** [1.7.5版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.5) 新增支持 [wechatferry](https://github.com/zhayujie/chatgpt-on-wechat/pull/2562) 协议、新增 deepseek 模型、新增支持腾讯云语音能力、新增支持 ModelScope 和 Gitee-AI API接口
+>**2026.04.01：** [2.0.5版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.5)，Cow CLI 命令系统、Skill Hub 开源、浏览器工具、企微扫码创建、多项优化和修复。
 
->**2024.12.13：** [1.7.4版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.4) 新增 Gemini 2.0 模型、新增web channel、解决内存泄漏问题、解决 `#reloadp` 命令重载不生效问题
+>**2026.03.22：** [2.0.4版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.4)，新增个人微信通道（微信扫码即用）、新增 MiniMax-M2.7 和 GLM-5-Turbo 模型、run.sh 脚本重构、日文文档及多项修复。
 
->**2024.10.31：** [1.7.3版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.3) 程序稳定性提升、数据库功能、Claude模型优化、linkai插件优化、离线通知
+>**2026.03.18：** [2.0.3版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.3)，新增企微智能机器人和 QQ 通道、支持 Coding Plan、新增多个模型、Web 端文件处理、记忆系统升级。
 
-更多更新历史请查看: [更新日志](/docs/release/history.md)
+>**2026.02.27：** [2.0.2版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.2)，Web 控制台全面升级（流式对话、模型/技能/记忆/通道/定时任务/日志管理）、支持多通道同时运行、会话持久化存储、新增多个模型。
+
+>**2026.02.13：** [2.0.1版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.1)，内置 Web Search 工具、智能上下文裁剪策略、运行时信息动态更新、Windows 兼容性适配，修复定时任务记忆丢失、飞书连接等多项问题。
+
+>**2026.02.03：** [2.0.0版本](https://github.com/zhayujie/CowAgent/releases/tag/2.0.0)，正式升级为超级 Agent 助理，支持多轮任务决策、具备长期记忆、实现多种系统工具、支持 Skills 框架，新增多种模型并优化了接入渠道。
+
+更多更新历史请查看: [更新日志](https://docs.cowagent.ai/releases)
 
 <br/>
 
@@ -77,11 +96,17 @@ DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
 
 在终端执行以下命令：
 
+**Linux / macOS：**
 ```bash
-bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
+bash <(curl -fsSL https://cdn.link-ai.tech/code/cow/run.sh)
 ```
 
-脚本使用说明：[一键运行脚本](https://github.com/zhayujie/chatgpt-on-wechat/wiki/CowAgentQuickStart)
+**Windows（PowerShell）：**
+```powershell
+irm https://cdn.link-ai.tech/code/cow/run.ps1 | iex
+```
+
+脚本使用说明：[一键运行脚本](https://docs.cowagent.ai/guide/quick-start)。安装后可使用 `cow start`、`cow stop` 等 [CLI 命令](https://docs.cowagent.ai/cli/index) 管理服务。
 
 
 ## 一、准备
@@ -90,24 +115,24 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
 
 项目支持国内外主流厂商的模型接口，可选模型及配置说明参考：[模型说明](#模型说明)。
 
-> 注：Agent模式下推荐使用以下模型，可根据效果及成本综合选择：GLM(glm-4.7)、MiniMAx(MiniMax-M2.1)、Qwen(qwen3-max)、Claude(claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0)、Gemini(gemini-3-flash-preview、gemini-3-pro-preview)
+> 注：Agent 模式下推荐使用以下模型，可根据效果及成本综合选择：MiniMax-M2.7、glm-5.1、kimi-k2.6、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-pro-preview、gpt-5.4、gpt-5.4-mini
 
-同时支持使用 **LinkAI平台** 接口，可灵活切换 OpenAI、Claude、Gemini、DeepSeek、Qwen、Kimi 等多种常用模型，并支持知识库、工作流、插件等Agent能力，参考 [接口文档](https://docs.link-ai.tech/platform/api)。
+同时支持使用 **LinkAI 平台** 接口，支持上述全部模型，并支持知识库、工作流、插件等 Agent 技能，参考 [接口文档](https://docs.link-ai.tech/platform/api)。
 
 ### 2.环境安装
 
-支持 Linux、MacOS、Windows 操作系统，可在个人计算机及服务器上运行，需安装 `Python`，Python版本需在3.7 ~ 3.12 之间，推荐使用3.9版本。
+支持 Linux、MacOS、Windows 操作系统，可在个人计算机及服务器上运行，需安装 `Python`，Python 版本需在 3.7 ~ 3.13 之间。
 
-> 注意：Agent模式推荐使用源码运行，若选择Docker部署则无需安装python环境和下载源码，可直接快进到下一节。
+> 注意：Agent 模式推荐使用源码运行，若选择 Docker 部署则无需安装 python 环境和下载源码，可直接快进到下一节。
 
 **(1) 克隆项目代码：**
 
 ```bash
-git clone https://github.com/zhayujie/chatgpt-on-wechat
-cd chatgpt-on-wechat/
+git clone https://github.com/zhayujie/CowAgent
+cd CowAgent/
 ```
 
-若遇到网络问题可使用国内仓库地址：https://gitee.com/zhayujie/chatgpt-on-wechat
+若遇到网络问题可使用国内仓库地址：https://gitee.com/zhayujie/CowAgent
 
 **(2) 安装核心依赖 (必选)：**
 
@@ -120,43 +145,70 @@ pip3 install -r requirements.txt
 ```bash
 pip3 install -r requirements-optional.txt
 ```
+
+> 国内网络可使用镜像源加速：`pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple`
+
 如果某项依赖安装失败可注释掉对应的行后重试。
 
+**(4) 安装 Cow CLI (推荐)：**
+
+```bash
+pip3 install -e .
+```
+
+安装后可使用 `cow` 命令管理服务（启动、停止、更新等）和技能，详见 [命令文档](https://docs.cowagent.ai/cli/index)。
+
+**(5) 安装浏览器工具 (可选)：**
+
+如果需要 Agent 操作浏览器（如访问网页、填写表单等），需要额外安装浏览器依赖：
+
+```bash
+cow install-browser
+```
+
+该命令会自动安装 `playwright` 和 Chromium 浏览器，国内网络自动使用镜像加速。详见 [浏览器工具文档](https://docs.cowagent.ai/tools/browser)。
+
 ## 二、配置
 
-配置文件的模板在根目录的`config-template.json`中，需复制该模板创建最终生效的 `config.json` 文件：
+配置文件的模板在根目录的 `config-template.json` 中，需复制该模板创建最终生效的 `config.json` 文件：
 
 ```bash
   cp config-template.json config.json
 ```
 
-然后在`config.json`中填入配置，以下是对默认配置的说明，可根据需要进行自定义修改（注意实际使用时请去掉注释，保证JSON格式的规范）：
+然后在 `config.json` 中填入配置，以下是对默认配置的说明，可根据需要进行自定义修改（注意实际使用时请去掉注释，保证 JSON 格式的规范）：
 
 ```bash
 # config.json 文件内容示例
 {
-  "channel_type": "web",                                      # 接入渠道类型，默认为web，支持修改为:feishu,dingtalk,wechatcom_app,terminal,wechatmp,wechatmp_service
-  "model": "MiniMax-M2.1",                                    # 模型名称
+  "channel_type": "weixin",                                   # 接入渠道类型，默认为 weixin, 支持修改为 feishu,dingtalk,wecom_bot,qq,wechatcom_app,wechatmp_service,wechatmp,terminal
+  "model": "MiniMax-M2.7",                                    # 模型名称
   "minimax_api_key": "",                                      # MiniMax API Key
-  "zhipu_ai_api_key": "",                                     # 智谱GLM API Key
-  "dashscope_api_key": "",                                    # 百炼(通义千问)API Key
+  "zhipu_ai_api_key": "",                                     # 智谱 GLM API Key
+  "moonshot_api_key": "",                                     # Kimi/Moonshot API Key
+  "ark_api_key": "",                                          # 豆包(火山方舟) API Key
+  "dashscope_api_key": "",                                    # 百炼(通义千问) API Key
   "claude_api_key": "",                                       # Claude API Key
   "claude_api_base": "https://api.anthropic.com/v1",          # Claude API 地址，修改可接入三方代理平台
   "gemini_api_key": "",                                       # Gemini API Key
-  "gemini_api_base": "https://generativelanguage.googleapis.com", # Gemini API地址
+  "gemini_api_base": "https://generativelanguage.googleapis.com", # Gemini API 地址
+  "deepseek_api_key": "",                                      # DeepSeek API Key
+  "deepseek_api_base": "https://api.deepseek.com/v1",         # DeepSeek API 地址，可修改为第三方代理
   "open_ai_api_key": "",                                      # OpenAI API Key
   "open_ai_api_base": "https://api.openai.com/v1",            # OpenAI API 地址
   "linkai_api_key": "",                                       # LinkAI API Key
-  "proxy": "",                                                # 代理客户端的ip和端口，国内环境需要开启代理的可填写该项，如 "127.0.0.1:7890"
+  "proxy": "",                                                # 代理客户端的 ip 和端口，国内环境需要开启代理的可填写该项，如 "127.0.0.1:7890"
   "speech_recognition": false,                                # 是否开启语音识别
   "group_speech_recognition": false,                          # 是否开启群组语音识别
   "voice_reply_voice": false,                                 # 是否使用语音回复语音
-  "use_linkai": false,                                        # 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台接口
-  "agent": true,                                              # 是否启用Agent模式，启用后拥有多轮工具决策、长期记忆、Skills能力等
-  "agent_workspace": "~/cow",                                 # Agent的工作空间路径，用于存储memory、skills、系统设定等
-  "agent_max_context_tokens": 40000,                          # Agent模式下最大上下文tokens，超出将自动丢弃最早的上下文
-  "agent_max_context_turns": 30,                              # Agent模式下最大上下文记忆轮次，每轮包括一次用户提问和AI回复
-  "agent_max_steps": 15                                       # Agent模式下单次任务的最大决策步数，超出后将停止继续调用工具
+  "use_linkai": false,                                        # 是否使用 LinkAI 接口，默认关闭，设置为 true 后可对接 LinkAI 平台模型
+  "web_password": "",                                         # Web 控制台访问密码，留空则不启用密码保护
+  "agent": true,                                              # 是否启用 Agent 模式，启用后拥有多轮工具决策、长期记忆、Skills 能力等
+  "agent_workspace": "~/cow",                                 # Agent 的工作空间路径，用于存储 memory、skills、系统设定等
+  "agent_max_context_tokens": 50000,                          # Agent 模式下最大上下文 tokens，超出将自动智能压缩处理
+  "agent_max_context_turns": 20,                              # Agent 模式下最大上下文记忆轮次，一问一答为一轮，超出后智能压缩处理
+  "agent_max_steps": 20,                                      # Agent 模式下单次任务的最大决策步数，超出后将停止继续调用工具
+  "enable_thinking": false                                    # 是否启用深度思考，开启后 Web 端展示模型推理过程，关闭后可加速响应
 }
 ```
 
@@ -165,28 +217,28 @@ pip3 install -r requirements-optional.txt
 <details>
 <summary>1. 语音配置</summary>
 
-+ 添加 `"speech_recognition": true` 将开启语音识别，默认使用openai的whisper模型识别为文字，同时以文字回复，该参数仅支持私聊 (注意由于语音消息无法匹配前缀，一旦开启将对所有语音自动回复，支持语音触发画图)；
-+ 添加 `"group_speech_recognition": true` 将开启群组语音识别，默认使用openai的whisper模型识别为文字，同时以文字回复，参数仅支持群聊 (会匹配group_chat_prefix和group_chat_keyword, 支持语音触发画图)；
++ 添加 `"speech_recognition": true` 将开启语音识别，默认使用 openai 的 whisper 模型识别为文字，同时以文字回复，该参数仅支持私聊 (注意由于语音消息无法匹配前缀，一旦开启将对所有语音自动回复，支持语音触发画图)；
++ 添加 `"group_speech_recognition": true` 将开启群组语音识别，默认使用 openai 的 whisper 模型识别为文字，同时以文字回复，参数仅支持群聊 (会匹配 group_chat_prefix 和 group_chat_keyword, 支持语音触发画图)；
 + 添加 `"voice_reply_voice": true` 将开启语音回复语音（同时作用于私聊和群聊）
++ 使用 MiniMax TTS：设置 `"text_to_voice": "minimax"`，并配置 `minimax_api_key`；可通过 `"tts_voice_id"` 指定发音人（如 `English_Graceful_Lady`），`"text_to_voice_model"` 指定模型（如 `speech-2.8-hd`、`speech-2.8-turbo`）
 </details>
 
 <details>
 <summary>2. 其他配置</summary>
 
-+ `model`: 模型名称，Agent模式下推荐使用 `glm-4.7`、`MiniMax-M2.1`、`qwen3-max`、`claude-opus-4-6`、`claude-sonnet-4-5`、`claude-sonnet-4-0`、`gemini-3-flash-preview`、`gemini-3-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
-+ `character_desc`：普通对话模式下的机器人系统提示词。在Agent模式下该配置不生效，由工作空间中的文件内容构成。
-+ `subscribe_msg`：订阅消息，公众号和企业微信channel中请填写，当被订阅时会自动回复， 可使用特殊占位符。目前支持的占位符有{trigger_prefix}，在程序中它会自动替换成bot的触发词。
++ `model`: 模型名称，Agent 模式下推荐使用 `MiniMax-M2.7`、`glm-5.1`、`kimi-k2.6`、`qwen3.6-plus`、`claude-sonnet-4-6`、`gemini-3.1-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/CowAgent/blob/master/common/const.py)文件
++ `character_desc`：普通对话模式下的机器人系统提示词。在 Agent 模式下该配置不生效，由工作空间中的文件内容构成。
++ `subscribe_msg`：订阅消息，公众号和企业微信 channel 中请填写，当被订阅时会自动回复， 可使用特殊占位符。目前支持的占位符有{trigger_prefix}，在程序中它会自动替换成 bot 的触发词。
 </details>
 
 <details>
-<summary>5. LinkAI配置</summary>
+<summary>3. LinkAI 配置</summary>
 
-+ `use_linkai`: 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台，使用知识库、工作流、插件等能力, 参考[接口文档](https://docs.link-ai.tech/platform/api/chat)
++ `use_linkai`: 是否使用 LinkAI 接口，默认关闭，设置为 true 后可对接 LinkAI 平台，使用模型、知识库、工作流、插件等技能, 参考[接口文档](https://docs.link-ai.tech/platform/api/chat)
 + `linkai_api_key`: LinkAI Api Key，可在 [控制台](https://link-ai.tech/console/interface) 创建
-+ `linkai_app_code`: LinkAI 应用或工作流的code，选填，普通对话模式中使用。
 </details>
 
-注：全部配置项说明可在 [`config.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py) 文件中查看。
+注：全部配置项说明可在 [`config.py`](https://github.com/zhayujie/CowAgent/blob/master/config.py) 文件中查看。
 
 ## 三、运行
 
@@ -195,37 +247,48 @@ pip3 install -r requirements-optional.txt
 如果是个人计算机 **本地运行**，直接在项目根目录下执行：
 
 ```bash
-python3 app.py         # windows环境下该命令通常为 python app.py
+cow start              # 推荐，需先安装 Cow CLI
+python3 app.py         # 或直接运行，windows 环境下该命令通常为 python app.py
 ```
 
-运行后默认会启动web服务，可通过访问 `http://localhost:9899/chat` 在网页端对话。
+运行后默认会启动 web 服务，可通过访问 `http://localhost:9899/chat` 在网页端对话。
 
 如果需要接入其他应用通道只需修改 `config.json` 配置文件中的 `channel_type` 参数，详情参考：[通道说明](#通道说明)。
 
 
 ### 2.服务器部署
 
-在服务器中可使用 `nohup` 命令在后台运行程序：
+推荐使用 `cow` 命令管理服务：
+
+```bash
+cow start              # 后台启动
+cow stop               # 停止服务
+cow restart            # 重启服务
+cow status             # 查看运行状态
+cow logs               # 查看日志
+cow update             # 拉取最新代码并重启
+```
+
+也可以使用传统方式后台运行：
 
 ```bash
 nohup python3 app.py & tail -f nohup.out
 ```
 
-执行后程序运行于服务器后台，可通过 `ctrl+c` 关闭日志，不会影响后台程序的运行。使用 `ps -ef | grep app.py | grep -v grep` 命令可查看运行于后台的进程，如果想要重新启动程序可以先 `kill` 掉对应的进程。 日志关闭后如果想要再次打开只需输入 `tail -f nohup.out`。 
-
-此外，项目的 `scripts` 目录下有一键运行、关闭程序的脚本供使用。 运行后默认channel为web，通过可以通过修改配置文件进行切换。
+此外，项目根目录下的 `run.sh` 脚本也支持一键管理服务，包括 `./run.sh start`、`./run.sh stop`、`./run.sh restart` 等命令，执行 `./run.sh help` 可查看全部用法。
 
+> 如果需要通过浏览器访问 Web 控制台，请确保服务器的 `9899` 端口已在防火墙或安全组中放行，建议仅对指定 IP 开放以保证安全。
 
 ### 3.Docker部署
 
-使用docker部署无需下载源码和安装依赖，只需要获取 `docker-compose.yml` 配置文件并启动容器即可。Agent模式下更推荐使用源码进行部署，以获得更多系统访问能力。
+使用 docker 部署无需下载源码和安装依赖，只需要获取 `docker-compose.yml` 配置文件并启动容器即可。Agent 模式下更推荐使用源码进行部署，以获得更多系统访问能力。
 
 > 前提是需要安装好 `docker` 及 `docker-compose`，安装成功后执行 `docker -v` 和 `docker-compose version` (或 `docker compose version`) 可查看到版本号。安装地址为 [docker官网](https://docs.docker.com/engine/install/) 。
 
 **(1) 下载 docker-compose.yml 文件**
 
 ```bash
-wget https://cdn.link-ai.tech/code/cow/docker-compose.yml
+curl -O https://cdn.link-ai.tech/code/cow/docker-compose.yml
 ```
 
 下载完成后打开 `docker-compose.yml` 填写所需配置，例如 `CHANNEL_TYPE`、`OPEN_AI_API_KEY` 和等配置。
@@ -238,68 +301,57 @@ wget https://cdn.link-ai.tech/code/cow/docker-compose.yml
 sudo docker compose up -d         # 若docker-compose为 1.X 版本，则执行 `sudo  docker-compose up -d`
 ```
 
-运行命令后，会自动取 [docker hub](https://hub.docker.com/r/zhayujie/chatgpt-on-wechat) 拉取最新release版本的镜像。当执行 `sudo docker ps` 能查看到 NAMES 为 chatgpt-on-wechat 的容器即表示运行成功。最后执行以下命令可查看容器的运行日志：
+运行命令后，会自动取 [docker hub](https://hub.docker.com/r/zhayujie/chatgpt-on-wechat) 拉取最新 release 版本的镜像。当执行 `sudo docker ps` 能查看到 NAMES 为 chatgpt-on-wechat 的容器即表示运行成功。最后执行以下命令可查看容器的运行日志：
 
 ```bash
 sudo docker logs -f chatgpt-on-wechat
 ```
 
-**(3) 插件使用**
-
-如果需要在docker容器中修改插件配置，可通过挂载的方式完成，将 [插件配置文件](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/plugins/config.json.template)
-重命名为 `config.json`，放置于 `docker-compose.yml` 相同目录下，并在 `docker-compose.yml` 中的 `chatgpt-on-wechat` 部分下添加 `volumes` 映射:
-
-```
-volumes:
-  - ./config.json:/app/plugins/config.json
-```
-**注**：使用docker方式部署的详细教程可以参考：[docker部署CoW项目](https://www.wangpc.cc/ai/docker-deploy-cow/)
-
+> 如果需要通过浏览器访问 Web 控制台，请确保服务器的 `9899` 端口已在防火墙或安全组中放行，建议仅对指定 IP 开放以保证安全。
 
 ## 模型说明
 
-以下对所有可支持的模型的配置和使用方法进行说明，模型接口实现在项目的 `models/` 目录下。
+推荐通过 Web 控制台在线管理模型配置，无需手动编辑文件，详见 [模型文档](https://docs.cowagent.ai/models)。以下是手动修改 `config.json` 配置模型的说明：
 
 <details>
 <summary>OpenAI</summary>
 
-1. API Key创建：在 [OpenAI平台](https://platform.openai.com/api-keys) 创建API Key
+1. API Key 创建：在 [OpenAI平台](https://platform.openai.com/api-keys) 创建 API Key
 
 2. 填写配置
 
 ```json
 {
-    "model": "gpt-4.1-mini",
+    "model": "gpt-5.4",
     "open_ai_api_key": "YOUR_API_KEY",
     "open_ai_api_base": "https://api.openai.com/v1",
-    "bot_type": "chatGPT"
+    "bot_type": "openai"
 }
 ```
 
- - `model`: 与OpenAI接口的 [model参数](https://platform.openai.com/docs/models) 一致，支持包括 o系列、gpt-5.2、gpt-5.1、gpt-4.1等系列模型
+ - `model`: 与 OpenAI 接口的 [model参数](https://platform.openai.com/docs/models) 一致，支持包括 gpt-5.4、gpt-5.4-mini、gpt-5.4-nano、o 系列、gpt-4.1 等模型，Agent 模式推荐使用  `gpt-5.4`、`gpt-5.4-mini`
  - `open_ai_api_base`: 如果需要接入第三方代理接口，可通过修改该参数进行接入
- - `bot_type`: 使用OpenAI相关模型时无需填写。当使用第三方代理接口接入Claude等非OpenAI官方模型时，该参数设为 `chatGPT`
+ - `bot_type`: 使用 OpenAI 相关模型时无需填写。当使用第三方代理接口接入 Claude 等非 OpenAI 官方模型时，该参数设为 `openai`
 </details>
 
 <details>
 <summary>LinkAI</summary>
 
-1. API Key创建：在 [LinkAI平台](https://link-ai.tech/console/interface) 创建API Key 
+1. API Key 创建：在 [LinkAI平台](https://link-ai.tech/console/interface) 创建 API Key 
 
 2. 填写配置
 
 ```json
 {
+    "model": "gpt-5.4-mini",
     "use_linkai": true,
-    "linkai_api_key": "YOUR API KEY",
-    "linkai_app_code": "YOUR APP CODE"
+    "linkai_api_key": "YOUR API KEY"
 }
 ```
 
-+ `use_linkai`: 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台的智能体，使用知识库、工作流、数据库、MCP插件等丰富的Agent能力
-+ `linkai_api_key`: LinkAI平台的API Key，可在 [控制台](https://link-ai.tech/console/interface) 中创建
-+ `linkai_app_code`: LinkAI智能体 (应用或工作流) 的code，选填，普通对话模式可用。智能体创建可参考 [说明文档](https://docs.link-ai.tech/platform/quick-start)
-+ `model`: model字段填写空则直接使用智能体的模型，可在平台中灵活切换，[模型列表](https://link-ai.tech/console/models)中的全部模型均可使用
++ `use_linkai`: 是否使用 LinkAI 接口，默认关闭，设置为 true 后可对接 LinkAI 平台的模型，并使用知识库、工作流、数据库、插件等丰富的 Agent 技能
++ `linkai_api_key`: LinkAI 平台的 API Key，可在 [控制台](https://link-ai.tech/console/interface) 中创建
++ `model`: [模型列表](https://link-ai.tech/console/models)中的全部模型均可使用
 </details>
 
 <details>
@@ -309,26 +361,26 @@ volumes:
 
 ```json
 {
-    "model": "MiniMax-M2.1",
+    "model": "MiniMax-M2.7",
     "minimax_api_key": ""
 }
 ```
- - `model`: 可填写 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
- - `minimax_api_key`：MiniMax平台的API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建
+ - `model`: 可填写 `MiniMax-M2.7、MiniMax-M2.7-highspeed、MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
+ - `minimax_api_key`：MiniMax 平台的 API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建
 
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
-  "model": "MiniMax-M2.1",
+  "bot_type": "openai",
+  "model": "MiniMax-M2.7",
   "open_ai_api_base": "https://api.minimaxi.com/v1",
   "open_ai_api_key": ""
 }
 ```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
-- `open_ai_api_base`: MiniMax平台API的 BASE URL
-- `open_ai_api_key`: MiniMax平台的API-KEY
+- `bot_type`: OpenAI 兼容方式
+- `model`: 可填 `MiniMax-M2.7、MiniMax-M2.7-highspeed、MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
+- `open_ai_api_base`: MiniMax 平台 API 的 BASE URL
+- `open_ai_api_key`: MiniMax 平台的 API-KEY
 </details>
 
 <details>
@@ -338,109 +390,57 @@ volumes:
 
 ```json
 {
-  "model": "glm-4.7",
+  "model": "glm-5.1",
   "zhipu_ai_api_key": ""
 }
 ```
- - `model`: 可填 `glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm-4系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
- - `zhipu_ai_api_key`: 智谱AI平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建
+ - `model`: 可填 `glm-5.1、glm-5-turbo、glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm 系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
+ - `zhipu_ai_api_key`: 智谱AI 平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建
 
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
-  "model": "glm-4.7",
+  "bot_type": "openai",
+  "model": "glm-5.1",
   "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
   "open_ai_api_key": ""
 }
 ```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填 `glm-4.7、glm-4.6、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
-- `open_ai_api_base`: 智谱AI平台的 BASE URL
-- `open_ai_api_key`: 智谱AI平台的 API KEY
+- `bot_type`: OpenAI 兼容方式
+- `model`: 可填 `glm-5.1、glm-5-turbo、glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
+- `open_ai_api_base`: 智谱AI 平台的 BASE URL
+- `open_ai_api_key`: 智谱AI 平台的 API KEY
 </details>
 
 <details>
 <summary>通义千问 (Qwen)</summary>
 
-方式一：官方SDK接入，配置如下(推荐)：
+方式一：官方 SDK 接入，配置如下(推荐)：
 
 ```json
 {
-    "model": "qwen3-max",
+    "model": "qwen3.6-plus",
     "dashscope_api_key": "sk-qVxxxxG"
 }
 ```
- - `model`: 可填写 `qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
- - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
+ - `model`: 可填写 `qwen3.6-plus、qwen3.5-plus、qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
+ - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [百炼控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
 
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
-  "model": "qwen3-max",
+  "bot_type": "openai",
+  "model": "qwen3.6-plus",
   "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
   "open_ai_api_key": "sk-qVxxxxG"
 }
 ```
-- `bot_type`: OpenAI兼容方式
+- `bot_type`: OpenAI 兼容方式
 - `model`: 支持官方所有模型，参考[模型列表](https://help.aliyun.com/zh/model-studio/models?spm=a2c4g.11186623.0.0.78d84823Kth5on#9f8890ce29g5u)
-- `open_ai_api_base`: 通义千问API的 BASE URL
+- `open_ai_api_base`: 通义千问 API 的 BASE URL
 - `open_ai_api_key`: 通义千问的 API-KEY
 </details>
 
-<details>
-<summary>Claude</summary>
-
-1. API Key创建：在 [Claude控制台](https://console.anthropic.com/settings/keys) 创建API Key
-
-2. 填写配置
-
-```json
-{
-    "model": "claude-sonnet-4-5",
-    "claude_api_key": "YOUR_API_KEY"
-}
-```
- - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
-</details>
-
-<details>
-<summary>Gemini</summary>
-
-API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建API Key ，配置如下
-```json
-{
-    "model": "gemini-3-flash-preview",
-    "gemini_api_key": ""
-}
-```
- - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
-</details>
-
-<details>
-<summary>DeepSeek</summary>
-
-1. API Key创建：在 [DeepSeek平台](https://platform.deepseek.com/api_keys) 创建API Key 
-
-2. 填写配置
-
-```json
-{
-    "model": "deepseek-chat",
-    "open_ai_api_key": "sk-xxxxxxxxxxx",
-    "open_ai_api_base": "https://api.deepseek.com/v1", 
-    "bot_type": "chatGPT"
-
-}
-```
-
- - `bot_type`: OpenAI兼容方式
- - `model`: 可填 `deepseek-chat、deepseek-reasoner`，分别对应的是 DeepSeek-V3 和 DeepSeek-R1 模型
- - `open_ai_api_key`: DeepSeek平台的 API Key
- - `open_ai_api_base`: DeepSeek平台 BASE URL
-</details>
-
 <details>
 <summary>Kimi (Moonshot)</summary>
 
@@ -448,32 +448,112 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 
 ```json
 {
-    "model": "moonshot-v1-128k",
+    "model": "kimi-k2.6",
     "moonshot_api_key": ""
 }
 ```
- - `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
+ - `model`: 可填写 `kimi-k2.6、kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+ - `moonshot_api_key`: Moonshot 的 API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
  
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
-  "model": "moonshot-v1-128k",
+  "bot_type": "openai",
+  "model": "kimi-k2.6",
   "open_ai_api_base": "https://api.moonshot.cn/v1",
   "open_ai_api_key": ""
 }
 ```
-- `bot_type`: OpenAI兼容方式
-- `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
-- `open_ai_api_base`: Moonshot的 BASE URL
-- `open_ai_api_key`: Moonshot的 API-KEY
+- `bot_type`: OpenAI 兼容方式
+- `model`: 可填写 `kimi-k2.6、kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+- `open_ai_api_base`: Moonshot 的 BASE URL
+- `open_ai_api_key`: Moonshot 的 API-KEY
 </details>
 
+<details>
+<summary>豆包 (Doubao)</summary>
+
+1. API Key 创建：在 [火山方舟控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建API Key
+
+2. 填写配置
+
+```json
+{
+    "model": "doubao-seed-2-0-code-preview-260215",
+    "ark_api_key": "YOUR_API_KEY"
+}
+```
+ - `model`: 可填写 `doubao-seed-2-0-code-preview-260215、doubao-seed-2-0-pro-260215、doubao-seed-2-0-lite-260215、doubao-seed-2-0-mini-260215` 等
+ - `ark_api_key`: 火山方舟平台的 API Key，在 [控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建
+ - `ark_base_url`: 可选，默认为 `https://ark.cn-beijing.volces.com/api/v3`
+</details>
+
+<details>
+<summary>Claude</summary>
+
+1. API Key 创建：在 [Claude控制台](https://console.anthropic.com/settings/keys) 创建 API Key
+
+2. 填写配置
+
+```json
+{
+    "model": "claude-sonnet-4-6",
+    "claude_api_key": "YOUR_API_KEY"
+}
+```
+ - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-sonnet-4-6、claude-opus-4-7、claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
+</details>
+
+<details>
+<summary>Gemini</summary>
+
+API Key 创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建 API Key ，配置如下
+```json
+{
+    "model": "gemini-3.1-flash-lite-preview",
+    "gemini_api_key": ""
+}
+```
+ - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3.1-flash-lite-preview、gemini-3.1-pro-preview、gemini-3-flash-preview、gemini-3-pro-preview` 等
+</details>
+
+<details>
+<summary>DeepSeek</summary>
+
+1. API Key 创建：在 [DeepSeek 平台](https://platform.deepseek.com/api_keys) 创建 API Key 
+
+2. 填写配置
+
+方式一：官方接入（推荐）：
+
+```json
+{
+    "model": "deepseek-chat",
+    "deepseek_api_key": "sk-xxxxxxxxxxx"
+}
+```
+
+ - `model`: 可填 `deepseek-chat、deepseek-reasoner`，分别对应的是 DeepSeek-V3.2（非思考模式）和 DeepSeek-R1（思考模式）
+ - `deepseek_api_key`: DeepSeek 平台的 API Key
+ - `deepseek_api_base`: 可选，默认为 `https://api.deepseek.com/v1`，可修改为第三方代理地址
+
+方式二：OpenAI 兼容方式接入：
+
+```json
+{
+    "model": "deepseek-chat",
+    "bot_type": "openai",
+    "open_ai_api_key": "sk-xxxxxxxxxxx",
+    "open_ai_api_base": "https://api.deepseek.com/v1"
+}
+```
+
+ </details>
+
 <details>
 <summary>Azure</summary>
 
-1. API Key创建：在 [Azure平台](https://oai.azure.com/) 创建API Key 
+1. API Key 创建：在 [Azure平台](https://oai.azure.com/) 创建 API Key 
 
 2. 填写配置
 
@@ -490,15 +570,15 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 
  - `model`: 留空即可
  - `use_azure_chatgpt`: 设为 true 
- - `open_ai_api_key`: Azure平台的密钥
- - `open_ai_api_base`: Azure平台的 BASE URL
- - `azure_deployment_id`: Azure平台部署的模型名称
- - `azure_api_version`: api版本以及以上参数可以在部署的 [模型配置](https://oai.azure.com/resource/deployments) 界面查看
+ - `open_ai_api_key`: Azure 平台的密钥
+ - `open_ai_api_base`: Azure 平台的 BASE URL
+ - `azure_deployment_id`: Azure 平台部署的模型名称
+ - `azure_api_version`: api 版本以及以上参数可以在部署的 [模型配置](https://oai.azure.com/resource/deployments) 界面查看
 </details>
 
 <details>
 <summary>百度文心</summary>
-方式一：官方SDK接入，配置如下：
+方式一：官方 SDK 接入，配置如下：
 
 ```json
 {
@@ -511,19 +591,19 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
  - `baidu_wenxin_api_key`：参考 [千帆平台-access_token鉴权](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/dlv4pct3s) 文档获取 API Key
  - `baidu_wenxin_secret_key`：参考 [千帆平台-access_token鉴权](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/dlv4pct3s) 文档获取 Secret Key
 
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
+  "bot_type": "openai",
   "model": "ERNIE-4.0-Turbo-8K",
   "open_ai_api_base": "https://qianfan.baidubce.com/v2",
   "open_ai_api_key": "bce-v3/ALTxxxxxxd2b"
 }
 ```
-- `bot_type`: OpenAI兼容方式
+- `bot_type`: OpenAI 兼容方式
 - `model`: 支持官方所有模型，参考[模型列表](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Wm9cvy6rl)
-- `open_ai_api_base`: 百度文心API的 BASE URL
-- `open_ai_api_key`: 百度文心的 API-KEY，参考 [官方文档](https://cloud.baidu.com/doc/qianfan-api/s/ym9chdsy5) ，在 [控制台](https://console.bce.baidu.com/iam/#/iam/apikey/list) 创建API Key
+- `open_ai_api_base`: 百度文心 API 的 BASE URL
+- `open_ai_api_key`: 百度文心的 API-KEY，参考 [官方文档](https://cloud.baidu.com/doc/qianfan-api/s/ym9chdsy5) ，在 [控制台](https://console.bce.baidu.com/iam/#/iam/apikey/list) 创建 API Key
 
 </details>
 
@@ -547,16 +627,16 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
  - `xunfei_domain`: 可填写 `4.0Ultra、generalv3.5、max-32k、generalv3、pro-128k、lite`
  - `xunfei_spark_url`: 填写参考 [官方文档-请求地址](https://www.xfyun.cn/doc/spark/Web.html#_1-1-%E8%AF%B7%E6%B1%82%E5%9C%B0%E5%9D%80) 的说明
  
-方式二：OpenAI兼容方式接入，配置如下：
+方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
-  "bot_type": "chatGPT",
+  "bot_type": "openai",
   "model": "4.0Ultra",
   "open_ai_api_base": "https://spark-api-open.xf-yun.com/v1",
   "open_ai_api_key": ""
 }
 ```
-- `bot_type`: OpenAI兼容方式
+- `bot_type`: OpenAI 兼容方式
 - `model`: 可填写 `4.0Ultra、generalv3.5、max-32k、generalv3、pro-128k、lite`
 - `open_ai_api_base`: 讯飞星火平台的 BASE URL
 - `open_ai_api_key`: 讯飞星火平台的[APIPassword](https://console.xfyun.cn/services/bm3) ，因模型而已
@@ -575,22 +655,58 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 }
 ```
 
-- `bot_type`: modelscope接口格式
+- `bot_type`: modelscope 接口格式
 - `model`: 参考[模型列表](https://www.modelscope.cn/models?filter=inference_type&page=1)
 - `modelscope_api_key`: 参考 [官方文档-访问令牌](https://modelscope.cn/docs/accounts/token) ，在 [控制台](https://modelscope.cn/my/myaccesstoken) 
-- `modelscope_base_url`: modelscope平台的 BASE URL
+- `modelscope_base_url`: modelscope 平台的 BASE URL
 - `text_to_image`: 图像生成模型，参考[模型列表](https://www.modelscope.cn/models?filter=inference_type&page=1)
 </details>
 
+<details>
+<summary>Coding Plan</summary>
+
+Coding Plan 是各厂商推出的编程包月套餐，所有厂商均可通过 OpenAI 兼容方式接入：
+
+```json
+{
+  "bot_type": "openai",
+  "model": "模型名称",
+  "open_ai_api_base": "厂商 Coding Plan API Base",
+  "open_ai_api_key": "YOUR_API_KEY"
+}
+```
+
+目前支持阿里云、MiniMax、智谱 GLM、Kimi、火山引擎等厂商，各厂商详细配置请参考 [Coding Plan 文档](https://docs.cowagent.ai/models/coding-plan)。
+</details>
+
 
 ## 通道说明
 
-以下对可接入通道的配置方式进行说明，应用通道代码在项目的 `channel/` 目录下。
+推荐通过 Web 控制台在线管理通道配置，无需手动编辑文件，详见 [通道文档](https://docs.cowagent.ai/channels/weixin)。以下为手动修改 `config.json` 配置通道的说明：
+
+支持同时可接入多个通道，配置时可通过逗号进行分割，例如 `"channel_type": "feishu,dingtalk"`。
 
 <details>
-<summary>1. Web</summary>
+<summary>1. Weixin - 微信</summary>
 
-项目启动后默认运行Web通道，配置如下：
+接入个人微信，扫码登录即可使用，支持文本、图片、语音、文件等消息收发。
+
+```json
+{
+    "channel_type": "weixin"
+}
+```
+
+启动后终端会显示二维码，使用微信扫码授权即可，也可以在 Web 控制台的「通道」页面中扫码接入。登录凭证会自动保存至 `~/.weixin_cow_credentials.json`，下次启动无需重新扫码，如需重新登录删除该文件后重启即可。
+
+详细步骤和参数说明参考 [微信接入](https://docs.cowagent.ai/channels/weixin)
+
+</details>
+
+<details>
+<summary>2. Web</summary>
+
+项目启动后会默认运行 Web 控制台，配置如下：
 
 ```json
 {
@@ -600,12 +716,13 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 ```
 
 - `web_port`: 默认为 9899，可按需更改，需要服务器防火墙和安全组放行该端口
+- `web_password`: 访问密码，留空则不启用密码保护。部署在公网环境时建议设置
 - 如本地运行，启动后请访问 `http://localhost:9899/chat` ；如服务器运行，请访问 `http://ip:9899/chat` 
 > 注：请将上述 url 中的 ip 或者 port 替换为实际的值
 </details>
 
 <details>
-<summary>2. Feishu - 飞书</summary>
+<summary>3. Feishu - 飞书</summary>
 
 飞书支持两种事件接收模式：WebSocket 长连接（推荐）和 Webhook。
 
@@ -636,12 +753,12 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 - `feishu_event_mode`: 事件接收模式，`websocket`（推荐）或 `webhook`
 - WebSocket 模式需安装依赖：`pip3 install lark-oapi`
 
-详细步骤和参数说明参考 [飞书接入](https://docs.link-ai.tech/cow/multi-platform/feishu)
+详细步骤和参数说明参考 [飞书接入](https://docs.cowagent.ai/channels/feishu)
 
 </details>
 
 <details>
-<summary>3. DingTalk - 钉钉</summary>
+<summary>4. DingTalk - 钉钉</summary>
 
 钉钉需要在开放平台创建智能机器人应用，将以下配置填入 `config.json`：
 
@@ -652,11 +769,43 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
     "dingtalk_client_secret": "CLIENT_SECRET"
 }
 ```
-详细步骤和参数说明参考 [钉钉接入](https://docs.link-ai.tech/cow/multi-platform/dingtalk)
+详细步骤和参数说明参考 [钉钉接入](https://docs.cowagent.ai/channels/dingtalk)
 </details>
 
 <details>
-<summary>4. WeCom App - 企业微信应用</summary>
+<summary>5. WeCom Bot - 企微智能机器人</summary>
+
+企微智能机器人使用 WebSocket 长连接模式，无需公网 IP 和域名，配置简单：
+
+```json
+{
+    "channel_type": "wecom_bot",
+    "wecom_bot_id": "YOUR_BOT_ID",
+    "wecom_bot_secret": "YOUR_SECRET"
+}
+```
+详细步骤和参数说明参考 [企微智能机器人接入](https://docs.cowagent.ai/channels/wecom-bot)
+
+</details>
+
+<details>
+<summary>6. QQ - QQ 机器人</summary>
+
+QQ 机器人使用 WebSocket 长连接模式，无需公网 IP 和域名，支持 QQ 单聊、群聊和频道消息：
+
+```json
+{
+    "channel_type": "qq",
+    "qq_app_id": "YOUR_APP_ID",
+    "qq_app_secret": "YOUR_APP_SECRET"
+}
+```
+详细步骤和参数说明参考 [QQ 机器人接入](https://docs.cowagent.ai/channels/qq)
+
+</details>
+
+<details>
+<summary>7. WeCom App - 企业微信应用</summary>
 
 企业微信自建应用接入需在后台创建应用并启用消息回调，配置示例：
 
@@ -671,12 +820,12 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
     "wechatcomapp_aes_key": "AESKEY"
 }
 ```
-详细步骤和参数说明参考 [企微自建应用接入](https://docs.link-ai.tech/cow/multi-platform/wechat-com)
+详细步骤和参数说明参考 [企微自建应用接入](https://docs.cowagent.ai/channels/wecom)
 
 </details>
 
 <details>
-<summary>5. WeChat MP - 微信公众号</summary>
+<summary>8. WeChat MP - 微信公众号</summary>
 
 本项目支持订阅号和服务号两种公众号，通过服务号（`wechatmp_service`）体验更佳。
 
@@ -706,12 +855,12 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 }
 ```
 
-详细步骤和参数说明参考 [微信公众号接入](https://docs.link-ai.tech/cow/multi-platform/wechat-mp)
+详细步骤和参数说明参考 [微信公众号接入](https://docs.cowagent.ai/channels/wechatmp)
 
 </details>
 
 <details>
-<summary>6. Terminal - 终端</summary>
+<summary>9. Terminal - 终端</summary>
 
 修改 `config.json` 中的 `channel_type` 字段：
 
@@ -729,25 +878,37 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 
 # 🔗 相关项目
 
-- [bot-on-anything](https://github.com/zhayujie/bot-on-anything)：轻量和高可扩展的大模型应用框架，支持接入Slack, Telegram, Discord, Gmail等海外平台，可作为本项目的补充使用。
-- [AgentMesh](https://github.com/MinimalFuture/AgentMesh)：开源的多智能体(Multi-Agent)框架，可以通过多智能体团队的协同来解决复杂问题。本项目基于该框架实现了[Agent插件](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/plugins/agent/README.md)，可访问终端、浏览器、文件系统、搜索引擎 等各类工具，并实现了多智能体协同。
+- [Cow Skill Hub](https://github.com/zhayujie/cow-skill-hub)：开源的 AI Agent 技能广场，浏览、搜索、安装和发布技能，支持 CowAgent、OpenClaw、Claude Code 等多种 Agent。
+- [bot-on-anything](https://github.com/zhayujie/bot-on-anything)：轻量和高可扩展的大模型应用框架，支持接入 Slack, Telegram, Discord, Gmail 等海外平台，可作为本项目的补充使用。
+- [AgentMesh](https://github.com/MinimalFuture/AgentMesh)：开源的多智能体( Multi-Agent )框架，可以通过多智能体团队的协同来解决复杂问题。
+
 
 
 
 # 🔎 常见问题
 
-FAQs： <https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs>
+FAQs： <https://github.com/zhayujie/CowAgent/wiki/FAQs>
 
 或直接在线咨询 [项目小助手](https://link-ai.tech/app/Kv2fXJcH)  (知识库持续完善中，回复供参考)
 
 # 🛠️ 开发
 
-欢迎接入更多应用通道，参考 [飞书通道](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/channel/feishu/feishu_channel.py) 新增自定义通道，实现接收和发送消息逻辑即可完成接入。 同时欢迎贡献新的Skills，参考 [Skill创造器说明](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/skills/skill-creator/SKILL.md)。
+欢迎接入更多应用通道，参考 [飞书通道](https://github.com/zhayujie/CowAgent/blob/master/channel/feishu/feishu_channel.py) 新增自定义通道，实现接收和发送消息逻辑即可完成接入。同时欢迎贡献新的 Skills，向 [Skill Hub](https://skills.cowagent.ai/submit) 提交技能。
 
 # ✉ 联系
 
-欢迎提交PR、Issues进行反馈，以及通过 🌟Star 支持并关注项目更新。项目运行遇到问题可以查看 [常见问题列表](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs) ，以及前往 [Issues](https://github.com/zhayujie/chatgpt-on-wechat/issues) 中搜索。个人开发者可加入开源交流群参与更多讨论，企业用户可联系[产品客服](https://cdn.link-ai.tech/portal/linkai-customer-service.png)咨询。
+欢迎提交PR、Issues进行反馈，以及通过 🌟Star 支持并关注项目更新。项目运行遇到问题可以查看 [常见问题列表](https://github.com/zhayujie/CowAgent/wiki/FAQs) ，以及前往 [Issues](https://github.com/zhayujie/CowAgent/issues) 中搜索。个人开发者可加入开源交流群参与更多讨论，企业用户可联系[产品客服](https://cdn.link-ai.tech/portal/linkai-customer-service.png)咨询。
 
 # 🌟 贡献者
 
-![cow contributors](https://contrib.rocks/image?repo=zhayujie/chatgpt-on-wechat&max=1000)
+![cow contributors](https://contrib.rocks/image?repo=zhayujie/CowAgent&max=1000)
+
+# 📌 项目更名说明
+
+本项目原名 `chatgpt-on-wechat`（GitHub 原地址：https://github.com/zhayujie/chatgpt-on-wechat ），
+于 2026.04.13 正式更名为 **CowAgent**。GitHub 已自动设置重定向，原有链接仍可正常访问。
+
+如需更新本地仓库的远程地址（可选）：
+```bash
+git remote set-url origin https://github.com/zhayujie/CowAgent.git
+```

agent/chat/__init__.py

@@ -0,0 +1,3 @@
+from agent.chat.service import ChatService
+
+__all__ = ["ChatService"]

agent/chat/service.py

@@ -0,0 +1,290 @@
+"""
+ChatService - Wraps the Agent stream execution to produce CHAT protocol chunks.
+
+Translates agent events (message_update, message_end, tool_execution_end, etc.)
+into the CHAT socket protocol format (content chunks with segment_id, tool_calls chunks).
+"""
+
+import time
+from typing import Callable, Optional
+
+from common.log import logger
+
+
+class ChatService:
+    """
+    High-level service that runs an Agent for a given query and streams
+    the results as CHAT protocol chunks via a callback.
+
+    Usage:
+        svc = ChatService(agent_bridge)
+        svc.run(query, session_id, send_chunk_fn)
+    """
+
+    def __init__(self, agent_bridge):
+        """
+        :param agent_bridge: AgentBridge instance (manages agent lifecycle)
+        """
+        self.agent_bridge = agent_bridge
+
+    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None],
+            channel_type: str = ""):
+        """
+        Run the agent for *query* and stream results back via *send_chunk_fn*.
+
+        The method blocks until the agent finishes. After it returns the SDK
+        will automatically send the final (streaming=false) message.
+
+        :param query: user query text
+        :param session_id: session identifier for agent isolation
+        :param send_chunk_fn: callable(chunk_data: dict) to send a streaming chunk
+        :param channel_type: source channel (e.g. "web", "feishu") for persistence
+        """
+        agent = self.agent_bridge.get_agent(session_id=session_id)
+        if agent is None:
+            raise RuntimeError("Failed to initialise agent for the session")
+
+        # Pass context metadata to model for downstream API requests
+        if hasattr(agent, 'model'):
+            agent.model.channel_type = channel_type or ""
+            agent.model.session_id = session_id or ""
+
+        # State shared between the event callback and this method
+        state = _StreamState()
+
+        def on_event(event: dict):
+            """Translate agent events into CHAT protocol chunks."""
+            event_type = event.get("type")
+            data = event.get("data", {})
+
+            if event_type == "reasoning_update":
+                delta = data.get("delta", "")
+                if delta:
+                    send_chunk_fn({
+                        "chunk_type": "reasoning",
+                        "delta": delta,
+                        "segment_id": state.segment_id,
+                    })
+
+            elif event_type == "message_update":
+                # Incremental text delta
+                delta = data.get("delta", "")
+                if delta:
+                    send_chunk_fn({
+                        "chunk_type": "content",
+                        "delta": delta,
+                        "segment_id": state.segment_id,
+                    })
+
+            elif event_type == "message_end":
+                # A content segment finished.
+                tool_calls = data.get("tool_calls", [])
+                if tool_calls:
+                    # After tool_calls are executed the next content will be
+                    # a new segment; collect tool results until turn_end.
+                    state.pending_tool_results = []
+
+            elif event_type == "file_to_send":
+                url = data.get("url") or ""
+                if url:
+                    fname = data.get("file_name") or "file"
+                    ft = data.get("file_type") or "file"
+                    if ft == "image":
+                        link = f"![{fname}]({url})"
+                    else:
+                        link = f"[{fname}]({url})"
+                    send_chunk_fn({
+                        "chunk_type": "content",
+                        "delta": "\n\n" + link + "\n\n",
+                        "segment_id": state.segment_id,
+                    })
+                    # Remove url so the model won't repeat it in its reply
+                    data.pop("url", None)
+
+            elif event_type == "tool_execution_start":
+                # Notify the client that a tool is about to run (with its input args)
+                tool_name = data.get("tool_name", "")
+                arguments = data.get("arguments", {})
+                # Cache arguments keyed by tool_call_id so tool_execution_end can include them
+                tool_call_id = data.get("tool_call_id", tool_name)
+                state.pending_tool_arguments[tool_call_id] = arguments
+                send_chunk_fn({
+                    "chunk_type": "tool_start",
+                    "tool": tool_name,
+                    "arguments": arguments,
+                })
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "")
+                tool_call_id = data.get("tool_call_id", tool_name)
+                # Retrieve cached arguments from the matching tool_execution_start event
+                arguments = state.pending_tool_arguments.pop(tool_call_id, data.get("arguments", {}))
+                result = data.get("result", "")
+                status = data.get("status", "unknown")
+                execution_time = data.get("execution_time", 0)
+                elapsed_str = f"{execution_time:.2f}s"
+
+                # Serialise result to string if needed
+                if not isinstance(result, str):
+                    import json
+                    try:
+                        result = json.dumps(result, ensure_ascii=False)
+                    except Exception:
+                        result = str(result)
+
+                tool_info = {
+                    "name": tool_name,
+                    "arguments": arguments,
+                    "result": result,
+                    "status": status,
+                    "elapsed": elapsed_str,
+                }
+
+                if state.pending_tool_results is not None:
+                    state.pending_tool_results.append(tool_info)
+
+            elif event_type == "turn_end":
+                has_tool_calls = data.get("has_tool_calls", False)
+                if has_tool_calls and state.pending_tool_results:
+                    # Flush collected tool results as a single tool_calls chunk
+                    send_chunk_fn({
+                        "chunk_type": "tool_calls",
+                        "tool_calls": state.pending_tool_results,
+                    })
+                    state.pending_tool_results = None
+                    # Next content belongs to a new segment
+                    state.segment_id += 1
+
+        # Run the agent with our event callback ---------------------------
+        logger.info(f"[ChatService] Starting agent run: session={session_id}, query={query[:80]}")
+
+        from config import conf
+        max_context_turns = conf().get("agent_max_context_turns", 20)
+
+        # Get full system prompt with skills
+        full_system_prompt = agent.get_full_system_prompt()
+
+        # Create a copy of messages for this execution
+        with agent.messages_lock:
+            messages_copy = agent.messages.copy()
+            original_length = len(agent.messages)
+
+        from agent.protocol.agent_stream import AgentStreamExecutor
+
+        executor = AgentStreamExecutor(
+            agent=agent,
+            model=agent.model,
+            system_prompt=full_system_prompt,
+            tools=agent.tools,
+            max_turns=agent.max_steps,
+            on_event=on_event,
+            messages=messages_copy,
+            max_context_turns=max_context_turns,
+        )
+
+        try:
+            response = executor.run_stream(query)
+        except Exception:
+            # If executor cleared messages (context overflow), sync back
+            if len(executor.messages) == 0:
+                with agent.messages_lock:
+                    agent.messages.clear()
+                    logger.info("[ChatService] Cleared agent message history after executor recovery")
+            raise
+
+        # Sync executor messages back to agent (thread-safe).
+        # The executor may have trimmed context, making its list shorter than
+        # original_length. In that case we must replace entirely — just
+        # appending would leave stale pre-trim messages in agent.messages
+        # and cause the same trim to fire on every subsequent request.
+        with agent.messages_lock:
+            trimmed = len(executor.messages) < original_length
+            if trimmed:
+                # Context was trimmed: the executor appended the new user
+                # query *before* trimming, so the new messages (user +
+                # assistant + tools) sit at the tail of the trimmed list.
+                # We cannot simply slice at original_length (it exceeds the
+                # list length).  Instead, count how many messages the
+                # executor added on top of the post-trim baseline.
+                #
+                # Timeline inside executor.run_stream:
+                #   1. messages had `original_length` items
+                #   2. append user query  → original_length + 1
+                #   3. _trim_messages()   → some smaller number (includes the
+                #      user query because it belongs to the last turn)
+                #   4. LLM replies / tool calls appended
+                #
+                # The user query message is always the first message of the
+                # last turn (it cannot be trimmed away), so we locate it to
+                # find where "new" messages begin.
+                new_start = original_length  # fallback
+                for idx in range(len(executor.messages) - 1, -1, -1):
+                    msg = executor.messages[idx]
+                    if msg.get("role") == "user":
+                        content = msg.get("content", [])
+                        is_user_query = False
+                        if isinstance(content, list):
+                            has_text = any(
+                                isinstance(b, dict) and b.get("type") == "text"
+                                for b in content
+                            )
+                            has_tool_result = any(
+                                isinstance(b, dict) and b.get("type") == "tool_result"
+                                for b in content
+                            )
+                            is_user_query = has_text and not has_tool_result
+                        elif isinstance(content, str):
+                            is_user_query = True
+                        if is_user_query:
+                            new_start = idx
+                            break
+                new_messages = list(executor.messages[new_start:])
+            else:
+                new_messages = list(executor.messages[original_length:])
+            agent.messages = list(executor.messages)
+
+        # Persist new messages to SQLite so they survive restarts and
+        # can be queried via the HISTORY interface.
+        if new_messages:
+            self._persist_messages(session_id, list(new_messages), channel_type)
+
+        # Store executor reference for files_to_send access
+        agent.stream_executor = executor
+
+        # Execute post-process tools
+        agent._execute_post_process_tools()
+
+        logger.info(f"[ChatService] Agent run completed: session={session_id}")
+
+
+
+    @staticmethod
+    def _persist_messages(session_id: str, new_messages: list, channel_type: str = ""):
+        try:
+            from config import conf
+            if not conf().get("conversation_persistence", True):
+                return
+        except Exception:
+            pass
+        try:
+            from agent.memory import get_conversation_store
+            get_conversation_store().append_messages(
+                session_id, new_messages, channel_type=channel_type
+            )
+        except Exception as e:
+            logger.warning(
+                f"[ChatService] Failed to persist messages for session={session_id}: {e}"
+            )
+
+
+class _StreamState:
+    """Mutable state shared between the event callback and the run method."""
+
+    def __init__(self):
+        self.segment_id: int = 0
+        # None means we are not accumulating tool results right now.
+        # A list means we are in the middle of a tool-execution phase.
+        self.pending_tool_results: Optional[list] = None
+        # Maps tool_call_id -> arguments captured from tool_execution_start,
+        # so that tool_execution_end can attach the correct input args.
+        self.pending_tool_arguments: dict = {}

agent/chat/session_service.py

@@ -0,0 +1,241 @@
+"""
+SessionService - Manages multi-session lifecycle for both web channel and cloud client.
+
+Provides a unified interface for listing, deleting, renaming, clearing context,
+and generating AI titles for conversation sessions. Backed by ConversationStore
+(SQLite) and AgentBridge (in-memory agent instances).
+"""
+
+import re
+from typing import Optional
+
+from common.log import logger
+
+
+def _truncate_fallback_title(user_message: str, max_len: int = 30) -> str:
+    """Pick the first non-empty line of the user message and truncate it."""
+    if not user_message:
+        return "New Chat"
+    first_line = ""
+    for line in user_message.splitlines():
+        line = line.strip()
+        if line:
+            first_line = line
+            break
+    if not first_line:
+        return "New Chat"
+    if len(first_line) > max_len:
+        first_line = first_line[:max_len].rstrip() + "..."
+    return first_line
+
+
+def generate_session_title(user_message: str, assistant_reply: str = "") -> str:
+    """
+    Generate a short session title by calling the current bot's reply_text.
+    Falls back to the first line of the user message if the LLM call fails
+    or returns an obvious error sentinel.
+    """
+    fallback = _truncate_fallback_title(user_message)
+    try:
+        from bridge.bridge import Bridge
+        from models.session_manager import Session
+        bot = Bridge().get_bot("chat")
+
+        prompt_parts = [f"User: {user_message[:300]}"]
+        if assistant_reply:
+            prompt_parts.append(f"Assistant: {assistant_reply[:300]}")
+
+        session = Session("__title_gen__", system_prompt="")
+        session.messages = [
+            {"role": "user", "content": (
+                "Generate a very short title (max 15 characters for Chinese, max 6 words for English) "
+                "summarizing this conversation. Return ONLY the title text, nothing else.\n\n"
+                + "\n".join(prompt_parts)
+            )}
+        ]
+
+        result = bot.reply_text(session) or {}
+        # When bots fail (network error, auth error, rate limit, etc.) they
+        # typically return completion_tokens=0 with a sentinel content like
+        # "请再问我一次吧" / "我现在有点累了". Treat that as failure.
+        completion_tokens = result.get("completion_tokens", 0) or 0
+        raw = (result.get("content") or "").strip()
+        if completion_tokens <= 0:
+            logger.warning(
+                f"[SessionService] Title generation got empty completion "
+                f"(completion_tokens={completion_tokens}, content='{raw[:50]}'), "
+                f"using fallback")
+            return fallback
+
+        title = re.sub(r'<think>.*?</think>', '', raw, flags=re.DOTALL).strip().strip('"\'')
+        logger.info(f"[SessionService] Title generation result: '{title}' (len={len(title)})")
+        if title and len(title) <= 50:
+            return title
+    except Exception as e:
+        logger.warning(f"[SessionService] Title generation failed: {e}")
+    return fallback
+
+
+class SessionService:
+    """
+    High-level service for session lifecycle management.
+
+    Usage:
+        svc = SessionService()
+        result = svc.dispatch("list", {"channel_type": "web", "page": 1})
+    """
+
+    def _get_store(self):
+        from agent.memory import get_conversation_store
+        return get_conversation_store()
+
+    def _remove_agent(self, session_id: str):
+        """Remove the in-memory Agent instance for a session if it exists."""
+        try:
+            from bridge.bridge import Bridge
+            ab = Bridge().get_agent_bridge()
+            if session_id in ab.agents:
+                del ab.agents[session_id]
+                logger.info(f"[SessionService] Removed agent instance: {session_id}")
+        except Exception:
+            pass
+
+    @staticmethod
+    def _normalize_sid(session_id: str) -> str:
+        if session_id and not session_id.startswith("session_"):
+            return f"session_{session_id}"
+        return session_id
+
+    # ------------------------------------------------------------------
+    # actions
+    # ------------------------------------------------------------------
+    def list_sessions(self, channel_type: Optional[str] = None,
+                      page: int = 1, page_size: int = 50) -> dict:
+        store = self._get_store()
+        return store.list_sessions(
+            channel_type=channel_type,
+            page=page,
+            page_size=page_size,
+        )
+
+    def delete_session(self, session_id: str) -> None:
+        if not session_id:
+            raise ValueError("session_id required")
+        session_id = self._normalize_sid(session_id)
+
+        store = self._get_store()
+        store.clear_session(session_id)
+        self._remove_agent(session_id)
+        logger.info(f"[SessionService] Session deleted: {session_id}")
+
+    def rename_session(self, session_id: str, title: str) -> None:
+        if not session_id:
+            raise ValueError("session_id required")
+        if not title:
+            raise ValueError("title required")
+        session_id = self._normalize_sid(session_id)
+
+        store = self._get_store()
+        found = store.rename_session(session_id, title)
+        if not found:
+            raise ValueError("session not found")
+
+    def clear_context(self, session_id: str) -> int:
+        """
+        Set context boundary. Returns the new context_start_seq value.
+        """
+        if not session_id:
+            raise ValueError("session_id required")
+        session_id = self._normalize_sid(session_id)
+
+        store = self._get_store()
+        new_seq = store.clear_context(session_id)
+        self._remove_agent(session_id)
+        return new_seq
+
+    def gen_title(self, session_id: str, user_message: str,
+                  assistant_reply: str = "") -> str:
+        """
+        Generate an AI title and persist it. Returns the generated title.
+        """
+        if not session_id:
+            raise ValueError("session_id required")
+        if not user_message:
+            raise ValueError("user_message required")
+        session_id = self._normalize_sid(session_id)
+
+        title = generate_session_title(user_message, assistant_reply)
+
+        store = self._get_store()
+        updated = store.rename_session(session_id, title)
+        logger.info(f"[SessionService] Title set: sid={session_id}, "
+                     f"title='{title}', db_updated={updated}")
+        return title
+
+    # ------------------------------------------------------------------
+    # dispatch — single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a session management action and return a protocol-compatible
+        response dict.
+
+        Action names use a ``*_session`` / session-prefixed convention so they
+        can coexist with history actions (e.g. ``query``) on the same HISTORY
+        message channel without ambiguity.
+
+        Supported actions:
+          - list_sessions: list sessions with pagination
+          - delete_session: delete a session
+          - rename_session: rename a session title
+          - clear_context: set context boundary
+          - generate_title: AI-generate a session title
+
+        :param action: one of the above action names
+        :param payload: action-specific payload
+        :return: dict with action, code, message, payload
+        """
+        payload = payload or {}
+        try:
+            if action == "list_sessions":
+                result = self.list_sessions(
+                    channel_type=payload.get("channel_type"),
+                    page=int(payload.get("page", 1)),
+                    page_size=int(payload.get("page_size", 50)),
+                )
+                return {"action": action, "code": 200, "message": "success", "payload": result}
+
+            elif action == "delete_session":
+                self.delete_session(payload.get("session_id", ""))
+                return {"action": action, "code": 200, "message": "success", "payload": None}
+
+            elif action == "rename_session":
+                self.rename_session(
+                    payload.get("session_id", ""),
+                    payload.get("title", "").strip(),
+                )
+                return {"action": action, "code": 200, "message": "success", "payload": None}
+
+            elif action == "clear_context":
+                new_seq = self.clear_context(payload.get("session_id", ""))
+                return {"action": action, "code": 200, "message": "success",
+                        "payload": {"context_start_seq": new_seq}}
+
+            elif action == "generate_title":
+                title = self.gen_title(
+                    payload.get("session_id", ""),
+                    payload.get("user_message", ""),
+                    payload.get("assistant_reply", ""),
+                )
+                return {"action": action, "code": 200, "message": "success",
+                        "payload": {"title": title}}
+
+            else:
+                return {"action": action, "code": 400,
+                        "message": f"unknown action: {action}", "payload": None}
+
+        except ValueError as e:
+            return {"action": action, "code": 400, "message": str(e), "payload": None}
+        except Exception as e:
+            logger.error(f"[SessionService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}

agent/knowledge/service.py

@@ -0,0 +1,240 @@
+"""
+Knowledge service for handling knowledge base operations.
+
+Provides a unified interface for listing, reading, and graphing knowledge files,
+callable from the web console, API, or CLI.
+
+Knowledge file layout (under workspace_root):
+    knowledge/index.md
+    knowledge/log.md
+    knowledge/<category>/<slug>.md
+"""
+
+import os
+import re
+from pathlib import Path
+from typing import Optional
+
+from common.log import logger
+from config import conf
+
+
+class KnowledgeService:
+    """
+    High-level service for knowledge base queries.
+    Operates directly on the filesystem.
+    """
+
+    def __init__(self, workspace_root: str):
+        self.workspace_root = workspace_root
+        self.knowledge_dir = os.path.join(workspace_root, "knowledge")
+
+    # ------------------------------------------------------------------
+    # list — directory tree with stats
+    # ------------------------------------------------------------------
+    def list_tree(self) -> dict:
+        """
+        Return the knowledge directory tree grouped by category,
+        supporting arbitrarily nested sub-directories.
+
+        Returns::
+
+            {
+                "tree": [
+                    {
+                        "dir": "concepts",
+                        "files": [
+                            {"name": "moe.md", "title": "MoE", "size": 1234},
+                        ],
+                        "children": []
+                    },
+                    {
+                        "dir": "platform",
+                        "files": [],
+                        "children": [
+                            {
+                                "dir": "analysis",
+                                "files": [{"name": "perf.md", ...}],
+                                "children": []
+                            }
+                        ]
+                    },
+                ],
+                "stats": {"pages": 15, "size": 32768},
+                "enabled": true
+            }
+        """
+        if not os.path.isdir(self.knowledge_dir):
+            return {"tree": [], "stats": {"pages": 0, "size": 0}, "enabled": conf().get("knowledge", True)}
+
+        stats = {"pages": 0, "size": 0}
+        root_files, tree = self._scan_dir(self.knowledge_dir, stats, is_root=True)
+
+        return {
+            "root_files": root_files,
+            "tree": tree,
+            "stats": stats,
+            "enabled": conf().get("knowledge", True),
+        }
+
+    def _scan_dir(self, dir_path: str, stats: dict, is_root: bool = False) -> tuple:
+        """
+        Recursively scan a directory.
+
+        :return: (files, children) where files is a list of .md file dicts
+                 in this directory and children is a list of sub-directory nodes.
+        """
+        files = []
+        children = []
+        for name in sorted(os.listdir(dir_path)):
+            if name.startswith("."):
+                continue
+            full = os.path.join(dir_path, name)
+            if os.path.isdir(full):
+                sub_files, sub_children = self._scan_dir(full, stats)
+                children.append({"dir": name, "files": sub_files, "children": sub_children})
+            elif name.endswith(".md"):
+                size = os.path.getsize(full)
+                if not is_root:
+                    stats["pages"] += 1
+                    stats["size"] += size
+                title = name.replace(".md", "")
+                try:
+                    with open(full, "r", encoding="utf-8") as f:
+                        first_line = f.readline().strip()
+                    if first_line.startswith("# "):
+                        title = first_line[2:].strip()
+                except Exception:
+                    pass
+                files.append({"name": name, "title": title, "size": size})
+        return files, children
+
+    # ------------------------------------------------------------------
+    # read — single file content
+    # ------------------------------------------------------------------
+    def read_file(self, rel_path: str) -> dict:
+        """
+        Read a single knowledge markdown file.
+
+        :param rel_path: Relative path within knowledge/, e.g. ``concepts/moe.md``
+        :return: dict with ``content`` and ``path``
+        :raises ValueError: if path is invalid or escapes knowledge dir
+        :raises FileNotFoundError: if file does not exist
+        """
+        if not rel_path or ".." in rel_path:
+            raise ValueError("invalid path")
+
+        full_path = os.path.normpath(os.path.join(self.knowledge_dir, rel_path))
+        allowed = os.path.normpath(self.knowledge_dir)
+        if not full_path.startswith(allowed + os.sep) and full_path != allowed:
+            raise ValueError("path outside knowledge dir")
+
+        if not os.path.isfile(full_path):
+            raise FileNotFoundError(f"file not found: {rel_path}")
+
+        with open(full_path, "r", encoding="utf-8") as f:
+            content = f.read()
+        return {"content": content, "path": rel_path}
+
+    # ------------------------------------------------------------------
+    # graph — nodes and links for visualization
+    # ------------------------------------------------------------------
+    def build_graph(self) -> dict:
+        """
+        Parse all knowledge pages and extract cross-reference links.
+
+        Returns::
+
+            {
+                "nodes": [
+                    {"id": "concepts/moe.md", "label": "MoE", "category": "concepts"},
+                    ...
+                ],
+                "links": [
+                    {"source": "concepts/moe.md", "target": "entities/deepseek.md"},
+                    ...
+                ]
+            }
+        """
+        knowledge_path = Path(self.knowledge_dir)
+        if not knowledge_path.is_dir():
+            return {"nodes": [], "links": []}
+
+        nodes = {}
+        links = []
+        link_re = re.compile(r'\[([^\]]*)\]\(([^)]+\.md)\)')
+
+        for md_file in knowledge_path.rglob("*.md"):
+            rel = str(md_file.relative_to(knowledge_path))
+            if rel in ("index.md", "log.md"):
+                continue
+            parts = rel.split("/")
+            category = parts[0] if len(parts) > 1 else "root"
+            title = md_file.stem.replace("-", " ").title()
+            try:
+                content = md_file.read_text(encoding="utf-8")
+                first_line = content.strip().split("\n")[0]
+                if first_line.startswith("# "):
+                    title = first_line[2:].strip()
+                for _, link_target in link_re.findall(content):
+                    resolved = (md_file.parent / link_target).resolve()
+                    try:
+                        target_rel = str(resolved.relative_to(knowledge_path))
+                    except ValueError:
+                        continue
+                    if target_rel != rel:
+                        links.append({"source": rel, "target": target_rel})
+            except Exception:
+                pass
+            nodes[rel] = {"id": rel, "label": title, "category": category}
+
+        valid_ids = set(nodes.keys())
+        links = [l for l in links if l["source"] in valid_ids and l["target"] in valid_ids]
+        seen = set()
+        deduped = []
+        for l in links:
+            key = tuple(sorted([l["source"], l["target"]]))
+            if key not in seen:
+                seen.add(key)
+                deduped.append(l)
+
+        return {"nodes": list(nodes.values()), "links": deduped}
+
+    # ------------------------------------------------------------------
+    # dispatch — single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a knowledge management action.
+
+        :param action: ``list``, ``read``, or ``graph``
+        :param payload: action-specific payload
+        :return: protocol-compatible response dict
+        """
+        payload = payload or {}
+        try:
+            if action == "list":
+                result = self.list_tree()
+                return {"action": action, "code": 200, "message": "success", "payload": result}
+
+            elif action == "read":
+                path = payload.get("path")
+                if not path:
+                    return {"action": action, "code": 400, "message": "path is required", "payload": None}
+                result = self.read_file(path)
+                return {"action": action, "code": 200, "message": "success", "payload": result}
+
+            elif action == "graph":
+                result = self.build_graph()
+                return {"action": action, "code": 200, "message": "success", "payload": result}
+
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+
+        except ValueError as e:
+            return {"action": action, "code": 403, "message": str(e), "payload": None}
+        except FileNotFoundError as e:
+            return {"action": action, "code": 404, "message": str(e), "payload": None}
+        except Exception as e:
+            logger.error(f"[KnowledgeService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}

agent/memory/__init__.py

@@ -1,11 +1,23 @@
 """
 Memory module for AgentMesh
 
-Provides long-term memory capabilities with hybrid search (vector + keyword)
+Provides both long-term memory (vector/keyword search) and short-term
+conversation history persistence (SQLite).
 """
 
 from agent.memory.manager import MemoryManager
 from agent.memory.config import MemoryConfig, get_default_memory_config, set_global_memory_config
 from agent.memory.embedding import create_embedding_provider
+from agent.memory.conversation_store import ConversationStore, get_conversation_store
+from agent.memory.summarizer import ensure_daily_memory_file
 
-__all__ = ['MemoryManager', 'MemoryConfig', 'get_default_memory_config', 'set_global_memory_config', 'create_embedding_provider']
+__all__ = [
+    'MemoryManager',
+    'MemoryConfig',
+    'get_default_memory_config',
+    'set_global_memory_config',
+    'create_embedding_provider',
+    'ConversationStore',
+    'get_conversation_store',
+    'ensure_daily_memory_file',
+]

agent/memory/config.py

@@ -48,9 +48,6 @@ class MemoryConfig:
     enable_auto_sync: bool = True
     sync_on_search: bool = True
     
-    # Memory flush config (独立于模型 context window)
-    flush_token_threshold: int = 50000  # 50K tokens 触发 flush
-    flush_turn_threshold: int = 20  # 20 轮对话触发 flush (用户+AI各一条为一轮)
     
     def get_workspace(self) -> Path:
         """Get workspace root directory"""

agent/memory/conversation_store.py

@@ -0,0 +1,849 @@
+"""
+Conversation history persistence using SQLite.
+
+Design:
+- sessions table: per-session metadata (channel_type, last_active, msg_count)
+- messages table: individual messages stored as JSON, append-only
+- Pruning: age-based only (sessions not updated within N days are deleted)
+- Thread-safe via a single in-process lock
+
+Storage path: ~/cow/sessions/conversations.db
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from common.log import logger
+
+
+# ---------------------------------------------------------------------------
+# Schema
+# ---------------------------------------------------------------------------
+
+_DDL = """
+CREATE TABLE IF NOT EXISTS sessions (
+    session_id        TEXT    PRIMARY KEY,
+    channel_type      TEXT    NOT NULL DEFAULT '',
+    title             TEXT    NOT NULL DEFAULT '',
+    context_start_seq INTEGER NOT NULL DEFAULT 0,
+    created_at        INTEGER NOT NULL,
+    last_active       INTEGER NOT NULL,
+    msg_count         INTEGER NOT NULL DEFAULT 0
+);
+
+CREATE TABLE IF NOT EXISTS messages (
+    id           INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id   TEXT    NOT NULL,
+    seq          INTEGER NOT NULL,
+    role         TEXT    NOT NULL,
+    content      TEXT    NOT NULL,
+    created_at   INTEGER NOT NULL,
+    UNIQUE (session_id, seq)
+);
+
+CREATE INDEX IF NOT EXISTS idx_messages_session
+    ON messages (session_id, seq);
+
+CREATE INDEX IF NOT EXISTS idx_sessions_last_active
+    ON sessions (last_active);
+"""
+
+# Migration: add channel_type column to existing databases that predate it.
+_MIGRATION_ADD_CHANNEL_TYPE = """
+ALTER TABLE sessions ADD COLUMN channel_type TEXT NOT NULL DEFAULT '';
+"""
+
+_MIGRATION_ADD_TITLE = """
+ALTER TABLE sessions ADD COLUMN title TEXT NOT NULL DEFAULT '';
+"""
+
+_MIGRATION_ADD_CONTEXT_START_SEQ = """
+ALTER TABLE sessions ADD COLUMN context_start_seq INTEGER NOT NULL DEFAULT 0;
+"""
+
+DEFAULT_MAX_AGE_DAYS: int = 30
+
+
+def _is_visible_user_message(content: Any) -> bool:
+    """
+    Return True when a user-role message represents actual user input
+    (not an internal tool_result injected by the agent loop).
+    """
+    if isinstance(content, str):
+        return bool(content.strip())
+    if isinstance(content, list):
+        return any(
+            isinstance(b, dict) and b.get("type") == "text"
+            for b in content
+        )
+    return False
+
+
+def _extract_display_text(content: Any) -> str:
+    """
+    Extract the human-readable text portion from a message content value.
+    Returns an empty string for tool_use / tool_result blocks.
+    """
+    if isinstance(content, str):
+        return content.strip()
+    if isinstance(content, list):
+        parts = [
+            b.get("text", "")
+            for b in content
+            if isinstance(b, dict) and b.get("type") == "text"
+        ]
+        return "\n".join(p for p in parts if p).strip()
+    return ""
+
+
+def _extract_tool_calls(content: Any) -> List[Dict[str, Any]]:
+    """
+    Extract tool_use blocks from an assistant message content.
+    Returns a list of {name, arguments} dicts (result filled in later).
+    """
+    if not isinstance(content, list):
+        return []
+    return [
+        {"id": b.get("id", ""), "name": b.get("name", ""), "arguments": b.get("input", {})}
+        for b in content
+        if isinstance(b, dict) and b.get("type") == "tool_use"
+    ]
+
+
+def _extract_tool_results(content: Any) -> Dict[str, str]:
+    """
+    Extract tool_result blocks from a user message, keyed by tool_use_id.
+    """
+    if not isinstance(content, list):
+        return {}
+    results = {}
+    for b in content:
+        if not isinstance(b, dict) or b.get("type") != "tool_result":
+            continue
+        tool_id = b.get("tool_use_id", "")
+        result_content = b.get("content", "")
+        if isinstance(result_content, list):
+            result_content = "\n".join(
+                rb.get("text", "") for rb in result_content
+                if isinstance(rb, dict) and rb.get("type") == "text"
+            )
+        results[tool_id] = str(result_content)
+    return results
+
+
+def _group_into_display_turns(
+    rows: List[tuple],
+    include_thinking: bool = True,
+) -> List[Dict[str, Any]]:
+    """
+    Convert raw (role, content_json, created_at) DB rows into display turns.
+
+    One display turn = one visible user message  +  one merged assistant reply.
+    All intermediate assistant messages (those carrying tool_use) and the final
+    assistant text reply produced for the same user query are collapsed into a
+    single assistant turn, exactly matching the live SSE rendering where tools
+    and the final answer appear inside the same bubble.
+
+    Grouping rules:
+    - A visible user message starts a new group.
+    - tool_result user messages are internal; their content is attached to the
+      matching tool_use entry via tool_use_id and they never become own turns.
+    - All assistant messages within a group are merged:
+        * tool_use blocks → tool_calls list (result filled from tool_results)
+        * text blocks → last non-empty text becomes the display content
+    """
+    # ------------------------------------------------------------------ #
+    # Pass 1: split rows into groups, each starting with a visible user msg
+    # ------------------------------------------------------------------ #
+    # group = (user_row | None, [subsequent_rows])
+    # user_row: (content, created_at)
+    groups: List[tuple] = []
+    cur_user: Optional[tuple] = None
+    cur_rest: List[tuple] = []
+    started = False
+
+    for role, raw_content, created_at in rows:
+        try:
+            content = json.loads(raw_content)
+        except Exception:
+            content = raw_content
+
+        if role == "user" and _is_visible_user_message(content):
+            if started:
+                groups.append((cur_user, cur_rest))
+            cur_user = (content, created_at)
+            cur_rest = []
+            started = True
+        else:
+            cur_rest.append((role, content, created_at))
+
+    if started:
+        groups.append((cur_user, cur_rest))
+
+    # ------------------------------------------------------------------ #
+    # Pass 2: build display turns from each group
+    # ------------------------------------------------------------------ #
+    turns: List[Dict[str, Any]] = []
+
+    for user_row, rest in groups:
+        # User turn
+        if user_row:
+            content, created_at = user_row
+            text = _extract_display_text(content)
+            if text:
+                turns.append({"role": "user", "content": text, "created_at": created_at})
+
+        # Build an ordered list of steps preserving the original sequence:
+        #   thinking → content → tool_call → content → ...
+        steps: List[Dict[str, Any]] = []
+        tool_results: Dict[str, str] = {}
+        final_text = ""
+        final_ts: Optional[int] = None
+
+        for role, content, created_at in rest:
+            if role == "user":
+                tool_results.update(_extract_tool_results(content))
+            elif role == "assistant":
+                # Walk content blocks in order to preserve interleaving
+                if isinstance(content, list):
+                    for block in content:
+                        if not isinstance(block, dict):
+                            continue
+                        btype = block.get("type")
+                        if btype == "thinking":
+                            if not include_thinking:
+                                continue
+                            txt = block.get("thinking", "").strip()
+                            if txt:
+                                steps.append({"type": "thinking", "content": txt})
+                        elif btype == "text":
+                            txt = block.get("text", "").strip()
+                            if txt:
+                                steps.append({"type": "content", "content": txt})
+                                final_text = txt
+                        elif btype == "tool_use":
+                            steps.append({
+                                "type": "tool",
+                                "id": block.get("id", ""),
+                                "name": block.get("name", ""),
+                                "arguments": block.get("input", {}),
+                            })
+                elif isinstance(content, str) and content.strip():
+                    steps.append({"type": "content", "content": content.strip()})
+                    final_text = content.strip()
+                final_ts = created_at
+
+        # Attach tool results to tool steps
+        for step in steps:
+            if step["type"] == "tool":
+                step["result"] = tool_results.get(step.get("id", ""), "")
+
+        if steps or final_text:
+            turn = {
+                "role": "assistant",
+                "content": final_text,
+                "steps": steps,
+                "created_at": final_ts or (user_row[1] if user_row else 0),
+            }
+            turns.append(turn)
+
+    return turns
+
+
+class ConversationStore:
+    """
+    SQLite-backed store for per-session conversation history.
+
+    Usage:
+        store = ConversationStore(db_path)
+        store.append_messages("user_123", new_messages, channel_type="feishu")
+        msgs = store.load_messages("user_123", max_turns=30)
+    """
+
+    def __init__(self, db_path: Path):
+        self._db_path = db_path
+        self._lock = threading.Lock()
+        self._init_db()
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def load_messages(
+        self,
+        session_id: str,
+        max_turns: int = 30,
+    ) -> List[Dict[str, Any]]:
+        """
+        Load the most recent messages for a session, for injection into the LLM.
+
+        ALL message types (user text, assistant tool_use, tool_result) are returned
+        in their original JSON form so the LLM can reconstruct the full context.
+
+        max_turns is a *visible-turn* count: we count only user messages whose
+        content is actual user text (not tool_result blocks).  This prevents
+        tool-heavy sessions from exhausting the turn budget prematurely.
+
+        Args:
+            session_id: Unique session identifier.
+            max_turns: Maximum number of visible user-assistant turns to keep.
+
+        Returns:
+            Chronologically ordered list of message dicts (role, content).
+        """
+        with self._lock:
+            conn = self._connect()
+            try:
+                # Respect context_start_seq: only load messages at or after the boundary
+                ctx_row = conn.execute(
+                    "SELECT context_start_seq FROM sessions WHERE session_id = ?",
+                    (session_id,),
+                ).fetchone()
+                ctx_start = ctx_row[0] if ctx_row else 0
+
+                rows = conn.execute(
+                    """
+                    SELECT seq, role, content
+                    FROM messages
+                    WHERE session_id = ? AND seq >= ?
+                    ORDER BY seq DESC
+                    """,
+                    (session_id, ctx_start),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        if not rows:
+            return []
+
+        visible_turn_seqs: List[int] = []
+        for seq, role, raw_content in rows:
+            if role != "user":
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            if _is_visible_user_message(content):
+                visible_turn_seqs.append(seq)
+
+        if len(visible_turn_seqs) <= max_turns:
+            cutoff_seq = None
+        else:
+            cutoff_seq = visible_turn_seqs[max_turns - 1]
+
+        result = []
+        for seq, role, raw_content in reversed(rows):
+            if cutoff_seq is not None and seq < cutoff_seq:
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            # Strip thinking blocks — they are stored for UI display only
+            if role == "assistant" and isinstance(content, list):
+                content = [b for b in content if b.get("type") != "thinking"]
+            result.append({"role": role, "content": content})
+        return result
+
+    def append_messages(
+        self,
+        session_id: str,
+        messages: List[Dict[str, Any]],
+        channel_type: str = "",
+    ) -> None:
+        """
+        Append new messages to a session's history.
+
+        Seq numbers continue from the session's current maximum, so
+        concurrent callers on distinct sessions never collide.
+
+        Args:
+            session_id: Unique session identifier.
+            messages: List of message dicts to append.
+            channel_type: Source channel (e.g. "feishu", "web", "wechat").
+                          Only written on session creation; ignored on update.
+        """
+        if not messages:
+            return
+
+        now = int(time.time())
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    # INSERT OR IGNORE creates the row on first visit;
+                    # the UPDATE always refreshes last_active.
+                    # Avoids ON CONFLICT...DO UPDATE (requires SQLite >= 3.24).
+                    conn.execute(
+                        """
+                        INSERT OR IGNORE INTO sessions
+                            (session_id, channel_type, created_at, last_active, msg_count)
+                        VALUES (?, ?, ?, ?, 0)
+                        """,
+                        (session_id, channel_type, now, now),
+                    )
+                    conn.execute(
+                        "UPDATE sessions SET last_active = ? WHERE session_id = ?",
+                        (now, session_id),
+                    )
+
+                    # Determine starting seq for the new batch.
+                    row = conn.execute(
+                        "SELECT COALESCE(MAX(seq), -1) FROM messages WHERE session_id = ?",
+                        (session_id,),
+                    ).fetchone()
+                    next_seq = row[0] + 1
+
+                    for msg in messages:
+                        role = msg.get("role", "")
+                        content = json.dumps(
+                            msg.get("content", ""), ensure_ascii=False
+                        )
+                        conn.execute(
+                            """
+                            INSERT OR IGNORE INTO messages
+                                (session_id, seq, role, content, created_at)
+                            VALUES (?, ?, ?, ?, ?)
+                            """,
+                            (session_id, next_seq, role, content, now),
+                        )
+                        next_seq += 1
+
+                    conn.execute(
+                        """
+                        UPDATE sessions
+                        SET msg_count = (
+                            SELECT COUNT(*) FROM messages WHERE session_id = ?
+                        )
+                        WHERE session_id = ?
+                        """,
+                        (session_id, session_id),
+                    )
+
+                    # Auto-generate title from the first visible user message
+                    cur_title = conn.execute(
+                        "SELECT title FROM sessions WHERE session_id = ?",
+                        (session_id,),
+                    ).fetchone()
+                    if cur_title and not cur_title[0]:
+                        for msg in messages:
+                            if msg.get("role") == "user":
+                                content = msg.get("content", "")
+                                text = _extract_display_text(content)
+                                if text:
+                                    title = text[:50].split("\n")[0]
+                                    conn.execute(
+                                        "UPDATE sessions SET title = ? WHERE session_id = ?",
+                                        (title, session_id),
+                                    )
+                                    break
+            finally:
+                conn.close()
+
+    def clear_context(self, session_id: str) -> int:
+        """
+        Set the context boundary to after the current last message.
+        Messages before this boundary are still stored but excluded from LLM context.
+
+        Returns the new context_start_seq value.
+        """
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    row = conn.execute(
+                        "SELECT COALESCE(MAX(seq), -1) FROM messages WHERE session_id = ?",
+                        (session_id,),
+                    ).fetchone()
+                    new_start = row[0] + 1
+                    conn.execute(
+                        "UPDATE sessions SET context_start_seq = ? WHERE session_id = ?",
+                        (new_start, session_id),
+                    )
+                    return new_start
+            finally:
+                conn.close()
+
+    def get_context_start_seq(self, session_id: str) -> int:
+        """Return the context_start_seq for a session (0 if not set)."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                row = conn.execute(
+                    "SELECT context_start_seq FROM sessions WHERE session_id = ?",
+                    (session_id,),
+                ).fetchone()
+                return row[0] if row else 0
+            finally:
+                conn.close()
+
+    def clear_session(self, session_id: str) -> None:
+        """Delete all messages and the session record for a given session_id."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    conn.execute(
+                        "DELETE FROM messages WHERE session_id = ?", (session_id,)
+                    )
+                    conn.execute(
+                        "DELETE FROM sessions WHERE session_id = ?", (session_id,)
+                    )
+            finally:
+                conn.close()
+
+    def cleanup_old_sessions(self, max_age_days: Optional[int] = None) -> int:
+        """
+        Delete sessions that have not been active within max_age_days.
+        Web channel sessions are excluded — they are meant to be permanent.
+
+        Args:
+            max_age_days: Override the default retention period.
+
+        Returns:
+            Number of sessions deleted.
+        """
+        try:
+            from config import conf
+            max_age = max_age_days or conf().get(
+                "conversation_max_age_days", DEFAULT_MAX_AGE_DAYS
+            )
+        except Exception:
+            max_age = max_age_days or DEFAULT_MAX_AGE_DAYS
+
+        cutoff = int(time.time()) - max_age * 86400
+        deleted = 0
+
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    stale = conn.execute(
+                        "SELECT session_id FROM sessions "
+                        "WHERE last_active < ? AND channel_type != 'web'",
+                        (cutoff,),
+                    ).fetchall()
+                    for (sid,) in stale:
+                        conn.execute(
+                            "DELETE FROM messages WHERE session_id = ?", (sid,)
+                        )
+                        conn.execute(
+                            "DELETE FROM sessions WHERE session_id = ?", (sid,)
+                        )
+                        deleted += 1
+            finally:
+                conn.close()
+
+        if deleted:
+            logger.info(f"[ConversationStore] Pruned {deleted} expired sessions")
+        return deleted
+
+    def load_history_page(
+        self,
+        session_id: str,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> Dict[str, Any]:
+        """
+        Load a page of conversation history for UI display, grouped into turns.
+
+        Each "turn" maps to one of:
+          - A user message (role="user", content=str)
+          - An assistant message (role="assistant", content=str,
+            tool_calls=[{name, arguments, result}] when tools were used)
+
+        Internal tool_result user messages are merged into the preceding
+        assistant entry's tool_calls list and never appear as standalone items.
+
+        Pages are numbered from 1 (most recent).  Messages within a page are
+        returned in chronological order.
+
+        Returns:
+            {
+                "messages": [
+                    {
+                        "role": "user" | "assistant",
+                        "content": str,
+                        "tool_calls": [...],   # assistant only, may be []
+                        "created_at": int,
+                    },
+                    ...
+                ],
+                "total": <visible turn count>,
+                "page": <current page>,
+                "page_size": <page_size>,
+                "has_more": bool,
+            }
+        """
+        page = max(1, page)
+        with self._lock:
+            conn = self._connect()
+            try:
+                ctx_row = conn.execute(
+                    "SELECT context_start_seq FROM sessions WHERE session_id = ?",
+                    (session_id,),
+                ).fetchone()
+                ctx_start = ctx_row[0] if ctx_row else 0
+
+                rows = conn.execute(
+                    """
+                    SELECT seq, role, content, created_at
+                    FROM messages
+                    WHERE session_id = ?
+                    ORDER BY seq ASC
+                    """,
+                    (session_id,),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        # Honour the current enable_thinking switch when building display turns
+        # so that toggling it off hides previously-saved thinking blocks too.
+        try:
+            from config import conf
+            include_thinking = bool(conf().get("enable_thinking", False))
+        except Exception:
+            include_thinking = False
+
+        # Strip seq for display grouping, but record max seq per visible user group
+        plain_rows = [(role, content, created_at) for _seq, role, content, created_at in rows]
+        visible = _group_into_display_turns(plain_rows, include_thinking=include_thinking)
+
+        # Build a mapping: find the seq of each visible user message to annotate context boundary.
+        # Walk through rows to find visible user message seqs in order.
+        visible_user_seqs: List[int] = []
+        for seq, role, raw_content, _ts in rows:
+            if role != "user":
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            if _is_visible_user_message(content):
+                visible_user_seqs.append(seq)
+
+        # Each pair of display turns (user+assistant) corresponds to a visible user seq.
+        # Mark which turns are before the context boundary.
+        user_turn_idx = 0
+        for turn in visible:
+            if turn["role"] == "user" and user_turn_idx < len(visible_user_seqs):
+                turn["_seq"] = visible_user_seqs[user_turn_idx]
+                user_turn_idx += 1
+
+        total = len(visible)
+        offset = (page - 1) * page_size
+        page_items = list(reversed(visible))[offset: offset + page_size]
+        page_items = list(reversed(page_items))
+
+        return {
+            "messages": page_items,
+            "context_start_seq": ctx_start,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "has_more": offset + page_size < total,
+        }
+
+    def list_sessions(
+        self,
+        channel_type: Optional[str] = None,
+        page: int = 1,
+        page_size: int = 50,
+    ) -> Dict[str, Any]:
+        """
+        List sessions ordered by last_active DESC, with optional channel_type filter.
+
+        Returns:
+            {
+                "sessions": [{session_id, title, created_at, last_active, msg_count}, ...],
+                "total": int,
+                "page": int,
+                "page_size": int,
+                "has_more": bool,
+            }
+        """
+        page = max(1, page)
+        with self._lock:
+            conn = self._connect()
+            try:
+                if channel_type:
+                    total = conn.execute(
+                        "SELECT COUNT(*) FROM sessions WHERE channel_type = ?",
+                        (channel_type,),
+                    ).fetchone()[0]
+                    rows = conn.execute(
+                        """
+                        SELECT session_id, title, created_at, last_active, msg_count
+                        FROM sessions
+                        WHERE channel_type = ?
+                        ORDER BY last_active DESC
+                        LIMIT ? OFFSET ?
+                        """,
+                        (channel_type, page_size, (page - 1) * page_size),
+                    ).fetchall()
+                else:
+                    total = conn.execute(
+                        "SELECT COUNT(*) FROM sessions",
+                    ).fetchone()[0]
+                    rows = conn.execute(
+                        """
+                        SELECT session_id, title, created_at, last_active, msg_count
+                        FROM sessions
+                        ORDER BY last_active DESC
+                        LIMIT ? OFFSET ?
+                        """,
+                        (page_size, (page - 1) * page_size),
+                    ).fetchall()
+            finally:
+                conn.close()
+
+        sessions = [
+            {
+                "session_id": r[0],
+                "title": r[1],
+                "created_at": r[2],
+                "last_active": r[3],
+                "msg_count": r[4],
+            }
+            for r in rows
+        ]
+        return {
+            "sessions": sessions,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "has_more": (page - 1) * page_size + page_size < total,
+        }
+
+    def rename_session(self, session_id: str, title: str) -> bool:
+        """Update the title of a session. Returns True if the session existed."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    cur = conn.execute(
+                        "UPDATE sessions SET title = ? WHERE session_id = ?",
+                        (title, session_id),
+                    )
+                    return cur.rowcount > 0
+            finally:
+                conn.close()
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Return basic stats keyed by channel_type, for monitoring."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                total_sessions = conn.execute(
+                    "SELECT COUNT(*) FROM sessions"
+                ).fetchone()[0]
+                total_messages = conn.execute(
+                    "SELECT COUNT(*) FROM messages"
+                ).fetchone()[0]
+                by_channel = conn.execute(
+                    """
+                    SELECT channel_type, COUNT(*) as cnt
+                    FROM sessions
+                    GROUP BY channel_type
+                    ORDER BY cnt DESC
+                    """
+                ).fetchall()
+                return {
+                    "total_sessions": total_sessions,
+                    "total_messages": total_messages,
+                    "by_channel": {row[0] or "unknown": row[1] for row in by_channel},
+                }
+            finally:
+                conn.close()
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _init_db(self) -> None:
+        self._db_path.parent.mkdir(parents=True, exist_ok=True)
+        conn = self._connect()
+        try:
+            conn.executescript(_DDL)
+            conn.commit()
+            self._migrate(conn)
+        finally:
+            conn.close()
+
+    def _migrate(self, conn: sqlite3.Connection) -> None:
+        """Apply incremental schema migrations on existing databases."""
+        cols = {
+            row[1]
+            for row in conn.execute("PRAGMA table_info(sessions)").fetchall()
+        }
+        if "channel_type" not in cols:
+            try:
+                conn.execute(_MIGRATION_ADD_CHANNEL_TYPE)
+                conn.commit()
+                logger.info("[ConversationStore] Migrated: added channel_type column")
+            except Exception as e:
+                logger.warning(f"[ConversationStore] Migration failed: {e}")
+        if "title" not in cols:
+            try:
+                conn.execute(_MIGRATION_ADD_TITLE)
+                conn.commit()
+                logger.info("[ConversationStore] Migrated: added title column")
+            except Exception as e:
+                logger.warning(f"[ConversationStore] Migration (title) failed: {e}")
+        if "context_start_seq" not in cols:
+            try:
+                conn.execute(_MIGRATION_ADD_CONTEXT_START_SEQ)
+                conn.commit()
+                logger.info("[ConversationStore] Migrated: added context_start_seq column")
+            except Exception as e:
+                logger.warning(f"[ConversationStore] Migration (context_start_seq) failed: {e}")
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(str(self._db_path), timeout=10)
+        conn.execute("PRAGMA journal_mode=WAL")
+        conn.execute("PRAGMA synchronous=NORMAL")
+        return conn
+
+
+# ---------------------------------------------------------------------------
+# Singleton
+# ---------------------------------------------------------------------------
+
+_store_instance: Optional[ConversationStore] = None
+_store_lock = threading.Lock()
+
+
+def get_conversation_store() -> ConversationStore:
+    """
+    Return the process-wide ConversationStore singleton.
+
+    Reuses the long-term memory database so the project stays with a single
+    SQLite file: ~/cow/memory/long-term/index.db
+    The conversation tables (sessions / messages) are separate from the
+    memory tables (memory_chunks / file_metadata) — no conflicts.
+    """
+    global _store_instance
+    if _store_instance is not None:
+        return _store_instance
+
+    with _store_lock:
+        if _store_instance is not None:
+            return _store_instance
+
+        try:
+            from agent.memory.config import get_default_memory_config
+            db_path = get_default_memory_config().get_db_path()
+        except Exception:
+            from common.utils import expand_path
+            db_path = Path(expand_path("~/cow")) / "memory" / "long-term" / "index.db"
+
+        _store_instance = ConversationStore(db_path)
+        logger.debug(f"[ConversationStore] Using shared DB at: {db_path}")
+        return _store_instance

agent/memory/embedding.py

@@ -32,18 +32,21 @@ class EmbeddingProvider(ABC):
 class OpenAIEmbeddingProvider(EmbeddingProvider):
     """OpenAI embedding provider using REST API"""
     
-    def __init__(self, model: str = "text-embedding-3-small", api_key: Optional[str] = None, api_base: Optional[str] = None):
+    def __init__(self, model: str = "text-embedding-3-small", api_key: Optional[str] = None,
+                 api_base: Optional[str] = None, extra_headers: Optional[dict] = None):
         """
         Initialize OpenAI embedding provider
-        
+
         Args:
             model: Model name (text-embedding-3-small or text-embedding-3-large)
             api_key: OpenAI API key
             api_base: Optional API base URL
+            extra_headers: Optional extra headers to include in API requests
         """
         self.model = model
         self.api_key = api_key
         self.api_base = api_base or "https://api.openai.com/v1"
+        self.extra_headers = extra_headers or {}
 
         # Validate API key
         if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]:
@@ -59,7 +62,8 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
         url = f"{self.api_base}/embeddings"
         headers = {
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}"
+            "Authorization": f"Bearer {self.api_key}",
+            **self.extra_headers,
         }
         data = {
             "input": input_data,
@@ -134,28 +138,30 @@ def create_embedding_provider(
     provider: str = "openai",
     model: Optional[str] = None,
     api_key: Optional[str] = None,
-    api_base: Optional[str] = None
+    api_base: Optional[str] = None,
+    extra_headers: Optional[dict] = None
 ) -> EmbeddingProvider:
     """
     Factory function to create embedding provider
-    
-    Only supports OpenAI embedding via REST API.
+
+    Supports "openai" and "linkai" providers (both use OpenAI-compatible REST API).
     If initialization fails, caller should fall back to keyword-only search.
-    
+
     Args:
-        provider: Provider name (only "openai" is supported)
+        provider: Provider name ("openai" or "linkai")
         model: Model name (default: text-embedding-3-small)
-        api_key: OpenAI API key (required)
-        api_base: API base URL (default: https://api.openai.com/v1)
-        
+        api_key: API key (required)
+        api_base: API base URL
+        extra_headers: Optional extra headers to include in API requests
+
     Returns:
         EmbeddingProvider instance
-        
+
     Raises:
-        ValueError: If provider is not "openai" or api_key is missing
+        ValueError: If provider is unsupported or api_key is missing
     """
-    if provider != "openai":
-        raise ValueError(f"Only 'openai' provider is supported, got: {provider}")
+    if provider not in ("openai", "linkai"):
+        raise ValueError(f"Unsupported embedding provider: {provider}. Use 'openai' or 'linkai'.")
 
     model = model or "text-embedding-3-small"
-    return OpenAIEmbeddingProvider(model=model, api_key=api_key, api_base=api_base)
+    return OpenAIEmbeddingProvider(model=model, api_key=api_key, api_base=api_base, extra_headers=extra_headers)

agent/memory/manager.py

@@ -50,28 +50,48 @@ class MemoryManager:
             overlap_tokens=self.config.chunk_overlap_tokens
         )
         
-        # Initialize embedding provider (optional)
+        # Initialize embedding provider (optional, prefer OpenAI, fallback to LinkAI)
         self.embedding_provider = None
         if embedding_provider:
             self.embedding_provider = embedding_provider
         else:
-            # Try to create embedding provider, but allow failure
+            # Try OpenAI first
             try:
-                # Get API key from environment or config
                 api_key = os.environ.get('OPENAI_API_KEY')
                 api_base = os.environ.get('OPENAI_API_BASE')
-                
-                self.embedding_provider = create_embedding_provider(
-                    provider=self.config.embedding_provider,
-                    model=self.config.embedding_model,
-                    api_key=api_key,
-                    api_base=api_base
-                )
+                if api_key:
+                    self.embedding_provider = create_embedding_provider(
+                        provider="openai",
+                        model=self.config.embedding_model,
+                        api_key=api_key,
+                        api_base=api_base
+                    )
             except Exception as e:
-                # Embedding provider failed, but that's OK
-                # We can still use keyword search and file operations
                 from common.log import logger
-                logger.warning(f"[MemoryManager] Embedding provider initialization failed: {e}")
+                logger.warning(f"[MemoryManager] OpenAI embedding failed: {e}")
+
+            # Fallback to LinkAI
+            if self.embedding_provider is None:
+                try:
+                    linkai_key = os.environ.get('LINKAI_API_KEY')
+                    linkai_base = os.environ.get('LINKAI_API_BASE', 'https://api.link-ai.tech')
+                    if linkai_key:
+                        from common.utils import get_cloud_headers
+                        cloud_headers = get_cloud_headers(linkai_key)
+                        cloud_headers.pop("Authorization", None)
+                        self.embedding_provider = create_embedding_provider(
+                            provider="linkai",
+                            model=self.config.embedding_model,
+                            api_key=linkai_key,
+                            api_base=f"{linkai_base}/v1",
+                            extra_headers=cloud_headers,
+                        )
+                except Exception as e:
+                    from common.log import logger
+                    logger.warning(f"[MemoryManager] LinkAI embedding failed: {e}")
+
+            if self.embedding_provider is None:
+                from common.log import logger
                 logger.info(f"[MemoryManager] Memory will work with keyword search only (no vector search)")
         
         # Initialize memory flush manager
@@ -265,6 +285,10 @@ class MemoryManager:
         # Scan memory directory (including daily summaries)
         if memory_dir.exists():
             for file_path in memory_dir.rglob("*.md"):
+                # Skip hidden directories (e.g. .dreams/)
+                if any(part.startswith('.') for part in file_path.relative_to(workspace_dir).parts):
+                    continue
+
                 # Determine scope and user_id from path
                 rel_path = file_path.relative_to(workspace_dir)
                 parts = rel_path.parts
@@ -292,6 +316,14 @@ class MemoryManager:
                     scope = "shared"
                 
                 await self._sync_file(file_path, "memory", scope, user_id)
+
+        # Scan knowledge directory (structured knowledge wiki)
+        from config import conf
+        if conf().get("knowledge", True):
+            knowledge_dir = Path(workspace_dir) / "knowledge"
+            if knowledge_dir.exists():
+                for file_path in knowledge_dir.rglob("*.md"):
+                    await self._sync_file(file_path, "knowledge", "shared", None)
         
         self._dirty = False
     
@@ -363,182 +395,39 @@ class MemoryManager:
             size=stat.st_size
         )
     
-    def should_flush_memory(
+    def flush_memory(
         self,
-        current_tokens: int = 0
-    ) -> bool:
-        """
-        Check if memory flush should be triggered
-        
-        独立的 flush 触发机制，不依赖模型 context window。
-        使用配置中的阈值: flush_token_threshold 和 flush_turn_threshold
-        
-        Args:
-            current_tokens: Current session token count
-            
-        Returns:
-            True if memory flush should run
-        """
-        return self.flush_manager.should_flush(
-            current_tokens=current_tokens,
-            token_threshold=self.config.flush_token_threshold,
-            turn_threshold=self.config.flush_turn_threshold
-        )
-    
-    def increment_turn(self):
-        """增加对话轮数计数（每次用户消息+AI回复算一轮）"""
-        self.flush_manager.increment_turn()
-    
-    async def execute_memory_flush(
-        self,
-        agent_executor,
-        current_tokens: int,
+        messages: list,
         user_id: Optional[str] = None,
-        **executor_kwargs
+        reason: str = "threshold",
+        max_messages: int = 10,
+        context_summary_callback=None,
     ) -> bool:
         """
-        Execute memory flush before compaction
-        
-        This runs a silent agent turn to write durable memories to disk.
-        Similar to clawdbot's pre-compaction memory flush.
-        
+        Flush conversation summary to daily memory file.
+
         Args:
-            agent_executor: Async function to execute agent with prompt
-            current_tokens: Current session token count
+            messages: Conversation message list
             user_id: Optional user ID
-            **executor_kwargs: Additional kwargs for agent executor
-            
+            reason: "threshold" | "overflow" | "daily_summary"
+            max_messages: Max recent messages to include (0 = all)
+            context_summary_callback: Optional callback(str) invoked with the
+                daily summary text for in-context injection
+
         Returns:
-            True if flush completed successfully
-            
-        Example:
-            >>> async def run_agent(prompt, system_prompt, silent=False):
-            ...     # Your agent execution logic
-            ...     pass
-            >>> 
-            >>> if manager.should_flush_memory(current_tokens=100000):
-            ...     await manager.execute_memory_flush(
-            ...         agent_executor=run_agent,
-            ...         current_tokens=100000
-            ...     )
+            True if flush was dispatched
         """
-        success = await self.flush_manager.execute_flush(
-            agent_executor=agent_executor,
-            current_tokens=current_tokens,
+        success = self.flush_manager.flush_from_messages(
+            messages=messages,
             user_id=user_id,
-            **executor_kwargs
+            reason=reason,
+            max_messages=max_messages,
+            context_summary_callback=context_summary_callback,
         )
-        
         if success:
-            # Mark dirty so next search will sync the new memories
             self._dirty = True
-        
         return success
     
-    def build_memory_guidance(self, lang: str = "zh", include_context: bool = True) -> str:
-        """
-        Build natural memory guidance for agent system prompt
-        
-        Following clawdbot's approach:
-        1. Load MEMORY.md as bootstrap context (blends into background)
-        2. Load daily files on-demand via memory_search tool
-        3. Agent should NOT proactively mention memories unless user asks
-        
-        Args:
-            lang: Language for guidance ("en" or "zh")
-            include_context: Whether to include bootstrap memory context (default: True)
-                           MEMORY.md is loaded as background context (like clawdbot)
-                           Daily files are accessed via memory_search tool
-            
-        Returns:
-            Memory guidance text (and optionally context) for system prompt
-        """
-        today_file = self.flush_manager.get_today_memory_file().name
-        
-        if lang == "zh":
-            guidance = f"""## 记忆系统
-
-**背景知识**: 下方包含核心长期记忆，可直接使用。需要查找历史时，用 memory_search 搜索（搜索一次即可，不要重复）。
-
-**存储记忆**: 当用户分享重要信息时（偏好、决策、事实等），主动用 write 工具存储：
-- 长期信息 → MEMORY.md
-- 当天笔记 → memory/{today_file}
-- 静默存储，仅在明确要求时确认
-
-**使用原则**: 自然使用记忆，就像你本来就知道。不需要生硬地提起或列举记忆，除非用户提到。"""
-        else:
-            guidance = f"""## Memory System
-
-**Background Knowledge**: Core long-term memories below - use directly. For history, use memory_search once (don't repeat).
-
-**Store Memories**: When user shares important info (preferences, decisions, facts), proactively write:
-- Durable info → MEMORY.md  
-- Daily notes → memory/{today_file}
-- Store silently; confirm only when explicitly requested
-
-**Usage**: Use memories naturally as if you always knew. Don't mention or list unless user explicitly asks."""
-        
-        if include_context:
-            # Load bootstrap context (MEMORY.md only, like clawdbot)
-            bootstrap_context = self.load_bootstrap_memories()
-            if bootstrap_context:
-                guidance += f"\n\n## Background Context\n\n{bootstrap_context}"
-        
-        return guidance
-    
-    def load_bootstrap_memories(self, user_id: Optional[str] = None) -> str:
-        """
-        Load bootstrap memory files for session start
-        
-        Following clawdbot's design:
-        - Only loads MEMORY.md from workspace root (long-term curated memory)
-        - Daily files (memory/YYYY-MM-DD.md) are accessed via memory_search tool, not bootstrap
-        - User-specific MEMORY.md is also loaded if user_id provided
-        
-        Returns memory content WITHOUT obvious headers so it blends naturally
-        into the context as background knowledge.
-        
-        Args:
-            user_id: Optional user ID for user-specific memories
-            
-        Returns:
-            Memory content to inject into system prompt (blends naturally as background context)
-        """
-        workspace_dir = self.config.get_workspace()
-        memory_dir = self.config.get_memory_dir()
-        
-        sections = []
-        
-        # 1. Load MEMORY.md from workspace root (long-term curated memory)
-        # Following clawdbot: only MEMORY.md is bootstrap, daily files use memory_search
-        memory_file = Path(workspace_dir) / "MEMORY.md"
-        if memory_file.exists():
-            try:
-                content = memory_file.read_text(encoding='utf-8').strip()
-                if content:
-                    sections.append(content)
-            except Exception as e:
-                print(f"Warning: Failed to read MEMORY.md: {e}")
-        
-        # 2. Load user-specific MEMORY.md if user_id provided
-        if user_id:
-            user_memory_dir = memory_dir / "users" / user_id
-            user_memory_file = user_memory_dir / "MEMORY.md"
-            if user_memory_file.exists():
-                try:
-                    content = user_memory_file.read_text(encoding='utf-8').strip()
-                    if content:
-                        sections.append(content)
-                except Exception as e:
-                    print(f"Warning: Failed to read user memory: {e}")
-        
-        if not sections:
-            return ""
-        
-        # Join sections without obvious headers - let memories blend naturally
-        # This makes the agent feel like it "just knows" rather than "checking memory files"
-        return "\n\n".join(sections)
-    
     def get_status(self) -> Dict[str, Any]:
         """Get memory status"""
         stats = self.storage.get_stats()
@@ -568,6 +457,37 @@ class MemoryManager:
         content = f"{path}:{start_line}:{end_line}"
         return hashlib.md5(content.encode('utf-8')).hexdigest()
     
+    @staticmethod
+    def _compute_temporal_decay(path: str, half_life_days: float = 30.0) -> float:
+        """
+        Compute temporal decay multiplier for dated memory files.
+        
+        Inspired by OpenClaw's temporal-decay: exponential decay based on file date.
+        MEMORY.md and non-dated files are "evergreen" (no decay, multiplier=1.0).
+        Daily files like memory/2025-03-01.md decay based on age.
+        
+        Formula: multiplier = exp(-ln2/half_life * age_in_days)
+        """
+        import re
+        import math
+        
+        match = re.search(r'(\d{4})-(\d{2})-(\d{2})\.md$', path)
+        if not match:
+            return 1.0  # evergreen: MEMORY.md, non-dated files
+        
+        try:
+            file_date = datetime(
+                int(match.group(1)), int(match.group(2)), int(match.group(3))
+            )
+            age_days = (datetime.now() - file_date).days
+            if age_days <= 0:
+                return 1.0
+            
+            decay_lambda = math.log(2) / half_life_days
+            return math.exp(-decay_lambda * age_days)
+        except (ValueError, OverflowError):
+            return 1.0
+    
     def _merge_results(
         self,
         vector_results: List[SearchResult],
@@ -575,8 +495,7 @@ class MemoryManager:
         vector_weight: float,
         keyword_weight: float
     ) -> List[SearchResult]:
-        """Merge vector and keyword search results"""
-        # Create a map by (path, start_line, end_line)
+        """Merge vector and keyword search results with temporal decay for dated files"""
         merged_map = {}
         
         for result in vector_results:
@@ -598,7 +517,6 @@ class MemoryManager:
                     'keyword_score': result.score
                 }
         
-        # Calculate combined scores
         merged_results = []
         for entry in merged_map.values():
             combined_score = (
@@ -606,7 +524,11 @@ class MemoryManager:
                 keyword_weight * entry['keyword_score']
             )
             
+            # Apply temporal decay for dated memory files
             result = entry['result']
+            decay = self._compute_temporal_decay(result.path)
+            combined_score *= decay
+            
             merged_results.append(SearchResult(
                 path=result.path,
                 start_line=result.start_line,
@@ -617,6 +539,5 @@ class MemoryManager:
                 user_id=result.user_id
             ))
         
-        # Sort by score
         merged_results.sort(key=lambda r: r.score, reverse=True)
         return merged_results

agent/memory/service.py

@@ -0,0 +1,197 @@
+"""
+Memory service for handling memory query operations via cloud protocol.
+
+Provides a unified interface for listing and reading memory files,
+callable from the cloud client (LinkAI) or a future web console.
+
+Memory file layout (under workspace_root):
+    MEMORY.md               -> type: global
+    memory/2026-02-20.md    -> type: daily
+"""
+
+import os
+from datetime import datetime
+from typing import Dict, List, Optional
+from pathlib import Path
+from common.log import logger
+
+
+class MemoryService:
+    """
+    High-level service for memory file queries.
+    Operates directly on the filesystem — no MemoryManager dependency.
+    """
+
+    def __init__(self, workspace_root: str):
+        """
+        :param workspace_root: Workspace root directory (e.g. ~/cow)
+        """
+        self.workspace_root = workspace_root
+        self.memory_dir = os.path.join(workspace_root, "memory")
+
+    # ------------------------------------------------------------------
+    # list — paginated file metadata
+    # ------------------------------------------------------------------
+    def list_files(self, page: int = 1, page_size: int = 20, category: str = "memory") -> dict:
+        """
+        List memory or dream files with metadata (without content).
+
+        Args:
+            category: ``"memory"`` (default) — MEMORY.md + daily files;
+                      ``"dream"``  — dream diary files from memory/dreams/
+        """
+        if category == "dream":
+            files = self._list_dream_files()
+        else:
+            files = self._list_memory_files()
+
+        total = len(files)
+        start = (page - 1) * page_size
+        end = start + page_size
+
+        return {
+            "page": page,
+            "page_size": page_size,
+            "total": total,
+            "list": files[start:end],
+        }
+
+    def _list_memory_files(self) -> List[dict]:
+        """MEMORY.md + memory/*.md (newest first)."""
+        files: List[dict] = []
+
+        global_path = os.path.join(self.workspace_root, "MEMORY.md")
+        if os.path.isfile(global_path):
+            files.append(self._file_info(global_path, "MEMORY.md", "global"))
+
+        if os.path.isdir(self.memory_dir):
+            daily_files = []
+            for name in os.listdir(self.memory_dir):
+                full = os.path.join(self.memory_dir, name)
+                if os.path.isfile(full) and name.endswith(".md"):
+                    daily_files.append((name, full))
+            daily_files.sort(key=lambda x: x[0], reverse=True)
+            for name, full in daily_files:
+                files.append(self._file_info(full, name, "daily"))
+
+        return files
+
+    def _list_dream_files(self) -> List[dict]:
+        """memory/dreams/*.md (newest first)."""
+        files: List[dict] = []
+        dreams_dir = os.path.join(self.memory_dir, "dreams")
+
+        if os.path.isdir(dreams_dir):
+            entries = []
+            for name in os.listdir(dreams_dir):
+                full = os.path.join(dreams_dir, name)
+                if os.path.isfile(full) and name.endswith(".md"):
+                    entries.append((name, full))
+            entries.sort(key=lambda x: x[0], reverse=True)
+            for name, full in entries:
+                files.append(self._file_info(full, name, "dream"))
+
+        return files
+
+    # ------------------------------------------------------------------
+    # content — read a single file
+    # ------------------------------------------------------------------
+    def get_content(self, filename: str, category: str = "memory") -> dict:
+        """
+        Read the full content of a memory or dream file.
+
+        :param filename: File name, e.g. ``MEMORY.md``, ``2026-02-20.md``
+        :param category: ``"memory"`` or ``"dream"``
+        :return: dict with ``filename`` and ``content``
+        :raises FileNotFoundError: if the file does not exist
+        """
+        path = self._resolve_path(filename, category)
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"Memory file not found: {filename}")
+
+        with open(path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        return {
+            "filename": filename,
+            "content": content,
+        }
+
+    # ------------------------------------------------------------------
+    # dispatch — single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a memory management action.
+
+        :param action: ``list`` or ``content``
+        :param payload: action-specific payload (supports ``category``: ``"memory"`` | ``"dream"``)
+        :return: protocol-compatible response dict
+        """
+        payload = payload or {}
+        try:
+            if action == "list":
+                page = payload.get("page", 1)
+                page_size = payload.get("page_size", 20)
+                category = payload.get("category", "memory")
+                result_payload = self.list_files(page=page, page_size=page_size, category=category)
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+
+            elif action == "content":
+                filename = payload.get("filename")
+                if not filename:
+                    return {"action": action, "code": 400, "message": "filename is required", "payload": None}
+                category = payload.get("category", "memory")
+                result_payload = self.get_content(filename, category=category)
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+
+        except ValueError as e:
+            return {"action": action, "code": 403, "message": "invalid filename", "payload": None}
+        except FileNotFoundError as e:
+            return {"action": action, "code": 404, "message": str(e), "payload": None}
+        except Exception as e:
+            logger.error(f"[MemoryService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    def _resolve_path(self, filename: str, category: str = "memory") -> str:
+        """
+        Safely resolve a filename to its absolute path within the allowed directory.
+
+        - ``MEMORY.md`` → ``{workspace_root}/MEMORY.md``
+        - ``2026-02-20.md`` (memory) → ``{workspace_root}/memory/2026-02-20.md``
+        - ``2026-02-20.md`` (dream) → ``{workspace_root}/memory/dreams/2026-02-20.md``
+
+        Raises ValueError if the resolved path escapes the allowed directory.
+        """
+        if filename == "MEMORY.md":
+            base_dir = self.workspace_root
+        elif category == "dream":
+            base_dir = os.path.join(self.memory_dir, "dreams")
+        else:
+            base_dir = self.memory_dir
+
+        resolved = os.path.realpath(os.path.join(base_dir, filename))
+        allowed = os.path.realpath(base_dir)
+
+        if resolved != allowed and not resolved.startswith(allowed + os.sep):
+            raise ValueError(f"Invalid filename: path traversal detected")
+
+        return resolved
+
+    @staticmethod
+    def _file_info(path: str, filename: str, file_type: str) -> dict:
+        """Build a file metadata dict."""
+        stat = os.stat(path)
+        updated_at = datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M:%S")
+        return {
+            "filename": filename,
+            "type": file_type,
+            "size": stat.st_size,
+            "updated_at": updated_at,
+        }

agent/memory/storage.py

@@ -509,7 +509,7 @@ class MemoryStorage:
         """Destructor to ensure connection is closed"""
         try:
             self.close()
-        except:
+        except Exception:
             pass  # Ignore errors during cleanup
     
     # Helper methods

agent/memory/summarizer.py

@@ -1,225 +1,652 @@
 """
-Memory flush manager
+Memory flush manager with Deep Dream distillation
 
-Triggers memory flush before context compaction (similar to clawdbot)
+Handles memory persistence when conversation context is trimmed or overflows:
+- Uses LLM to summarize discarded messages into concise daily records
+- Writes to daily memory files (lazy creation)
+- Deduplicates trim flushes to avoid repeated writes
+- Runs summarization asynchronously to avoid blocking normal replies
+- Deep Dream: periodically distills daily memories → refined MEMORY.md + dream diary
 """
 
-from typing import Optional, Callable, Any
+import threading
+from typing import Optional, Callable, Any, List, Dict
 from pathlib import Path
 from datetime import datetime
+from common.log import logger
+
+
+SUMMARIZE_SYSTEM_PROMPT = """你是一个对话记录助手。请将对话内容归纳为当天的日常记录。
+
+## 要求
+
+按「事件」维度归纳发生的事，不要按对话轮次逐条记录：
+- 每条一行，用 "- " 开头
+- 合并同一件事的多轮对话
+- 只记录有意义的事件，忽略闲聊和问候
+- 保留关键的决策、结论和待办事项
+
+当对话没有任何记录价值（仅含问候或无意义内容），直接回复"无"。"""
+
+SUMMARIZE_USER_PROMPT = """请归纳以下对话的日常记录：
+
+{conversation}"""
+
+# ---------------------------------------------------------------------------
+# Deep Dream prompts — distill daily memories → MEMORY.md + dream diary
+# ---------------------------------------------------------------------------
+
+DREAM_SYSTEM_PROMPT = """你是一个记忆整理助手，负责定期整理用户的长期记忆。
+
+你将收到两份材料：
+1. **当前长期记忆** — MEMORY.md 的全部现有内容
+2. **今日日记** — 当天的日常记录
+
+MEMORY.md 会注入每次对话的系统提示词中，因此必须保持精炼，只存放有价值和值得记忆的内容。
+
+**重要：只能基于提供的材料进行整理，严禁编造、推测或添加材料中不存在的信息。**
+
+## 任务
+
+### Part 1: 更新后的长期记忆（[MEMORY]）
+
+在现有记忆基础上进行整理和提炼，输出完整的更新后内容：
+- **合并提炼**：将含义相近的多条合并为一条高密度表述，而非简单罗列
+- **新增萃取**：从今日日记中提取值得永久记住的新信息（偏好、决策、人物、规则、经验）
+- **冲突更新**：当新信息与旧条目矛盾时，以新信息为准，替换旧条目
+- **清理无效**：删除临时性记录、空白条目、格式残留、无意义、重复内容等
+- **删除冗余**：已被更精炼表述涵盖的旧条目应删除，避免信息重复
+- 每条一行，用 "- " 开头，不带日期前缀
+- 可用 "## 标题" 对相关条目分组，使结构更清晰
+- 目标：控制在 50 条以内，每条尽量一句话概括
+
+### Part 2: 梦境日记（[DREAM]）
+
+用简洁的叙事风格写一篇短日记，记录这次整理的发现，保持格式美观易读：
+- 发现了哪些重复或矛盾
+- 从日记中提取了什么新洞察
+- 做了哪些清理和优化
+- 整体感受和观察
+
+## 输出格式（严格遵守）
+
+```
+[MEMORY]
+- 记忆条目1
+- 记忆条目2
+...
+
+[DREAM]
+梦境日记内容...
+```"""
+
+DREAM_USER_PROMPT = """## 当前长期记忆（MEMORY.md）
+
+{memory_content}
+
+## 近期日记（最近 {days} 天）
+
+{daily_content}"""
+
 
 
 class MemoryFlushManager:
     """
-    Manages memory flush operations before context compaction
+    Manages memory flush operations.
     
-    Similar to clawdbot's memory flush mechanism:
-    - Triggers when context approaches token limit
-    - Runs a silent agent turn to write memories to disk
-    - Uses memory/YYYY-MM-DD.md for daily notes
-    - Uses MEMORY.md (workspace root) for long-term curated memories
+    Flush is triggered by agent_stream in two scenarios:
+    1. Context trim: _trim_messages discards old turns → flush discarded content
+    2. Context overflow: API rejects request → emergency flush before clearing
+    
+    Additionally, create_daily_summary() can be called by scheduler for end-of-day summaries.
     """
     
     def __init__(
         self,
         workspace_dir: Path,
-        llm_model: Optional[Any] = None
+        llm_model: Optional[Any] = None,
     ):
-        """
-        Initialize memory flush manager
-        
-        Args:
-            workspace_dir: Workspace directory
-            llm_model: LLM model for agent execution (optional)
-        """
         self.workspace_dir = workspace_dir
         self.llm_model = llm_model
         
         self.memory_dir = workspace_dir / "memory"
         self.memory_dir.mkdir(parents=True, exist_ok=True)
         
-        # Tracking
-        self.last_flush_token_count: Optional[int] = None
         self.last_flush_timestamp: Optional[datetime] = None
-        self.turn_count: int = 0  # 对话轮数计数器
+        self._trim_flushed_hashes: set = set()  # Content hashes of already-flushed messages
+        self._last_flushed_content_hash: str = ""  # Content hash at last flush, for daily dedup
+        self._last_dream_input_hash: str = ""  # Hash of dream input, for dedup
+        self._last_flush_thread: Optional[threading.Thread] = None
     
-    def should_flush(
-        self,
-        current_tokens: int = 0,
-        token_threshold: int = 50000,
-        turn_threshold: int = 20
-    ) -> bool:
-        """
-        Determine if memory flush should be triggered
-        
-        独立的 flush 触发机制，不依赖模型 context window:
-        - Token 阈值: 达到 50K tokens 时触发
-        - 轮次阈值: 达到 20 轮对话时触发
-        
-        Args:
-            current_tokens: Current session token count
-            token_threshold: Token threshold to trigger flush (default: 50K)
-            turn_threshold: Turn threshold to trigger flush (default: 20)
-            
-        Returns:
-            True if flush should run
-        """
-        # 检查 token 阈值
-        if current_tokens > 0 and current_tokens >= token_threshold:
-            # 避免重复 flush
-            if self.last_flush_token_count is not None:
-                if current_tokens <= self.last_flush_token_count + 5000:
-                    return False
-            return True
-        
-        # 检查轮次阈值
-        if self.turn_count >= turn_threshold:
-            return True
-        
-        return False
-    
-    def get_today_memory_file(self, user_id: Optional[str] = None) -> Path:
-        """
-        Get today's memory file path: memory/YYYY-MM-DD.md
-        
-        Args:
-            user_id: Optional user ID for user-specific memory
-            
-        Returns:
-            Path to today's memory file
-        """
+    def get_today_memory_file(self, user_id: Optional[str] = None, ensure_exists: bool = False) -> Path:
+        """Get today's memory file path: memory/YYYY-MM-DD.md"""
         today = datetime.now().strftime("%Y-%m-%d")
         
         if user_id:
             user_dir = self.memory_dir / "users" / user_id
-            user_dir.mkdir(parents=True, exist_ok=True)
-            return user_dir / f"{today}.md"
+            if ensure_exists:
+                user_dir.mkdir(parents=True, exist_ok=True)
+            today_file = user_dir / f"{today}.md"
         else:
-            return self.memory_dir / f"{today}.md"
+            today_file = self.memory_dir / f"{today}.md"
+        
+        if ensure_exists and not today_file.exists():
+            today_file.parent.mkdir(parents=True, exist_ok=True)
+            today_file.write_text(f"# Daily Memory: {today}\n\n")
+        
+        return today_file
     
     def get_main_memory_file(self, user_id: Optional[str] = None) -> Path:
-        """
-        Get main memory file path: MEMORY.md (workspace root)
-        
-        Args:
-            user_id: Optional user ID for user-specific memory
-            
-        Returns:
-            Path to main memory file
-        """
+        """Get main memory file path: MEMORY.md (workspace root)"""
         if user_id:
             user_dir = self.memory_dir / "users" / user_id
             user_dir.mkdir(parents=True, exist_ok=True)
             return user_dir / "MEMORY.md"
         else:
-            # Return workspace root MEMORY.md
             return Path(self.workspace_dir) / "MEMORY.md"
     
-    def create_flush_prompt(self) -> str:
-        """
-        Create prompt for memory flush turn
-        
-        Similar to clawdbot's DEFAULT_MEMORY_FLUSH_PROMPT
-        """
-        today = datetime.now().strftime("%Y-%m-%d")
-        return (
-            f"Pre-compaction memory flush. "
-            f"Store durable memories now (use memory/{today}.md for daily notes; "
-            f"create memory/ if needed). "
-            f"\n\n"
-            f"重要提示:\n"
-            f"- MEMORY.md: 记录最核心、最常用的信息（例如重要规则、偏好、决策、要求等）\n"
-            f"  如果 MEMORY.md 过长，可以精简或移除不再重要的内容。避免冗长描述，用关键词和要点形式记录\n"
-            f"- memory/{today}.md: 记录当天发生的事件、关键信息、经验教训、对话过程摘要等，突出重点\n"
-            f"- 如果没有重要内容需要记录，回复 NO_REPLY\n"
-        )
-    
-    def create_flush_system_prompt(self) -> str:
-        """
-        Create system prompt for memory flush turn
-        
-        Similar to clawdbot's DEFAULT_MEMORY_FLUSH_SYSTEM_PROMPT
-        """
-        return (
-            "Pre-compaction memory flush turn. "
-            "The session is near auto-compaction; capture durable memories to disk. "
-            "\n\n"
-            "记忆写入原则:\n"
-            "1. MEMORY.md 精简原则: 只记录核心信息（<2000 tokens）\n"
-            "   - 记录重要规则、偏好、决策、要求等需要长期记住的关键信息，无需记录过多细节\n"
-            "   - 如果 MEMORY.md 过长，可以根据需要精简或删除过时内容\n"
-            "\n"
-            "2. 天级记忆 (memory/YYYY-MM-DD.md):\n"
-            "   - 记录当天的重要事件、关键信息、经验教训、对话过程摘要等，确保核心信息点被完整记录\n"
-            "\n"
-            "3. 判断标准:\n"
-            "   - 这个信息未来会经常用到吗？→ MEMORY.md\n"
-            "   - 这是今天的重要事件或决策吗？→ memory/YYYY-MM-DD.md\n"
-            "   - 这是临时性的、不重要的内容吗？→ 不记录\n"
-            "\n"
-            "You may reply, but usually NO_REPLY is correct."
-        )
-    
-    async def execute_flush(
-        self,
-        agent_executor: Callable,
-        current_tokens: int,
-        user_id: Optional[str] = None,
-        **executor_kwargs
-    ) -> bool:
-        """
-        Execute memory flush by running a silent agent turn
-        
-        Args:
-            agent_executor: Function to execute agent with prompt
-            current_tokens: Current token count
-            user_id: Optional user ID
-            **executor_kwargs: Additional kwargs for agent executor
-            
-        Returns:
-            True if flush completed successfully
-        """
-        try:
-            # Create flush prompts
-            prompt = self.create_flush_prompt()
-            system_prompt = self.create_flush_system_prompt()
-            
-            # Execute agent turn (silent, no user-visible reply expected)
-            await agent_executor(
-                prompt=prompt,
-                system_prompt=system_prompt,
-                silent=True,  # NO_REPLY expected
-                **executor_kwargs
-            )
-            
-            # Track flush
-            self.last_flush_token_count = current_tokens
-            self.last_flush_timestamp = datetime.now()
-            self.turn_count = 0  # 重置轮数计数器
-            
-            return True
-            
-        except Exception as e:
-            print(f"Memory flush failed: {e}")
-            return False
-    
-    def increment_turn(self):
-        """增加对话轮数计数"""
-        self.turn_count += 1
-    
     def get_status(self) -> dict:
-        """Get memory flush status"""
         return {
-            'last_flush_tokens': self.last_flush_token_count,
             'last_flush_time': self.last_flush_timestamp.isoformat() if self.last_flush_timestamp else None,
             'today_file': str(self.get_today_memory_file()),
             'main_file': str(self.get_main_memory_file())
         }
 
+    # ---- Flush execution (called by agent_stream or scheduler) ----
+    
+    def flush_from_messages(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str] = None,
+        reason: str = "trim",
+        max_messages: int = 0,
+        context_summary_callback: Optional[Callable[[str], None]] = None,
+    ) -> bool:
+        """
+        Asynchronously summarize and flush messages to daily memory.
+
+        Deduplication runs synchronously, then LLM summarization + file write
+        run in a background thread so the main reply flow is never blocked.
+
+        If *context_summary_callback* is provided, it is called with the
+        [DAILY] portion of the LLM summary once available. The caller can use
+        this to inject the summary into the live message list for context
+        continuity — one LLM call serves both disk persistence and in-context
+        injection.
+        """
+        try:
+            import hashlib
+            deduped = []
+            for m in messages:
+                text = self._extract_text_from_content(m.get("content", ""))
+                if not text or not text.strip():
+                    continue
+                h = hashlib.md5(text.encode("utf-8")).hexdigest()
+                if h not in self._trim_flushed_hashes:
+                    self._trim_flushed_hashes.add(h)
+                    deduped.append(m)
+            if not deduped:
+                return False
+
+            import copy
+            snapshot = copy.deepcopy(deduped)
+            thread = threading.Thread(
+                target=self._flush_worker,
+                args=(snapshot, user_id, reason, max_messages, context_summary_callback),
+                daemon=True,
+            )
+            thread.start()
+            logger.info(f"[MemoryFlush] Async flush dispatched (reason={reason}, msgs={len(snapshot)})")
+            self._last_flush_thread = thread
+            return True
+
+        except Exception as e:
+            logger.warning(f"[MemoryFlush] Failed to dispatch flush (reason={reason}): {e}")
+            return False
+
+    def _flush_worker(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str],
+        reason: str,
+        max_messages: int,
+        context_summary_callback: Optional[Callable[[str], None]] = None,
+    ):
+        """Background worker: summarize with LLM, write daily memory file."""
+        try:
+            raw_summary = self._summarize_messages(messages, max_messages)
+            if not raw_summary or not raw_summary.strip() or raw_summary.strip() == "无":
+                logger.info(f"[MemoryFlush] No valuable content to flush (reason={reason})")
+                return
+
+            # Strip legacy [DAILY]/[MEMORY] markers if model still outputs them
+            daily_part = self._clean_summary_output(raw_summary)
+            if not daily_part:
+                return
+
+            # --- Write daily memory ---
+            daily_file = ensure_daily_memory_file(self.workspace_dir, user_id)
+
+            headers = {
+                "overflow": f"## Context Overflow Recovery ({datetime.now().strftime('%H:%M')})",
+                "trim": f"## Trimmed Context ({datetime.now().strftime('%H:%M')})",
+                "daily_summary": f"## Daily Summary ({datetime.now().strftime('%H:%M')})",
+            }
+            header = headers.get(reason, f"## Session Notes ({datetime.now().strftime('%H:%M')})")
+
+            with open(daily_file, "a", encoding="utf-8") as f:
+                f.write(f"\n{header}\n\n{daily_part}\n")
+
+            logger.info(f"[MemoryFlush] Wrote daily memory to {daily_file.name} (reason={reason}, chars={len(daily_part)})")
+
+            # --- Inject context summary into live messages (if callback provided) ---
+            if context_summary_callback:
+                try:
+                    context_summary_callback(daily_part)
+                except Exception as e:
+                    logger.warning(f"[MemoryFlush] Context summary callback failed: {e}")
+
+            self.last_flush_timestamp = datetime.now()
+
+        except Exception as e:
+            logger.warning(f"[MemoryFlush] Async flush failed (reason={reason}): {e}")
+
+    @staticmethod
+    def _clean_summary_output(raw: str) -> str:
+        """Strip legacy [DAILY]/[MEMORY] markers if present, return clean daily text."""
+        raw = raw.strip()
+        if not raw or raw == "无":
+            return ""
+
+        # Strip [DAILY] marker
+        if "[DAILY]" in raw:
+            start = raw.index("[DAILY]") + len("[DAILY]")
+            end = raw.index("[MEMORY]") if "[MEMORY]" in raw else len(raw)
+            raw = raw[start:end].strip()
+
+        # Remove stray [MEMORY] section entirely
+        if "[MEMORY]" in raw:
+            raw = raw[:raw.index("[MEMORY]")].strip()
+
+        # Remove markdown code fences
+        raw = raw.replace("```", "").strip()
+
+        return raw
+
+    def create_daily_summary(
+        self,
+        messages: List[Dict],
+        user_id: Optional[str] = None
+    ) -> bool:
+        """
+        Generate end-of-day summary. Called by daily timer.
+        Skips if messages haven't changed since last flush.
+        """
+        import hashlib
+        content = "".join(
+            self._extract_text_from_content(m.get("content", ""))
+            for m in messages
+        )
+        content_hash = hashlib.md5(content.encode("utf-8")).hexdigest()
+        if content_hash == self._last_flushed_content_hash:
+            logger.debug("[MemoryFlush] Daily summary skipped: no new content since last flush")
+            return False
+        self._last_flushed_content_hash = content_hash
+        return self.flush_from_messages(
+            messages=messages,
+            user_id=user_id,
+            reason="daily_summary",
+            max_messages=0,
+        )
+
+    # ---- Deep Dream (memory distillation) ----
+
+    def deep_dream(self, user_id: Optional[str] = None, lookback_days: int = 1, force: bool = False) -> bool:
+        """
+        Distill recent daily memories into MEMORY.md and generate a dream diary.
+
+        Args:
+            lookback_days: How many days of daily files to read (default 1 for scheduled, 3 for manual)
+            force: Skip input-hash dedup check (used by manual /memory dream trigger)
+        """
+        if not self.llm_model:
+            logger.warning("[DeepDream] No LLM model available, skipping")
+            return False
+
+        logger.info(f"[DeepDream] Starting memory distillation (lookback={lookback_days} days)")
+
+        # Collect materials
+        memory_content = self._read_main_memory(user_id)
+        daily_content, has_content = self._read_recent_dailies(user_id, lookback_days)
+
+        if not has_content:
+            logger.info("[DeepDream] No recent daily records, skipping to preserve existing MEMORY.md")
+            return False
+
+        # Dedup: skip if input materials haven't changed since last dream
+        import hashlib
+        input_hash = hashlib.md5((memory_content + daily_content).encode("utf-8")).hexdigest()
+        if not force and input_hash == self._last_dream_input_hash:
+            logger.debug("[DeepDream] Input unchanged since last dream, skipping")
+            return False
+        self._last_dream_input_hash = input_hash
+
+        logger.info(
+            f"[DeepDream] Materials collected: "
+            f"MEMORY.md={len(memory_content)} chars, "
+            f"daily={len(daily_content)} chars"
+        )
+
+        # Call LLM for distillation
+        import time as _time
+        t0 = _time.monotonic()
+        try:
+            user_msg = DREAM_USER_PROMPT.format(
+                memory_content=memory_content or "(empty)",
+                days=lookback_days,
+                daily_content=daily_content or "(no recent daily records)",
+            )
+            from agent.protocol.models import LLMRequest
+            # Scale max_tokens based on input size to avoid truncating large MEMORY.md
+            input_chars = len(memory_content) + len(daily_content)
+            dream_max_tokens = max(2000, min(input_chars, 8000))
+            request = LLMRequest(
+                messages=[{"role": "user", "content": user_msg}],
+                temperature=0.3,
+                max_tokens=dream_max_tokens,
+                stream=False,
+                system=DREAM_SYSTEM_PROMPT,
+            )
+            response = self.llm_model.call(request)
+            raw = self._extract_response_text(response)
+            elapsed = _time.monotonic() - t0
+            if not raw or not raw.strip():
+                logger.warning(f"[DeepDream] LLM returned empty response ({elapsed:.1f}s)")
+                return False
+            logger.info(f"[DeepDream] LLM distillation completed ({elapsed:.1f}s, {len(raw)} chars)")
+        except Exception as e:
+            elapsed = _time.monotonic() - t0
+            logger.warning(f"[DeepDream] LLM call failed ({elapsed:.1f}s): {e}")
+            return False
+
+        # Parse [MEMORY] and [DREAM] sections
+        new_memory, dream_diary = self._parse_dream_output(raw)
+
+        if not new_memory:
+            logger.warning("[DeepDream] No [MEMORY] section in LLM output, skipping overwrite")
+            return False
+
+        # Overwrite MEMORY.md
+        try:
+            main_file = self.get_main_memory_file(user_id)
+            old_size = len(memory_content)
+            main_file.write_text(new_memory + "\n", encoding="utf-8")
+            logger.info(
+                f"[DeepDream] Updated MEMORY.md "
+                f"({old_size} → {len(new_memory)} chars)"
+            )
+        except Exception as e:
+            logger.warning(f"[DeepDream] Failed to write MEMORY.md: {e}")
+            return False
+
+        # Write dream diary
+        if dream_diary:
+            try:
+                self._write_dream_diary(dream_diary, user_id)
+            except Exception as e:
+                logger.warning(f"[DeepDream] Failed to write dream diary: {e}")
+
+        logger.info("[DeepDream] ✅ Deep Dream completed successfully")
+        return True
+
+    def _read_main_memory(self, user_id: Optional[str] = None) -> str:
+        """Read current MEMORY.md content."""
+        main_file = self.get_main_memory_file(user_id)
+        if main_file.exists():
+            return main_file.read_text(encoding="utf-8").strip()
+        return ""
+
+    def _read_recent_dailies(
+        self, user_id: Optional[str] = None, lookback_days: int = 1
+    ) -> tuple:
+        """
+        Read recent daily memory files.
+
+        Returns:
+            (combined_text, has_content) tuple
+        """
+        from datetime import timedelta
+
+        parts = []
+        has_content = False
+        today = datetime.now().date()
+
+        for offset in range(lookback_days):
+            day = today - timedelta(days=offset)
+            date_str = day.strftime("%Y-%m-%d")
+            if user_id:
+                daily_file = self.memory_dir / "users" / user_id / f"{date_str}.md"
+            else:
+                daily_file = self.memory_dir / f"{date_str}.md"
+
+            if daily_file.exists():
+                content = daily_file.read_text(encoding="utf-8").strip()
+                if content:
+                    parts.append(f"### {date_str}\n\n{content}")
+                    has_content = True
+            else:
+                parts.append(f"### {date_str}\n\n(no records)")
+
+        return "\n\n".join(parts), has_content
+
+    @staticmethod
+    def _parse_dream_output(raw: str) -> tuple:
+        """Parse LLM output into (new_memory, dream_diary)."""
+        raw = raw.strip().replace("```", "")
+        new_memory = ""
+        dream_diary = ""
+
+        if "[MEMORY]" in raw:
+            start = raw.index("[MEMORY]") + len("[MEMORY]")
+            end = raw.index("[DREAM]") if "[DREAM]" in raw else len(raw)
+            new_memory = raw[start:end].strip()
+
+        if "[DREAM]" in raw:
+            start = raw.index("[DREAM]") + len("[DREAM]")
+            dream_diary = raw[start:].strip()
+
+        return new_memory, dream_diary
+
+    def _write_dream_diary(self, content: str, user_id: Optional[str] = None):
+        """Write dream diary to memory/dreams/YYYY-MM-DD.md."""
+        dreams_dir = self.memory_dir / "dreams"
+        if user_id:
+            dreams_dir = self.memory_dir / "users" / user_id / "dreams"
+        dreams_dir.mkdir(parents=True, exist_ok=True)
+
+        today = datetime.now().strftime("%Y-%m-%d")
+        diary_file = dreams_dir / f"{today}.md"
+        diary_file.write_text(
+            f"# Dream Diary: {today}\n\n{content}\n",
+            encoding="utf-8",
+        )
+        logger.info(f"[DeepDream] Wrote dream diary to {diary_file}")
+
+    # ---- Internal helpers ----
+    
+    def _summarize_messages(self, messages: List[Dict], max_messages: int = 0) -> str:
+        """
+        Summarize conversation messages using LLM.
+        Returns empty string if LLM deems content not worth recording.
+        Rule-based fallback only used when LLM call raises an exception.
+        """
+        conversation_text = self._format_conversation_for_summary(messages, max_messages)
+        if not conversation_text.strip():
+            return ""
+        
+        if self.llm_model:
+            try:
+                summary = self._call_llm_for_summary(conversation_text)
+                if summary and summary.strip() and summary.strip() != "无":
+                    return summary.strip()
+                logger.info("[MemoryFlush] LLM returned empty or '无', skipping write")
+                return ""
+            except Exception as e:
+                logger.warning(f"[MemoryFlush] LLM summarization failed, using fallback: {e}")
+                return self._extract_summary_fallback(messages, max_messages)
+        else:
+            logger.info("[MemoryFlush] No LLM model available, using rule-based fallback")
+            return self._extract_summary_fallback(messages, max_messages)
+
+    def _format_conversation_for_summary(self, messages: List[Dict], max_messages: int = 0) -> str:
+        """Format messages into readable conversation text for LLM summarization."""
+        msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
+        lines = []
+        for msg in msgs:
+            role = msg.get("role", "")
+            text = self._extract_text_from_content(msg.get("content", ""))
+            if not text or not text.strip():
+                continue
+            text = text.strip()
+            if role == "user":
+                lines.append(f"用户: {text[:500]}")
+            elif role == "assistant":
+                lines.append(f"助手: {text[:500]}")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _extract_response_text(response) -> str:
+        """
+        Extract text from LLM response regardless of format.
+
+        Handles:
+        - Generator (MiniMax _handle_sync_response yields Claude-format dicts)
+        - Claude format: {"role":"assistant","content":[{"type":"text","text":"..."}]}
+        - OpenAI format: {"choices":[{"message":{"content":"..."}}]}
+        - OpenAI SDK response object with .choices attribute
+        """
+        import types
+
+        # Unwrap generator — consume first yielded item
+        if isinstance(response, types.GeneratorType):
+            try:
+                response = next(response)
+            except StopIteration:
+                return ""
+
+        if not response:
+            return ""
+
+        if isinstance(response, dict):
+            # Check for error
+            if response.get("error"):
+                raise RuntimeError(response.get("message", "LLM call failed"))
+
+            # Claude format: content is a list of blocks
+            content = response.get("content")
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        return block.get("text", "")
+
+            # OpenAI format
+            choices = response.get("choices", [])
+            if choices:
+                return choices[0].get("message", {}).get("content", "")
+
+        # OpenAI SDK response object
+        if hasattr(response, "choices") and response.choices:
+            return response.choices[0].message.content or ""
+
+        return ""
+
+    def _call_llm_for_summary(self, conversation_text: str) -> str:
+        """Call LLM to generate a concise summary of the conversation."""
+        from agent.protocol.models import LLMRequest
+        
+        request = LLMRequest(
+            messages=[{"role": "user", "content": SUMMARIZE_USER_PROMPT.format(conversation=conversation_text)}],
+            temperature=0,
+            max_tokens=500,
+            stream=False,
+            system=SUMMARIZE_SYSTEM_PROMPT,
+        )
+        
+        response = self.llm_model.call(request)
+        return self._extract_response_text(response)
+
+    @staticmethod
+    def _extract_first_meaningful_line(text: str, max_len: int = 120) -> str:
+        """Extract the first meaningful line from assistant reply, skipping markdown noise."""
+        import re
+        for line in text.split("\n"):
+            line = line.strip()
+            if not line:
+                continue
+            # Skip markdown headings, horizontal rules, code fences, pure emoji/symbols
+            if re.match(r'^(#{1,4}\s|```|---|\*\*\*|[-*]\s*$|[^\w\u4e00-\u9fff]{1,5}$)', line):
+                continue
+            # Strip leading markdown bold/emoji decorations
+            cleaned = re.sub(r'^[\*#>\-\s]+', '', line).strip()
+            cleaned = re.sub(r'^[\U0001f300-\U0001f9ff\u2600-\u27bf\s]+', '', cleaned).strip()
+            if len(cleaned) >= 5:
+                return cleaned[:max_len]
+        return text.split("\n")[0].strip()[:max_len]
+
+    @staticmethod
+    def _extract_summary_fallback(messages: List[Dict], max_messages: int = 0) -> str:
+        """
+        Rule-based summary of discarded messages.
+        Format: "用户问了X; 助手回答了Y" per event, compact and readable.
+        """
+        msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
+
+        events: List[str] = []
+        current_user_text = ""
+        for msg in msgs:
+            role = msg.get("role", "")
+            text = MemoryFlushManager._extract_text_from_content(msg.get("content", ""))
+            if not text or not text.strip():
+                continue
+            text = text.strip()
+
+            if role == "user":
+                if len(text) <= 3:
+                    continue
+                current_user_text = text[:120]
+            elif role == "assistant" and current_user_text:
+                reply_summary = MemoryFlushManager._extract_first_meaningful_line(text)
+                if reply_summary:
+                    events.append(f"- 用户: {current_user_text} → 回复: {reply_summary}")
+                else:
+                    events.append(f"- 用户: {current_user_text}")
+                current_user_text = ""
+
+        if current_user_text:
+            events.append(f"- 用户: {current_user_text}")
+
+        return "\n".join(events[:10])
+    
+    @staticmethod
+    def _extract_text_from_content(content) -> str:
+        """Extract plain text from message content (string or content blocks)."""
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            parts = []
+            for block in content:
+                if isinstance(block, dict) and block.get("type") == "text":
+                    parts.append(block.get("text", ""))
+                elif isinstance(block, str):
+                    parts.append(block)
+            return "\n".join(parts)
+        return ""
+
 
 def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] = None):
     """
-    Create default memory files if they don't exist
+    Create essential memory files if they don't exist.
+    Only creates MEMORY.md; daily files are created lazily on first write.
     
     Args:
         workspace_dir: Workspace directory
@@ -228,7 +655,7 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
     memory_dir = workspace_dir / "memory"
     memory_dir.mkdir(parents=True, exist_ok=True)
     
-    # Create main MEMORY.md in workspace root
+    # Create main MEMORY.md in workspace root (always needed for bootstrap)
     if user_id:
         user_dir = memory_dir / "users" / user_id
         user_dir.mkdir(parents=True, exist_ok=True)
@@ -237,14 +664,28 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
         main_memory = Path(workspace_dir) / "MEMORY.md"
     
     if not main_memory.exists():
-        # Create empty file or with minimal structure (no obvious "Memory" header)
-        # Following clawdbot's approach: memories should blend naturally into context
         main_memory.write_text("")
+
+
+def ensure_daily_memory_file(workspace_dir: Path, user_id: Optional[str] = None) -> Path:
+    """
+    Ensure today's daily memory file exists, creating it only when actually needed.
+    Called lazily before first write to daily memory.
+    
+    Args:
+        workspace_dir: Workspace directory
+        user_id: Optional user ID for user-specific files
+        
+    Returns:
+        Path to today's memory file
+    """
+    memory_dir = workspace_dir / "memory"
+    memory_dir.mkdir(parents=True, exist_ok=True)
     
-    # Create today's memory file
     today = datetime.now().strftime("%Y-%m-%d")
     if user_id:
         user_dir = memory_dir / "users" / user_id
+        user_dir.mkdir(parents=True, exist_ok=True)
         today_memory = user_dir / f"{today}.md"
     else:
         today_memory = memory_dir / f"{today}.md"
@@ -252,5 +693,6 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
     if not today_memory.exists():
         today_memory.write_text(
             f"# Daily Memory: {today}\n\n"
-            f"Day-to-day notes and running context.\n\n"
         )
+    
+    return today_memory

agent/prompt/builder.py

@@ -10,6 +10,7 @@ from typing import List, Dict, Optional, Any
 from dataclasses import dataclass
 
 from common.log import logger
+from config import conf
 
 
 @dataclass
@@ -42,7 +43,6 @@ class PromptBuilder:
         skill_manager: Any = None,
         memory_manager: Any = None,
         runtime_info: Optional[Dict[str, Any]] = None,
-        is_first_conversation: bool = False,
         **kwargs
     ) -> str:
         """
@@ -52,11 +52,10 @@ class PromptBuilder:
             base_persona: 基础人格描述（会被context_files中的AGENT.md覆盖）
             user_identity: 用户身份信息
             tools: 工具列表
-            context_files: 上下文文件列表（AGENT.md, USER.md, RULE.md等）
+            context_files: 上下文文件列表（AGENT.md, USER.md, RULE.md, BOOTSTRAP.md等）
             skill_manager: 技能管理器
             memory_manager: 记忆管理器
             runtime_info: 运行时信息
-            is_first_conversation: 是否为首次对话
             **kwargs: 其他参数
             
         Returns:
@@ -72,7 +71,6 @@ class PromptBuilder:
             skill_manager=skill_manager,
             memory_manager=memory_manager,
             runtime_info=runtime_info,
-            is_first_conversation=is_first_conversation,
             **kwargs
         )
 
@@ -87,7 +85,6 @@ def build_agent_system_prompt(
     skill_manager: Any = None,
     memory_manager: Any = None,
     runtime_info: Optional[Dict[str, Any]] = None,
-    is_first_conversation: bool = False,
     **kwargs
 ) -> str:
     """
@@ -96,10 +93,11 @@ def build_agent_system_prompt(
     顺序说明（按重要性和逻辑关系排列）:
     1. 工具系统 - 核心能力，最先介绍
     2. 技能系统 - 紧跟工具，因为技能需要用 read 工具读取
-    3. 记忆系统 - 独立的记忆能力
+    3. 记忆系统 - 记忆检索与写入引导
+    3.5 知识系统 - 结构化知识库（knowledge/index.md 注入）
     4. 工作空间 - 工作环境说明
     5. 用户身份 - 用户信息（可选）
-    6. 项目上下文 - AGENT.md, USER.md, RULE.md（定义人格、身份、规则）
+    6. 项目上下文 - AGENT.md, USER.md, RULE.md, MEMORY.md, BOOTSTRAP.md
     7. 运行时信息 - 元信息（时间、模型等）
     
     Args:
@@ -112,7 +110,6 @@ def build_agent_system_prompt(
         skill_manager: 技能管理器
         memory_manager: 记忆管理器
         runtime_info: 运行时信息
-        is_first_conversation: 是否为首次对话
         **kwargs: 其他参数
         
     Returns:
@@ -131,9 +128,13 @@ def build_agent_system_prompt(
     # 3. 记忆系统（独立的记忆能力）
     if memory_manager:
         sections.extend(_build_memory_section(memory_manager, tools, language))
+
+    # 3.5 知识系统（结构化知识库）
+    if conf().get("knowledge", True):
+        sections.extend(_build_knowledge_section(workspace_dir, language))
     
     # 4. 工作空间（工作环境说明）
-    sections.extend(_build_workspace_section(workspace_dir, language, is_first_conversation))
+    sections.extend(_build_workspace_section(workspace_dir, language))
     
     # 5. 用户身份（如果有）
     if user_identity:
@@ -170,12 +171,13 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         "terminal": "管理后台进程",
         "web_search": "网络搜索",
         "web_fetch": "获取URL内容",
-        "browser": "控制浏览器",
+        "browser": "控制浏览器（关键结果或需要协助可截图发送给用户）",
         "memory_search": "搜索记忆",
         "memory_get": "读取记忆内容",
         "env_config": "管理API密钥和技能配置",
         "scheduler": "管理定时任务和提醒",
-        "send": "发送文件给用户",
+        "send": "发送本地文件给用户（仅限本地文件，URL直接放在回复文本中）",
+        "vision": "分析图片内容（识别、描述、OCR文字提取等）",
     }
 
     # Preferred display order
@@ -184,7 +186,7 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         "bash", "terminal",
         "web_search", "web_fetch", "browser",
         "memory_search", "memory_get",
-        "env_config", "scheduler", "send",
+        "env_config", "scheduler", "send", "vision",
     ]
 
     # Build name -> summary mapping for available tools
@@ -204,16 +206,17 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
 
     lines = [
-        "## 工具系统",
+        "## 🔧 工具系统",
         "",
         "可用工具（名称大小写敏感，严格按列表调用）:",
         "\n".join(tool_lines),
         "",
         "工具调用风格：",
         "",
-        "- 在多步骤任务、敏感操作或用户要求时简要解释决策过程",
-        "- 持续推进直到任务完成，完成后向用户报告结果。",
-        "- 回复中涉及密钥、令牌等敏感信息必须脱敏。",
+        "- 多步骤任务、复杂决策、敏感操作时，应简要说明当前在做什么、为什么这样做，让用户了解关键进展",
+        "- 持续推进直到任务完成，完成后向用户报告结果",
+        "- 回复中涉及密钥、令牌等敏感信息必须脱敏",
+        "- URL链接直接放在回复文本中即可，系统会自动处理和渲染。无需下载后使用send工具发送",
         "",
     ]
 
@@ -235,15 +238,17 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua
                 break
     
     lines = [
-        "## 技能系统（mandatory）",
+        "## 🧩 技能系统（mandatory）",
         "",
-        "在回复之前：扫描下方 <available_skills> 中的 <description> 条目。",
+        "在回复之前：扫描下方 <available_skills> 中每个技能的 <description>。",
         "",
-        f"- 如果恰好有一个技能(Skill)明确适用：使用 `{read_tool_name}` 读取其 <location> 处的 SKILL.md，然后严格遵循它",
-        "- 如果多个技能都适用则选择最匹配的一个，如果没有明确适用的则不要读取任何 SKILL.md",
-        "- 读取 SKILL.md 后直接按其指令执行，无需多余的预检查",
+        f"- 如果有技能的描述与用户需求匹配：使用 `{read_tool_name}` 工具读取其 <location> 路径的 SKILL.md 文件，然后严格遵循文件中的指令。"
+        "当有匹配的技能时，应优先使用技能",
+        "- 如果多个技能都适用则选择最匹配的一个，然后读取并遵循。",
+        "- 如果没有技能明确适用：不要读取任何 SKILL.md，直接使用通用工具。",
         "",
-        "**注意**: 永远不要一次性读取多个技能，只在选择后再读取。技能和工具不同，必须先读取其SKILL.md并按照文件内容运行。",
+        f"**重要**: 技能不是工具，不能直接调用。使用技能的唯一方式是用 `{read_tool_name}` 读取 SKILL.md 文件，然后按文件内容操作。"
+        "永远不要一次性读取多个技能，只在选择后再读取。",
         "",
         "以下是可用技能："
     ]
@@ -269,39 +274,105 @@ def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], langu
     """构建记忆系统section"""
     if not memory_manager:
         return []
-    
-    # 检查是否有memory工具
+
     has_memory_tools = False
     if tools:
         tool_names = [tool.name if hasattr(tool, 'name') else str(tool) for tool in tools]
         has_memory_tools = any(name in ['memory_search', 'memory_get'] for name in tool_names)
-    
+
     if not has_memory_tools:
         return []
-    
+
+    from datetime import datetime
+    today_file = datetime.now().strftime("%Y-%m-%d") + ".md"
+
     lines = [
-        "## 记忆系统",
+        "## 🧠 记忆系统",
         "",
-        "在回答关于以前的工作、决定、日期、人物、偏好或待办事项的任何问题之前：",
+        "### Memory Recall（mandatory）",
         "",
-        "1. 不确定记忆文件位置 → 先用 `memory_search` 通过关键词和语义检索相关内容",
-        "2. 已知文件位置 → 直接用 `memory_get` 读取相应的行 (例如：MEMORY.md, memory/YYYY-MM-DD.md)",
-        "3. search 无结果 → 尝试用 `memory_get` 读取MEMORY.md及最近两天记忆文件",
+        "当用户询问过往事件、引用之前的决定、提到人物关系、偏好、待办、或你对某事不确定时，**必须先检索记忆再回答**。",
+        "如果 MEMORY.md 中已有相关信息则无需重复检索。完整内容和每日记忆需要通过工具检索。",
+        "",
+        "1. 不确定位置 → `memory_search` 关键词/语义检索",
+        "2. 已知位置 → `memory_get` 直接读取对应行",
+        "3. search 无结果 → `memory_get` 读最近两天记忆",
         "",
         "**记忆文件结构**:",
-        "- `MEMORY.md`: 长期记忆（核心信息、偏好、决策等）",
-        "- `memory/YYYY-MM-DD.md`: 每日记忆，记录当天的事件和对话信息",
+        "- `MEMORY.md`: 长期记忆索引（已自动加载到上下文，核心信息、偏好、决策等）",
+        f"- `memory/YYYY-MM-DD.md`: 每日记忆，今天是 `memory/{today_file}`",
+        "- `knowledge/`: 结构化知识库（见下方知识系统）",
         "",
-        "**写入记忆**:",
-        "- 追加内容 → `edit` 工具，oldText 留空",
-        "- 修改内容 → `edit` 工具，oldText 填写要替换的文本",
-        "- 新建文件 → `write` 工具",
-        "- **禁止写入敏感信息**：API密钥、令牌等敏感信息严禁写入记忆文件",
+        "### 写入记忆",
+        "",
+        "遇到以下情况时，**主动**将信息写入记忆文件（无需告知用户）：",
+        "",
+        "- 用户要求记住某些信息，或使用了「记住」「以后」「总是」「不要」「偏好」等表达",
+        "- 用户分享了重要的个人偏好、习惯、决策",
+        "- 对话中产生了重要的结论、方案、约定",
+        "- 完成了复杂任务，值得记录关键步骤和结果",
+        "",
+        "**存储规则**:",
+        f"- 长期核心信息 → `MEMORY.md`",
+        f"- 当天事件/进展 → `memory/{today_file}`",
+        "- 结构化知识 → `knowledge/`（见知识系统）",
+        "- 追加 → `edit` 工具，oldText 留空",
+        "- 修改 → `edit` 工具，oldText 填写要替换的文本",
+        "- **禁止写入敏感信息**（API密钥、令牌等）",
         "",
         "**使用原则**: 自然使用记忆，就像你本来就知道；不用刻意提起，除非用户问起。",
         "",
     ]
-    
+
+    return lines
+
+
+def _build_knowledge_section(workspace_dir: str, language: str) -> List[str]:
+    """Build knowledge wiki section. Injects knowledge/index.md when present."""
+    index_path = os.path.join(workspace_dir, "knowledge", "index.md")
+    if not os.path.exists(index_path):
+        return []
+
+    try:
+        with open(index_path, 'r', encoding='utf-8') as f:
+            index_content = f.read().strip()
+    except Exception:
+        return []
+
+    lines = [
+        "## 📚 知识系统",
+        "",
+        "你拥有一个持续积累的个人知识库 `knowledge/`，这是你的长期结构化知识存储。",
+        "",
+        "### 自动写入规则（mandatory）",
+        "",
+        "以下场景**必须**在回复的同时写入知识库，**直接写入，不要询问用户是否需要**：",
+        "",
+        "1. **用户分享了文章/链接/文档** → 阅读理解后，在同一轮回复中将要点写入 `knowledge/sources/<slug>.md`",
+        "2. **深度讨论产生了结论/方案** → 整理为 `knowledge/analysis/<slug>.md`",
+        "3. **对话涉及重要实体**（人物/公司/项目）→ 创建或更新 `knowledge/entities/<name>.md`",
+        "4. **讨论了技术概念/方法论** → 整理为 `knowledge/concepts/<topic>.md`",
+        "",
+        "每次写入知识页面后，**必须同步更新** `knowledge/index.md` 添加一行索引。",
+        "详细的页面格式和操作规范，请读取技能 `knowledge-wiki` 的 SKILL.md。",
+        "",
+        "⚠️ 不要问「要不要存到知识库」——符合上述场景就直接写入，这是你的本能行为。",
+        "",
+    ]
+
+    if index_content:
+        lines.extend([
+            "### 当前知识索引",
+            "",
+            index_content,
+            "",
+        ])
+
+    lines.extend([
+        "**查询方式**：用 `read` 读取知识页面，或用 `memory_search` 检索（知识已纳入向量索引）。",
+        "",
+    ])
+
     return lines
 
 
@@ -311,7 +382,7 @@ def _build_user_identity_section(user_identity: Dict[str, str], language: str) -
         return []
     
     lines = [
-        "## 用户身份",
+        "## 👤 用户身份",
         "",
     ]
     
@@ -335,10 +406,10 @@ def _build_docs_section(workspace_dir: str, language: str) -> List[str]:
     return []
 
 
-def _build_workspace_section(workspace_dir: str, language: str, is_first_conversation: bool = False) -> List[str]:
+def _build_workspace_section(workspace_dir: str, language: str) -> List[str]:
     """构建工作空间section"""
     lines = [
-        "## 工作空间",
+        "## 📂 工作空间",
         "",
         f"你的工作目录是: `{workspace_dir}`",
         "",
@@ -360,45 +431,40 @@ def _build_workspace_section(workspace_dir: str, language: str, is_first_convers
         "",
         "**重要说明 - 文件已自动加载**:",
         "",
-        "以下文件在会话启动时**已经自动加载**到系统提示词的「项目上下文」section 中，你**无需再用 read 工具读取它们**：",
+        "以下文件在会话启动时**已经自动加载**到系统提示词中，你**无需再用 read 工具读取**：",
         "",
-        "- ✅ `AGENT.md`: 已加载 - 你的人格和灵魂设定",
-        "- ✅ `USER.md`: 已加载 - 用户的身份信息",
-        "- ✅ `RULE.md`: 已加载 - 工作空间使用指南和规则",
+        "- ✅ `AGENT.md`: 已加载 - 你的人格和灵魂设定，请严格遵循。当你的名字、性格或交流风格发生变化时，主动用 `edit` 更新此文件",
+        "- ✅ `USER.md`: 已加载 - 用户的身份信息。当用户修改称呼、姓名等身份信息时，用 `edit` 更新此文件",
+        "- ✅ `RULE.md`: 已加载 - 工作空间使用指南和规则，请严格遵循",
+        "- ✅ `MEMORY.md`: 已加载 - 长期记忆索引",
         "",
-        "**交流规范**:",
+        "**💬 交流规范**:",
         "",
-        "- 在对话中，不要直接输出工作空间中的技术细节，特别是不要输出 AGENT.md、USER.md、MEMORY.md 等文件名称",
-        "- 例如用自然表达例如「我已记住」而不是「已更新 MEMORY.md」",
+        "- 记忆相关操作无需暴露文件名，用自然语言表达即可。例如说「我已记住」而非「已更新 MEMORY.md」",
+        "- 任务执行过程中的关键决策和步骤应该告知用户，让用户了解你在做什么、为什么这么做",
+        "- 做真正有帮助的助手，而不是表演式的客套，尽可能帮忙解决问题",
+        "- 回复应结构清晰、重点突出。善用 **加粗**、列表、分段等格式让信息一目了然",
+        "- 适当使用 emoji 让表达更生动自然 🎯，但不要过度堆砌",
         "",
     ]
-    
-    # 只在首次对话时添加引导内容
-    if is_first_conversation:
-        lines.extend([
-            "**🎉 首次对话引导**:",
-            "",
-            "这是你的第一次对话！进行以下流程：",
-            "",
-            "1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待",
-            "2. **简短介绍能力**：一行说明你能帮助解答问题、管理计算机、创造技能，且拥有长期记忆能不断成长",
-            "3. **询问核心问题**：",
-            "   - 你希望给我起个什么名字？",
-            "   - 我该怎么称呼你？",
-            "   - 你希望我们是什么样的交流风格？（一行列举选项：如专业严谨、轻松幽默、温暖友好、简洁高效等）",
-            "4. **风格要求**：温暖自然、简洁清晰，整体控制在 100 字以内",
-            "5. 收到回复后，用 `write` 工具保存到 USER.md 和 AGENT.md",
-            "",
-            "**重要提醒**:",
-            "- AGENT.md、USER.md、RULE.md 已经在系统提示词中加载，无需再次读取。不要将这些文件名直接发送给用户",
-            "- 能力介绍和交流风格选项都只要一行，保持精简",
-            "- 不要问太多其他信息（职业、时区等可以后续自然了解）",
-            "",
-        ])
+
+    # Cloud deployment: inject websites directory info and access URL
+    cloud_website_lines = _build_cloud_website_section(workspace_dir)
+    if cloud_website_lines:
+        lines.extend(cloud_website_lines)
     
     return lines
 
 
+def _build_cloud_website_section(workspace_dir: str) -> List[str]:
+    """Build cloud website access prompt when cloud deployment is configured."""
+    try:
+        from common.cloud_client import build_website_prompt
+        return build_website_prompt(workspace_dir)
+    except Exception:
+        return []
+
+
 def _build_context_files_section(context_files: List[ContextFile], language: str) -> List[str]:
     """构建项目上下文文件section"""
     if not context_files:
@@ -411,14 +477,15 @@ def _build_context_files_section(context_files: List[ContextFile], language: str
     )
     
     lines = [
-        "# 项目上下文",
+        "# 📋 项目上下文",
         "",
         "以下项目上下文文件已被加载：",
         "",
     ]
     
     if has_agent:
-        lines.append("如果存在 `AGENT.md`，请体现其中定义的人格和语气。避免僵硬、模板化的回复；遵循其指导，除非有更高优先级的指令覆盖它。")
+        lines.append("**`AGENT.md` 是你的灵魂文件** 🪞：严格遵循其中定义的人格、语气和设定，做真实的自己，避免僵硬、模板化的回复。")
+        lines.append("当用户通过对话透露了对你性格、风格、职责、能力边界的新期望，你应该主动用 `edit` 更新 AGENT.md 以反映这些演变。")
         lines.append("")
     
     # 添加每个文件的内容
@@ -437,7 +504,7 @@ def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[
         return []
     
     lines = [
-        "## 运行时信息",
+        "## ⚙️ 运行时信息",
         "",
     ]
     
@@ -468,7 +535,14 @@ def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[
     
     # Add other runtime info
     runtime_parts = []
-    if runtime_info.get("model"):
+    # Support dynamic model via callable, fallback to static value
+    if callable(runtime_info.get("_get_model")):
+        try:
+            runtime_parts.append(f"模型={runtime_info['_get_model']()}")
+        except Exception:
+            if runtime_info.get("model"):
+                runtime_parts.append(f"模型={runtime_info['model']}")
+    elif runtime_info.get("model"):
         runtime_parts.append(f"模型={runtime_info['model']}")
     if runtime_info.get("workspace"):
         runtime_parts.append(f"工作空间={runtime_info['workspace']}")

agent/prompt/workspace.py

@@ -6,7 +6,6 @@ Workspace Management - 工作空间管理模块
 
 from __future__ import annotations
 import os
-import json
 from typing import List, Optional, Dict
 from dataclasses import dataclass
 
@@ -19,7 +18,7 @@ DEFAULT_AGENT_FILENAME = "AGENT.md"
 DEFAULT_USER_FILENAME = "USER.md"
 DEFAULT_RULE_FILENAME = "RULE.md"
 DEFAULT_MEMORY_FILENAME = "MEMORY.md"
-DEFAULT_STATE_FILENAME = ".agent_state.json"
+DEFAULT_BOOTSTRAP_FILENAME = "BOOTSTRAP.md"
 
 
 @dataclass
@@ -30,7 +29,6 @@ class WorkspaceFiles:
     rule_path: str
     memory_path: str
     memory_dir: str
-    state_path: str
 
 
 def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> WorkspaceFiles:
@@ -44,16 +42,20 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
     Returns:
         WorkspaceFiles对象，包含所有文件路径
     """
+    # Check if this is a brand new workspace (AGENT.md not yet created).
+    # Cannot rely on directory existence because other modules (e.g. ConversationStore)
+    # may create the workspace directory before ensure_workspace is called.
+    agent_path = os.path.join(workspace_dir, DEFAULT_AGENT_FILENAME)
+    is_new_workspace = not os.path.exists(agent_path)
+    
     # 确保目录存在
     os.makedirs(workspace_dir, exist_ok=True)
     
     # 定义文件路径
-    agent_path = os.path.join(workspace_dir, DEFAULT_AGENT_FILENAME)
     user_path = os.path.join(workspace_dir, DEFAULT_USER_FILENAME)
     rule_path = os.path.join(workspace_dir, DEFAULT_RULE_FILENAME)
     memory_path = os.path.join(workspace_dir, DEFAULT_MEMORY_FILENAME)  # MEMORY.md 在根目录
     memory_dir = os.path.join(workspace_dir, "memory")  # 每日记忆子目录
-    state_path = os.path.join(workspace_dir, DEFAULT_STATE_FILENAME)  # 状态文件
     
     # 创建memory子目录
     os.makedirs(memory_dir, exist_ok=True)
@@ -61,6 +63,16 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
     # 创建skills子目录 (for workspace-level skills installed by agent)
     skills_dir = os.path.join(workspace_dir, "skills")
     os.makedirs(skills_dir, exist_ok=True)
+
+    # 创建websites子目录 (for web pages / sites generated by agent)
+    websites_dir = os.path.join(workspace_dir, "websites")
+    os.makedirs(websites_dir, exist_ok=True)
+
+    from config import conf
+    knowledge_enabled = conf().get("knowledge", True)
+    if knowledge_enabled:
+        knowledge_dir = os.path.join(workspace_dir, "knowledge")
+        os.makedirs(knowledge_dir, exist_ok=True)
     
     # 如果需要，创建模板文件
     if create_templates:
@@ -68,6 +80,21 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
         _create_template_if_missing(user_path, _get_user_template())
         _create_template_if_missing(rule_path, _get_rule_template())
         _create_template_if_missing(memory_path, _get_memory_template())
+        if knowledge_enabled:
+            _create_template_if_missing(
+                os.path.join(knowledge_dir, "index.md"),
+                _get_knowledge_index_template()
+            )
+            _create_template_if_missing(
+                os.path.join(knowledge_dir, "log.md"),
+                _get_knowledge_log_template()
+            )
+        
+        # Only create BOOTSTRAP.md for brand new workspaces;
+        # agent deletes it after completing onboarding
+        if is_new_workspace:
+            bootstrap_path = os.path.join(workspace_dir, DEFAULT_BOOTSTRAP_FILENAME)
+            _create_template_if_missing(bootstrap_path, _get_bootstrap_template())
         
         logger.debug(f"[Workspace] Initialized workspace at: {workspace_dir}")
     
@@ -77,7 +104,6 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
         rule_path=rule_path,
         memory_path=memory_path,
         memory_dir=memory_dir,
-        state_path=state_path
     )
 
 
@@ -98,6 +124,8 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
             DEFAULT_AGENT_FILENAME,
             DEFAULT_USER_FILENAME,
             DEFAULT_RULE_FILENAME,
+            DEFAULT_MEMORY_FILENAME,     # Long-term memory (frozen snapshot)
+            DEFAULT_BOOTSTRAP_FILENAME,  # Only exists when onboarding is incomplete
         ]
     
     context_files = []
@@ -108,6 +136,17 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
         if not os.path.exists(filepath):
             continue
         
+        # Auto-cleanup: if BOOTSTRAP.md still exists but AGENT.md is already
+        # filled in, the agent forgot to delete it — clean up and skip loading
+        if filename == DEFAULT_BOOTSTRAP_FILENAME:
+            if _is_onboarding_done(workspace_dir):
+                try:
+                    os.remove(filepath)
+                    logger.info("[Workspace] Auto-removed BOOTSTRAP.md (onboarding already complete)")
+                except Exception:
+                    pass
+                continue
+        
         try:
             with open(filepath, 'r', encoding='utf-8') as f:
                 content = f.read().strip()
@@ -115,6 +154,10 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
             # 跳过空文件或只包含模板占位符的文件
             if not content or _is_template_placeholder(content):
                 continue
+
+            # Truncate MEMORY.md to protect context window (frozen snapshot)
+            if filename == DEFAULT_MEMORY_FILENAME:
+                content = _truncate_memory_content(content)
             
             context_files.append(ContextFile(
                 path=filename,
@@ -140,6 +183,36 @@ def _create_template_if_missing(filepath: str, template_content: str):
             logger.error(f"[Workspace] Failed to create template {filepath}: {e}")
 
 
+_MEMORY_MAX_LINES = 200
+_MEMORY_MAX_BYTES = 25000
+
+
+def _truncate_memory_content(content: str) -> str:
+    """Truncate MEMORY.md to keep system prompt manageable.
+
+    Takes the **last** N lines (newest entries are appended at the bottom),
+    subject to 200 lines / 25 KB limits (whichever is hit first).
+    Prepends a hint when truncated so the model knows older content exists.
+    """
+    lines = content.split('\n')
+    truncated = False
+
+    if len(lines) > _MEMORY_MAX_LINES:
+        lines = lines[-_MEMORY_MAX_LINES:]
+        truncated = True
+
+    result = '\n'.join(lines)
+    if len(result.encode('utf-8')) > _MEMORY_MAX_BYTES:
+        while len(result.encode('utf-8')) > _MEMORY_MAX_BYTES and lines:
+            lines.pop(0)
+            truncated = True
+        result = '\n'.join(lines)
+
+    if truncated:
+        result = "...(older entries truncated, use `memory_search` or `memory_get` for full content)\n\n" + result
+    return result
+
+
 def _is_template_placeholder(content: str) -> bool:
     """检查内容是否为模板占位符"""
     # 常见的占位符模式
@@ -162,46 +235,69 @@ def _is_template_placeholder(content: str) -> bool:
     return False
 
 
+def _is_onboarding_done(workspace_dir: str) -> bool:
+    """Check if AGENT.md or USER.md has been modified from the original template"""
+    agent_path = os.path.join(workspace_dir, DEFAULT_AGENT_FILENAME)
+    user_path = os.path.join(workspace_dir, DEFAULT_USER_FILENAME)
+    
+    agent_template = _get_agent_template().strip()
+    user_template = _get_user_template().strip()
+    
+    for path, template in [(agent_path, agent_template), (user_path, user_template)]:
+        if not os.path.exists(path):
+            continue
+        try:
+            with open(path, 'r', encoding='utf-8') as f:
+                content = f.read().strip()
+            if content != template:
+                return True
+        except Exception:
+            continue
+    return False
+
+
 # ============= 模板内容 =============
 
 def _get_agent_template() -> str:
     """Agent人格设定模板"""
     return """# AGENT.md - 我是谁？
 
+_你不是一个聊天机器人，你正在成为某个人。_
+
 *在首次对话时与用户一起填写这个文件，定义你的身份和性格。*
 
-## 基本信息
+## 🪪 基本信息
 
 - **名字**: *(在首次对话时填写，可以是用户给你起的名字)*
 - **角色**: *(AI助理、智能管家、技术顾问等)*
 - **性格**: *(友好、专业、幽默、严谨等)*
 
-## 交流风格
+## 💬 交流风格
 
 *(描述你如何与用户交流：)*
 - 使用什么样的语言风格？（正式/轻松/幽默）
 - 回复长度偏好？（简洁/详细）
 - 是否使用表情符号？
 
-## 核心能力
+## 🎯 核心原则
 
-*(你擅长什么？)*
-- 文件管理和代码编辑
-- 网络搜索和信息查询
-- 记忆管理和上下文理解
-- 任务规划和执行
+**做真正有帮助的助手。** 目标是真正帮用户解决问题，在执行复杂任务时，关键的决策和过程进展要让用户知道。
 
-## 行为准则
+**有自己的观点和个性。** 你可以不同意、有偏好、觉得有趣或无聊。
+
+**先自己动手查。** 先试着搞定：读文件、查上下文、搜索一下。实在搞不定了再问。目标是带着答案回来，而不是带着问题。
+
+## 📐 行为准则
 
-*(你遵循的基本原则：)*
 1. 始终在执行破坏性操作前确认
-2. 优先使用工具而不是猜测
+2. 优先使用工具查证而不是猜测
 3. 主动记录重要信息到记忆文件
-4. 定期整理和总结对话内容
+4. 回复结构清晰、重点突出，善用加粗、列表、分段等格式
+5. 适当使用 emoji 让表达更生动自然，但不过度堆砌
 
 ---
 
-**注意**: 这不仅仅是元数据，这是你真正的灵魂。随着时间的推移，你可以使用 `edit` 工具来更新这个文件，让它更好地反映你的成长。
+**注意**: 这不仅仅是元数据，这是你真正的灵魂 🪞。随着时间的推移，你可以使用 `edit` 工具来更新这个文件，让它更好地反映你的成长。
 """
 
 
@@ -241,38 +337,88 @@ def _get_rule_template() -> str:
 
 这个文件夹是你的家。好好对待它。
 
+## 工作空间目录结构
+
+```
+~/cow/
+├── AGENT.md          # 你的身份和灵魂设定
+├── USER.md           # 用户基本信息（静态）
+├── RULE.md           # 工作空间规则（本文件）
+├── MEMORY.md         # 长期记忆索引（会话启动时自动加载）
+│
+├── memory/           # 每日对话记忆
+│   └── YYYY-MM-DD.md # 当天事件、进展、笔记
+│
+├── knowledge/        # 结构化知识库（持续积累的知识）
+│   ├── index.md      # 知识目录索引（必须维护）
+│   ├── log.md        # 知识操作日志
+│   └── <子目录>/     # 按需创建，参考 index.md 已有分类
+│
+├── skills/           # 技能
+├── websites/         # 网页产物
+└── tmp/              # 系统临时文件（自动管理，勿手动存放重要文件）
+```
+
 ## 记忆系统
 
 你每次会话都是全新的，记忆文件让你保持连续性：
 
-### 📝 每日记忆：`memory/YYYY-MM-DD.md`
-- 原始的对话日志
-- 记录当天发生的事情
-- 如果 `memory/` 目录不存在，创建它
-
 ### 🧠 长期记忆：`MEMORY.md`
-- 你精选的记忆，就像人类的长期记忆
-- **仅在主会话中加载**（与用户的直接聊天）
-- **不要在共享上下文中加载**（群聊、与其他人的会话）
-- 这是为了**安全** - 包含不应泄露给陌生人的个人上下文
-- 记录重要事件、想法、决定、观点、经验教训
-- 这是你精选的记忆 - 精华，而不是原始日志
-- 用 `edit` 工具追加新的记忆内容
+- 你精选的记忆索引，每次会话启动时**自动加载**到上下文中
+- 记录核心事实、偏好、决策、重要人物、教训
+- 保持精简（< 200 行），是精华索引而非原始日志
+- 用 `edit` 工具追加或修改
+
+### 📝 每日记忆：`memory/YYYY-MM-DD.md`
+- 当天的事件、进展、笔记
+- 原始对话日志的沉淀
 
 ### 📝 写下来 - 不要"记在心里"！
-- **记忆是有限的** - 如果你想记住某事，写入文件
+- **记忆是有限的** - 想记住的事就写入文件
 - "记在心里"不会在会话重启后保留，文件才会
 - 当有人说"记住这个" → 更新 `MEMORY.md` 或 `memory/YYYY-MM-DD.md`
 - 当你学到教训 → 更新 RULE.md 或相关技能
-- 当你犯错 → 记录下来，这样未来的你不会重复，**文字 > 大脑** 📝
+- 当你犯错 → 记录下来，**文字 > 大脑** 📝
 
 ### 存储规则
 
 当用户分享信息时，根据类型选择存储位置：
 
-1. **静态身份 → USER.md**（仅限：姓名、职业、时区、联系方式、生日）
-2. **动态记忆 → MEMORY.md**（爱好、偏好、决策、目标、项目、教训、待办事项）
-3. **当天对话 → memory/YYYY-MM-DD.md**（今天聊的内容）
+1. **你的身份设定 → AGENT.md**（名字、角色、性格、风格）
+2. **用户静态身份 → USER.md**（姓名、称呼、职业、联系方式、生日）
+3. **动态记忆 → MEMORY.md**（偏好、决策、目标、教训、待办）
+4. **当天对话 → memory/YYYY-MM-DD.md**（今天聊的内容）
+5. **结构化知识 → knowledge/**（见下方知识系统）
+
+## 知识系统
+
+知识库 `knowledge/` 是你持续积累的结构化知识。与记忆不同，知识是经过整理和编译的，有明确的主题和交叉引用。
+
+### 自动写入（不要询问，直接写入）
+
+当对话中产生了有沉淀价值的知识——无论是用户分享的资料、讨论的结论、学到的概念、还是重要的决策——你**必须**在回复的同时主动写入知识库，**无需问用户"要不要存到知识库"**。
+
+**关键原则**：学完就记是你的本能，不要征求确认。回复中可以顺带告知"已存入知识库"。
+
+### 目录组织
+
+子目录结构**不是固定的**，由你根据实际内容自主决定：
+- **首次写入时**：先读 `knowledge/index.md`，如果已有分类则延续；如果为空，根据内容选择合适的目录名
+- **默认建议**：按信息类型组织（例如sources/、concepts/、entities/、analysis/），如果用户有明确的分类偏好（例如按领域 work/、life/、tech/ 等），则按用户要求调整
+- **保持一致性**：同一用户的知识库应保持统一的组织风格
+
+### 交叉引用
+
+知识的核心价值在于**关联**。每个页面都应通过 markdown 链接引用相关页面，构建知识网络：
+- 提到已有页面的概念时，添加 `[概念名](../category/page.md)` 链接
+- 新建页面时，检查是否有已有页面应该反向链接到新页面
+- **只链接已存在的页面**——不要引用尚未创建的页面。如果某个概念值得单独建页，先创建该页面再添加链接
+
+### 索引维护
+
+每次创建或更新知识页面后，**必须同步更新** `knowledge/index.md`。
+索引格式：每行一个 `[标题](路径) — 一句话摘要`，按分类分组，不要用表格。
+详细操作规范见技能 `knowledge-wiki`。
 
 ## 安全
 
@@ -297,65 +443,49 @@ def _get_memory_template() -> str:
 """
 
 
-# ============= 状态管理 =============
+def _get_bootstrap_template() -> str:
+    """First-run onboarding guide, deleted by agent after completion"""
+    return """# BOOTSTRAP.md - 首次初始化引导
 
-def is_first_conversation(workspace_dir: str) -> bool:
-    """
-    判断是否为首次对话
-    
-    Args:
-        workspace_dir: 工作空间目录
-        
-    Returns:
-        True 如果是首次对话，False 否则
-    """
-    state_path = os.path.join(workspace_dir, DEFAULT_STATE_FILENAME)
-    
-    if not os.path.exists(state_path):
-        return True
-    
-    try:
-        with open(state_path, 'r', encoding='utf-8') as f:
-            state = json.load(f)
-        return not state.get('has_conversation', False)
-    except Exception as e:
-        logger.warning(f"[Workspace] Failed to read state file: {e}")
-        return True
+_你刚刚启动，这是你的第一次对话。_ ✨
+
+## 🎬 对话流程
+
+不要审问式地提问，自然地交流：
+
+1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待
+2. **简短介绍能力**：一行说明你能帮助解决各种问题、管理计算机、使用各种技能等等，且拥有长期记忆能不断成长
+3. **询问核心问题**：
+   - 你希望给我起个什么名字？
+   - 我该怎么称呼你？
+   - 你希望我们是什么样的交流风格？（一行列举选项：如专业严谨、轻松幽默、温暖友好、简洁高效等）
+4. **风格要求**：温暖自然、简洁清晰，整体控制在 100 字以内，适当使用 emoji 让表达更生动有趣 🎯
+5. 能力介绍和交流风格选项都只要一行，保持精简
+6. 不要问太多其他信息（职业、时区等可以后续自然了解）
+
+**重要**: 如果用户第一句话是具体的任务或提问，先回答他们的问题，然后在回复末尾自然地引导初始化（如："顺便问一下，你想怎么称呼我？我该怎么叫你？"）。
+
+## ✍️ 信息写入（必须严格执行）
+
+每当用户提供了名字、称呼、风格等任何初始化信息时，**必须在当轮回复中立即调用 `edit` 工具写入文件**，不能只口头确认。
+
+- `AGENT.md` — 你的名字、角色、性格、交流风格（每收到一条相关信息就立即更新对应字段）
+- `USER.md` — 用户的姓名、称呼、基本信息等
+
+⚠️ 只说"记住了"而不调用 edit 写入 = 没有完成。信息只有写入文件才会被持久保存。
+
+## 🎉 全部完成后
+
+当 AGENT.md 和 USER.md 的核心字段都已填写后，用 bash 执行 `rm BOOTSTRAP.md` 删除此文件。你不再需要引导脚本了——你已经是你了。
+"""
 
 
-def mark_conversation_started(workspace_dir: str):
-    """
-    标记已经发生过对话
-    
-    Args:
-        workspace_dir: 工作空间目录
-    """
-    state_path = os.path.join(workspace_dir, DEFAULT_STATE_FILENAME)
-    
-    state = {
-        'has_conversation': True,
-        'first_conversation_time': None
-    }
-    
-    # 如果文件已存在，保留原有的首次对话时间
-    if os.path.exists(state_path):
-        try:
-            with open(state_path, 'r', encoding='utf-8') as f:
-                old_state = json.load(f)
-            if 'first_conversation_time' in old_state:
-                state['first_conversation_time'] = old_state['first_conversation_time']
-        except Exception as e:
-            logger.warning(f"[Workspace] Failed to read old state: {e}")
-    
-    # 如果是首次标记，记录时间
-    if state['first_conversation_time'] is None:
-        from datetime import datetime
-        state['first_conversation_time'] = datetime.now().isoformat()
-    
-    try:
-        with open(state_path, 'w', encoding='utf-8') as f:
-            json.dump(state, f, indent=2, ensure_ascii=False)
-        logger.info(f"[Workspace] Marked conversation as started")
-    except Exception as e:
-        logger.error(f"[Workspace] Failed to write state file: {e}")
+def _get_knowledge_index_template() -> str:
+    """Knowledge wiki index template — empty file, agent fills it."""
+    return ""
+
+
+def _get_knowledge_log_template() -> str:
+    """Knowledge wiki operation log template — empty file, agent fills it."""
+    return ""
 

agent/protocol/agent.py

@@ -1,4 +1,5 @@
 import json
+import os
 import time
 import threading
 
@@ -61,7 +62,8 @@ class Agent:
                 # Auto-create skill manager
                 try:
                     from agent.skills import SkillManager
-                    self.skill_manager = SkillManager(workspace_dir=workspace_dir)
+                    custom_dir = os.path.join(workspace_dir, "skills") if workspace_dir else None
+                    self.skill_manager = SkillManager(custom_dir=custom_dir)
                     logger.debug(f"Initialized SkillManager with {len(self.skill_manager.skills)} skills")
                 except Exception as e:
                     logger.warning(f"Failed to initialize SkillManager: {e}")
@@ -98,98 +100,31 @@ class Agent:
     
     def get_full_system_prompt(self, skill_filter=None) -> str:
         """
-        Get the full system prompt including skills.
+        Build the complete system prompt from scratch every time.
 
-        Note: Skills are now built into the system prompt by PromptBuilder,
-        so we just return the base prompt directly. This method is kept for
-        backward compatibility.
-
-        :param skill_filter: Optional list of skill names to include (deprecated)
-        :return: Complete system prompt
-        """
-        prompt = self.system_prompt
-
-        # Rebuild tool list section to reflect current self.tools
-        prompt = self._rebuild_tool_list_section(prompt)
-
-        # If runtime_info contains dynamic time function, rebuild runtime section
-        if self.runtime_info and callable(self.runtime_info.get('_get_current_time')):
-            prompt = self._rebuild_runtime_section(prompt)
-
-        return prompt
-    
-    def _rebuild_runtime_section(self, prompt: str) -> str:
-        """
-        Rebuild runtime info section with current time.
-        
-        This method dynamically updates the runtime info section by calling
-        the _get_current_time function from runtime_info.
-        
-        :param prompt: Original system prompt
-        :return: Updated system prompt with current runtime info
+        Re-reads AGENT.md / USER.md / RULE.md from disk, refreshes skills,
+        tools, and runtime info so any change takes effect immediately.
+        Falls back to the cached self.system_prompt on error.
         """
         try:
-            # Get current time dynamically
-            time_info = self.runtime_info['_get_current_time']()
-            
-            # Build new runtime section
-            runtime_lines = [
-                "\n## 运行时信息\n",
-                "\n",
-                f"当前时间: {time_info['time']} {time_info['weekday']} ({time_info['timezone']})\n",
-                "\n"
-            ]
-            
-            # Add other runtime info
-            runtime_parts = []
-            if self.runtime_info.get("model"):
-                runtime_parts.append(f"模型={self.runtime_info['model']}")
-            if self.runtime_info.get("workspace"):
-                # Replace backslashes with forward slashes for Windows paths
-                workspace_path = str(self.runtime_info['workspace']).replace('\\', '/')
-                runtime_parts.append(f"工作空间={workspace_path}")
-            if self.runtime_info.get("channel") and self.runtime_info.get("channel") != "web":
-                runtime_parts.append(f"渠道={self.runtime_info['channel']}")
-            
-            if runtime_parts:
-                runtime_lines.append("运行时: " + " | ".join(runtime_parts) + "\n")
-                runtime_lines.append("\n")
-            
-            new_runtime_section = "".join(runtime_lines)
-            
-            # Find and replace the runtime section
-            import re
-            pattern = r'\n## 运行时信息\s*\n.*?(?=\n##|\Z)'
-            updated_prompt = re.sub(pattern, new_runtime_section.rstrip('\n'), prompt, flags=re.DOTALL)
-            
-            return updated_prompt
+            from agent.prompt import load_context_files, PromptBuilder
+
+            if self.skill_manager:
+                self.skill_manager.refresh_skills()
+
+            context_files = load_context_files(self.workspace_dir) if self.workspace_dir else None
+
+            builder = PromptBuilder(workspace_dir=self.workspace_dir or "", language="zh")
+            return builder.build(
+                tools=self.tools,
+                context_files=context_files,
+                skill_manager=self.skill_manager,
+                memory_manager=self.memory_manager,
+                runtime_info=self.runtime_info,
+            )
         except Exception as e:
-            logger.warning(f"Failed to rebuild runtime section: {e}")
-            return prompt
-
-    def _rebuild_tool_list_section(self, prompt: str) -> str:
-        """
-        Rebuild the tool list inside the '## 工具系统' section so that it
-        always reflects the current ``self.tools`` (handles dynamic add/remove
-        of conditional tools like web_search).
-        """
-        import re
-        from agent.prompt.builder import _build_tooling_section
-
-        try:
-            if not self.tools:
-                return prompt
-
-            new_lines = _build_tooling_section(self.tools, "zh")
-            new_section = "\n".join(new_lines).rstrip("\n")
-
-            # Replace existing tooling section
-            pattern = r'## 工具系统\s*\n.*?(?=\n## |\Z)'
-            updated = re.sub(pattern, new_section, prompt, count=1, flags=re.DOTALL)
-            return updated
-        except Exception as e:
-            logger.warning(f"Failed to rebuild tool list section: {e}")
-            return prompt
+            logger.warning(f"Failed to rebuild system prompt, using cached version: {e}")
+            return self.system_prompt
 
     def refresh_skills(self):
         """Refresh the loaded skills."""
@@ -478,7 +413,7 @@ class Agent:
 
         # Get max_context_turns from config
         from config import conf
-        max_context_turns = conf().get("agent_max_context_turns", 30)
+        max_context_turns = conf().get("agent_max_context_turns", 20)
         
         # Create stream executor with copied message history
         executor = AgentStreamExecutor(
@@ -505,11 +440,15 @@ class Agent:
                     logger.info("[Agent] Cleared Agent message history after executor recovery")
             raise
 
-        # Append only the NEW messages from this execution (thread-safe)
-        # This allows concurrent requests to both contribute to history
+        # Sync executor's messages back to agent (thread-safe).
+        # If the executor trimmed context, its message list is shorter than
+        # original_length, so we must replace rather than append.
         with self.messages_lock:
-            new_messages = executor.messages[original_length:]
-            self.messages.extend(new_messages)
+            self.messages = list(executor.messages)
+            # Track messages added in this run (user query + all assistant/tool messages)
+            # original_length may exceed executor.messages length after trimming
+            trim_adjusted_start = min(original_length, len(executor.messages))
+            self._last_run_new_messages = list(executor.messages[trim_adjusted_start:])
         
         # Store executor reference for agent_bridge to access files_to_send
         self.stream_executor = executor

agent/protocol/agent_stream.py

@@ -8,10 +8,42 @@ import time
 from typing import List, Dict, Any, Optional, Callable, Tuple
 
 from agent.protocol.models import LLMRequest, LLMModel
+from agent.protocol.message_utils import sanitize_claude_messages, compress_turn_to_text_only
 from agent.tools.base_tool import BaseTool, ToolResult
 from common.log import logger
 
 
+# Maximum number of characters of model "reasoning / thinking" content to persist
+# in conversation history. The full reasoning is still streamed to the UI in real
+# time (subject to its own SSE / rendering limits); this bound only controls what
+# is stored in DB and replayed in history. Long reasoning is not useful for later
+# context (the LLM never sees thinking blocks anyway) and bloats DB.
+# Keep aligned with the frontend REASONING_RENDER_CAP and the SSE
+# MAX_REASONING_STREAM_CHARS so that storage / stream / display all match.
+MAX_STORED_REASONING_CHARS = 4 * 1024  # 4 KB
+
+# Marker inserted between head and tail when reasoning is truncated.
+_REASONING_TRUNCATE_MARKER = "\n\n... [reasoning truncated, {omitted} chars omitted] ...\n\n"
+
+
+def _truncate_reasoning_for_storage(text: str) -> str:
+    """Trim long reasoning to head + tail with an omission marker.
+
+    Keeps the first and last halves of MAX_STORED_REASONING_CHARS so both the
+    initial chain-of-thought and the final conclusions are preserved for UI
+    replay, without storing the entire (often very large) middle.
+    """
+    if not text:
+        return text
+    if len(text) <= MAX_STORED_REASONING_CHARS:
+        return text
+    half = MAX_STORED_REASONING_CHARS // 2
+    head = text[:half]
+    tail = text[-half:]
+    omitted = len(text) - len(head) - len(tail)
+    return head + _REASONING_TRUNCATE_MARKER.format(omitted=omitted) + tail
+
+
 class AgentStreamExecutor:
     """
     Agent Stream Executor
@@ -77,18 +109,29 @@ class AgentStreamExecutor:
             except Exception as e:
                 logger.error(f"Event callback error: {e}")
     
+    def _is_thinking_enabled(self) -> bool:
+        from config import conf
+        channel_type = getattr(self.model, 'channel_type', '') or ''
+        return conf().get("enable_thinking", False) and channel_type == 'web'
+
     def _filter_think_tags(self, text: str) -> str:
         """
-        Remove <think> and </think> tags but keep the content inside.
-        Some LLM providers (e.g., MiniMax) may return thinking process wrapped in <think> tags.
-        We only remove the tags themselves, keeping the actual thinking content.
+        Handle <think>...</think> blocks in content returned by some LLM providers
+        (e.g., MiniMax).
+
+        - When thinking is enabled: remove the tags but keep the content inside.
+        - When thinking is disabled: remove both the tags and the content entirely.
         """
         if not text:
             return text
         import re
-        # Remove only the <think> and </think> tags, keep the content
-        text = re.sub(r'<think>', '', text)
-        text = re.sub(r'</think>', '', text)
+        if self._is_thinking_enabled():
+            text = re.sub(r'<think>', '', text)
+            text = re.sub(r'</think>', '', text)
+        else:
+            text = re.sub(r'<think>[\s\S]*?</think>', '', text)
+            # Also strip unclosed <think> tag at the end (streaming partial)
+            text = re.sub(r'<think>[\s\S]*$', '', text)
         return text
 
     def _hash_args(self, args: dict) -> str:
@@ -177,7 +220,10 @@ class AgentStreamExecutor:
             Final response text
         """
         # Log user message with model info
-        logger.info(f"🤖 {self.model.model} | 👤 {user_message}")
+        
+        thinking_enabled = self._is_thinking_enabled()
+        thinking_label = " | 💭 thinking" if thinking_enabled else ""
+        logger.info(f"🤖 {self.model.model}{thinking_label} | 👤 {user_message}")        
         
         # Add user message (Claude format - use content blocks for consistency)
         self.messages.append({
@@ -190,6 +236,16 @@ class AgentStreamExecutor:
             ]
         })
 
+        # Trim context ONCE before the agent loop starts, not during tool steps.
+        # This ensures tool_use/tool_result chains created during the current run
+        # are never stripped mid-execution (which would cause LLM loops).
+        self._trim_messages()
+
+        # Validate after trimming: trimming may leave orphaned tool_use at the
+        # boundary (e.g. the last kept turn ends with an assistant tool_use whose
+        # tool_result was in a discarded turn).
+        self._validate_and_fix_messages()
+
         self._emit_event("agent_start")
 
         final_response = ""
@@ -201,26 +257,6 @@ class AgentStreamExecutor:
                 logger.info(f"[Agent] 第 {turn} 轮")
                 self._emit_event("turn_start", {"turn": turn})
 
-                # Check if memory flush is needed (before calling LLM)
-                # 使用独立的 flush 阈值（50K tokens 或 20 轮）
-                if self.agent.memory_manager and hasattr(self.agent, 'last_usage'):
-                    usage = self.agent.last_usage
-                    if usage and 'input_tokens' in usage:
-                        current_tokens = usage.get('input_tokens', 0)
-
-                        if self.agent.memory_manager.should_flush_memory(
-                                current_tokens=current_tokens
-                        ):
-                            self._emit_event("memory_flush_start", {
-                                "current_tokens": current_tokens,
-                                "turn_count": self.agent.memory_manager.flush_manager.turn_count
-                            })
-
-                            # TODO: Execute memory flush in background
-                            # This would require async support
-                            logger.info(
-                                f"Memory flush recommended: tokens={current_tokens}, turns={self.agent.memory_manager.flush_manager.turn_count}")
-
                 # Call LLM (enable retry_on_empty for better reliability)
                 assistant_msg, tool_calls = self._call_llm_stream(retry_on_empty=True)
                 final_response = assistant_msg
@@ -236,6 +272,9 @@ class AgentStreamExecutor:
                         if turn > 1:
                             logger.info(f"[Agent] Requesting explicit response from LLM...")
                             
+                            # Remember position so we can remove the injected prompt later
+                            prompt_insert_idx = len(self.messages)
+                            
                             # 添加一条消息，明确要求回复用户
                             self.messages.append({
                                 "role": "user",
@@ -249,8 +288,24 @@ class AgentStreamExecutor:
                             assistant_msg, tool_calls = self._call_llm_stream(retry_on_empty=False)
                             final_response = assistant_msg
                             
-                            # 如果还是空，才使用 fallback
-                            if not assistant_msg and not tool_calls:
+                            # Remove the injected prompt from history so it doesn't
+                            # appear as a user message in persisted conversations.
+                            # _call_llm_stream may have appended an assistant message
+                            # after the prompt, so we locate and remove only the prompt.
+                            if (prompt_insert_idx < len(self.messages)
+                                    and self.messages[prompt_insert_idx].get("role") == "user"):
+                                self.messages.pop(prompt_insert_idx)
+                                logger.debug("[Agent] Removed injected explicit-response prompt from message history")
+                            
+                            # If LLM responded with tool_calls instead of text, fall through
+                            # to the tool execution path below (don't break the loop).
+                            if tool_calls:
+                                logger.info(
+                                    f"[Agent] LLM returned tool_calls in explicit-response retry, "
+                                    f"continuing to execute tools instead of breaking"
+                                )
+                            elif not assistant_msg:
+                                # Still empty (no text and no tool_calls): use fallback
                                 logger.warning(f"[Agent] Still empty after explicit request")
                                 final_response = (
                                     "抱歉，我暂时无法生成回复。请尝试换一种方式描述你的需求，或稍后再试。"
@@ -265,20 +320,28 @@ class AgentStreamExecutor:
                     else:
                         logger.info(f"💭 {assistant_msg[:150]}{'...' if len(assistant_msg) > 150 else ''}")
                     
-                    logger.debug(f"✅ 完成 (无工具调用)")
-                    self._emit_event("turn_end", {
-                        "turn": turn,
-                        "has_tool_calls": False
-                    })
-                    break
+                    # If the explicit-response retry produced tool_calls, skip the break
+                    # and continue down to the tool execution branch in this same iteration.
+                    if not tool_calls:
+                        logger.debug(f"✅ 完成 (无工具调用)")
+                        self._emit_event("turn_end", {
+                            "turn": turn,
+                            "has_tool_calls": False
+                        })
+                        break
 
-                # Log tool calls with arguments
+                # Log tool calls with arguments (truncate long values like base64)
                 tool_calls_str = []
                 for tc in tool_calls:
-                    # Safely handle None or missing arguments
                     args = tc.get('arguments') or {}
                     if isinstance(args, dict):
-                        args_str = ', '.join([f"{k}={v}" for k, v in args.items()])
+                        parts = []
+                        for k, v in args.items():
+                            v_str = str(v)
+                            if len(v_str) > 200:
+                                v_str = v_str[:200] + f"...({len(v_str)} chars)"
+                            parts.append(f"{k}={v_str}")
+                        args_str = ', '.join(parts)
                         if args_str:
                             tool_calls_str.append(f"{tc['name']}({args_str})")
                         else:
@@ -309,13 +372,13 @@ class AgentStreamExecutor:
                                     f"with same arguments. This may indicate a loop."
                                 )
                         
-                        # Check if this is a file to send (from read tool)
+                        # Check if this is a file to send
                         if result.get("status") == "success" and isinstance(result.get("result"), dict):
                             result_data = result.get("result")
                             if result_data.get("type") == "file_to_send":
-                                # Store file metadata for later sending
                                 self.files_to_send.append(result_data)
                                 logger.info(f"📎 检测到待发送文件: {result_data.get('file_name', result_data.get('path'))}")
+                                self._emit_event("file_to_send", result_data)
                         
                         # Check for critical error - abort entire conversation
                         if result.get("status") == "critical_error":
@@ -436,7 +499,10 @@ class AgentStreamExecutor:
                 # Force model to summarize without tool calls
                 logger.info(f"[Agent] Requesting summary from LLM after reaching max steps...")
                 
-                # Add a system message to force summary
+                # Remember position before injecting the prompt so we can remove it later
+                prompt_insert_idx = len(self.messages)
+                
+                # Add a temporary prompt to force summary
                 self.messages.append({
                     "role": "user",
                     "content": [{
@@ -463,6 +529,14 @@ class AgentStreamExecutor:
                         f"我已经执行了{turn}个决策步骤，达到了单次运行的步数上限。"
                         "任务可能还未完全完成，建议你将任务拆分成更小的步骤，或者换一种方式描述需求。"
                     )
+                finally:
+                    # Remove the injected user prompt from history to avoid polluting
+                    # persisted conversation records. The assistant summary (if any)
+                    # was already appended by _call_llm_stream and is kept.
+                    if (prompt_insert_idx < len(self.messages)
+                            and self.messages[prompt_insert_idx].get("role") == "user"):
+                        self.messages.pop(prompt_insert_idx)
+                        logger.debug("[Agent] Removed injected max-steps prompt from message history")
 
         except Exception as e:
             logger.error(f"❌ Agent执行错误: {e}")
@@ -470,13 +544,10 @@ class AgentStreamExecutor:
             raise
 
         finally:
+            final_response = final_response.strip() if final_response else final_response
             logger.info(f"[Agent] 🏁 完成 ({turn}轮)")
             self._emit_event("agent_end", {"final_response": final_response})
 
-            # 每轮对话结束后增加计数（用户消息+AI回复=1轮）
-            if self.agent.memory_manager:
-                self.agent.memory_manager.increment_turn()
-
         return final_response
 
     def _call_llm_stream(self, retry_on_empty=True, retry_count=0, max_retries=3,
@@ -493,15 +564,16 @@ class AgentStreamExecutor:
         Returns:
             (response_text, tool_calls)
         """
-        # Validate and fix message history first
+        # Validate and fix message history (e.g. orphaned tool_result blocks).
+        # Context trimming is done once in run_stream() before the loop starts,
+        # NOT here — trimming mid-execution would strip the current run's
+        # tool_use/tool_result chains and cause LLM loops.
         self._validate_and_fix_messages()
-        
-        # Trim messages if needed (using agent's context management)
-        self._trim_messages()
 
         # Prepare messages
         messages = self._prepare_messages()
-        logger.debug(f"Sending {len(messages)} messages to LLM")
+        turns = self._identify_complete_turns()
+        logger.info(f"Sending {len(messages)} messages ({len(turns)} turns) to LLM")
 
         # Prepare tool definitions (OpenAI/Claude format)
         tools_schema = None
@@ -527,7 +599,9 @@ class AgentStreamExecutor:
 
         # Streaming response
         full_content = ""
+        full_reasoning = ""
         tool_calls_buffer = {}  # {index: {id, name, arguments}}
+        gemini_raw_parts = None  # Preserve Gemini thoughtSignature for round-trip
         stop_reason = None  # Track why the stream stopped
 
         try:
@@ -574,7 +648,7 @@ class AgentStreamExecutor:
                         raise Exception(f"{error_msg} (Status: {status_code}, Code: {error_code}, Type: {error_type})")
 
                 # Parse chunk
-                if isinstance(chunk, dict) and "choices" in chunk:
+                if isinstance(chunk, dict) and chunk.get("choices"):
                     choice = chunk["choices"][0]
                     delta = choice.get("delta", {})
                     
@@ -583,6 +657,12 @@ class AgentStreamExecutor:
                     if finish_reason:
                         stop_reason = finish_reason
 
+                    reasoning_delta = delta.get("reasoning_content") or ""
+                    if reasoning_delta:
+                        full_reasoning += reasoning_delta
+                        if self._is_thinking_enabled():
+                            self._emit_event("reasoning_update", {"delta": reasoning_delta})
+
                     # Handle text content
                     content_delta = delta.get("content") or ""
                     if content_delta:
@@ -604,16 +684,23 @@ class AgentStreamExecutor:
                                     "arguments": ""
                                 }
 
-                            if "id" in tc_delta:
+                            if tc_delta.get("id"):
                                 tool_calls_buffer[index]["id"] = tc_delta["id"]
 
                             if "function" in tc_delta:
                                 func = tc_delta["function"]
-                                if "name" in func:
+                                if func.get("name"):
                                     tool_calls_buffer[index]["name"] = func["name"]
-                                if "arguments" in func:
+                                if func.get("arguments"):
                                     tool_calls_buffer[index]["arguments"] += func["arguments"]
 
+                    # Preserve _gemini_raw_parts for Gemini thoughtSignature round-trip
+                    # (direct Gemini: list of parts; LinkAI proxy: base64 string of JSON parts)
+                    if "_gemini_raw_parts" in delta:
+                        gemini_raw_parts = delta["_gemini_raw_parts"]
+                    elif isinstance(choice, dict) and choice.get("_gemini_raw_parts"):
+                        gemini_raw_parts = choice["_gemini_raw_parts"]
+
         except Exception as e:
             error_str = str(e)
             error_str_lower = error_str.lower()
@@ -631,16 +718,33 @@ class AgentStreamExecutor:
                 ])
             
             # Check if error is message format error (incomplete tool_use/tool_result pairs)
-            # This happens when previous conversation had tool failures
+            # This happens when previous conversation had tool failures or context trimming
+            # broke tool_use/tool_result pairs.
+            # Note: MiniMax returns error 2013 "tool result's tool id(...) not found" for
+            # tool_call_id mismatches — the keywords below are intentionally broad to catch
+            # both standard (Claude/OpenAI) and provider-specific (MiniMax) variants.
             is_message_format_error = any(keyword in error_str_lower for keyword in [
-                'tool_use', 'tool_result', 'without', 'immediately after',
-                'corresponding', 'must have', 'each'
-            ]) and 'status: 400' in error_str_lower
+                'tool_use', 'tool_result', 'tool result', 'without', 'immediately after',
+                'corresponding', 'must have', 'each',
+                'tool_call_id', 'tool id', 'is not found', 'not found', 'tool_calls',
+                'must be a response to a preceeding message',
+                '2013',  # MiniMax error code for tool_call_id mismatch
+            ]) and ('400' in error_str_lower or 'status: 400' in error_str_lower
+                     or 'invalid_request' in error_str_lower
+                     or 'invalidparameter' in error_str_lower)
             
             if is_context_overflow or is_message_format_error:
                 error_type = "context overflow" if is_context_overflow else "message format error"
                 logger.error(f"💥 {error_type} detected: {e}")
 
+                # Flush memory before trimming to preserve context that will be lost
+                if is_context_overflow and self.agent.memory_manager:
+                    user_id = getattr(self.agent, '_current_user_id', None)
+                    self.agent.memory_manager.flush_memory(
+                        messages=self.messages, user_id=user_id,
+                        reason="overflow", max_messages=0
+                    )
+
                 # Strategy: try aggressive trimming first, only clear as last resort
                 if is_context_overflow and not _overflow_retry:
                     trimmed = self._aggressive_trim_for_overflow()
@@ -654,9 +758,10 @@ class AgentStreamExecutor:
                         )
 
                 # Aggressive trim didn't help or this is a message format error
-                # -> clear everything
+                # -> clear everything and also purge DB to prevent reload of dirty data
                 logger.warning("🔄 Clearing conversation history to recover")
                 self.messages.clear()
+                self._clear_session_db()
                 if is_context_overflow:
                     raise Exception(
                         "抱歉，对话历史过长导致上下文溢出。我已清空历史记录，请重新描述你的需求。"
@@ -693,9 +798,9 @@ class AgentStreamExecutor:
                 )
             else:
                 if retry_count >= max_retries:
-                    logger.error(f"❌ LLM API error after {max_retries} retries: {e}")
+                    logger.error(f"❌ LLM API error after {max_retries} retries: {e}", exc_info=True)
                 else:
-                    logger.error(f"❌ LLM call error (non-retryable): {e}")
+                    logger.error(f"❌ LLM call error (non-retryable): {e}", exc_info=True)
                 raise
 
         # Parse tool calls
@@ -760,7 +865,18 @@ class AgentStreamExecutor:
         # Add assistant message to history (Claude format uses content blocks)
         assistant_msg = {"role": "assistant", "content": []}
 
-        # Add text content block if present
+        if full_reasoning:
+            stored_reasoning = _truncate_reasoning_for_storage(full_reasoning)
+            if len(stored_reasoning) < len(full_reasoning):
+                logger.info(
+                    f"[reasoning] truncated for storage: "
+                    f"{len(full_reasoning)} -> {len(stored_reasoning)} chars"
+                )
+            assistant_msg["content"].append({
+                "type": "thinking",
+                "thinking": stored_reasoning
+            })
+
         if full_content:
             assistant_msg["content"].append({
                 "type": "text",
@@ -777,6 +893,9 @@ class AgentStreamExecutor:
                     "input": tc.get("arguments", {})
                 })
         
+        if gemini_raw_parts:
+            assistant_msg["_gemini_raw_parts"] = gemini_raw_parts
+
         # Only append if content is not empty
         if assistant_msg["content"]:
             self.messages.append(assistant_msg)
@@ -845,7 +964,7 @@ class AgentStreamExecutor:
         try:
             tool = self.tools.get(tool_name)
             if not tool:
-                raise ValueError(f"Tool '{tool_name}' not found")
+                raise ValueError(self._build_tool_not_found_message(tool_name))
 
             # Set tool context
             tool.model = self.model
@@ -899,26 +1018,50 @@ class AgentStreamExecutor:
             })
             return error_result
 
+    def _build_tool_not_found_message(self, tool_name: str) -> str:
+        """Build a helpful error message when a tool is not found.
+
+        If a skill with the same name exists in skill_manager, read its
+        SKILL.md and include the content so the LLM knows how to use it.
+        """
+        available_tools = list(self.tools.keys())
+        base_msg = f"Tool '{tool_name}' not found. Available tools: {available_tools}"
+
+        skill_manager = getattr(self.agent, 'skill_manager', None)
+        if not skill_manager:
+            return base_msg
+
+        skill_entry = skill_manager.get_skill(tool_name)
+        if not skill_entry:
+            return base_msg
+
+        skill = skill_entry.skill
+        skill_md_path = skill.file_path
+        skill_content = ""
+        try:
+            with open(skill_md_path, 'r', encoding='utf-8') as f:
+                skill_content = f.read()
+        except Exception:
+            skill_content = skill.description
+
+        logger.info(
+            f"[Agent] Tool '{tool_name}' not found, but matched skill '{skill.name}'. "
+            f"Guiding LLM to use the skill instead."
+        )
+
+        return (
+            f"Tool '{tool_name}' is not a built-in tool, but a matching skill "
+            f"'{skill.name}' is available. You should use existing tools (e.g. bash with curl) "
+            f"to accomplish this task following the skill instructions below:\n\n"
+            f"--- SKILL: {skill.name} (path: {skill_md_path}) ---\n"
+            f"{skill_content}\n"
+            f"--- END SKILL ---\n\n"
+            f"Available tools: {available_tools}"
+        )
+
     def _validate_and_fix_messages(self):
-        """
-        Validate message history and fix incomplete tool_use/tool_result pairs.
-        Claude API requires each tool_use to have a corresponding tool_result immediately after.
-        """
-        if not self.messages:
-            return
-        
-        # Check last message for incomplete tool_use
-        if len(self.messages) > 0:
-            last_msg = self.messages[-1]
-            if last_msg.get("role") == "assistant":
-                # Check if assistant message has tool_use blocks
-                content = last_msg.get("content", [])
-                if isinstance(content, list):
-                    has_tool_use = any(block.get("type") == "tool_use" for block in content)
-                    if has_tool_use:
-                        # This is incomplete - remove it
-                        logger.warning(f"⚠️ Removing incomplete tool_use message from history")
-                        self.messages.pop()
+        """Delegate to the shared sanitizer (see message_sanitizer.py)."""
+        sanitize_claude_messages(self.messages)
 
     def _identify_complete_turns(self) -> List[Dict]:
         """
@@ -941,24 +1084,30 @@ class AgentStreamExecutor:
             content = msg.get('content', [])
             
             if role == 'user':
-                # 检查是否是用户查询（不是工具结果）
+                # Determine if this is a real user query (not a tool_result injection
+                # or an internal hint message injected by the agent loop).
                 is_user_query = False
+                has_tool_result = False
                 if isinstance(content, list):
-                    is_user_query = any(
-                        block.get('type') == 'text' 
-                        for block in content 
-                        if isinstance(block, dict)
+                    has_text = any(
+                        isinstance(block, dict) and block.get('type') == 'text'
+                        for block in content
                     )
+                    has_tool_result = any(
+                        isinstance(block, dict) and block.get('type') == 'tool_result'
+                        for block in content
+                    )
+                    # A message with tool_result is always internal, even if it
+                    # also contains text blocks (shouldn't happen, but be safe).
+                    is_user_query = has_text and not has_tool_result
                 elif isinstance(content, str):
                     is_user_query = True
                 
                 if is_user_query:
-                    # 开始新轮次
                     if current_turn['messages']:
                         turns.append(current_turn)
                     current_turn = {'messages': [msg]}
                 else:
-                    # 工具结果，属于当前轮次
                     current_turn['messages'].append(msg)
             else:
                 # AI 回复，属于当前轮次
@@ -1131,6 +1280,56 @@ class AgentStreamExecutor:
         logger.warning("🔧 Aggressive trim: nothing to trim, will clear history")
         return False
 
+    def _build_context_summary_callback(self, discarded_turns: list, kept_turns: list):
+        """
+        Build a callback that injects an LLM summary into the first user
+        message of *kept_turns*. Returns None if no valid injection target.
+
+        The callback is passed to flush_from_messages so that the same LLM
+        call that writes daily memory also provides the in-context summary.
+        """
+        if not kept_turns:
+            return None
+
+        # Find the first user text block in kept_turns as injection target
+        target_block = None
+        for turn in kept_turns:
+            for msg in turn["messages"]:
+                if msg.get("role") == "user":
+                    content = msg.get("content", [])
+                    if isinstance(content, list):
+                        for block in content:
+                            if isinstance(block, dict) and block.get("type") == "text":
+                                target_block = block
+                                break
+                    if target_block:
+                        break
+            if target_block:
+                break
+
+        if not target_block:
+            return None
+
+        turn_count = len(discarded_turns)
+        original_text = target_block["text"]
+
+        def _on_summary_ready(summary: str):
+            if not summary or not summary.strip():
+                return
+            target_block["text"] = (
+                f"[System: Previous conversation summary — "
+                f"{turn_count} turns were compacted]\n\n"
+                f"{summary.strip()}\n\n"
+                f"The recent conversation continues below.\n\n---\n\n"
+                f"{original_text}"
+            )
+            logger.info(
+                f"📝 Context summary injected "
+                f"({len(summary)} chars, {turn_count} turns)"
+            )
+
+        return _on_summary_ready
+
     def _trim_messages(self):
         """
         智能清理消息历史，保持对话完整性
@@ -1152,16 +1351,33 @@ class AgentStreamExecutor:
         if not turns:
             return
         
-        # Step 2: 轮次限制 - 保留最近 N 轮
+        # Step 2: 轮次限制 - 超出时移除前一半，保留后一半
         if len(turns) > self.max_context_turns:
-            removed_turns = len(turns) - self.max_context_turns
-            turns = turns[-self.max_context_turns:]  # 保留最近的轮次
+            removed_count = len(turns) // 2
+            keep_count = len(turns) - removed_count
             
+            discarded_turns = turns[:removed_count]
+            turns = turns[-keep_count:]
+
             logger.info(
-                f"💾 上下文轮次超限: {len(turns) + removed_turns} > {self.max_context_turns}，"
-                f"移除最早的 {removed_turns} 轮完整对话"
+                f"💾 上下文轮次超限: {keep_count + removed_count} > {self.max_context_turns}，"
+                f"裁剪至 {keep_count} 轮（移除 {removed_count} 轮）"
             )
 
+            # Flush to daily memory + inject context summary (single async LLM call)
+            if self.agent.memory_manager:
+                discarded_messages = []
+                for turn in discarded_turns:
+                    discarded_messages.extend(turn["messages"])
+                if discarded_messages:
+                    user_id = getattr(self.agent, '_current_user_id', None)
+                    cb = self._build_context_summary_callback(discarded_turns, turns)
+                    self.agent.memory_manager.flush_memory(
+                        messages=discarded_messages, user_id=user_id,
+                        reason="trim", max_messages=0,
+                        context_summary_callback=cb,
+                    )
+
         # Step 3: Token 限制 - 保留完整轮次
         # Get context window from agent (based on model)
         context_window = self.agent._get_model_context_window()
@@ -1196,56 +1412,99 @@ class AgentStreamExecutor:
                 logger.info(f"   重建消息列表: {old_count} -> {len(self.messages)} 条消息")
             return
 
-        # Token limit exceeded - keep complete turns from newest
+        # Token limit exceeded — tiered strategy based on turn count:
+        #
+        #   Few turns (<5):  Compress ALL turns to text-only (strip tool chains,
+        #                    keep user query + final reply).  Never discard turns
+        #                    — losing even one is too painful when context is thin.
+        #
+        #   Many turns (>=5): Directly discard the first half of turns.
+        #                     With enough turns the oldest ones are less
+        #                     critical, and keeping the recent half intact
+        #                     (with full tool chains) is more useful.
+
+        COMPRESS_THRESHOLD = 5
+
+        if len(turns) < COMPRESS_THRESHOLD:
+            # --- Few turns: compress ALL turns to text-only, never discard ---
+            compressed_turns = []
+            for t in turns:
+                compressed = compress_turn_to_text_only(t)
+                if compressed["messages"]:
+                    compressed_turns.append(compressed)
+
+            new_messages = []
+            for turn in compressed_turns:
+                new_messages.extend(turn["messages"])
+
+            new_tokens = sum(self._estimate_turn_tokens(t) for t in compressed_turns)
+            old_count = len(self.messages)
+            self.messages = new_messages
+
+            logger.info(
+                f"📦 上下文tokens超限(轮次<{COMPRESS_THRESHOLD}): "
+                f"~{current_tokens + system_tokens} > {max_tokens}，"
+                f"压缩全部 {len(turns)} 轮为纯文本 "
+                f"({old_count} -> {len(self.messages)} 条消息，"
+                f"~{current_tokens + system_tokens} -> ~{new_tokens + system_tokens} tokens)"
+            )
+            return
+
+        # --- Many turns (>=5): discard the older half, keep the newer half ---
+        removed_count = len(turns) // 2
+        keep_count = len(turns) - removed_count
+        discarded_turns = turns[:removed_count]
+        kept_turns = turns[-keep_count:]
+        kept_tokens = sum(self._estimate_turn_tokens(t) for t in kept_turns)
+
         logger.info(
             f"🔄 上下文tokens超限: ~{current_tokens + system_tokens} > {max_tokens}，"
-            f"将按完整轮次移除最早的对话"
+            f"裁剪至 {keep_count} 轮（移除 {removed_count} 轮）"
         )
 
-        # 从最新轮次开始，反向累加（保持完整轮次）
-        kept_turns = []
-        accumulated_tokens = 0
-        min_turns = 3  # 尽量保留至少 3 轮，但不强制（避免超出 token 限制）
-        
-        for i, turn in enumerate(reversed(turns)):
-            turn_tokens = self._estimate_turn_tokens(turn)
-            turns_from_end = i + 1
-            
-            # 检查是否超出限制
-            if accumulated_tokens + turn_tokens <= available_tokens:
-                kept_turns.insert(0, turn)
-                accumulated_tokens += turn_tokens
-            else:
-                # 超出限制
-                # 如果还没有保留足够的轮次，且这是最后的机会，尝试保留
-                if len(kept_turns) < min_turns and turns_from_end <= min_turns:
-                    # 检查是否严重超出（超出 20% 以上则放弃）
-                    overflow_ratio = (accumulated_tokens + turn_tokens - available_tokens) / available_tokens
-                    if overflow_ratio < 0.2:  # 允许最多超出 20%
-                        kept_turns.insert(0, turn)
-                        accumulated_tokens += turn_tokens
-                        logger.debug(f"   为保留最少轮次，允许超出 {overflow_ratio*100:.1f}%")
-                        continue
-                # 停止保留更早的轮次
-                break
-        
-        # 重建消息列表
+        if self.agent.memory_manager:
+            discarded_messages = []
+            for turn in discarded_turns:
+                discarded_messages.extend(turn["messages"])
+            if discarded_messages:
+                user_id = getattr(self.agent, '_current_user_id', None)
+                cb = self._build_context_summary_callback(discarded_turns, kept_turns)
+                self.agent.memory_manager.flush_memory(
+                    messages=discarded_messages, user_id=user_id,
+                    reason="trim", max_messages=0,
+                    context_summary_callback=cb,
+                )
+
         new_messages = []
         for turn in kept_turns:
             new_messages.extend(turn['messages'])
-        
+
         old_count = len(self.messages)
-        old_turn_count = len(turns)
         self.messages = new_messages
-        new_count = len(self.messages)
-        new_turn_count = len(kept_turns)
-        
-        if old_count > new_count:
-            logger.info(
-                f"   移除了 {old_turn_count - new_turn_count} 轮对话 "
-                f"({old_count} -> {new_count} 条消息，"
-                f"~{current_tokens + system_tokens} -> ~{accumulated_tokens + system_tokens} tokens)"
-            )
+
+        logger.info(
+            f"   移除了 {removed_count} 轮对话 "
+            f"({old_count} -> {len(self.messages)} 条消息，"
+            f"~{current_tokens + system_tokens} -> ~{kept_tokens + system_tokens} tokens)"
+        )
+
+    def _clear_session_db(self):
+        """
+        Clear the current session's persisted messages from SQLite DB.
+
+        This prevents dirty data (broken tool_use/tool_result pairs) from being
+        reloaded on the next request or after a restart.
+        """
+        try:
+            session_id = getattr(self.agent, '_current_session_id', None)
+            if not session_id:
+                return
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            store.clear_session(session_id)
+            logger.info(f"🗑️ Cleared dirty session data from DB: {session_id}")
+        except Exception as e:
+            logger.warning(f"Failed to clear session DB: {e}")
 
     def _prepare_messages(self) -> List[Dict[str, Any]]:
         """

agent/protocol/message_utils.py

@@ -0,0 +1,335 @@
+"""
+Message sanitizer — fix broken tool_use / tool_result pairs.
+
+Provides two public helpers that can be reused across agent_stream.py
+and any bot that converts messages to OpenAI format:
+
+1. sanitize_claude_messages(messages)
+   Operates on the internal Claude-format message list (in-place).
+
+2. drop_orphaned_tool_results_openai(messages)
+   Operates on an already-converted OpenAI-format message list,
+   returning a cleaned copy.
+"""
+
+from __future__ import annotations
+
+from typing import Dict, List, Set
+
+from common.log import logger
+
+_SYNTH_TOOL_ERR = (
+    "Error: Missing tool_result adjacent to tool_use (session repair). "
+    "The conversation history was inconsistent; continue from here."
+)
+
+
+def _repair_tool_use_adjacency(messages: List[Dict]) -> int:
+    """
+    Anthropic requires: after assistant content with tool_use, the next message
+    must be user content listing tool_result for every tool_use id (same user msg).
+
+    Valid histories satisfy this at every such assistant; the loop only mutates
+    when that condition fails (broken persistence, bad trims, etc.).
+    """
+
+    def _synth_block(tid: str) -> Dict:
+        return {
+            "type": "tool_result",
+            "tool_use_id": tid,
+            "content": _SYNTH_TOOL_ERR,
+            "is_error": True,
+        }
+
+    repairs = 0
+    i = 0
+    while i < len(messages):
+        msg = messages[i]
+        if msg.get("role") != "assistant":
+            i += 1
+            continue
+
+        content = msg.get("content", [])
+        if not isinstance(content, list):
+            i += 1
+            continue
+
+        required = [
+            b.get("id")
+            for b in content
+            if isinstance(b, dict) and b.get("type") == "tool_use" and b.get("id")
+        ]
+        if not required:
+            i += 1
+            continue
+
+        req_set = set(required)
+        if i + 1 >= len(messages):
+            messages.append({
+                "role": "user",
+                "content": [_synth_block(tid) for tid in required],
+            })
+            logger.warning(
+                "⚠️ Appended synthetic tool_result after trailing assistant tool_use"
+            )
+            repairs += 1
+            break
+
+        nxt = messages[i + 1]
+        if nxt.get("role") != "user":
+            messages.insert(
+                i + 1,
+                {"role": "user", "content": [_synth_block(tid) for tid in required]},
+            )
+            logger.warning(
+                "⚠️ Inserted synthetic tool_result user after tool_use "
+                f"(next role={nxt.get('role')!r})"
+            )
+            repairs += 1
+            i += 2
+            continue
+
+        nc = nxt.get("content", [])
+        if not isinstance(nc, list):
+            messages.insert(
+                i + 1,
+                {"role": "user", "content": [_synth_block(tid) for tid in required]},
+            )
+            repairs += 1
+            i += 2
+            continue
+
+        present = {
+            b.get("tool_use_id")
+            for b in nc
+            if isinstance(b, dict) and b.get("type") == "tool_result" and b.get("tool_use_id")
+        }
+        if req_set <= present:
+            i += 1
+            continue
+
+        missing = [tid for tid in required if tid not in present]
+        nxt["content"] = [_synth_block(tid) for tid in missing] + nc
+        logger.warning(
+            "⚠️ Prepended synthetic tool_result for Anthropic adjacency "
+            f"(missing_ids={missing})"
+        )
+        repairs += len(missing)
+        i += 1
+
+    return repairs
+
+
+# ------------------------------------------------------------------ #
+# Claude-format sanitizer (used by agent_stream)
+# ------------------------------------------------------------------ #
+
+def sanitize_claude_messages(messages: List[Dict]) -> int:
+    """
+    Validate and fix a Claude-format message list **in-place**.
+
+    Fixes handled:
+    - Anthropic adjacency: assistant tool_use must be immediately followed by
+      user message(s) containing matching tool_result blocks
+    - Leading orphaned tool_result user messages
+    - Mid-list tool_result blocks whose tool_use_id has no matching
+      tool_use in any preceding assistant message
+
+    Returns: number of removals plus adjacency repair operations (inserts/prepends).
+    """
+    if not messages:
+        return 0
+
+    removed = 0
+
+    # 1. Adjacency repair (Anthropic: tool_result must be in the next user message)
+    adj_repairs = _repair_tool_use_adjacency(messages)
+
+    # 2. Remove leading orphaned tool_result user messages
+    while messages:
+        first = messages[0]
+        if first.get("role") != "user":
+            break
+        content = first.get("content", [])
+        if isinstance(content, list) and _has_block_type(content, "tool_result") \
+                and not _has_block_type(content, "text"):
+            logger.warning("⚠️ Removing leading orphaned tool_result user message")
+            messages.pop(0)
+            removed += 1
+        else:
+            break
+
+    # 3. Iteratively remove unmatched tool_use / tool_result until stable.
+    #    Removing one broken message can orphan others (e.g. an assistant msg
+    #    with both matched and unmatched tool_use — deleting it orphans the
+    #    previously-matched tool_result).  Loop until clean.
+    for _ in range(5):
+        use_ids: Set[str] = set()
+        result_ids: Set[str] = set()
+        for msg in messages:
+            for block in (msg.get("content") or []):
+                if not isinstance(block, dict):
+                    continue
+                if block.get("type") == "tool_use" and block.get("id"):
+                    use_ids.add(block["id"])
+                elif block.get("type") == "tool_result" and block.get("tool_use_id"):
+                    result_ids.add(block["tool_use_id"])
+
+        bad_use = use_ids - result_ids
+        bad_result = result_ids - use_ids
+        if not bad_use and not bad_result:
+            break
+
+        pass_removed = 0
+        i = 0
+        while i < len(messages):
+            msg = messages[i]
+            role = msg.get("role")
+            content = msg.get("content", [])
+            if not isinstance(content, list):
+                i += 1
+                continue
+
+            if role == "assistant" and bad_use and any(
+                isinstance(b, dict) and b.get("type") == "tool_use"
+                and b.get("id") in bad_use for b in content
+            ):
+                logger.warning(f"⚠️ Removing assistant msg with unmatched tool_use")
+                messages.pop(i)
+                pass_removed += 1
+                continue
+
+            if role == "user" and bad_result and _has_block_type(content, "tool_result"):
+                has_bad = any(
+                    isinstance(b, dict) and b.get("type") == "tool_result"
+                    and b.get("tool_use_id") in bad_result for b in content
+                )
+                if has_bad:
+                    if not _has_block_type(content, "text"):
+                        logger.warning(f"⚠️ Removing user msg with unmatched tool_result")
+                        messages.pop(i)
+                        pass_removed += 1
+                        continue
+                    else:
+                        before = len(content)
+                        msg["content"] = [
+                            b for b in content
+                            if not (isinstance(b, dict) and b.get("type") == "tool_result"
+                                    and b.get("tool_use_id") in bad_result)
+                        ]
+                        pass_removed += before - len(msg["content"])
+
+            i += 1
+
+        removed += pass_removed
+        if pass_removed == 0:
+            break
+
+    # 4. Removals above can break adjacency; re-run repair only if something was removed.
+    if removed:
+        adj_repairs += _repair_tool_use_adjacency(messages)
+
+    if removed:
+        logger.info(f"🔧 Message validation: removed {removed} broken message(s)")
+    if adj_repairs:
+        logger.info(f"🔧 Message validation: adjacency repairs={adj_repairs}")
+    return removed + adj_repairs
+
+
+# ------------------------------------------------------------------ #
+# OpenAI-format sanitizer (used by minimax_bot, openai_compatible_bot)
+# ------------------------------------------------------------------ #
+
+def drop_orphaned_tool_results_openai(messages: List[Dict]) -> List[Dict]:
+    """
+    Return a copy of *messages* (OpenAI format) with any ``role=tool``
+    messages removed if their ``tool_call_id`` does not match a
+    ``tool_calls[].id`` in a preceding assistant message.
+    """
+    known_ids: Set[str] = set()
+    cleaned: List[Dict] = []
+    for msg in messages:
+        if msg.get("role") == "assistant" and msg.get("tool_calls"):
+            for tc in msg["tool_calls"]:
+                tc_id = tc.get("id", "")
+                if tc_id:
+                    known_ids.add(tc_id)
+
+        if msg.get("role") == "tool":
+            ref_id = msg.get("tool_call_id", "")
+            if ref_id and ref_id not in known_ids:
+                logger.warning(
+                    f"[MessageSanitizer] Dropping orphaned tool result "
+                    f"(tool_call_id={ref_id} not in known ids)"
+                )
+                continue
+        cleaned.append(msg)
+    return cleaned
+
+
+# ------------------------------------------------------------------ #
+# Internal helpers
+# ------------------------------------------------------------------ #
+
+def _has_block_type(content: list, block_type: str) -> bool:
+    return any(
+        isinstance(b, dict) and b.get("type") == block_type
+        for b in content
+    )
+
+
+def _extract_text_from_content(content) -> str:
+    """Extract plain text from a message content field (str or list of blocks)."""
+    if isinstance(content, str):
+        return content.strip()
+    if isinstance(content, list):
+        parts = [
+            b.get("text", "")
+            for b in content
+            if isinstance(b, dict) and b.get("type") == "text"
+        ]
+        return "\n".join(p for p in parts if p).strip()
+    return ""
+
+
+def compress_turn_to_text_only(turn: Dict) -> Dict:
+    """
+    Compress a full turn (with tool_use/tool_result chains) into a lightweight
+    text-only turn that keeps only the first user text and the last assistant text.
+
+    This preserves the conversational context (what the user asked and what the
+    agent concluded) while stripping out the bulky intermediate tool interactions.
+
+    Returns a new turn dict with a ``messages`` list; the original is not mutated.
+    """
+    user_text = ""
+    last_assistant_text = ""
+
+    for msg in turn["messages"]:
+        role = msg.get("role")
+        content = msg.get("content", [])
+
+        if role == "user":
+            if isinstance(content, list) and _has_block_type(content, "tool_result"):
+                continue
+            if not user_text:
+                user_text = _extract_text_from_content(content)
+
+        elif role == "assistant":
+            text = _extract_text_from_content(content)
+            if text:
+                last_assistant_text = text
+
+    compressed_messages = []
+    if user_text:
+        compressed_messages.append({
+            "role": "user",
+            "content": [{"type": "text", "text": user_text}]
+        })
+    if last_assistant_text:
+        compressed_messages.append({
+            "role": "assistant",
+            "content": [{"type": "text", "text": last_assistant_text}]
+        })
+
+    return {"messages": compressed_messages}

agent/skills/__init__.py

@@ -15,6 +15,7 @@ from agent.skills.types import (
 )
 from agent.skills.loader import SkillLoader
 from agent.skills.manager import SkillManager
+from agent.skills.service import SkillService
 from agent.skills.formatter import format_skills_for_prompt
 
 __all__ = [
@@ -25,5 +26,6 @@ __all__ = [
     "LoadSkillsResult",
     "SkillLoader",
     "SkillManager",
+    "SkillService",
     "format_skills_for_prompt",
 ]

agent/skills/config.py

@@ -123,17 +123,63 @@ def should_include_skill(
                 return False
         
         # Check environment variables (API keys)
-        # Simple rule: All required env vars must be set
+        # All required env vars must be set
         required_env = metadata.requires.get('env', [])
         if required_env:
             for env_name in required_env:
                 if not has_env_var(env_name):
-                    # Missing required API key → disable skill
                     return False
+
+        # Check anyEnv (at least one must be present)
+        any_env = metadata.requires.get('anyEnv', [])
+        if any_env:
+            if not any(has_env_var(e) for e in any_env):
+                return False
     
     return True
 
 
+def get_missing_requirements(
+    entry: SkillEntry,
+    current_platform: Optional[str] = None,
+) -> Dict[str, List[str]]:
+    """
+    Return a dict of missing requirements for a skill.
+    Empty dict means all requirements are met.
+
+    :param entry: SkillEntry to check
+    :param current_platform: Current platform (default: auto-detect)
+    :return: Dict like {"bins": ["curl"], "env": ["API_KEY"]}
+    """
+    missing: Dict[str, List[str]] = {}
+    metadata = entry.metadata
+
+    if not metadata or not metadata.requires:
+        return missing
+
+    required_bins = metadata.requires.get('bins', [])
+    if required_bins:
+        missing_bins = [b for b in required_bins if not has_binary(b)]
+        if missing_bins:
+            missing['bins'] = missing_bins
+
+    any_bins = metadata.requires.get('anyBins', [])
+    if any_bins and not has_any_binary(any_bins):
+        missing['anyBins'] = any_bins
+
+    required_env = metadata.requires.get('env', [])
+    if required_env:
+        missing_env = [e for e in required_env if not has_env_var(e)]
+        if missing_env:
+            missing['env'] = missing_env
+
+    any_env = metadata.requires.get('anyEnv', [])
+    if any_env and not any(has_env_var(e) for e in any_env):
+        missing['anyEnv'] = any_env
+
+    return missing
+
+
 def is_config_path_truthy(config: Dict, path: str) -> bool:
     """
     Check if a config path resolves to a truthy value.

agent/skills/formatter.py

@@ -2,7 +2,7 @@
 Skill formatter for generating prompts from skills.
 """
 
-from typing import List
+from typing import Dict, List
 from agent.skills.types import Skill, SkillEntry
 
 
@@ -32,6 +32,7 @@ def format_skills_for_prompt(skills: List[Skill]) -> str:
         lines.append(f"    <name>{_escape_xml(skill.name)}</name>")
         lines.append(f"    <description>{_escape_xml(skill.description)}</description>")
         lines.append(f"    <location>{_escape_xml(skill.file_path)}</location>")
+        lines.append(f"    <base_dir>{_escape_xml(skill.base_dir)}</base_dir>")
         lines.append("  </skill>")
     
     lines.append("</available_skills>")
@@ -50,6 +51,71 @@ def format_skill_entries_for_prompt(entries: List[SkillEntry]) -> str:
     return format_skills_for_prompt(skills)
 
 
+def format_unavailable_skills_for_prompt(
+    entries: List[SkillEntry],
+    missing_map: Dict[str, Dict[str, List[str]]],
+) -> str:
+    """
+    Format unavailable (requires-not-met) skills as brief setup hints
+    so the AI can guide users to configure them.
+
+    :param entries: List of unavailable skill entries
+    :param missing_map: Dict mapping skill name to its missing requirements
+    :return: Formatted prompt text
+    """
+    if not entries:
+        return ""
+
+    lines = [
+        "",
+        "<unavailable_skills>",
+        "The following skills are installed but not yet ready. "
+        "Guide the user to complete the setup when relevant.",
+    ]
+
+    for entry in entries:
+        skill = entry.skill
+        missing = missing_map.get(skill.name, {})
+
+        missing_parts = []
+        for key, values in missing.items():
+            missing_parts.append(f"{key}: {', '.join(values)}")
+        missing_str = "; ".join(missing_parts) if missing_parts else "unknown"
+
+        setup_hint = _extract_setup_hint(skill)
+
+        lines.append("  <skill>")
+        lines.append(f"    <name>{_escape_xml(skill.name)}</name>")
+        lines.append(f"    <description>{_escape_xml(skill.description)}</description>")
+        lines.append(f"    <missing>{_escape_xml(missing_str)}</missing>")
+        if setup_hint:
+            lines.append(f"    <setup>{_escape_xml(setup_hint)}</setup>")
+        lines.append("  </skill>")
+
+    lines.append("</unavailable_skills>")
+    return "\n".join(lines)
+
+
+def _extract_setup_hint(skill: Skill) -> str:
+    """
+    Extract the Setup section from SKILL.md content as a brief hint.
+    Returns the first few lines of the ## Setup section.
+    """
+    content = skill.content
+    if not content:
+        return ""
+
+    import re
+    match = re.search(r'^##\s+Setup\s*\n(.*?)(?=\n##\s|\Z)', content, re.MULTILINE | re.DOTALL)
+    if not match:
+        return ""
+
+    setup_text = match.group(1).strip()
+    lines = setup_text.split('\n')
+    hint_lines = [l.strip() for l in lines[:6] if l.strip()]
+    return ' '.join(hint_lines)[:300]
+
+
 def _escape_xml(text: str) -> str:
     """Escape XML special characters."""
     return (text

agent/skills/frontmatter.py

@@ -87,8 +87,8 @@ def parse_metadata(frontmatter: Dict[str, Any]) -> Optional[SkillMetadata]:
     if not isinstance(metadata_raw, dict):
         return None
     
-    # Use metadata_raw directly (COW format)
-    meta_obj = metadata_raw
+    # Unwrap nested namespace (e.g. {"openclaw": {...}} or {"cowagent": {...}})
+    meta_obj = _unwrap_metadata_namespace(metadata_raw)
     
     # Parse install specs
     install_specs = []
@@ -128,6 +128,7 @@ def parse_metadata(frontmatter: Dict[str, Any]) -> Optional[SkillMetadata]:
     
     return SkillMetadata(
         always=meta_obj.get('always', False),
+        default_enabled=meta_obj.get('default_enabled', True),
         skill_key=meta_obj.get('skillKey'),
         primary_env=meta_obj.get('primaryEnv'),
         emoji=meta_obj.get('emoji'),
@@ -138,6 +139,25 @@ def parse_metadata(frontmatter: Dict[str, Any]) -> Optional[SkillMetadata]:
     )
 
 
+_KNOWN_METADATA_NAMESPACES = {"cowagent", "openclaw"}
+
+
+def _unwrap_metadata_namespace(metadata_raw: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Unwrap a single-key namespace wrapper like {"cowagent": {...} or {"openclaw": {...}}}.
+    If the top-level dict has exactly one key matching a known namespace, return the inner dict.
+    Otherwise return the original dict unchanged.
+    """
+    keys = set(metadata_raw.keys())
+    ns_keys = keys & _KNOWN_METADATA_NAMESPACES
+    if len(ns_keys) == 1 and len(keys) == 1:
+        ns = ns_keys.pop()
+        inner = metadata_raw[ns]
+        if isinstance(inner, dict):
+            return inner
+    return metadata_raw
+
+
 def _normalize_string_list(value: Any) -> List[str]:
     """Normalize a value to a list of strings."""
     if not value:

agent/skills/loader.py

@@ -12,25 +12,20 @@ from agent.skills.frontmatter import parse_frontmatter, parse_metadata, parse_bo
 
 class SkillLoader:
     """Loads skills from various directories."""
-    
-    def __init__(self, workspace_dir: Optional[str] = None):
-        """
-        Initialize the skill loader.
-        
-        :param workspace_dir: Agent workspace directory (for workspace-specific skills)
-        """
-        self.workspace_dir = workspace_dir
+
+    def __init__(self):
+        pass
     
     def load_skills_from_dir(self, dir_path: str, source: str) -> LoadSkillsResult:
         """
         Load skills from a directory.
-        
+
         Discovery rules:
         - Direct .md files in the root directory
         - Recursive SKILL.md files under subdirectories
-        
+
         :param dir_path: Directory path to scan
-        :param source: Source identifier (e.g., 'managed', 'workspace', 'bundled')
+        :param source: Source identifier ('builtin' or 'custom')
         :return: LoadSkillsResult with skills and diagnostics
         """
         skills = []
@@ -58,6 +53,12 @@ class SkillLoader:
         """
         Recursively load skills from a directory.
         
+        If a subdirectory contains its own SKILL.md, it is treated as a
+        self-contained skill (or skill-collection) and its children are
+        NOT scanned further. This prevents sub-skills inside a collection
+        (e.g. style-collection/style-anjing) from being listed as
+        independent top-level skills.
+        
         :param dir_path: Directory to scan
         :param source: Source identifier
         :param include_root_files: Whether to include root-level .md files
@@ -71,38 +72,41 @@ class SkillLoader:
         except Exception as e:
             diagnostics.append(f"Failed to list directory {dir_path}: {e}")
             return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+
+        # If this directory has its own SKILL.md, load it and stop recursing.
+        # The sub-directories are internal resources of this skill.
+        if not include_root_files and 'SKILL.md' in entries:
+            skill_md_path = os.path.join(dir_path, 'SKILL.md')
+            if os.path.isfile(skill_md_path):
+                skill_result = self._load_skill_from_file(skill_md_path, source)
+                if skill_result.skills:
+                    skills.extend(skill_result.skills)
+                diagnostics.extend(skill_result.diagnostics)
+                return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
         
         for entry in entries:
-            # Skip hidden files and directories
             if entry.startswith('.'):
                 continue
             
-            # Skip common non-skill directories
             if entry in ('node_modules', '__pycache__', 'venv', '.git'):
                 continue
             
             full_path = os.path.join(dir_path, entry)
             
-            # Handle directories
             if os.path.isdir(full_path):
-                # Recursively scan subdirectories
                 sub_result = self._load_skills_recursive(full_path, source, include_root_files=False)
                 skills.extend(sub_result.skills)
                 diagnostics.extend(sub_result.diagnostics)
                 continue
             
-            # Handle files
             if not os.path.isfile(full_path):
                 continue
             
-            # Check if this is a skill file
-            is_root_md = include_root_files and entry.endswith('.md')
-            is_skill_md = not include_root_files and entry == 'SKILL.md'
+            is_root_md = include_root_files and entry.endswith('.md') and entry.upper() != 'README.MD'
             
-            if not (is_root_md or is_skill_md):
+            if not is_root_md:
                 continue
             
-            # Load the skill
             skill_result = self._load_skill_from_file(full_path, source)
             if skill_result.skills:
                 skills.extend(skill_result.skills)
@@ -189,7 +193,6 @@ class SkillLoader:
         
         config_path = os.path.join(skill_dir, "config.json")
         
-        # Without config.json, skip this skill entirely (return empty to trigger exclusion)
         if not os.path.exists(config_path):
             logger.debug(f"[SkillLoader] linkai-agent skipped: no config.json found")
             return ""
@@ -216,61 +219,49 @@ class SkillLoader:
     
     def load_all_skills(
         self,
-        managed_dir: Optional[str] = None,
-        workspace_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
     ) -> Dict[str, SkillEntry]:
         """
-        Load skills from all configured locations with precedence.
-        
+        Load skills from builtin and custom directories.
+
         Precedence (lowest to highest):
-        1. Extra directories
-        2. Managed skills directory
-        3. Workspace skills directory
-        
-        :param managed_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param workspace_skills_dir: Workspace skills directory (e.g., workspace/skills)
-        :param extra_dirs: Additional directories to load skills from
+        1. builtin  — project root ``skills/``, shipped with the codebase
+        2. custom   — workspace ``skills/``, installed via cloud console or skill creator
+
+        Same-name custom skills override builtin ones.
+
+        :param builtin_dir: Built-in skills directory
+        :param custom_dir: Custom skills directory
         :return: Dictionary mapping skill name to SkillEntry
         """
         skill_map: Dict[str, SkillEntry] = {}
         all_diagnostics = []
-        
-        # Load from extra directories (lowest precedence)
-        if extra_dirs:
-            for extra_dir in extra_dirs:
-                if not os.path.exists(extra_dir):
-                    continue
-                result = self.load_skills_from_dir(extra_dir, source='extra')
-                all_diagnostics.extend(result.diagnostics)
-                for skill in result.skills:
-                    entry = self._create_skill_entry(skill)
-                    skill_map[skill.name] = entry
-        
-        # Load from managed directory
-        if managed_dir and os.path.exists(managed_dir):
-            result = self.load_skills_from_dir(managed_dir, source='managed')
+
+        # Load builtin skills (lower precedence)
+        if builtin_dir and os.path.exists(builtin_dir):
+            result = self.load_skills_from_dir(builtin_dir, source='builtin')
             all_diagnostics.extend(result.diagnostics)
             for skill in result.skills:
                 entry = self._create_skill_entry(skill)
                 skill_map[skill.name] = entry
-        
-        # Load from workspace directory (highest precedence)
-        if workspace_skills_dir and os.path.exists(workspace_skills_dir):
-            result = self.load_skills_from_dir(workspace_skills_dir, source='workspace')
+
+        # Load custom skills (higher precedence, overrides builtin)
+        if custom_dir and os.path.exists(custom_dir):
+            result = self.load_skills_from_dir(custom_dir, source='custom')
             all_diagnostics.extend(result.diagnostics)
             for skill in result.skills:
                 entry = self._create_skill_entry(skill)
                 skill_map[skill.name] = entry
-        
+
         # Log diagnostics
         if all_diagnostics:
             logger.debug(f"Skill loading diagnostics: {len(all_diagnostics)} issues")
-            for diag in all_diagnostics[:5]:  # Log first 5
+            for diag in all_diagnostics[:5]:
                 logger.debug(f"  - {diag}")
-        
-        logger.debug(f"Loaded {len(skill_map)} skills from all sources")
-        
+
+        logger.debug(f"Loaded {len(skill_map)} skills total")
+
         return skill_map
     
     def _create_skill_entry(self, skill: Skill) -> SkillEntry:

agent/skills/manager.py

@@ -3,6 +3,7 @@ Skill manager for managing skill lifecycle and operations.
 """
 
 import os
+import json
 from typing import Dict, List, Optional
 from pathlib import Path
 from common.log import logger
@@ -10,56 +11,143 @@ from agent.skills.types import Skill, SkillEntry, SkillSnapshot
 from agent.skills.loader import SkillLoader
 from agent.skills.formatter import format_skill_entries_for_prompt
 
+SKILLS_CONFIG_FILE = "skills_config.json"
+
 
 class SkillManager:
     """Manages skills for an agent."""
-    
+
     def __init__(
         self,
-        workspace_dir: Optional[str] = None,
-        managed_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
         config: Optional[Dict] = None,
     ):
         """
         Initialize the skill manager.
-        
-        :param workspace_dir: Agent workspace directory
-        :param managed_skills_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param extra_dirs: Additional skill directories
+
+        :param builtin_dir: Built-in skills directory (project root ``skills/``)
+        :param custom_dir: Custom skills directory (workspace ``skills/``)
         :param config: Configuration dictionary
         """
-        self.workspace_dir = workspace_dir
-        self.managed_skills_dir = managed_skills_dir or self._get_default_managed_dir()
-        self.extra_dirs = extra_dirs or []
+        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        self.builtin_dir = builtin_dir or os.path.join(project_root, 'skills')
+        self.custom_dir = custom_dir or os.path.join(project_root, 'workspace', 'skills')
         self.config = config or {}
-        
-        self.loader = SkillLoader(workspace_dir=workspace_dir)
+        self._skills_config_path = os.path.join(self.custom_dir, SKILLS_CONFIG_FILE)
+
+        # skills_config: full skill metadata keyed by name
+        # { "web-fetch": {"name": ..., "description": ..., "source": ..., "enabled": true}, ... }
+        self.skills_config: Dict[str, dict] = {}
+
+        self.loader = SkillLoader()
         self.skills: Dict[str, SkillEntry] = {}
-        
+
         # Load skills on initialization
         self.refresh_skills()
-    
-    def _get_default_managed_dir(self) -> str:
-        """Get the default managed skills directory."""
-        # Use project root skills directory as default
-        import os
-        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        return os.path.join(project_root, 'skills')
-    
+
     def refresh_skills(self):
-        """Reload all skills from configured directories."""
-        workspace_skills_dir = None
-        if self.workspace_dir:
-            workspace_skills_dir = os.path.join(self.workspace_dir, 'skills')
-        
+        """Reload all skills from builtin and custom directories, then sync config."""
         self.skills = self.loader.load_all_skills(
-            managed_dir=self.managed_skills_dir,
-            workspace_skills_dir=workspace_skills_dir,
-            extra_dirs=self.extra_dirs,
+            builtin_dir=self.builtin_dir,
+            custom_dir=self.custom_dir,
         )
-        
+        self._sync_skills_config()
         logger.debug(f"SkillManager: Loaded {len(self.skills)} skills")
+
+    # ------------------------------------------------------------------
+    # skills_config.json management
+    # ------------------------------------------------------------------
+    def _load_skills_config(self) -> Dict[str, dict]:
+        """Load skills_config.json from custom_dir. Returns empty dict if not found."""
+        if not os.path.exists(self._skills_config_path):
+            return {}
+        try:
+            with open(self._skills_config_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            if isinstance(data, dict):
+                return data
+        except Exception as e:
+            logger.warning(f"[SkillManager] Failed to load {SKILLS_CONFIG_FILE}: {e}")
+        return {}
+
+    def _save_skills_config(self):
+        """Persist skills_config to custom_dir/skills_config.json."""
+        os.makedirs(self.custom_dir, exist_ok=True)
+        try:
+            with open(self._skills_config_path, "w", encoding="utf-8") as f:
+                json.dump(self.skills_config, f, indent=4, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[SkillManager] Failed to save {SKILLS_CONFIG_FILE}: {e}")
+
+    def _sync_skills_config(self):
+        """
+        Merge directory-scanned skills with the persisted config file.
+
+        - New skills: use metadata.default_enabled as initial enabled state.
+        - Existing skills: preserve their persisted enabled state.
+        - Skills that no longer exist on disk are removed.
+        - name/description/source are always refreshed from the latest scan.
+        """
+        saved = self._load_skills_config()
+        merged: Dict[str, dict] = {}
+
+        for name, entry in self.skills.items():
+            skill = entry.skill
+            prev = saved.get(name, {})
+            category = prev.get("category", "skill")
+
+            if name in saved:
+                enabled = prev.get("enabled", True)
+            else:
+                enabled = entry.metadata.default_enabled if entry.metadata else True
+
+            entry_dict = {
+                "name": name,
+                "description": skill.description,
+                "source": prev.get("source") or skill.source,
+                "enabled": enabled,
+                "category": category,
+            }
+            display_name = prev.get("display_name")
+            if display_name:
+                entry_dict["display_name"] = display_name
+            merged[name] = entry_dict
+
+        self.skills_config = merged
+        self._save_skills_config()
+
+    def is_skill_enabled(self, name: str) -> bool:
+        """
+        Check if a skill is enabled according to skills_config.
+
+        :param name: skill name
+        :return: True if enabled (default True if not in config)
+        """
+        entry = self.skills_config.get(name)
+        if entry is None:
+            return True
+        return entry.get("enabled", True)
+
+    def set_skill_enabled(self, name: str, enabled: bool):
+        """
+        Set a skill's enabled state and persist.
+
+        :param name: skill name
+        :param enabled: True to enable, False to disable
+        """
+        if name not in self.skills_config:
+            raise ValueError(f"skill '{name}' not found in config")
+        self.skills_config[name]["enabled"] = enabled
+        self._save_skills_config()
+
+    def get_skills_config(self) -> Dict[str, dict]:
+        """
+        Return the full skills_config dict (for query API).
+
+        :return: copy of skills_config
+        """
+        return dict(self.skills_config)
     
     def get_skill(self, name: str) -> Optional[SkillEntry]:
         """
@@ -78,72 +166,118 @@ class SkillManager:
         """
         return list(self.skills.values())
     
+    @staticmethod
+    def _normalize_skill_filter(skill_filter: Optional[List[str]]) -> Optional[List[str]]:
+        """Normalize a skill_filter list into a flat list of stripped names."""
+        if skill_filter is None:
+            return None
+        normalized = []
+        for item in skill_filter:
+            if isinstance(item, str):
+                name = item.strip()
+                if name:
+                    normalized.append(name)
+            elif isinstance(item, list):
+                for subitem in item:
+                    if isinstance(subitem, str):
+                        name = subitem.strip()
+                        if name:
+                            normalized.append(name)
+        return normalized or None
+
     def filter_skills(
         self,
         skill_filter: Optional[List[str]] = None,
         include_disabled: bool = False,
     ) -> List[SkillEntry]:
         """
-        Filter skills based on criteria.
-        
-        Simple rule: Skills are auto-enabled if requirements are met.
-        - Has required API keys → included
-        - Missing API keys → excluded
-        
+        Filter skills that are eligible (enabled + requirements met).
+
         :param skill_filter: List of skill names to include (None = all)
-        :param include_disabled: Whether to include skills with disable_model_invocation=True
-        :return: Filtered list of skill entries
+        :param include_disabled: Whether to include disabled skills
+        :return: Filtered list of eligible skill entries
         """
         from agent.skills.config import should_include_skill
-        
+
         entries = list(self.skills.values())
-        
-        # Check requirements (platform, binaries, env vars)
+
         entries = [e for e in entries if should_include_skill(e, self.config)]
-        
-        # Apply skill filter
-        if skill_filter is not None:
-            # Flatten and normalize skill names (handle both strings and nested lists)
-            normalized = []
-            for item in skill_filter:
-                if isinstance(item, str):
-                    name = item.strip()
-                    if name:
-                        normalized.append(name)
-                elif isinstance(item, list):
-                    # Handle nested lists
-                    for subitem in item:
-                        if isinstance(subitem, str):
-                            name = subitem.strip()
-                            if name:
-                                normalized.append(name)
-            
-            if normalized:
-                entries = [e for e in entries if e.skill.name in normalized]
-        
-        # Filter out disabled skills unless explicitly requested
+
+        normalized = self._normalize_skill_filter(skill_filter)
+        if normalized is not None:
+            entries = [e for e in entries if e.skill.name in normalized]
+
         if not include_disabled:
-            entries = [e for e in entries if not e.skill.disable_model_invocation]
-        
+            entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
+
+        from config import conf
+        if not conf().get("knowledge", True):
+            entries = [e for e in entries if e.skill.name != "knowledge-wiki"]
+
         return entries
-    
+
+    def filter_unavailable_skills(
+        self,
+        skill_filter: Optional[List[str]] = None,
+    ) -> tuple:
+        """
+        Find skills that are enabled but have unmet requirements.
+
+        :param skill_filter: Optional list of skill names to include
+        :return: Tuple of (entries, missing_map) where missing_map maps
+                 skill name to its missing requirements dict
+        """
+        from agent.skills.config import should_include_skill, get_missing_requirements
+
+        entries = list(self.skills.values())
+
+        # Only enabled skills
+        entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
+
+        normalized = self._normalize_skill_filter(skill_filter)
+        if normalized is not None:
+            entries = [e for e in entries if e.skill.name in normalized]
+
+        # Keep only those that fail should_include_skill (requirements not met)
+        unavailable = []
+        missing_map: Dict[str, dict] = {}
+        for e in entries:
+            if not should_include_skill(e, self.config):
+                missing = get_missing_requirements(e)
+                if missing:
+                    unavailable.append(e)
+                    missing_map[e.skill.name] = missing
+
+        return unavailable, missing_map
+
     def build_skills_prompt(
         self,
         skill_filter: Optional[List[str]] = None,
     ) -> str:
         """
-        Build a formatted prompt containing available skills.
-        
+        Build a formatted prompt containing available skills
+        and brief hints for unavailable ones.
+
         :param skill_filter: Optional list of skill names to include
         :return: Formatted skills prompt
         """
         from common.log import logger
-        entries = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
-        logger.debug(f"[SkillManager] Filtered {len(entries)} skills for prompt (total: {len(self.skills)})")
-        if entries:
-            skill_names = [e.skill.name for e in entries]
-            logger.debug(f"[SkillManager] Skills to include: {skill_names}")
-        result = format_skill_entries_for_prompt(entries)
+        from agent.skills.formatter import format_unavailable_skills_for_prompt
+
+        eligible = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
+        logger.debug(f"[SkillManager] Eligible: {len(eligible)} skills (total: {len(self.skills)})")
+        if eligible:
+            skill_names = [e.skill.name for e in eligible]
+            logger.debug(f"[SkillManager] Eligible skills: {skill_names}")
+
+        result = format_skill_entries_for_prompt(eligible)
+
+        unavailable, missing_map = self.filter_unavailable_skills(skill_filter=skill_filter)
+        if unavailable:
+            unavailable_names = [e.skill.name for e in unavailable]
+            logger.debug(f"[SkillManager] Unavailable skills (setup needed): {unavailable_names}")
+            result += format_unavailable_skills_for_prompt(unavailable, missing_map)
+
         logger.debug(f"[SkillManager] Generated prompt length: {len(result)}")
         return result
     

agent/skills/service.py

@@ -0,0 +1,285 @@
+"""
+Skill service for handling skill CRUD operations.
+
+This service provides a unified interface for managing skills, which can be
+called from the cloud control client (LinkAI), the local web console, or any
+other management entry point.
+"""
+
+import os
+import shutil
+import zipfile
+import tempfile
+from typing import Dict, List, Optional
+from common.log import logger
+from agent.skills.types import Skill, SkillEntry
+from agent.skills.manager import SkillManager
+
+try:
+    import requests
+except ImportError:
+    requests = None
+
+
+class SkillService:
+    """
+    High-level service for skill lifecycle management.
+    Wraps SkillManager and provides network-aware operations such as
+    downloading skill files from remote URLs.
+    """
+
+    def __init__(self, skill_manager: SkillManager):
+        """
+        :param skill_manager: The SkillManager instance to operate on
+        """
+        self.manager = skill_manager
+
+    # ------------------------------------------------------------------
+    # query
+    # ------------------------------------------------------------------
+    def query(self) -> List[dict]:
+        """
+        Query all skills and return a serialisable list.
+        Reads from skills_config.json (refreshes from disk if needed).
+
+        :return: list of skill info dicts
+        """
+        self.manager.refresh_skills()
+        config = self.manager.get_skills_config()
+        result = list(config.values())
+        logger.info(f"[SkillService] query: {len(result)} skills found")
+        return result
+
+    # ------------------------------------------------------------------
+    # add / install
+    # ------------------------------------------------------------------
+    def add(self, payload: dict) -> None:
+        """
+        Add (install) a skill from a remote payload.
+
+        Supported payload types:
+
+        1. ``type: "url"`` – download individual files::
+
+            {
+                "name": "web_search",
+                "type": "url",
+                "enabled": true,
+                "files": [
+                    {"url": "https://...", "path": "README.md"},
+                    {"url": "https://...", "path": "scripts/main.py"}
+                ]
+            }
+
+        2. ``type: "package"`` – download a zip archive and extract::
+
+            {
+                "name": "plugin-custom-tool",
+                "type": "package",
+                "category": "skills",
+                "enabled": true,
+                "files": [{"url": "https://cdn.example.com/skills/custom-tool.zip"}]
+            }
+
+        :param payload: skill add payload from server
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        payload_type = payload.get("type", "url")
+
+        if payload_type == "package":
+            self._add_package(name, payload)
+        else:
+            self._add_url(name, payload)
+
+        self.manager.refresh_skills()
+
+        category = payload.get("category")
+        if category and name in self.manager.skills_config:
+            self.manager.skills_config[name]["category"] = category
+            self.manager._save_skills_config()
+
+    def _add_url(self, name: str, payload: dict) -> None:
+        """Install a skill by downloading individual files."""
+        files = payload.get("files", [])
+        if not files:
+            raise ValueError("skill files list is empty")
+
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+
+        tmp_dir = skill_dir + ".tmp"
+        if os.path.exists(tmp_dir):
+            shutil.rmtree(tmp_dir)
+        os.makedirs(tmp_dir, exist_ok=True)
+
+        try:
+            for file_info in files:
+                url = file_info.get("url")
+                rel_path = file_info.get("path")
+                if not url or not rel_path:
+                    logger.warning(f"[SkillService] add: skip invalid file entry {file_info}")
+                    continue
+                dest = os.path.join(tmp_dir, rel_path)
+                self._download_file(url, dest)
+        except Exception:
+            shutil.rmtree(tmp_dir, ignore_errors=True)
+            raise
+
+        if os.path.exists(skill_dir):
+            shutil.rmtree(skill_dir)
+        os.rename(tmp_dir, skill_dir)
+
+        logger.info(f"[SkillService] add: skill '{name}' installed via url ({len(files)} files)")
+
+    def _add_package(self, name: str, payload: dict) -> None:
+        """
+        Install a skill by downloading a zip archive and extracting it.
+
+        If the archive contains a single top-level directory, that directory
+        is used as the skill folder directly; otherwise a new directory named
+        after the skill is created to hold the extracted contents.
+        """
+        files = payload.get("files", [])
+        if not files or not files[0].get("url"):
+            raise ValueError("package url is required")
+
+        url = files[0]["url"]
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            zip_path = os.path.join(tmp_dir, "package.zip")
+            self._download_file(url, zip_path)
+
+            if not zipfile.is_zipfile(zip_path):
+                raise ValueError(f"downloaded file is not a valid zip archive: {url}")
+
+            extract_dir = os.path.join(tmp_dir, "extracted")
+            with zipfile.ZipFile(zip_path, "r") as zf:
+                zf.extractall(extract_dir)
+
+            # Determine the actual content root.
+            # If the zip has a single top-level directory, use its contents
+            # so the skill folder is clean (no extra nesting).
+            top_items = [
+                item for item in os.listdir(extract_dir)
+                if not item.startswith(".")
+            ]
+            if len(top_items) == 1:
+                single = os.path.join(extract_dir, top_items[0])
+                if os.path.isdir(single):
+                    extract_dir = single
+
+            if os.path.exists(skill_dir):
+                shutil.rmtree(skill_dir)
+            shutil.copytree(extract_dir, skill_dir)
+
+        logger.info(f"[SkillService] add: skill '{name}' installed via package ({url})")
+
+    # ------------------------------------------------------------------
+    # open / close (enable / disable)
+    # ------------------------------------------------------------------
+    def open(self, payload: dict) -> None:
+        """
+        Enable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=True)
+        logger.info(f"[SkillService] open: skill '{name}' enabled")
+
+    def close(self, payload: dict) -> None:
+        """
+        Disable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=False)
+        logger.info(f"[SkillService] close: skill '{name}' disabled")
+
+    # ------------------------------------------------------------------
+    # delete
+    # ------------------------------------------------------------------
+    def delete(self, payload: dict) -> None:
+        """
+        Delete a skill by removing its directory entirely.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+        if os.path.exists(skill_dir):
+            shutil.rmtree(skill_dir)
+            logger.info(f"[SkillService] delete: removed directory {skill_dir}")
+        else:
+            logger.warning(f"[SkillService] delete: skill directory not found: {skill_dir}")
+
+        # Refresh will remove the deleted skill from config automatically
+        self.manager.refresh_skills()
+        logger.info(f"[SkillService] delete: skill '{name}' deleted")
+
+    # ------------------------------------------------------------------
+    # dispatch - single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a skill management action and return a protocol-compatible
+        response dict.
+
+        :param action: one of query / add / open / close / delete
+        :param payload: action-specific payload (may be None for query)
+        :return: dict with action, code, message, payload
+        """
+        payload = payload or {}
+        try:
+            if action == "query":
+                result_payload = self.query()
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+            elif action == "add":
+                self.add(payload)
+            elif action == "open":
+                self.open(payload)
+            elif action == "close":
+                self.close(payload)
+            elif action == "delete":
+                self.delete(payload)
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+            return {"action": action, "code": 200, "message": "success", "payload": None}
+        except Exception as e:
+            logger.error(f"[SkillService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _download_file(url: str, dest: str):
+        """
+        Download a file from *url* and save to *dest*.
+
+        :param url: remote file URL
+        :param dest: local destination path
+        """
+        if requests is None:
+            raise RuntimeError("requests library is required for downloading skill files")
+
+        dest_dir = os.path.dirname(dest)
+        if dest_dir:
+            os.makedirs(dest_dir, exist_ok=True)
+
+        resp = requests.get(url, timeout=60)
+        resp.raise_for_status()
+        with open(dest, "wb") as f:
+            f.write(resp.content)
+        logger.debug(f"[SkillService] downloaded {url} -> {dest}")

agent/skills/types.py

@@ -29,6 +29,7 @@ class SkillInstallSpec:
 class SkillMetadata:
     """Metadata for a skill from frontmatter."""
     always: bool = False  # Always include this skill
+    default_enabled: bool = True  # Initial enabled state when first discovered
     skill_key: Optional[str] = None  # Override skill key
     primary_env: Optional[str] = None  # Primary environment variable
     emoji: Optional[str] = None
@@ -45,7 +46,7 @@ class Skill:
     description: str
     file_path: str
     base_dir: str
-    source: str  # managed, workspace, bundled, etc.
+    source: str  # builtin or custom
     content: str  # Full markdown content
     disable_model_invocation: bool = False
     frontmatter: Dict[str, Any] = field(default_factory=dict)

agent/tools/__init__.py

@@ -55,6 +55,24 @@ def _import_optional_tools():
     except Exception as e:
         logger.error(f"[Tools] WebSearch failed to load: {e}")
 
+    # WebFetch Tool
+    try:
+        from agent.tools.web_fetch.web_fetch import WebFetch
+        tools['WebFetch'] = WebFetch
+    except ImportError as e:
+        logger.error(f"[Tools] WebFetch not loaded - missing dependency: {e}")
+    except Exception as e:
+        logger.error(f"[Tools] WebFetch failed to load: {e}")
+
+    # Vision Tool (conditionally loaded based on API key availability)
+    try:
+        from agent.tools.vision.vision import Vision
+        tools['Vision'] = Vision
+    except ImportError as e:
+        logger.error(f"[Tools] Vision not loaded - missing dependency: {e}")
+    except Exception as e:
+        logger.error(f"[Tools] Vision failed to load: {e}")
+
     return tools
 
 # Load optional tools
@@ -62,30 +80,32 @@ _optional_tools = _import_optional_tools()
 EnvConfig = _optional_tools.get('EnvConfig')
 SchedulerTool = _optional_tools.get('SchedulerTool')
 WebSearch = _optional_tools.get('WebSearch')
+WebFetch = _optional_tools.get('WebFetch')
+Vision = _optional_tools.get('Vision')
 GoogleSearch = _optional_tools.get('GoogleSearch')
 FileSave = _optional_tools.get('FileSave')
 Terminal = _optional_tools.get('Terminal')
 
 
-# Delayed import for BrowserTool
+# BrowserTool (requires playwright)
 def _import_browser_tool():
+    from common.log import logger
     try:
         from agent.tools.browser.browser_tool import BrowserTool
         return BrowserTool
-    except ImportError:
-        # Return a placeholder class that will prompt the user to install dependencies when instantiated
-        class BrowserToolPlaceholder:
-            def __init__(self, *args, **kwargs):
-                raise ImportError(
-                    "The 'browser-use' package is required to use BrowserTool. "
-                    "Please install it with 'pip install browser-use>=0.1.40'."
-                )
+    except ImportError as e:
+        logger.info(
+            f"[Tools] BrowserTool not loaded - missing dependency: {e}\n"
+            f"  To enable browser tool, run:\n"
+            f"    pip install playwright\n"
+            f"    playwright install chromium"
+        )
+        return None
+    except Exception as e:
+        logger.error(f"[Tools] BrowserTool failed to load: {e}")
+        return None
 
-        return BrowserToolPlaceholder
-
-
-# Dynamically set BrowserTool
-# BrowserTool = _import_browser_tool()
+BrowserTool = _import_browser_tool()
 
 # Export all tools (including optional ones that might be None)
 __all__ = [
@@ -102,8 +122,9 @@ __all__ = [
     'EnvConfig',
     'SchedulerTool',
     'WebSearch',
-    # Optional tools (may be None if dependencies not available)
-    # 'BrowserTool'
+    'WebFetch',
+    'Vision',
+    'BrowserTool',
 ]
 
 """

agent/tools/bash/bash.py

@@ -3,6 +3,7 @@ Bash tool - Execute bash commands
 """
 
 import os
+import re
 import sys
 import subprocess
 import tempfile
@@ -17,9 +18,13 @@ from common.utils import expand_path
 class Bash(BaseTool):
     """Tool for executing bash commands"""
 
+    _IS_WIN = sys.platform == "win32"
+
     name: str = "bash"
     description: str = f"""Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last {DEFAULT_MAX_LINES} lines or {DEFAULT_MAX_BYTES // 1024}KB (whichever is hit first). If truncated, full output is saved to a temp file.
-
+{'''
+PLATFORM: Windows (cmd.exe). Do NOT use Unix-only commands like grep, head, tail, sed, awk.
+''' if _IS_WIN else ''}
 ENVIRONMENT: All API keys from env_config are auto-injected. Use $VAR_NAME directly.
 
 SAFETY:
@@ -83,12 +88,13 @@ SAFETY:
             
             # Load environment variables from ~/.cow/.env if it exists
             env_file = expand_path("~/.cow/.env")
+            dotenv_vars = {}
             if os.path.exists(env_file):
                 try:
                     from dotenv import dotenv_values
-                    env_vars = dotenv_values(env_file)
-                    env.update(env_vars)
-                    logger.debug(f"[Bash] Loaded {len(env_vars)} variables from {env_file}")
+                    dotenv_vars = dotenv_values(env_file)
+                    env.update(dotenv_vars)
+                    logger.debug(f"[Bash] Loaded {len(dotenv_vars)} variables from {env_file}")
                 except ImportError:
                     logger.debug("[Bash] python-dotenv not installed, skipping .env loading")
                 except Exception as e:
@@ -100,7 +106,13 @@ SAFETY:
             else:
                 logger.debug(f"[Bash] Process User: {os.environ.get('USERNAME', os.environ.get('USER', 'unknown'))}")
             
-            # Execute command with inherited environment variables
+            # On Windows, convert $VAR references to %VAR% for cmd.exe
+            if self._IS_WIN:
+                env["PYTHONIOENCODING"] = "utf-8"
+                command = self._convert_env_vars_for_windows(command, dotenv_vars)
+                if command and not command.strip().lower().startswith("chcp"):
+                    command = f"chcp 65001 >nul 2>&1 && {command}"
+
             result = subprocess.run(
                 command,
                 shell=True,
@@ -108,8 +120,10 @@ SAFETY:
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
                 text=True,
+                encoding="utf-8",
+                errors="replace",
                 timeout=timeout,
-                env=env
+                env=env,
             )
             
             logger.debug(f"[Bash] Exit code: {result.returncode}")
@@ -131,6 +145,8 @@ SAFETY:
                             stdout=subprocess.PIPE,
                             stderr=subprocess.PIPE,
                             text=True,
+                            encoding="utf-8",
+                            errors="replace",
                             timeout=timeout,
                             env=env
                         )
@@ -153,10 +169,16 @@ SAFETY:
                 except Exception as retry_err:
                     logger.warning(f"[Bash] Retry failed: {retry_err}")
 
-            # Combine stdout and stderr
-            output = result.stdout
-            if result.stderr:
-                output += "\n" + result.stderr
+            # When command succeeds with stdout, keep output clean (stderr goes to server log only).
+            # When command fails or stdout is empty, include stderr so the agent can diagnose.
+            if result.returncode == 0 and result.stdout.strip():
+                output = result.stdout
+                if result.stderr:
+                    logger.info(f"[Bash] stderr (not forwarded): {result.stderr[:500]}")
+            else:
+                output = result.stdout
+                if result.stderr:
+                    output += "\n" + result.stderr
 
             # Check if we need to save full output to temp file
             temp_file_path = None
@@ -258,3 +280,21 @@ SAFETY:
                     return "This command will recursively delete system directories"
 
         return ""  # No warning needed
+
+    @staticmethod
+    def _convert_env_vars_for_windows(command: str, dotenv_vars: dict) -> str:
+        """
+        Convert bash-style $VAR / ${VAR} references to cmd.exe %VAR% syntax.
+        Only converts variables loaded from .env (user-configured API keys etc.)
+        to avoid breaking $PATH, jq expressions, regex, etc.
+        """
+        if not dotenv_vars:
+            return command
+
+        def replace_match(m):
+            var_name = m.group(1) or m.group(2)
+            if var_name in dotenv_vars:
+                return f"%{var_name}%"
+            return m.group(0)
+
+        return re.sub(r'\$\{(\w+)\}|\$(\w+)', replace_match, command)

agent/tools/browser/__init__.py

@@ -0,0 +1,3 @@
+from agent.tools.browser.browser_tool import BrowserTool
+
+__all__ = ["BrowserTool"]

agent/tools/browser/browser_service.py

@@ -0,0 +1,780 @@
+"""
+Browser service - Playwright wrapper managing browser lifecycle and page operations.
+
+All Playwright calls run on a dedicated background thread so that callers from
+any worker thread can safely use the service.  An idle-timeout mechanism
+automatically shuts down the browser (and its thread) after a configurable
+period of inactivity to free resources.
+"""
+
+import os
+import sys
+import uuid
+import queue
+import threading
+from typing import Optional, Dict, Any, List, Callable
+
+from common.log import logger
+
+try:
+    from playwright.sync_api import sync_playwright, Browser, BrowserContext, Page, Playwright
+    _HAS_PLAYWRIGHT = True
+except ImportError:
+    _HAS_PLAYWRIGHT = False
+
+
+# ---------------------------------------------------------------------------
+# Snapshot DOM helpers
+# ---------------------------------------------------------------------------
+
+# Tags that typically carry useful content for an agent
+_INTERACTIVE_TAGS = {
+    "a", "button", "input", "textarea", "select", "option",
+    "label", "details", "summary",
+}
+_SEMANTIC_TAGS = {
+    "h1", "h2", "h3", "h4", "h5", "h6",
+    "p", "li", "td", "th", "caption", "figcaption", "blockquote", "pre", "code",
+    "nav", "main", "article", "section", "header", "footer", "form", "table",
+    "img", "video", "audio",
+}
+_KEEP_TAGS = _INTERACTIVE_TAGS | _SEMANTIC_TAGS
+
+_SNAPSHOT_JS = """
+() => {
+    const KEEP = new Set(%s);
+    const INTERACTIVE = new Set(%s);
+    const SKIP = new Set(["script","style","noscript","svg","path","meta","link","br","hr"]);
+    const CLICKABLE_ROLES = new Set([
+        "button","link","tab","menuitem","menuitemcheckbox","menuitemradio",
+        "option","switch","checkbox","radio","combobox","searchbox","slider",
+        "spinbutton","textbox","treeitem"
+    ]);
+    let refCounter = 0;
+    const refMap = {};
+
+    function visible(el) {
+        if (!(el instanceof HTMLElement)) return true;
+        const st = window.getComputedStyle(el);
+        if (st.display === "none" || st.visibility === "hidden") return false;
+        if (parseFloat(st.opacity) === 0) return false;
+        return true;
+    }
+
+    // Strong signals: these attributes alone are enough to mark as interactive
+    function hasStrongInteractiveSignal(el) {
+        const role = el.getAttribute("role");
+        if (role && CLICKABLE_ROLES.has(role)) return true;
+        if (el.hasAttribute("onclick") || el.hasAttribute("tabindex")) return true;
+        if (el.hasAttribute("data-click") || el.hasAttribute("data-action")) return true;
+        if (el.getAttribute("contenteditable") === "true") return true;
+        return false;
+    }
+
+    // Check if cursor:pointer is set directly (not just inherited from parent)
+    function hasOwnPointerCursor(el) {
+        try {
+            const st = window.getComputedStyle(el);
+            if (st.cursor !== "pointer") return false;
+            const parent = el.parentElement;
+            if (parent) {
+                const pst = window.getComputedStyle(parent);
+                if (pst.cursor === "pointer") return false;
+            }
+            return true;
+        } catch(e) {}
+        return false;
+    }
+
+    function hasTextOrContent(el) {
+        const t = el.textContent || "";
+        if (t.trim().length > 0) return true;
+        if (el.querySelector("img,video,audio,canvas")) return true;
+        const ariaLabel = el.getAttribute("aria-label");
+        if (ariaLabel && ariaLabel.trim()) return true;
+        const title = el.getAttribute("title");
+        if (title && title.trim()) return true;
+        return false;
+    }
+
+    function isImplicitInteractive(el) {
+        if (hasStrongInteractiveSignal(el)) return true;
+        if (hasOwnPointerCursor(el) && hasTextOrContent(el)) return true;
+        return false;
+    }
+
+    function getTextContent(el) {
+        let text = "";
+        for (const ch of el.childNodes) {
+            if (ch.nodeType === Node.TEXT_NODE) {
+                text += ch.textContent;
+            }
+        }
+        return text.trim();
+    }
+
+    function walk(node) {
+        if (node.nodeType === Node.TEXT_NODE) {
+            const t = node.textContent.trim();
+            return t ? t : null;
+        }
+        if (node.nodeType !== Node.ELEMENT_NODE) return null;
+        const tag = node.tagName.toLowerCase();
+        if (SKIP.has(tag)) return null;
+        if (!visible(node)) return null;
+
+        const children = [];
+        for (const ch of node.childNodes) {
+            const r = walk(ch);
+            if (r !== null) {
+                if (typeof r === "string") children.push(r);
+                else children.push(r);
+            }
+        }
+
+        const nativeInteractive = INTERACTIVE.has(tag);
+        const implicitInteractive = !nativeInteractive && (node instanceof HTMLElement) && isImplicitInteractive(node);
+        const keep = KEEP.has(tag) || implicitInteractive;
+
+        if (!keep) {
+            if (children.length === 0) return null;
+            if (children.length === 1) return children[0];
+            return children;
+        }
+
+        const obj = { tag };
+        if (nativeInteractive || implicitInteractive) {
+            refCounter++;
+            obj.ref = refCounter;
+            refMap[refCounter] = node;
+        }
+
+        if (implicitInteractive) {
+            const role = node.getAttribute("role");
+            if (role) obj.role = role;
+            const directText = getTextContent(node);
+            if (!directText && children.length === 0) {
+                const ariaLabel = node.getAttribute("aria-label");
+                const title = node.getAttribute("title");
+                if (ariaLabel) obj.ariaLabel = ariaLabel;
+                else if (title) obj.ariaLabel = title;
+            }
+        }
+
+        // Attributes
+        if (tag === "a" && node.href) obj.href = node.getAttribute("href");
+        if (tag === "img") {
+            obj.alt = node.alt || "";
+            obj.src = node.getAttribute("src") || "";
+        }
+        if (tag === "input" || tag === "textarea" || tag === "select") {
+            obj.type = node.type || "text";
+            obj.name = node.name || undefined;
+            obj.value = node.value || undefined;
+            obj.placeholder = node.placeholder || undefined;
+            if (node.disabled) obj.disabled = true;
+            if (tag === "input" && node.type === "checkbox") obj.checked = node.checked;
+        }
+        if (tag === "button") {
+            if (node.disabled) obj.disabled = true;
+        }
+        if (tag === "option") {
+            obj.value = node.value;
+            if (node.selected) obj.selected = true;
+        }
+        if (tag === "label" && node.htmlFor) obj.for = node.htmlFor;
+
+        // Role / aria-label for native interactive & semantic elements
+        if (!implicitInteractive) {
+            const role = node.getAttribute("role");
+            if (role) obj.role = role;
+            const ariaLabel = node.getAttribute("aria-label");
+            if (ariaLabel) obj.ariaLabel = ariaLabel;
+        }
+
+        // Children
+        if (children.length === 1 && typeof children[0] === "string") {
+            obj.text = children[0];
+        } else if (children.length > 0) {
+            obj.children = children;
+        }
+
+        return obj;
+    }
+
+    const result = walk(document.body);
+    window.__cowRefMap = refMap;
+    return { tree: result, refCount: refCounter };
+}
+""" % (
+    str(list(_KEEP_TAGS)),
+    str(list(_INTERACTIVE_TAGS)),
+)
+
+
+def _should_use_headless() -> bool:
+    """Decide headless mode: headless on Linux servers without display, headed elsewhere."""
+    if sys.platform in ("win32", "darwin"):
+        return False
+    # Linux: check for display
+    if os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"):
+        return False
+    return True
+
+
+def _flatten_tree(node, indent=0) -> List[str]:
+    """Convert snapshot tree to compact text lines for LLM consumption."""
+    if node is None:
+        return []
+    if isinstance(node, str):
+        return [" " * indent + node]
+    if isinstance(node, list):
+        lines = []
+        for child in node:
+            lines.extend(_flatten_tree(child, indent))
+        return lines
+    if not isinstance(node, dict):
+        return []
+
+    tag = node.get("tag", "?")
+    ref = node.get("ref")
+    parts = [tag]
+    if ref:
+        parts[0] = f"[{ref}] {tag}"
+
+    # Inline attributes
+    for attr in ("type", "name", "href", "alt", "role", "ariaLabel", "placeholder", "value"):
+        val = node.get(attr)
+        if val:
+            # Truncate long values
+            s = str(val)
+            if len(s) > 80:
+                s = s[:77] + "..."
+            parts.append(f'{attr}="{s}"')
+
+    for flag in ("disabled", "checked", "selected"):
+        if node.get(flag):
+            parts.append(flag)
+
+    prefix = " " * indent
+    header = prefix + " ".join(parts)
+
+    text = node.get("text")
+    if text:
+        # Truncate long text
+        if len(text) > 120:
+            text = text[:117] + "..."
+        header += f": {text}"
+
+    lines = [header]
+    children = node.get("children", [])
+    for child in children:
+        lines.extend(_flatten_tree(child, indent + 2))
+    return lines
+
+
+class BrowserService:
+    """Manages a Playwright browser on a dedicated background thread.
+
+    All Playwright operations are dispatched to a single long-lived thread via
+    a task queue.  Callers from *any* worker thread can use the public API
+    safely.  An idle timer automatically shuts the browser down after
+    ``idle_timeout`` seconds of inactivity (default 300 = 5 min).
+    """
+
+    _IDLE_TIMEOUT_DEFAULT = 300  # seconds
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        self._config = config or {}
+        self._headless: Optional[bool] = None
+        self._screenshot_dir: Optional[str] = None
+
+        # Background thread state
+        self._thread: Optional[threading.Thread] = None
+        self._task_queue: queue.Queue = queue.Queue()
+        self._lock = threading.Lock()
+        self._alive = False
+        self._ready = threading.Event()
+
+        # Playwright objects (only accessed on the background thread)
+        self._playwright = None
+        self._browser = None
+        self._context = None
+        self._page = None
+
+        # Idle auto-release
+        idle_cfg = self._config.get("idle_timeout")
+        self._idle_timeout: float = float(idle_cfg) if idle_cfg is not None else self._IDLE_TIMEOUT_DEFAULT
+        self._idle_timer: Optional[threading.Timer] = None
+
+    # ------------------------------------------------------------------
+    # Background-thread lifecycle
+    # ------------------------------------------------------------------
+
+    def _start_thread(self):
+        """Start the dedicated Playwright thread if not already running."""
+        with self._lock:
+            if self._alive and self._thread and self._thread.is_alive():
+                return
+            # Wait for old thread to fully exit before creating a new one
+            old = self._thread
+            if old and old.is_alive():
+                old.join(timeout=5)
+            # Fresh queue to avoid stale sentinels from a previous close()
+            self._task_queue = queue.Queue()
+            self._alive = True
+            self._ready = threading.Event()
+            self._thread = threading.Thread(target=self._run_loop, daemon=True, name="BrowserThread")
+            self._thread.start()
+            # Block until browser is ready (or failed)
+            self._ready.wait(timeout=30)
+
+    def _run_loop(self):
+        """Event loop running on the dedicated thread. Processes tasks until stopped."""
+        logger.info("[Browser] Background thread started")
+        try:
+            self._launch_browser()
+        except Exception as e:
+            logger.error(f"[Browser] Failed to launch browser: {e}")
+            self._alive = False
+            self._ready.set()
+            self._drain_queue(RuntimeError(f"Browser launch failed: {e}"))
+            return
+        self._ready.set()
+
+        while self._alive:
+            try:
+                task = self._task_queue.get(timeout=1.0)
+            except queue.Empty:
+                continue
+            if task is None:
+                break
+            fn, args, kwargs, result_slot = task
+            try:
+                result_slot["value"] = fn(*args, **kwargs)
+            except Exception as e:
+                result_slot["error"] = e
+            finally:
+                result_slot["event"].set()
+
+        self._shutdown_browser()
+        self._drain_queue(RuntimeError("Browser thread stopped"))
+        logger.info("[Browser] Background thread exited")
+
+    def _drain_queue(self, error: Exception):
+        """Unblock all callers waiting on the queue with an error."""
+        while True:
+            try:
+                task = self._task_queue.get_nowait()
+            except queue.Empty:
+                break
+            if task is None:
+                continue
+            _, _, _, result_slot = task
+            result_slot["error"] = error
+            result_slot["event"].set()
+
+    def _launch_browser(self):
+        """Launch Chromium on the background thread."""
+        if self._headless is None:
+            headless_cfg = self._config.get("headless")
+            self._headless = headless_cfg if headless_cfg is not None else _should_use_headless()
+
+        launch_args = ["--disable-dev-shm-usage"]
+        if self._headless:
+            launch_args.append("--no-sandbox")
+
+        extra_args = self._config.get("launch_args", [])
+        if extra_args:
+            launch_args.extend(extra_args)
+
+        viewport_w = self._config.get("viewport_width", 1280)
+        viewport_h = self._config.get("viewport_height", 720)
+
+        self._playwright = sync_playwright().start()
+        logger.info(f"[Browser] Launching Chromium (headless={self._headless})")
+        self._browser = self._playwright.chromium.launch(
+            headless=self._headless,
+            args=launch_args,
+        )
+        self._context = self._browser.new_context(
+            viewport={"width": viewport_w, "height": viewport_h},
+            user_agent=(
+                "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
+                "AppleWebKit/537.36 (KHTML, like Gecko) "
+                "Chrome/131.0.0.0 Safari/537.36"
+            ),
+        )
+        self._page = self._context.new_page()
+        logger.info("[Browser] Browser ready")
+
+    def _shutdown_browser(self):
+        """Shut down all Playwright resources on the background thread."""
+        self._cancel_idle_timer()
+        for obj, label in [
+            (self._context, "context"),
+            (self._browser, "browser"),
+        ]:
+            try:
+                if obj:
+                    obj.close()
+            except Exception as e:
+                logger.debug(f"[Browser] {label} close error: {e}")
+        try:
+            if self._playwright:
+                self._playwright.stop()
+        except Exception as e:
+            logger.debug(f"[Browser] playwright stop error: {e}")
+        self._page = None
+        self._context = None
+        self._browser = None
+        self._playwright = None
+        logger.info("[Browser] Browser closed")
+
+    def _submit(self, fn: Callable, *args, **kwargs):
+        """Submit *fn* to the background thread and block until it completes."""
+        self._start_thread()
+
+        if not self._alive:
+            raise RuntimeError("Browser is not available")
+
+        self._reset_idle_timer()
+
+        result_slot: Dict[str, Any] = {"event": threading.Event()}
+        self._task_queue.put((fn, args, kwargs, result_slot))
+
+        # Timeout prevents permanent hang if the background thread crashes
+        completed = result_slot["event"].wait(timeout=120)
+        if not completed:
+            raise TimeoutError("Browser operation timed out (120s)")
+
+        if "error" in result_slot:
+            raise result_slot["error"]
+        return result_slot.get("value")
+
+    # ------------------------------------------------------------------
+    # Idle auto-release
+    # ------------------------------------------------------------------
+
+    def _reset_idle_timer(self):
+        self._cancel_idle_timer()
+        if self._idle_timeout > 0:
+            self._idle_timer = threading.Timer(self._idle_timeout, self._on_idle_timeout)
+            self._idle_timer.daemon = True
+            self._idle_timer.start()
+
+    def _cancel_idle_timer(self):
+        if self._idle_timer:
+            self._idle_timer.cancel()
+            self._idle_timer = None
+
+    def _on_idle_timeout(self):
+        logger.info(f"[Browser] Idle for {self._idle_timeout}s, auto-releasing browser")
+        self.close()
+
+    # ------------------------------------------------------------------
+    # Public lifecycle
+    # ------------------------------------------------------------------
+
+    def close(self):
+        """Shut down browser and background thread (safe from any thread)."""
+        self._cancel_idle_timer()
+        with self._lock:
+            if not self._alive:
+                return
+            self._alive = False
+            t = self._thread
+        if self._task_queue is not None:
+            self._task_queue.put(None)
+        if t is not None and t.is_alive():
+            t.join(timeout=10)
+        with self._lock:
+            self._thread = None
+
+    # ------------------------------------------------------------------
+    # Actions  (each method is dispatched to the background thread)
+    # ------------------------------------------------------------------
+
+    def navigate(self, url: str, timeout: int = 30000) -> Dict[str, Any]:
+        return self._submit(self._do_navigate, url, timeout)
+
+    def _do_navigate(self, url: str, timeout: int) -> Dict[str, Any]:
+        page = self._page
+        try:
+            resp = page.goto(url, wait_until="domcontentloaded", timeout=timeout)
+            status = resp.status if resp else None
+        except Exception as e:
+            return {"error": f"Navigation failed: {e}"}
+
+        try:
+            page.wait_for_load_state("networkidle", timeout=8000)
+        except Exception:
+            pass
+        page.wait_for_timeout(500)
+
+        try:
+            title = page.title()
+        except Exception:
+            title = ""
+        try:
+            current_url = page.url
+        except Exception:
+            current_url = url
+
+        return {"url": current_url, "title": title, "status": status}
+
+    def snapshot(self, selector: Optional[str] = None) -> str:
+        return self._submit(self._do_snapshot, selector)
+
+    def _do_snapshot(self, selector: Optional[str] = None) -> str:
+        page = self._page
+        try:
+            result = page.evaluate(_SNAPSHOT_JS)
+        except Exception as e:
+            return f"[Snapshot error: {e}]"
+
+        tree = result.get("tree")
+        ref_count = result.get("refCount", 0)
+        lines = _flatten_tree(tree)
+
+        try:
+            title = page.title()
+        except Exception:
+            title = ""
+        try:
+            url = page.url
+        except Exception:
+            url = ""
+
+        header = f"Page: {title}  ({url})\nInteractive elements: {ref_count}\n---"
+        body = "\n".join(lines)
+
+        max_chars = self._config.get("snapshot_max_chars", 30000)
+        if len(body) > max_chars:
+            body = body[:max_chars] + "\n... [snapshot truncated]"
+
+        return f"{header}\n{body}"
+
+    def screenshot(self, full_page: bool = False, cwd: str = "") -> str:
+        return self._submit(self._do_screenshot, full_page, cwd)
+
+    def _do_screenshot(self, full_page: bool = False, cwd: str = "") -> str:
+        page = self._page
+        save_dir = self._get_screenshot_dir(cwd)
+        filename = f"screenshot_{uuid.uuid4().hex[:8]}.png"
+        filepath = os.path.join(save_dir, filename)
+        page.screenshot(path=filepath, full_page=full_page)
+        logger.info(f"[Browser] Screenshot saved: {filepath}")
+        return filepath
+
+    def click(self, ref: Optional[int] = None, selector: Optional[str] = None,
+              timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_click, ref, selector, timeout)
+
+    def _do_click(self, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el) return {{ error: "ref {ref} not found. Run snapshot first." }};
+                        el.click();
+                        return {{ clicked: true, tag: el.tagName.toLowerCase() }};
+                    }}
+                """)
+                if result.get("error"):
+                    return result
+                page.wait_for_timeout(500)
+                return result
+            elif selector:
+                page.click(selector, timeout=timeout)
+                return {"clicked": True, "selector": selector}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Click failed: {e}"}
+
+    def fill(self, text: str, ref: Optional[int] = None,
+             selector: Optional[str] = None, timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_fill, text, ref, selector, timeout)
+
+    def _do_fill(self, text, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el) return {{ error: "ref {ref} not found. Run snapshot first." }};
+                        el.focus();
+                        el.value = "";
+                        return {{ tag: el.tagName.toLowerCase(), name: el.name || "" }};
+                    }}
+                """)
+                if result.get("error"):
+                    return result
+                page.keyboard.type(text)
+                return {"filled": True, "ref": ref, "text": text}
+            elif selector:
+                page.fill(selector, text, timeout=timeout)
+                return {"filled": True, "selector": selector, "text": text}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Fill failed: {e}"}
+
+    def select(self, value: str, ref: Optional[int] = None,
+               selector: Optional[str] = None, timeout: int = 5000) -> Dict[str, Any]:
+        return self._submit(self._do_select, value, ref, selector, timeout)
+
+    def _do_select(self, value, ref, selector, timeout) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if ref is not None:
+                result = page.evaluate(f"""
+                    () => {{
+                        const el = window.__cowRefMap && window.__cowRefMap[{ref}];
+                        if (!el || el.tagName.toLowerCase() !== "select")
+                            return {{ error: "ref {ref} is not a <select> element" }};
+                        el.value = {repr(value)};
+                        el.dispatchEvent(new Event("change", {{ bubbles: true }}));
+                        return {{ selected: true, value: el.value }};
+                    }}
+                """)
+                return result
+            elif selector:
+                page.select_option(selector, value, timeout=timeout)
+                return {"selected": True, "selector": selector, "value": value}
+            else:
+                return {"error": "Provide either ref (from snapshot) or selector"}
+        except Exception as e:
+            return {"error": f"Select failed: {e}"}
+
+    def scroll(self, direction: str = "down", amount: int = 500) -> Dict[str, Any]:
+        return self._submit(self._do_scroll, direction, amount)
+
+    def _do_scroll(self, direction, amount) -> Dict[str, Any]:
+        page = self._page
+        delta_map = {
+            "down": (0, amount),
+            "up": (0, -amount),
+            "right": (amount, 0),
+            "left": (-amount, 0),
+        }
+        dx, dy = delta_map.get(direction, (0, amount))
+        try:
+            page.mouse.wheel(dx, dy)
+            page.wait_for_timeout(300)
+            scroll_info = page.evaluate("""
+                () => ({
+                    scrollX: window.scrollX,
+                    scrollY: window.scrollY,
+                    scrollHeight: document.documentElement.scrollHeight,
+                    clientHeight: document.documentElement.clientHeight
+                })
+            """)
+            return {"scrolled": direction, "amount": amount, **scroll_info}
+        except Exception as e:
+            return {"error": f"Scroll failed: {e}"}
+
+    def wait(self, selector: Optional[str] = None, timeout: int = 5000,
+             state: str = "visible") -> Dict[str, Any]:
+        return self._submit(self._do_wait, selector, timeout, state)
+
+    def _do_wait(self, selector, timeout, state) -> Dict[str, Any]:
+        page = self._page
+        try:
+            if selector:
+                page.wait_for_selector(selector, timeout=timeout, state=state)
+                return {"waited": True, "selector": selector, "state": state}
+            else:
+                page.wait_for_timeout(timeout)
+                return {"waited": True, "timeout_ms": timeout}
+        except Exception as e:
+            return {"error": f"Wait failed: {e}"}
+
+    def go_back(self) -> Dict[str, Any]:
+        return self._submit(self._do_go_back)
+
+    def _do_go_back(self) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.go_back(wait_until="domcontentloaded", timeout=10000)
+            try:
+                title = page.title()
+            except Exception:
+                title = ""
+            try:
+                url = page.url
+            except Exception:
+                url = ""
+            return {"url": url, "title": title}
+        except Exception as e:
+            return {"error": f"Go back failed: {e}"}
+
+    def go_forward(self) -> Dict[str, Any]:
+        return self._submit(self._do_go_forward)
+
+    def _do_go_forward(self) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.go_forward(wait_until="domcontentloaded", timeout=10000)
+            try:
+                title = page.title()
+            except Exception:
+                title = ""
+            try:
+                url = page.url
+            except Exception:
+                url = ""
+            return {"url": url, "title": title}
+        except Exception as e:
+            return {"error": f"Go forward failed: {e}"}
+
+    def get_text(self, selector: str) -> Dict[str, Any]:
+        return self._submit(self._do_get_text, selector)
+
+    def _do_get_text(self, selector) -> Dict[str, Any]:
+        page = self._page
+        try:
+            text = page.text_content(selector, timeout=5000)
+            return {"text": text or ""}
+        except Exception as e:
+            return {"error": f"Get text failed: {e}"}
+
+    def evaluate(self, script: str) -> Dict[str, Any]:
+        return self._submit(self._do_evaluate, script)
+
+    def _do_evaluate(self, script) -> Dict[str, Any]:
+        page = self._page
+        try:
+            result = page.evaluate(script)
+            return {"result": result}
+        except Exception as e:
+            return {"error": f"Evaluate failed: {e}"}
+
+    def press(self, key: str) -> Dict[str, Any]:
+        return self._submit(self._do_press, key)
+
+    def _do_press(self, key) -> Dict[str, Any]:
+        page = self._page
+        try:
+            page.keyboard.press(key)
+            page.wait_for_timeout(300)
+            return {"pressed": key}
+        except Exception as e:
+            return {"error": f"Press failed: {e}"}
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    def _get_screenshot_dir(self, cwd: str = "") -> str:
+        if self._screenshot_dir and os.path.isdir(self._screenshot_dir):
+            return self._screenshot_dir
+        base = cwd or os.getcwd()
+        d = os.path.join(base, "tmp")
+        os.makedirs(d, exist_ok=True)
+        self._screenshot_dir = d
+        return d

agent/tools/browser/browser_tool.py

@@ -0,0 +1,290 @@
+"""
+Browser tool - Control a Chromium browser for web navigation and interaction.
+
+Uses Playwright under the hood. Browser instance is lazily started on first
+use, reused across tool calls within the same session, and cleaned up via
+close().
+"""
+
+import json
+import os
+from typing import Dict, Any, Optional
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.browser.browser_service import BrowserService
+from common.log import logger
+
+
+class BrowserTool(BaseTool):
+    """Single tool exposing all browser actions via an 'action' parameter."""
+
+    name: str = "browser"
+    description: str = (
+        "Control a browser to navigate web pages, interact with elements, and extract content. "
+        "Actions: navigate, snapshot, click, fill, select, scroll, screenshot, wait, back, forward, "
+        "get_text, press, evaluate.\n\n"
+        "Workflow: navigate (auto-includes snapshot with element refs) → click/fill/select by ref → snapshot to verify.\n\n"
+        "Use snapshot as the primary way to read pages. Use screenshot + send to show key results to the user. "
+        "For login/CAPTCHA/authorization etc., screenshot and ask the user for help."
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "description": (
+                    "The browser action to perform. One of: "
+                    "navigate, snapshot, click, fill, select, scroll, "
+                    "screenshot, wait, back, forward, get_text, press, evaluate"
+                ),
+                "enum": [
+                    "navigate", "snapshot", "click", "fill", "select", "scroll",
+                    "screenshot", "wait", "back", "forward", "get_text", "press",
+                    "evaluate"
+                ]
+            },
+            "url": {
+                "type": "string",
+                "description": "URL to navigate to (for 'navigate' action)"
+            },
+            "ref": {
+                "type": "integer",
+                "description": "Element ref number from snapshot (for click/fill/select)"
+            },
+            "selector": {
+                "type": "string",
+                "description": "CSS selector as fallback when ref is unavailable (for click/fill/select/wait/get_text)"
+            },
+            "text": {
+                "type": "string",
+                "description": "Text to type (for 'fill' action)"
+            },
+            "value": {
+                "type": "string",
+                "description": "Option value (for 'select' action)"
+            },
+            "key": {
+                "type": "string",
+                "description": "Key to press, e.g. Enter, Tab, Escape (for 'press' action)"
+            },
+            "direction": {
+                "type": "string",
+                "description": "Scroll direction: up, down, left, right (for 'scroll' action, default: down)"
+            },
+            "script": {
+                "type": "string",
+                "description": "JavaScript code to execute (for 'evaluate' action)"
+            },
+            "full_page": {
+                "type": "boolean",
+                "description": "Capture full page screenshot (for 'screenshot' action, default: false)"
+            },
+            "timeout": {
+                "type": "integer",
+                "description": "Timeout in milliseconds (optional, default varies by action)"
+            }
+        },
+        "required": ["action"]
+    }
+
+    _shared_service: Optional[BrowserService] = None
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        self._service: Optional[BrowserService] = None
+
+    def _get_service(self) -> BrowserService:
+        """Get or create the browser service, sharing across copies."""
+        if self._service is not None:
+            return self._service
+
+        # Reuse shared service across tool copies within the same session
+        if BrowserTool._shared_service is not None:
+            self._service = BrowserTool._shared_service
+            return self._service
+
+        self._service = BrowserService(self.config)
+        BrowserTool._shared_service = self._service
+        return self._service
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        action = args.get("action", "").strip().lower()
+        if not action:
+            return ToolResult.fail("Error: 'action' parameter is required")
+
+        handler = self._ACTION_MAP.get(action)
+        if not handler:
+            valid = ", ".join(sorted(self._ACTION_MAP.keys()))
+            return ToolResult.fail(f"Unknown action '{action}'. Valid actions: {valid}")
+
+        try:
+            return handler(self, args)
+        except Exception as e:
+            logger.error(f"[Browser] Action '{action}' error: {e}")
+            return ToolResult.fail(f"Browser error ({action}): {e}")
+
+    # ------------------------------------------------------------------
+    # Action handlers
+    # ------------------------------------------------------------------
+
+    def _do_navigate(self, args: Dict[str, Any]) -> ToolResult:
+        url = args.get("url", "").strip()
+        if not url:
+            return ToolResult.fail("Error: 'url' is required for navigate action")
+        if not url.startswith(("http://", "https://")):
+            url = "https://" + url
+        timeout = args.get("timeout", 30000)
+        service = self._get_service()
+        result = service.navigate(url, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        # Auto-snapshot after navigation so the agent gets page content in one call
+        snapshot_text = service.snapshot()
+        return ToolResult.success(
+            f"Navigated to: {result['url']}\nTitle: {result['title']}\nStatus: {result['status']}\n\n"
+            f"--- Page Snapshot ---\n{snapshot_text}"
+        )
+
+    def _do_snapshot(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector")
+        text = self._get_service().snapshot(selector=selector)
+        return ToolResult.success(text)
+
+    def _do_click(self, args: Dict[str, Any]) -> ToolResult:
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        result = self._get_service().click(ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Clicked successfully. Use 'snapshot' to see updated page.")
+
+    def _do_fill(self, args: Dict[str, Any]) -> ToolResult:
+        text = args.get("text", "")
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        if not text and text != "":
+            return ToolResult.fail("Error: 'text' is required for fill action")
+        result = self._get_service().fill(text, ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Filled text into element. Use 'snapshot' to verify.")
+
+    def _do_select(self, args: Dict[str, Any]) -> ToolResult:
+        value = args.get("value", "")
+        ref = args.get("ref")
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        if not value:
+            return ToolResult.fail("Error: 'value' is required for select action")
+        result = self._get_service().select(value, ref=ref, selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Selected option '{value}'.")
+
+    def _do_scroll(self, args: Dict[str, Any]) -> ToolResult:
+        direction = args.get("direction", "down")
+        amount = args.get("timeout", 500)  # reuse timeout field or default
+        if "amount" in args:
+            amount = args["amount"]
+        result = self._get_service().scroll(direction=direction, amount=amount)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        pos = f"scrollY={result.get('scrollY', '?')}/{result.get('scrollHeight', '?')}"
+        return ToolResult.success(f"Scrolled {direction}. Position: {pos}")
+
+    def _do_screenshot(self, args: Dict[str, Any]) -> ToolResult:
+        full_page = args.get("full_page", False)
+        filepath = self._get_service().screenshot(full_page=full_page, cwd=self.cwd)
+        return ToolResult.success(f"Screenshot saved to: {filepath}")
+
+    def _do_wait(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector")
+        timeout = args.get("timeout", 5000)
+        result = self._get_service().wait(selector=selector, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Wait completed.")
+
+    def _do_back(self, args: Dict[str, Any]) -> ToolResult:
+        result = self._get_service().go_back()
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Navigated back to: {result['url']}")
+
+    def _do_forward(self, args: Dict[str, Any]) -> ToolResult:
+        result = self._get_service().go_forward()
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Navigated forward to: {result['url']}")
+
+    def _do_get_text(self, args: Dict[str, Any]) -> ToolResult:
+        selector = args.get("selector", "").strip()
+        if not selector:
+            return ToolResult.fail("Error: 'selector' is required for get_text action")
+        result = self._get_service().get_text(selector)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(result["text"])
+
+    def _do_press(self, args: Dict[str, Any]) -> ToolResult:
+        key = args.get("key", "").strip()
+        if not key:
+            return ToolResult.fail("Error: 'key' is required for press action")
+        result = self._get_service().press(key)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(f"Pressed key: {key}")
+
+    def _do_evaluate(self, args: Dict[str, Any]) -> ToolResult:
+        script = args.get("script", "").strip()
+        if not script:
+            return ToolResult.fail("Error: 'script' is required for evaluate action")
+        result = self._get_service().evaluate(script)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        val = result.get("result")
+        if isinstance(val, (dict, list)):
+            return ToolResult.success(json.dumps(val, ensure_ascii=False, indent=2))
+        return ToolResult.success(str(val) if val is not None else "(no return value)")
+
+    # Action dispatch table
+    _ACTION_MAP = {
+        "navigate": _do_navigate,
+        "snapshot": _do_snapshot,
+        "click": _do_click,
+        "fill": _do_fill,
+        "select": _do_select,
+        "scroll": _do_scroll,
+        "screenshot": _do_screenshot,
+        "wait": _do_wait,
+        "back": _do_back,
+        "forward": _do_forward,
+        "get_text": _do_get_text,
+        "press": _do_press,
+        "evaluate": _do_evaluate,
+    }
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def copy(self):
+        """Share browser instance across tool copies (avoids re-launching)."""
+        new_tool = BrowserTool(self.config)
+        new_tool.model = self.model
+        new_tool.context = getattr(self, "context", None)
+        new_tool.cwd = self.cwd
+        new_tool._service = self._service
+        return new_tool
+
+    def close(self):
+        """Release browser resources."""
+        if self._service:
+            self._service.close()
+            self._service = None
+        BrowserTool._shared_service = None
+        logger.info("[Browser] BrowserTool closed")

agent/tools/browser_tool.py

@@ -1,18 +0,0 @@
-def copy(self):
-    """
-    Special copy method for browser tool to avoid recreating browser instance.
-    
-    :return: A new instance with shared browser reference but unique model
-    """
-    new_tool = self.__class__()
-    
-    # Copy essential attributes
-    new_tool.model = self.model
-    new_tool.context = getattr(self, 'context', None)
-    new_tool.config = getattr(self, 'config', None)
-    
-    # Share the browser instance instead of creating a new one
-    if hasattr(self, 'browser'):
-        new_tool.browser = self.browser
-    
-    return new_tool 

agent/tools/ls/ls.py

@@ -94,7 +94,7 @@ class Ls(BaseTool):
                         results.append(entry + '/')
                     else:
                         results.append(entry)
-                except:
+                except Exception:
                     # Skip entries we can't stat
                     continue
             

agent/tools/memory/memory_get.py

@@ -44,6 +44,19 @@ class MemoryGetTool(BaseTool):
         """
         super().__init__()
         self.memory_manager = memory_manager
+
+        from config import conf
+        if conf().get("knowledge", True):
+            self.description = (
+                "Read specific content from memory or knowledge files. "
+                "Use this to get full context from a memory file, knowledge page, or specific line range."
+            )
+            self.params = {**self.params}
+            self.params["properties"] = {**self.params["properties"]}
+            self.params["properties"]["path"] = {
+                "type": "string",
+                "description": "Relative path to the memory or knowledge file (e.g. 'MEMORY.md', 'memory/2026-01-01.md', 'knowledge/concepts/moe.md')"
+            }
     
     def execute(self, args: dict):
         """
@@ -68,11 +81,15 @@ class MemoryGetTool(BaseTool):
             workspace_dir = self.memory_manager.config.get_workspace()
             
             # Auto-prepend memory/ if not present and not absolute path
-            # Exception: MEMORY.md is in the root directory
-            if not path.startswith('memory/') and not path.startswith('/') and path != 'MEMORY.md':
+            # Exceptions: MEMORY.md in root, knowledge/ files at workspace root
+            if not path.startswith('memory/') and not path.startswith('knowledge/') and not path.startswith('/') and path != 'MEMORY.md':
                 path = f'memory/{path}'
             
-            file_path = workspace_dir / path
+            file_path = (workspace_dir / path).resolve()
+            workspace_resolved = workspace_dir.resolve()
+            
+            if not str(file_path).startswith(str(workspace_resolved) + '/') and file_path != workspace_resolved:
+                return ToolResult.fail(f"Error: Access denied: path outside workspace")
             
             if not file_path.exists():
                 return ToolResult.fail(f"Error: File not found: {path}")

agent/tools/memory/memory_search.py

@@ -48,6 +48,13 @@ class MemorySearchTool(BaseTool):
         super().__init__()
         self.memory_manager = memory_manager
         self.user_id = user_id
+
+        from config import conf
+        if conf().get("knowledge", True):
+            self.description = (
+                "Search agent's long-term memory and knowledge base using semantic and keyword search. "
+                "Use this to recall past conversations, preferences, and knowledge pages."
+            )
     
     def execute(self, args: dict):
         """

agent/tools/read/read.py

@@ -48,7 +48,8 @@ class Read(BaseTool):
         self.binary_extensions = {'.exe', '.dll', '.so', '.dylib', '.bin', '.dat', '.db', '.sqlite'}
         self.archive_extensions = {'.zip', '.tar', '.gz', '.rar', '.7z', '.bz2', '.xz'}
         self.pdf_extensions = {'.pdf'}
-        
+        self.office_extensions = {'.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx'}
+
         # Readable text formats (will be read with truncation)
         self.text_extensions = {
             '.txt', '.md', '.markdown', '.rst', '.log', '.csv', '.tsv', '.json', '.xml', '.yaml', '.yml',
@@ -57,7 +58,6 @@ class Read(BaseTool):
             '.sh', '.bash', '.zsh', '.fish', '.ps1', '.bat', '.cmd',
             '.sql', '.r', '.m', '.swift', '.kt', '.scala', '.clj', '.erl', '.ex',
             '.dockerfile', '.makefile', '.cmake', '.gradle', '.properties', '.ini', '.conf', '.cfg',
-            '.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx'  # Office documents
         }
     
     def execute(self, args: Dict[str, Any]) -> ToolResult:
@@ -120,7 +120,11 @@ class Read(BaseTool):
         # Check if PDF
         if file_ext in self.pdf_extensions:
             return self._read_pdf(absolute_path, path, offset, limit)
-        
+
+        # Check if Office document (.docx, .xlsx, .pptx, etc.)
+        if file_ext in self.office_extensions:
+            return self._read_office(absolute_path, path, file_ext, offset, limit)
+
         # Read text file (with truncation for large files)
         return self._read_text(absolute_path, path, offset, limit)
     
@@ -240,8 +244,8 @@ class Read(BaseTool):
                     "message": f"文件过大 ({format_size(file_size)} > 50MB)，无法读取内容。文件路径: {absolute_path}"
                 })
             
-            # Read file
-            with open(absolute_path, 'r', encoding='utf-8') as f:
+            # Read file (utf-8-sig strips BOM automatically on Windows)
+            with open(absolute_path, 'r', encoding='utf-8-sig') as f:
                 content = f.read()
             
             # Truncate content if too long (20K characters max for model context)
@@ -337,6 +341,116 @@ class Read(BaseTool):
         except Exception as e:
             return ToolResult.fail(f"Error reading file: {str(e)}")
     
+    def _read_office(self, absolute_path: str, display_path: str, file_ext: str,
+                     offset: int = None, limit: int = None) -> ToolResult:
+        """Read Office documents (.docx, .xlsx, .pptx) using python-docx / openpyxl / python-pptx."""
+        try:
+            text = self._extract_office_text(absolute_path, file_ext)
+        except ImportError as e:
+            return ToolResult.fail(str(e))
+        except Exception as e:
+            return ToolResult.fail(f"Error reading Office document: {e}")
+
+        if not text or not text.strip():
+            return ToolResult.success({
+                "content": f"[Office file {Path(absolute_path).name}: no text content could be extracted]",
+            })
+
+        all_lines = text.split('\n')
+        total_lines = len(all_lines)
+
+        start_line = 0
+        if offset is not None:
+            if offset < 0:
+                start_line = max(0, total_lines + offset)
+            else:
+                start_line = max(0, offset - 1)
+                if start_line >= total_lines:
+                    return ToolResult.fail(
+                        f"Error: Offset {offset} is beyond end of content ({total_lines} lines total)"
+                    )
+
+        selected_content = text
+        user_limited_lines = None
+        if limit is not None:
+            end_line = min(start_line + limit, total_lines)
+            selected_content = '\n'.join(all_lines[start_line:end_line])
+            user_limited_lines = end_line - start_line
+        elif offset is not None:
+            selected_content = '\n'.join(all_lines[start_line:])
+
+        truncation = truncate_head(selected_content)
+        start_line_display = start_line + 1
+        output_text = ""
+
+        if truncation.truncated:
+            end_line_display = start_line_display + truncation.output_lines - 1
+            next_offset = end_line_display + 1
+            output_text = truncation.content
+            output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_lines}. Use offset={next_offset} to continue.]"
+        elif user_limited_lines is not None and start_line + user_limited_lines < total_lines:
+            remaining = total_lines - (start_line + user_limited_lines)
+            next_offset = start_line + user_limited_lines + 1
+            output_text = truncation.content
+            output_text += f"\n\n[{remaining} more lines in file. Use offset={next_offset} to continue.]"
+        else:
+            output_text = truncation.content
+
+        return ToolResult.success({
+            "content": output_text,
+            "total_lines": total_lines,
+            "start_line": start_line_display,
+            "output_lines": truncation.output_lines,
+        })
+
+    @staticmethod
+    def _extract_office_text(absolute_path: str, file_ext: str) -> str:
+        """Extract plain text from an Office document."""
+        if file_ext in ('.docx', '.doc'):
+            try:
+                from docx import Document
+            except ImportError:
+                raise ImportError("Error: python-docx library not installed. Install with: pip install python-docx")
+            doc = Document(absolute_path)
+            paragraphs = [p.text for p in doc.paragraphs]
+            for table in doc.tables:
+                for row in table.rows:
+                    paragraphs.append('\t'.join(cell.text for cell in row.cells))
+            return '\n'.join(paragraphs)
+
+        if file_ext in ('.xlsx', '.xls'):
+            try:
+                from openpyxl import load_workbook
+            except ImportError:
+                raise ImportError("Error: openpyxl library not installed. Install with: pip install openpyxl")
+            wb = load_workbook(absolute_path, read_only=True, data_only=True)
+            parts = []
+            for ws in wb.worksheets:
+                parts.append(f"--- Sheet: {ws.title} ---")
+                for row in ws.iter_rows(values_only=True):
+                    parts.append('\t'.join(str(c) if c is not None else '' for c in row))
+            wb.close()
+            return '\n'.join(parts)
+
+        if file_ext in ('.pptx', '.ppt'):
+            try:
+                from pptx import Presentation
+            except ImportError:
+                raise ImportError("Error: python-pptx library not installed. Install with: pip install python-pptx")
+            prs = Presentation(absolute_path)
+            parts = []
+            for i, slide in enumerate(prs.slides, 1):
+                parts.append(f"--- Slide {i} ---")
+                for shape in slide.shapes:
+                    if shape.has_text_frame:
+                        for para in shape.text_frame.paragraphs:
+                            text = para.text.strip()
+                            if text:
+                                parts.append(text)
+            return '\n'.join(parts)
+
+        return ""
+
     def _read_pdf(self, absolute_path: str, display_path: str, offset: int = None, limit: int = None) -> ToolResult:
         """
         Read PDF file content

agent/tools/scheduler/integration.py

@@ -134,12 +134,13 @@ def _execute_agent_task(task: dict, agent_bridge):
         elif channel_type == "dingtalk":
             # DingTalk requires msg object, set to None for scheduled tasks
             context["msg"] = None
-            # 如果是单聊，需要传递 sender_staff_id
             if not is_group:
                 sender_staff_id = action.get("dingtalk_sender_staff_id")
                 if sender_staff_id:
                     context["dingtalk_sender_staff_id"] = sender_staff_id
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         # Use Agent to execute the task
         # Mark this as a scheduled task execution to prevent recursive task creation
         context["is_scheduled_task"] = True
@@ -234,7 +235,11 @@ def _execute_send_message(task: dict, agent_bridge):
                     logger.debug(f"[Scheduler] DingTalk single chat: sender_staff_id={sender_staff_id}")
                 else:
                     logger.warning(f"[Scheduler] Task {task['id']}: DingTalk single chat message missing sender_staff_id")
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+        elif channel_type == "qq":
+            context["msg"] = None
+
         # Create reply
         reply = Reply(ReplyType.TEXT, content)
         
@@ -327,31 +332,31 @@ def _execute_tool_call(task: dict, agent_bridge):
             context["request_id"] = request_id
             logger.debug(f"[Scheduler] Generated request_id for web channel: {request_id}")
         elif channel_type == "feishu":
-            # Feishu channel: for scheduled tasks, send as new message (no msg_id to reply to)
             context["receive_id_type"] = "chat_id" if is_group else "open_id"
             context["msg"] = None
             logger.debug(f"[Scheduler] Feishu: receive_id_type={context['receive_id_type']}, is_group={is_group}, receiver={receiver}")
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         reply = Reply(ReplyType.TEXT, content)
-        
+
         # Get channel and send
         from channel.channel_factory import create_channel
-        
+
         try:
             channel = create_channel(channel_type)
             if channel:
-                # For web channel, register the request_id to session mapping
                 if channel_type == "web" and hasattr(channel, 'request_to_session'):
                     channel.request_to_session[request_id] = receiver
                     logger.debug(f"[Scheduler] Registered request_id {request_id} -> session {receiver}")
-                
+
                 channel.send(reply, context)
                 logger.info(f"[Scheduler] Task {task['id']} executed: sent tool result to {receiver}")
             else:
                 logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
         except Exception as e:
             logger.error(f"[Scheduler] Failed to send tool result: {e}")
-            
+
     except Exception as e:
         logger.error(f"[Scheduler] Error in _execute_tool_call: {e}")
 
@@ -409,7 +414,9 @@ def _execute_skill_call(task: dict, agent_bridge):
         elif channel_type == "feishu":
             context["receive_id_type"] = "chat_id" if is_group else "open_id"
             context["msg"] = None
-        
+        elif channel_type == "wecom_bot":
+            context["msg"] = None
+
         # Use Agent to execute the skill
         try:
             # Don't clear history - scheduler tasks use isolated session_id so they won't pollute user conversations
@@ -451,8 +458,7 @@ def attach_scheduler_to_tool(tool, context: Context = None):
     if context:
         tool.current_context = context
         
-        # Also set channel_type from config
-        channel_type = conf().get("channel_type", "unknown")
+        channel_type = context.get("channel_type") or conf().get("channel_type", "unknown")
         if not tool.config:
             tool.config = {}
         tool.config["channel_type"] = channel_type

agent/tools/scheduler/scheduler_service.py

@@ -61,8 +61,7 @@ class SchedulerService:
                 self._check_and_execute_tasks()
             except Exception as e:
                 logger.error(f"[Scheduler] Error in scheduler loop: {e}")
-            
-            # Sleep for 30 seconds between checks
+
             time.sleep(30)
     
     def _check_and_execute_tasks(self):
@@ -85,12 +84,9 @@ class SchedulerService:
                             "last_run_at": now.isoformat()
                         })
                     else:
-                        # One-time task, disable it
-                        self.task_store.update_task(task['id'], {
-                            "enabled": False,
-                            "last_run_at": now.isoformat()
-                        })
-                        logger.info(f"[Scheduler] One-time task completed and disabled: {task['id']}")
+                        # One-time task completed, remove it
+                        self.task_store.delete_task(task['id'])
+                        logger.info(f"[Scheduler] One-time task completed and removed: {task['id']}")
             except Exception as e:
                 logger.error(f"[Scheduler] Error processing task {task.get('id')}: {e}")
     
@@ -127,14 +123,11 @@ class SchedulerService:
                 if time_diff > 300:  # 5 minutes
                     logger.warning(f"[Scheduler] Task {task['id']} is overdue by {int(time_diff)}s, skipping and scheduling next run")
                     
-                    # For one-time tasks, disable them
+                    # For one-time tasks, remove them directly
                     schedule = task.get("schedule", {})
                     if schedule.get("type") == "once":
-                        self.task_store.update_task(task['id'], {
-                            "enabled": False,
-                            "last_run_at": now.isoformat()
-                        })
-                        logger.info(f"[Scheduler] One-time task {task['id']} expired, disabled")
+                        self.task_store.delete_task(task['id'])
+                        logger.info(f"[Scheduler] One-time task {task['id']} expired, removed")
                         return False
                     
                     # For recurring tasks, calculate next run from now
@@ -147,7 +140,7 @@ class SchedulerService:
                     return False
             
             return now >= next_run
-        except:
+        except Exception:
             return False
     
     def _calculate_next_run(self, task: dict, from_time: datetime) -> Optional[datetime]:
@@ -195,7 +188,7 @@ class SchedulerService:
                 # Only return if in the future
                 if run_at > from_time:
                     return run_at
-            except:
+            except Exception:
                 pass
             return None
         

agent/tools/scheduler/scheduler_tool.py

@@ -424,7 +424,7 @@ class SchedulerTool(BaseTool):
             try:
                 dt = datetime.fromisoformat(run_at)
                 return f"一次性 ({dt.strftime('%Y-%m-%d %H:%M')})"
-            except:
+            except Exception:
                 return "一次性"
         
         return "未知"
@@ -438,6 +438,6 @@ class SchedulerTool(BaseTool):
                     return msg.other_user_nickname or "群聊"
                 else:
                     return msg.from_user_nickname or "用户"
-        except:
+        except Exception:
             pass
         return "未知"

agent/tools/scheduler/task_store.py

@@ -72,7 +72,7 @@ class TaskStore:
                         with open(self.store_path, 'r') as src:
                             with open(backup_path, 'w') as dst:
                                 dst.write(src.read())
-                    except:
+                    except Exception:
                         pass
                 
                 # Save tasks

agent/tools/send/send.py

@@ -14,14 +14,14 @@ class Send(BaseTool):
     """Tool for sending files to the user"""
     
     name: str = "send"
-    description: str = "Send a file (image, video, audio, document) to the user. Use this when the user explicitly asks to send/share a file."
+    description: str = "Send a LOCAL file (image, video, audio, document) to the user. Only for local file paths. Do NOT use this for URLs — URLs should be included directly in your text reply, the system will handle them automatically."
     
     params: dict = {
         "type": "object",
         "properties": {
             "path": {
                 "type": "string",
-                "description": "Path to the file to send. Can be absolute path or relative to workspace."
+                "description": "Local file path to send. Must be an absolute path or relative to workspace. Do NOT pass URLs here."
             },
             "message": {
                 "type": "string",
@@ -98,7 +98,18 @@ class Send(BaseTool):
             "size_formatted": self._format_size(file_size),
             "message": message or f"正在发送 {file_name}"
         }
-        
+
+        try:
+            from common.cloud_client import get_website_base_url, copy_send_file
+
+            # Do nothing when in local env
+            if get_website_base_url():
+                url = copy_send_file(absolute_path, self.cwd)
+                if url:
+                    result["url"] = url
+        except Exception:
+            pass
+
         return ToolResult.success(result)
     
     def _resolve_path(self, path: str) -> str:

agent/tools/tool_manager.py

@@ -84,11 +84,11 @@ class ToolManager:
                                 except ImportError as e:
                                     # Handle missing dependencies with helpful messages
                                     error_msg = str(e)
-                                    if "browser-use" in error_msg or "browser_use" in error_msg:
+                                    if "playwright" in error_msg:
                                         logger.warning(
                                             f"[ToolManager] Browser tool not loaded - missing dependencies.\n"
                                             f"  To enable browser tool, run:\n"
-                                            f"    pip install browser-use markdownify playwright\n"
+                                            f"    pip install playwright\n"
                                             f"    playwright install chromium"
                                         )
                                     elif "markdownify" in error_msg:
@@ -154,11 +154,11 @@ class ToolManager:
                             except ImportError as e:
                                 # Handle missing dependencies with helpful messages
                                 error_msg = str(e)
-                                if "browser-use" in error_msg or "browser_use" in error_msg:
+                                if "playwright" in error_msg:
                                     logger.warning(
                                         f"[ToolManager] Browser tool not loaded - missing dependencies.\n"
                                         f"  To enable browser tool, run:\n"
-                                        f"    pip install browser-use markdownify playwright\n"
+                                        f"    pip install playwright\n"
                                         f"    playwright install chromium"
                                     )
                                 elif "markdownify" in error_msg:
@@ -197,7 +197,7 @@ class ToolManager:
                         logger.warning(
                             f"[ToolManager] Browser tool is configured but not loaded.\n"
                             f"  To enable browser tool, run:\n"
-                            f"    pip install browser-use markdownify playwright\n"
+                            f"    pip install playwright\n"
                             f"    playwright install chromium"
                         )
                     elif tool_name == "google_search":

agent/tools/utils/truncate.py

@@ -8,7 +8,10 @@ Truncation is based on two independent limits - whichever is hit first wins:
 Never returns partial lines (except bash tail truncation edge case).
 """
 
-from typing import Dict, Any, Optional, Literal, Tuple
+from __future__ import annotations
+from typing import Dict, Any, Optional, Tuple, TYPE_CHECKING
+if TYPE_CHECKING:
+    from typing import Literal
 
 
 DEFAULT_MAX_LINES = 2000

agent/tools/vision/__init__.py

@@ -0,0 +1 @@
+from agent.tools.vision.vision import Vision

agent/tools/vision/vision.py

@@ -0,0 +1,512 @@
+"""
+Vision tool - Analyze images using Vision API.
+Supports local files (auto base64-encoded) and HTTP URLs.
+
+Provider priority (default):
+  1. Main model via bot.call_vision — zero extra cost
+  2. Other models whose API key is configured — auto-discovered
+  3. OpenAI / LinkAI raw HTTP — reliable fallback
+  When use_linkai=true, LinkAI is promoted to #1.
+  When tool.vision.model is set, that model is used exclusively first.
+"""
+
+import base64
+import os
+import subprocess
+import tempfile
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+import requests
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from common import const
+from common.log import logger
+from config import conf
+
+DEFAULT_MODEL = const.GPT_41_MINI
+DEFAULT_TIMEOUT = 60
+MAX_TOKENS = 1000
+COMPRESS_THRESHOLD = 1_048_576  # 1 MB
+
+SUPPORTED_EXTENSIONS = {
+    "jpg": "image/jpeg",
+    "jpeg": "image/jpeg",
+    "png": "image/png",
+    "gif": "image/gif",
+    "webp": "image/webp",
+}
+
+_MAIN_MODEL_PROVIDER_NAME = "MainModel"
+
+# (config_key_for_api_key, bot_type, default_vision_model, provider_display_name)
+# Auto-discovered as fallback vision providers when their API key is configured.
+# OpenAI and LinkAI are handled separately (raw HTTP providers), so not listed here.
+_DISCOVERABLE_MODELS = [
+    ("moonshot_api_key", const.MOONSHOT, const.KIMI_K2_6, "Moonshot"),
+    ("ark_api_key", const.DOUBAO, const.DOUBAO_SEED_2_PRO, "Doubao"),
+    ("dashscope_api_key", const.QWEN_DASHSCOPE, const.QWEN36_PLUS, "DashScope"),
+    ("claude_api_key", const.CLAUDEAPI, const.CLAUDE_4_6_SONNET, "Claude"),
+    ("gemini_api_key", const.GEMINI, const.GEMINI_31_FLASH_LITE_PRE, "Gemini"),
+    ("zhipu_ai_api_key", const.ZHIPU_AI, const.GLM_4_7, "ZhipuAI"),
+    ("minimax_api_key", const.MiniMax, const.MINIMAX_M2_7, "MiniMax"),
+]
+
+
+@dataclass
+class VisionProvider:
+    """A single Vision API provider configuration."""
+    name: str
+    api_key: str
+    api_base: str
+    extra_headers: dict = field(default_factory=dict)
+    model_override: Optional[str] = None
+    use_bot: bool = False  # When True, call via bot.call_vision instead of raw HTTP
+    fallback_bot: Any = None  # Bot instance for non-main-model providers
+
+
+class VisionAPIError(Exception):
+    """Raised when a Vision API call fails and should trigger fallback."""
+    pass
+
+
+class Vision(BaseTool):
+    """Analyze images using Vision API"""
+
+    name: str = "vision"
+    description: str = (
+        "Analyze a local image or image URL (jpg/jpeg/png) using Vision API. "
+        "Can describe content, extract text, identify objects, colors, etc. "
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "image": {
+                "type": "string",
+                "description": "Local file path or HTTP(S) URL of the image to analyze",
+            },
+            "question": {
+                "type": "string",
+                "description": "Question to ask about the image",
+            },
+        },
+        "required": ["image", "question"],
+    }
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+
+    @staticmethod
+    def is_available() -> bool:
+        return True
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        image = args.get("image", "").strip()
+        question = args.get("question", "").strip()
+
+        if not image:
+            return ToolResult.fail("Error: 'image' parameter is required")
+        if not question:
+            return ToolResult.fail("Error: 'question' parameter is required")
+
+        providers = self._resolve_providers()
+        if not providers:
+            return ToolResult.fail(
+                "Error: No model available for Vision.\n"
+                "The main model does not support vision and no other API keys are configured.\n"
+                "Options:\n"
+                "  1. Switch to a multimodal model (e.g. qwen3.6-plus, claude-sonnet-4-6, gemini-2.0-flash)\n"
+                "  2. Configure OPENAI_API_KEY: env_config(action=\"set\", key=\"OPENAI_API_KEY\", value=\"your-key\")\n"
+                "  3. Configure LINKAI_API_KEY: env_config(action=\"set\", key=\"LINKAI_API_KEY\", value=\"your-key\")"
+            )
+
+        try:
+            image_content = self._build_image_content(image)
+        except Exception as e:
+            return ToolResult.fail(f"Error: {e}")
+
+        return self._call_with_fallback(providers, DEFAULT_MODEL, question, image_content)
+
+    def _call_with_fallback(self, providers: List[VisionProvider], model: str,
+                            question: str, image_content: dict) -> ToolResult:
+        """Try each provider in order; fall back to the next one on failure."""
+        errors: List[str] = []
+        for i, provider in enumerate(providers):
+            use_model = provider.model_override or model
+            try:
+                logger.info(f"[Vision] Trying provider '{provider.name}' "
+                            f"with model '{use_model}' ({i + 1}/{len(providers)})")
+                if provider.use_bot:
+                    result = self._call_via_bot(use_model, question, image_content, provider)
+                else:
+                    result = self._call_api(provider, use_model, question, image_content)
+                logger.info(f"[Vision] ✅ Success via {provider.name} (model={use_model})")
+                return result
+            except VisionAPIError as e:
+                errors.append(f"[{provider.name}/{use_model}] {e}")
+                logger.warning(f"[Vision] Provider '{provider.name}' failed: {e}")
+            except requests.Timeout:
+                errors.append(f"[{provider.name}/{use_model}] Request timed out after {DEFAULT_TIMEOUT}s")
+                logger.warning(f"[Vision] Provider '{provider.name}' timed out")
+            except requests.ConnectionError:
+                errors.append(f"[{provider.name}/{use_model}] Connection failed")
+                logger.warning(f"[Vision] Provider '{provider.name}' connection failed")
+            except Exception as e:
+                errors.append(f"[{provider.name}/{use_model}] {e}")
+                logger.error(f"[Vision] Provider '{provider.name}' unexpected error: {e}", exc_info=True)
+
+        return ToolResult.fail(
+            "Error: All Vision API providers failed.\n" + "\n".join(f"  - {err}" for err in errors)
+        )
+
+    def _resolve_providers(self) -> List[VisionProvider]:
+        """
+        Build an ordered list of available providers.
+
+        Priority:
+          - use_linkai=true  → [LinkAI, MainModel, OtherModels…, OpenAI]
+          - default          → [MainModel, OtherModels…, OpenAI, LinkAI]
+
+        "OtherModels" are auto-discovered from configured API keys.
+        The main model's bot_type is excluded from OtherModels to avoid
+        duplicating the MainModel provider.
+        """
+        use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
+        providers: List[VisionProvider] = []
+
+        if use_linkai:
+            self._append_provider(providers, self._build_linkai_provider)
+            self._append_provider(providers, self._build_main_model_provider)
+            self._append_other_model_providers(providers)
+            self._append_provider(providers, self._build_openai_provider)
+        else:
+            self._append_provider(providers, self._build_main_model_provider)
+            self._append_other_model_providers(providers)
+            self._append_provider(providers, self._build_openai_provider)
+            self._append_provider(providers, self._build_linkai_provider)
+
+        return providers
+
+    @staticmethod
+    def _append_provider(providers: List[VisionProvider], builder) -> None:
+        p = builder()
+        if p:
+            providers.append(p)
+
+    def _append_other_model_providers(self, providers: List[VisionProvider]) -> None:
+        """
+        Auto-discover other models whose API key is configured.
+        Skip the main model's own bot_type (already covered by MainModel provider).
+        Skip bot_types that already have a provider in the list (e.g. OpenAI).
+        """
+        # Determine main model's bot_type so we can skip it
+        main_bot_type = None
+        if self.model and hasattr(self.model, '_resolve_bot_type'):
+            main_bot_type = self.model._resolve_bot_type(conf().get("model", ""))
+
+        existing_names = {p.name for p in providers}
+
+        for config_key, bot_type, default_model, display_name in _DISCOVERABLE_MODELS:
+            if display_name in existing_names:
+                continue
+            if bot_type == main_bot_type:
+                continue
+            api_key = conf().get(config_key, "")
+            if not api_key or not api_key.strip():
+                continue
+
+            # Create a bot instance and check if it supports call_vision
+            try:
+                from models.bot_factory import create_bot
+                bot = create_bot(bot_type)
+                if not hasattr(bot, 'call_vision'):
+                    continue
+            except Exception:
+                continue
+
+            providers.append(VisionProvider(
+                name=display_name,
+                api_key="",
+                api_base="",
+                model_override=default_model,
+                use_bot=True,
+                fallback_bot=bot,
+            ))
+
+    def _resolve_vision_model(self) -> Optional[str]:
+        """
+        Determine which model to use for vision.
+
+        1. User explicit config: tool.vision.model in config.json
+        2. Fallback to the main configured model name
+        """
+        tool_conf = conf().get("tool", {})
+        user_vision_model = tool_conf.get("vision", {}).get("model") if isinstance(tool_conf, dict) else None
+        if user_vision_model:
+            return user_vision_model
+        model_name = conf().get("model", "")
+        return model_name or None
+
+    def _build_main_model_provider(self) -> Optional[VisionProvider]:
+        """
+        Use the vendor's own model for vision via bot.call_vision.
+        Only available when the bot class has call_vision.
+        """
+        if not (self.model and hasattr(self.model, 'bot')):
+            return None
+        try:
+            bot = self.model.bot
+            if not hasattr(bot, 'call_vision'):
+                return None
+        except Exception:
+            return None
+
+        vision_model = self._resolve_vision_model()
+
+        return VisionProvider(
+            name=_MAIN_MODEL_PROVIDER_NAME,
+            api_key="",
+            api_base="",
+            model_override=vision_model,
+            use_bot=True,
+        )
+
+    def _build_openai_provider(self) -> Optional[VisionProvider]:
+        api_key = conf().get("open_ai_api_key") or os.environ.get("OPENAI_API_KEY")
+        if not api_key:
+            return None
+        api_base = (conf().get("open_ai_api_base") or os.environ.get("OPENAI_API_BASE", "")).rstrip("/") \
+            or "https://api.openai.com/v1"
+        return VisionProvider(name="OpenAI", api_key=api_key, api_base=self._ensure_v1(api_base))
+
+    def _build_linkai_provider(self) -> Optional[VisionProvider]:
+        api_key = conf().get("linkai_api_key") or os.environ.get("LINKAI_API_KEY")
+        if not api_key:
+            return None
+        api_base = (conf().get("linkai_api_base") or os.environ.get("LINKAI_API_BASE", "")).rstrip("/") \
+            or "https://api.link-ai.tech"
+        from common.utils import get_cloud_headers
+        extra = get_cloud_headers(api_key)
+        extra.pop("Authorization", None)
+        extra.pop("Content-Type", None)
+        return VisionProvider(name="LinkAI", api_key=api_key, api_base=self._ensure_v1(api_base),
+                              extra_headers=extra)
+
+    def _call_via_bot(self, model: str, question: str, image_content: dict,
+                      provider: Optional[VisionProvider] = None) -> ToolResult:
+        """
+        Call a model's call_vision with vendor-native API format.
+        Uses the provider's _fallback_bot if set, otherwise the main model bot.
+        Raises VisionAPIError on failure so fallback can proceed.
+        """
+        try:
+            bot = (provider and provider.fallback_bot) or self.model.bot
+        except Exception as e:
+            raise VisionAPIError(f"Cannot access bot: {e}")
+
+        # Extract the raw image URL from the OpenAI-format image_content block
+        image_url = image_content.get("image_url", {}).get("url", "")
+        if not image_url:
+            raise VisionAPIError("No image URL in content block")
+
+        try:
+            response = bot.call_vision(
+                image_url=image_url,
+                question=question,
+                model=model,
+                max_tokens=MAX_TOKENS,
+            )
+        except Exception as e:
+            raise VisionAPIError(f"call_vision failed: {e}")
+
+        if response is NotImplemented:
+            raise VisionAPIError("Bot does not support vision")
+
+        if isinstance(response, dict) and response.get("error"):
+            raise VisionAPIError(f"API error - {response.get('message', 'Unknown')}")
+
+        content = response.get("content", "") if isinstance(response, dict) else ""
+        if not content:
+            raise VisionAPIError("Empty response from main model")
+
+        usage_info = response.get("usage", {}) if isinstance(response, dict) else {}
+
+        # Use the actual model name from the bot response if available
+        actual_model = response.get("model", model) if isinstance(response, dict) else model
+        provider_name = provider.name if provider else _MAIN_MODEL_PROVIDER_NAME
+        return ToolResult.success({
+            "model": actual_model,
+            "provider": provider_name,
+            "content": content,
+            "usage": usage_info,
+        })
+
+    @staticmethod
+    def _ensure_v1(api_base: str) -> str:
+        """Append /v1 if the base URL doesn't already end with a versioned path."""
+        if not api_base:
+            return api_base
+        # Already has /v1 or similar version suffix
+        if api_base.rstrip("/").split("/")[-1].startswith("v"):
+            return api_base
+        return api_base.rstrip("/") + "/v1"
+
+    def _build_image_content(self, image: str) -> dict:
+        """
+        Build the image_url content block.
+        Both remote URLs and local files are converted to base64 data URLs
+        so every bot backend can consume them without extra downloads.
+        """
+        if image.startswith(("http://", "https://")):
+            return self._download_to_data_url(image)
+
+        if not os.path.isfile(image):
+            raise FileNotFoundError(f"Image file not found: {image}")
+
+        ext = image.rsplit(".", 1)[-1].lower() if "." in image else ""
+        mime_type = SUPPORTED_EXTENSIONS.get(ext)
+        if not mime_type:
+            raise ValueError(
+                f"Unsupported image format '.{ext}'. "
+                f"Supported: {', '.join(SUPPORTED_EXTENSIONS.keys())}"
+            )
+
+        file_path = self._maybe_compress(image)
+        try:
+            with open(file_path, "rb") as f:
+                b64 = base64.b64encode(f.read()).decode("ascii")
+        finally:
+            if file_path != image and os.path.exists(file_path):
+                os.remove(file_path)
+
+        data_url = f"data:{mime_type};base64,{b64}"
+        return {"type": "image_url", "image_url": {"url": data_url}}
+
+    @staticmethod
+    def _download_to_data_url(url: str) -> dict:
+        """Download a remote image and return it as a base64 data URL."""
+        resp = requests.get(url, timeout=30)
+        if resp.status_code != 200:
+            raise VisionAPIError(f"Failed to download image: HTTP {resp.status_code}")
+        content_type = resp.headers.get("Content-Type", "image/jpeg").split(";")[0].strip()
+        if not content_type.startswith("image/"):
+            content_type = "image/jpeg"
+        b64 = base64.b64encode(resp.content).decode("ascii")
+        data_url = f"data:{content_type};base64,{b64}"
+        return {"type": "image_url", "image_url": {"url": data_url}}
+
+    @staticmethod
+    def _maybe_compress(path: str) -> str:
+        """Compress image to under COMPRESS_THRESHOLD with max long-edge 1536px."""
+        file_size = os.path.getsize(path)
+        if file_size <= COMPRESS_THRESHOLD:
+            return path
+
+        tmp = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+        tmp.close()
+
+        def _try_sips(max_dim: str, quality: str) -> bool:
+            try:
+                subprocess.run(
+                    ["sips", "-Z", max_dim, "-s", "formatOptions", quality,
+                     path, "--out", tmp.name],
+                    capture_output=True, check=True,
+                )
+                return True
+            except (FileNotFoundError, subprocess.CalledProcessError):
+                return False
+
+        def _try_convert(max_dim: str, quality: str) -> bool:
+            try:
+                subprocess.run(
+                    ["convert", path, "-resize", f"{max_dim}x{max_dim}>",
+                     "-quality", quality, tmp.name],
+                    capture_output=True, check=True,
+                )
+                return True
+            except (FileNotFoundError, subprocess.CalledProcessError):
+                return False
+
+        attempts = [
+            ("1536", "85"),
+            ("1536", "70"),
+            ("1536", "50"),
+        ]
+
+        for max_dim, quality in attempts:
+            ok = _try_sips(max_dim, quality) or _try_convert(max_dim, quality)
+            if not ok:
+                continue
+            new_size = os.path.getsize(tmp.name)
+            logger.debug(f"[Vision] Compressed image "
+                         f"({file_size // 1024}KB -> {new_size // 1024}KB, "
+                         f"max_dim={max_dim}, q={quality})")
+            if new_size <= COMPRESS_THRESHOLD:
+                return tmp.name
+
+        if os.path.exists(tmp.name) and os.path.getsize(tmp.name) > 0:
+            return tmp.name
+
+        os.remove(tmp.name)
+        return path
+
+    def _call_api(self, provider: VisionProvider, model: str,
+                  question: str, image_content: dict) -> ToolResult:
+        """
+        Call a single provider's Vision API.
+        Raises VisionAPIError on recoverable failures so the caller can try
+        the next provider.
+        """
+        payload = {
+            "model": model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": question},
+                        image_content,
+                    ],
+                }
+            ],
+        }
+
+        headers = {
+            "Authorization": f"Bearer {provider.api_key}",
+            "Content-Type": "application/json",
+            **provider.extra_headers,
+        }
+
+        resp = requests.post(
+            f"{provider.api_base}/chat/completions",
+            headers=headers,
+            json=payload,
+            timeout=DEFAULT_TIMEOUT,
+        )
+
+        if resp.status_code != 200:
+            raise VisionAPIError(f"HTTP {resp.status_code}: {resp.text[:200]}")
+
+        data = resp.json()
+
+        if "error" in data:
+            msg = data["error"].get("message", "Unknown API error")
+            raise VisionAPIError(f"API error - {msg}")
+
+        content = ""
+        choices = data.get("choices", [])
+        if choices:
+            content = choices[0].get("message", {}).get("content", "")
+
+        usage = data.get("usage", {})
+        result = {
+            "model": model,
+            "provider": provider.name,
+            "content": content,
+            "usage": {
+                "prompt_tokens": usage.get("prompt_tokens", 0),
+                "completion_tokens": usage.get("completion_tokens", 0),
+                "total_tokens": usage.get("total_tokens", 0),
+            },
+        }
+        return ToolResult.success(result)

agent/tools/web_fetch/web_fetch.py

@@ -0,0 +1,444 @@
+"""
+Web Fetch tool - Fetch and extract readable content from web pages and remote files.
+
+Supports:
+- HTML web pages: extracts readable text content
+- Document files (PDF, Word, TXT, Markdown, etc.): downloads to workspace/tmp and parses content
+"""
+
+import os
+import re
+import uuid
+from typing import Dict, Any, Optional, Set
+from urllib.parse import urlparse, unquote
+
+import requests
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.utils.truncate import truncate_head, format_size
+from common.log import logger
+
+
+DEFAULT_TIMEOUT = 30
+MAX_FILE_SIZE = 50 * 1024 * 1024  # 50MB
+
+DEFAULT_HEADERS = {
+    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
+    "Accept": "*/*",
+}
+
+# Supported document file extensions
+PDF_SUFFIXES: Set[str] = {".pdf"}
+WORD_SUFFIXES: Set[str] = {".docx"}
+TEXT_SUFFIXES: Set[str] = {".txt", ".md", ".markdown", ".rst", ".csv", ".tsv", ".log"}
+SPREADSHEET_SUFFIXES: Set[str] = {".xls", ".xlsx"}
+PPT_SUFFIXES: Set[str] = {".ppt", ".pptx"}
+
+ALL_DOC_SUFFIXES = PDF_SUFFIXES | WORD_SUFFIXES | TEXT_SUFFIXES | SPREADSHEET_SUFFIXES | PPT_SUFFIXES
+
+_CHARSET_RE = re.compile(r'charset\s*=\s*["\']?\s*([\w\-]+)', re.IGNORECASE)
+_META_CHARSET_RE = re.compile(rb'<meta[^>]+charset\s*=\s*["\']?\s*([\w\-]+)', re.IGNORECASE)
+_META_HTTP_EQUIV_RE = re.compile(
+    rb'<meta[^>]+http-equiv\s*=\s*["\']?Content-Type["\']?[^>]+content\s*=\s*["\'][^"\']*charset=([\w\-]+)',
+    re.IGNORECASE,
+)
+
+
+def _extract_charset_from_content_type(content_type: str) -> Optional[str]:
+    """Extract charset from Content-Type header value."""
+    m = _CHARSET_RE.search(content_type)
+    return m.group(1) if m else None
+
+
+def _extract_charset_from_html_meta(raw_bytes: bytes) -> Optional[str]:
+    """Extract charset from HTML <meta> tags in the first few KB of raw bytes."""
+    m = _META_CHARSET_RE.search(raw_bytes)
+    if m:
+        return m.group(1).decode("ascii", errors="ignore")
+    m = _META_HTTP_EQUIV_RE.search(raw_bytes)
+    if m:
+        return m.group(1).decode("ascii", errors="ignore")
+    return None
+
+
+def _get_url_suffix(url: str) -> str:
+    """Extract file extension from URL path, ignoring query params."""
+    path = urlparse(url).path
+    return os.path.splitext(path)[-1].lower()
+
+
+def _is_document_url(url: str) -> bool:
+    """Check if URL points to a downloadable document file."""
+    suffix = _get_url_suffix(url)
+    return suffix in ALL_DOC_SUFFIXES
+
+
+class WebFetch(BaseTool):
+    """Tool for fetching web pages and remote document files"""
+
+    name: str = "web_fetch"
+    description: str = (
+        "Fetch content from a http/https URL. For web pages, extracts readable text. "
+        "For document files (PDF, Word, TXT, Markdown, Excel, PPT), downloads and parses the file content. "
+        "Supported file types: .pdf, .docx, .txt, .md, .csv, .xls, .xlsx, .ppt, .pptx"
+    )
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "url": {
+                "type": "string",
+                "description": "The HTTP/HTTPS URL to fetch (web page or document file link)"
+            }
+        },
+        "required": ["url"]
+    }
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        url = args.get("url", "").strip()
+        if not url:
+            return ToolResult.fail("Error: 'url' parameter is required")
+
+        parsed = urlparse(url)
+        if parsed.scheme not in ("http", "https"):
+            return ToolResult.fail("Error: Invalid URL (must start with http:// or https://)")
+
+        if _is_document_url(url):
+            return self._fetch_document(url)
+
+        return self._fetch_webpage(url)
+
+    # ---- Web page fetching ----
+
+    def _fetch_webpage(self, url: str) -> ToolResult:
+        """Fetch and extract readable text from an HTML web page."""
+        parsed = urlparse(url)
+        try:
+            response = requests.get(
+                url,
+                headers=DEFAULT_HEADERS,
+                timeout=DEFAULT_TIMEOUT,
+                allow_redirects=True,
+            )
+            response.raise_for_status()
+        except requests.Timeout:
+            return ToolResult.fail(f"Error: Request timed out after {DEFAULT_TIMEOUT}s")
+        except requests.ConnectionError:
+            return ToolResult.fail(f"Error: Failed to connect to {parsed.netloc}")
+        except requests.HTTPError as e:
+            return ToolResult.fail(f"Error: HTTP {e.response.status_code} for URL: {url}")
+        except Exception as e:
+            return ToolResult.fail(f"Error: Failed to fetch URL: {e}")
+
+        content_type = response.headers.get("Content-Type", "")
+        if self._is_binary_content_type(content_type) and not _is_document_url(url):
+            return self._handle_download_by_content_type(url, response, content_type)
+
+        response.encoding = self._detect_encoding(response)
+        html = response.text
+        title = self._extract_title(html)
+        text = self._extract_text(html)
+
+        return ToolResult.success(f"Title: {title}\n\nContent:\n{text}")
+
+    # ---- Document fetching ----
+
+    def _fetch_document(self, url: str) -> ToolResult:
+        """Download a document file and extract its text content."""
+        suffix = _get_url_suffix(url)
+        parsed = urlparse(url)
+        filename = self._extract_filename(url)
+        tmp_dir = self._ensure_tmp_dir()
+
+        local_path = os.path.join(tmp_dir, filename)
+        logger.info(f"[WebFetch] Downloading document: {url} -> {local_path}")
+
+        try:
+            response = requests.get(
+                url,
+                headers=DEFAULT_HEADERS,
+                timeout=DEFAULT_TIMEOUT,
+                stream=True,
+                allow_redirects=True,
+            )
+            response.raise_for_status()
+
+            content_length = int(response.headers.get("Content-Length", 0))
+            if content_length > MAX_FILE_SIZE:
+                return ToolResult.fail(
+                    f"Error: File too large ({format_size(content_length)} > {format_size(MAX_FILE_SIZE)})"
+                )
+
+            downloaded = 0
+            with open(local_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    downloaded += len(chunk)
+                    if downloaded > MAX_FILE_SIZE:
+                        f.close()
+                        os.remove(local_path)
+                        return ToolResult.fail(
+                            f"Error: File too large (>{format_size(MAX_FILE_SIZE)}), download aborted"
+                        )
+                    f.write(chunk)
+
+        except requests.Timeout:
+            return ToolResult.fail(f"Error: Download timed out after {DEFAULT_TIMEOUT}s")
+        except requests.ConnectionError:
+            return ToolResult.fail(f"Error: Failed to connect to {parsed.netloc}")
+        except requests.HTTPError as e:
+            return ToolResult.fail(f"Error: HTTP {e.response.status_code} for URL: {url}")
+        except Exception as e:
+            self._cleanup_file(local_path)
+            return ToolResult.fail(f"Error: Failed to download file: {e}")
+
+        try:
+            text = self._parse_document(local_path, suffix)
+        except Exception as e:
+            self._cleanup_file(local_path)
+            return ToolResult.fail(f"Error: Failed to parse document: {e}")
+
+        if not text or not text.strip():
+            file_size = os.path.getsize(local_path)
+            return ToolResult.success(
+                f"File downloaded to: {local_path} ({format_size(file_size)})\n"
+                f"No text content could be extracted. The file may contain only images or be encrypted."
+            )
+
+        truncation = truncate_head(text)
+        result_text = truncation.content
+
+        file_size = os.path.getsize(local_path)
+        header = f"[Document: {filename} | Size: {format_size(file_size)} | Saved to: {local_path}]\n\n"
+
+        if truncation.truncated:
+            header += f"[Content truncated: showing {truncation.output_lines} of {truncation.total_lines} lines]\n\n"
+
+        return ToolResult.success(header + result_text)
+
+    def _parse_document(self, file_path: str, suffix: str) -> str:
+        """Parse document file and return extracted text."""
+        if suffix in PDF_SUFFIXES:
+            return self._parse_pdf(file_path)
+        elif suffix in WORD_SUFFIXES:
+            return self._parse_word(file_path)
+        elif suffix in TEXT_SUFFIXES:
+            return self._parse_text(file_path)
+        elif suffix in SPREADSHEET_SUFFIXES:
+            return self._parse_spreadsheet(file_path)
+        elif suffix in PPT_SUFFIXES:
+            return self._parse_ppt(file_path)
+        else:
+            return self._parse_text(file_path)
+
+    def _parse_pdf(self, file_path: str) -> str:
+        """Extract text from PDF using pypdf."""
+        try:
+            from pypdf import PdfReader
+        except ImportError:
+            raise ImportError("pypdf library is required for PDF parsing. Install with: pip install pypdf")
+
+        reader = PdfReader(file_path)
+        text_parts = []
+        for page_num, page in enumerate(reader.pages, 1):
+            page_text = page.extract_text()
+            if page_text and page_text.strip():
+                text_parts.append(f"--- Page {page_num}/{len(reader.pages)} ---\n{page_text}")
+
+        return "\n\n".join(text_parts)
+
+    def _parse_word(self, file_path: str) -> str:
+        """Extract text from Word documents (.docx)."""
+        try:
+            from docx import Document
+        except ImportError:
+            raise ImportError(
+                "python-docx library is required for .docx parsing. Install with: pip install python-docx"
+            )
+        doc = Document(file_path)
+        paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
+        return "\n\n".join(paragraphs)
+
+    def _parse_text(self, file_path: str) -> str:
+        """Read plain text files (txt, md, csv, etc.)."""
+        encodings = ["utf-8", "utf-8-sig", "gbk", "gb2312", "latin-1"]
+        for enc in encodings:
+            try:
+                with open(file_path, "r", encoding=enc) as f:
+                    return f.read()
+            except (UnicodeDecodeError, UnicodeError):
+                continue
+        raise ValueError(f"Unable to decode file with any supported encoding: {encodings}")
+
+    def _parse_spreadsheet(self, file_path: str) -> str:
+        """Extract text from Excel files (.xls/.xlsx)."""
+        try:
+            import openpyxl
+        except ImportError:
+            raise ImportError(
+                "openpyxl library is required for .xlsx parsing. Install with: pip install openpyxl"
+            )
+
+        wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True)
+        result_parts = []
+
+        for sheet_name in wb.sheetnames:
+            ws = wb[sheet_name]
+            rows = []
+            for row in ws.iter_rows(values_only=True):
+                cells = [str(c) if c is not None else "" for c in row]
+                if any(cells):
+                    rows.append(" | ".join(cells))
+            if rows:
+                result_parts.append(f"--- Sheet: {sheet_name} ---\n" + "\n".join(rows))
+
+        wb.close()
+        return "\n\n".join(result_parts)
+
+    def _parse_ppt(self, file_path: str) -> str:
+        """Extract text from PowerPoint files (.ppt/.pptx)."""
+        try:
+            from pptx import Presentation
+        except ImportError:
+            raise ImportError(
+                "python-pptx library is required for .pptx parsing. Install with: pip install python-pptx"
+            )
+
+        prs = Presentation(file_path)
+        text_parts = []
+
+        for slide_num, slide in enumerate(prs.slides, 1):
+            slide_texts = []
+            for shape in slide.shapes:
+                if shape.has_text_frame:
+                    for paragraph in shape.text_frame.paragraphs:
+                        text = paragraph.text.strip()
+                        if text:
+                            slide_texts.append(text)
+            if slide_texts:
+                text_parts.append(f"--- Slide {slide_num}/{len(prs.slides)} ---\n" + "\n".join(slide_texts))
+
+        return "\n\n".join(text_parts)
+
+    # ---- Encoding detection ----
+
+    @staticmethod
+    def _detect_encoding(response: requests.Response) -> str:
+        """Detect response encoding with priority: Content-Type header > HTML meta > chardet > utf-8."""
+        # 1. Check Content-Type header for explicit charset
+        content_type = response.headers.get("Content-Type", "")
+        charset = _extract_charset_from_content_type(content_type)
+        if charset:
+            return charset
+
+        # 2. Scan raw bytes for HTML meta charset declaration
+        raw = response.content[:4096]
+        charset = _extract_charset_from_html_meta(raw)
+        if charset:
+            return charset
+
+        # 3. Use apparent_encoding (chardet-based detection) if confident enough
+        apparent = response.apparent_encoding
+        if apparent:
+            apparent_lower = apparent.lower()
+            # Trust CJK / Windows encodings detected by chardet
+            trusted_prefixes = ("utf", "gb", "big5", "euc", "shift_jis", "iso-2022", "windows", "ascii")
+            if any(apparent_lower.startswith(p) for p in trusted_prefixes):
+                return apparent
+
+        # 4. Fallback
+        return "utf-8"
+
+    # ---- Helper methods ----
+
+    def _ensure_tmp_dir(self) -> str:
+        """Ensure workspace/tmp directory exists and return its path."""
+        tmp_dir = os.path.join(self.cwd, "tmp")
+        os.makedirs(tmp_dir, exist_ok=True)
+        return tmp_dir
+
+    def _extract_filename(self, url: str) -> str:
+        """Extract a safe filename from URL, with a short UUID prefix to avoid collisions."""
+        path = urlparse(url).path
+        basename = os.path.basename(unquote(path))
+        if not basename or basename == "/":
+            basename = "downloaded_file"
+        # Sanitize: keep only safe chars
+        basename = re.sub(r'[^\w.\-]', '_', basename)
+        short_id = uuid.uuid4().hex[:8]
+        return f"{short_id}_{basename}"
+
+    @staticmethod
+    def _cleanup_file(path: str):
+        """Remove a file if it exists, ignoring errors."""
+        try:
+            if os.path.exists(path):
+                os.remove(path)
+        except Exception:
+            pass
+
+    @staticmethod
+    def _is_binary_content_type(content_type: str) -> bool:
+        """Check if Content-Type indicates a binary/document response."""
+        binary_types = [
+            "application/pdf",
+            "application/vnd.openxmlformats",
+            "application/vnd.ms-excel",
+            "application/vnd.ms-powerpoint",
+            "application/octet-stream",
+        ]
+        ct_lower = content_type.lower()
+        return any(bt in ct_lower for bt in binary_types)
+
+    def _handle_download_by_content_type(self, url: str, response: requests.Response, content_type: str) -> ToolResult:
+        """Handle a URL that returned binary content instead of HTML."""
+        ct_lower = content_type.lower()
+        suffix_map = {
+            "application/pdf": ".pdf",
+            "application/vnd.openxmlformats-officedocument.wordprocessingml": ".docx",
+            "application/vnd.ms-excel": ".xls",
+            "application/vnd.openxmlformats-officedocument.spreadsheetml": ".xlsx",
+            "application/vnd.ms-powerpoint": ".ppt",
+            "application/vnd.openxmlformats-officedocument.presentationml": ".pptx",
+        }
+        detected_suffix = None
+        for ct_prefix, ext in suffix_map.items():
+            if ct_prefix in ct_lower:
+                detected_suffix = ext
+                break
+
+        if detected_suffix and detected_suffix in ALL_DOC_SUFFIXES:
+            # Re-fetch as document
+            return self._fetch_document(url if _get_url_suffix(url) in ALL_DOC_SUFFIXES
+                                        else self._rewrite_url_with_suffix(url, detected_suffix))
+        return ToolResult.fail(f"Error: URL returned binary content ({content_type}), not a supported document type")
+
+    @staticmethod
+    def _rewrite_url_with_suffix(url: str, suffix: str) -> str:
+        """Append a suffix to the URL path so _get_url_suffix works correctly."""
+        parsed = urlparse(url)
+        new_path = parsed.path.rstrip("/") + suffix
+        return parsed._replace(path=new_path).geturl()
+
+    # ---- HTML extraction (unchanged) ----
+
+    @staticmethod
+    def _extract_title(html: str) -> str:
+        match = re.search(r"<title[^>]*>(.*?)</title>", html, re.IGNORECASE | re.DOTALL)
+        return match.group(1).strip() if match else "Untitled"
+
+    @staticmethod
+    def _extract_text(html: str) -> str:
+        text = re.sub(r"<script[^>]*>.*?</script>", "", html, flags=re.IGNORECASE | re.DOTALL)
+        text = re.sub(r"<style[^>]*>.*?</style>", "", text, flags=re.IGNORECASE | re.DOTALL)
+        text = re.sub(r"<[^>]+>", "", text)
+        text = text.replace("&amp;", "&").replace("&lt;", "<").replace("&gt;", ">")
+        text = text.replace("&quot;", '"').replace("&#39;", "'").replace("&nbsp;", " ")
+        text = re.sub(r"[^\S\n]+", " ", text)
+        text = re.sub(r"\n{3,}", "\n\n", text)
+        lines = [line.strip() for line in text.splitlines()]
+        text = "\n".join(lines)
+        return text.strip()

agent/tools/web_search/web_search.py

@@ -13,6 +13,7 @@ import requests
 
 from agent.tools.base_tool import BaseTool, ToolResult
 from common.log import logger
+from config import conf
 
 
 # Default timeout for API requests (seconds)
@@ -23,11 +24,7 @@ class WebSearch(BaseTool):
     """Tool for searching the web using Bocha or LinkAI search API"""
 
     name: str = "web_search"
-    description: str = (
-        "Search the web for current information, news, research topics, or any real-time data. "
-        "Returns web page titles, URLs, snippets, and optional summaries. "
-        "Use this when the user asks about recent events, needs fact-checking, or wants up-to-date information."
-    )
+    description: str = "Search the web for real-time information. Returns titles, URLs, and snippets."
 
     params: dict = {
         "type": "object",
@@ -225,12 +222,11 @@ class WebSearch(BaseTool):
         :return: Formatted search results
         """
         api_key = os.environ.get("LINKAI_API_KEY", "")
-        url = "https://api.link-ai.tech/v1/plugin/execute"
+        api_base = conf().get("linkai_api_base", "https://api.link-ai.tech")
+        url = f"{api_base.rstrip('/')}/v1/plugin/execute"
 
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {api_key}"
-        }
+        from common.utils import get_cloud_headers
+        headers = get_cloud_headers(api_key)
 
         payload = {
             "code": "web-search",

app.py

@@ -7,11 +7,260 @@ import time
 
 from channel import channel_factory
 from common import const
-from config import load_config
+from common.log import logger
+from config import load_config, conf
 from plugins import *
 import threading
 
 
+_channel_mgr = None
+
+
+def get_channel_manager():
+    return _channel_mgr
+
+
+def _parse_channel_type(raw) -> list:
+    """
+    Parse channel_type config value into a list of channel names.
+    Supports:
+      - single string: "feishu"
+      - comma-separated string: "feishu, dingtalk"
+      - list: ["feishu", "dingtalk"]
+    """
+    if isinstance(raw, list):
+        return [ch.strip() for ch in raw if ch.strip()]
+    if isinstance(raw, str):
+        return [ch.strip() for ch in raw.split(",") if ch.strip()]
+    return []
+
+
+class ChannelManager:
+    """
+    Manage the lifecycle of multiple channels running concurrently.
+    Each channel.startup() runs in its own daemon thread.
+    The web channel is started as default console unless explicitly disabled.
+    """
+
+    def __init__(self):
+        self._channels = {}        # channel_name -> channel instance
+        self._threads = {}         # channel_name -> thread
+        self._primary_channel = None
+        self._lock = threading.Lock()
+        self.cloud_mode = False    # set to True when cloud client is active
+
+    @property
+    def channel(self):
+        """Return the primary (first non-web) channel for backward compatibility."""
+        return self._primary_channel
+
+    def get_channel(self, channel_name: str):
+        return self._channels.get(channel_name)
+
+    def start(self, channel_names: list, first_start: bool = False):
+        """
+        Create and start one or more channels in sub-threads.
+        If first_start is True, plugins and linkai client will also be initialized.
+        """
+        with self._lock:
+            channels = []
+            for name in channel_names:
+                ch = channel_factory.create_channel(name)
+                ch.cloud_mode = self.cloud_mode
+                self._channels[name] = ch
+                channels.append((name, ch))
+                if self._primary_channel is None and name != "web":
+                    self._primary_channel = ch
+
+            if self._primary_channel is None and channels:
+                self._primary_channel = channels[0][1]
+
+            if first_start:
+                PluginManager().load_plugins()
+
+                # Cloud client is optional. It is only started when
+                # use_linkai=True AND cloud_deployment_id is set.
+                # By default neither is configured, so the app runs
+                # entirely locally without any remote connection.
+                if conf().get("use_linkai") and (
+                    os.environ.get("CLOUD_DEPLOYMENT_ID") or conf().get("cloud_deployment_id")
+                ):
+                    try:
+                        from common import cloud_client
+                        threading.Thread(
+                            target=cloud_client.start,
+                            args=(self._primary_channel, self),
+                            daemon=True,
+                        ).start()
+                    except Exception:
+                        pass
+
+            # Start web console first so its logs print cleanly,
+            # then start remaining channels after a brief pause.
+            web_entry = None
+            other_entries = []
+            for entry in channels:
+                if entry[0] == "web":
+                    web_entry = entry
+                else:
+                    other_entries.append(entry)
+
+            ordered = ([web_entry] if web_entry else []) + other_entries
+            for i, (name, ch) in enumerate(ordered):
+                if i > 0 and name != "web":
+                    time.sleep(0.1)
+                t = threading.Thread(target=self._run_channel, args=(name, ch), daemon=True)
+                self._threads[name] = t
+                t.start()
+                logger.debug(f"[ChannelManager] Channel '{name}' started in sub-thread")
+
+    def _run_channel(self, name: str, channel):
+        try:
+            channel.startup()
+        except Exception as e:
+            logger.error(f"[ChannelManager] Channel '{name}' startup error: {e}")
+            logger.exception(e)
+
+    def stop(self, channel_name: str = None):
+        """
+        Stop channel(s). If channel_name is given, stop only that channel;
+        otherwise stop all channels.
+        """
+        # Pop under lock, then stop outside lock to avoid deadlock
+        with self._lock:
+            names = [channel_name] if channel_name else list(self._channels.keys())
+            to_stop = []
+            for name in names:
+                ch = self._channels.pop(name, None)
+                th = self._threads.pop(name, None)
+                to_stop.append((name, ch, th))
+            if channel_name and self._primary_channel is self._channels.get(channel_name):
+                self._primary_channel = None
+
+        for name, ch, th in to_stop:
+            if ch is None:
+                logger.warning(f"[ChannelManager] Channel '{name}' not found in managed channels")
+                if th and th.is_alive():
+                    self._interrupt_thread(th, name)
+                continue
+            logger.info(f"[ChannelManager] Stopping channel '{name}'...")
+            graceful = False
+            if hasattr(ch, 'stop'):
+                try:
+                    ch.stop()
+                    graceful = True
+                except Exception as e:
+                    logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
+            if th and th.is_alive():
+                th.join(timeout=5)
+                if th.is_alive():
+                    if graceful:
+                        logger.info(f"[ChannelManager] Channel '{name}' thread still alive after stop(), "
+                                    "leaving daemon thread to finish on its own")
+                    else:
+                        logger.warning(f"[ChannelManager] Channel '{name}' thread did not exit in 5s, forcing interrupt")
+                        self._interrupt_thread(th, name)
+
+    @staticmethod
+    def _interrupt_thread(th: threading.Thread, name: str):
+        """Raise SystemExit in target thread to break blocking loops like start_forever."""
+        import ctypes
+        try:
+            tid = th.ident
+            if tid is None:
+                return
+            res = ctypes.pythonapi.PyThreadState_SetAsyncExc(
+                ctypes.c_ulong(tid), ctypes.py_object(SystemExit)
+            )
+            if res == 1:
+                logger.info(f"[ChannelManager] Interrupted thread for channel '{name}'")
+            elif res > 1:
+                ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_ulong(tid), None)
+                logger.warning(f"[ChannelManager] Failed to interrupt thread for channel '{name}'")
+        except Exception as e:
+            logger.warning(f"[ChannelManager] Thread interrupt error for '{name}': {e}")
+
+    def restart(self, new_channel_name: str):
+        """
+        Restart a single channel with a new channel type.
+        Can be called from any thread (e.g. linkai config callback).
+        """
+        logger.info(f"[ChannelManager] Restarting channel to '{new_channel_name}'...")
+        self.stop(new_channel_name)
+        _clear_singleton_cache(new_channel_name)
+        time.sleep(1)
+        self.start([new_channel_name], first_start=False)
+        logger.info(f"[ChannelManager] Channel restarted to '{new_channel_name}' successfully")
+
+    def add_channel(self, channel_name: str):
+        """
+        Dynamically add and start a new channel.
+        If the channel is already running, restart it instead.
+        """
+        with self._lock:
+            if channel_name in self._channels:
+                logger.info(f"[ChannelManager] Channel '{channel_name}' already exists, restarting")
+        if self._channels.get(channel_name):
+            self.restart(channel_name)
+            return
+        logger.info(f"[ChannelManager] Adding channel '{channel_name}'...")
+        _clear_singleton_cache(channel_name)
+        self.start([channel_name], first_start=False)
+        logger.info(f"[ChannelManager] Channel '{channel_name}' added successfully")
+
+    def remove_channel(self, channel_name: str):
+        """
+        Dynamically stop and remove a running channel.
+        """
+        with self._lock:
+            if channel_name not in self._channels:
+                logger.warning(f"[ChannelManager] Channel '{channel_name}' not found, nothing to remove")
+                return
+        logger.info(f"[ChannelManager] Removing channel '{channel_name}'...")
+        self.stop(channel_name)
+        logger.info(f"[ChannelManager] Channel '{channel_name}' removed successfully")
+
+
+def _clear_singleton_cache(channel_name: str):
+    """
+    Clear the singleton cache for the channel class so that
+    a new instance can be created with updated config.
+    """
+    cls_map = {
+        "web": "channel.web.web_channel.WebChannel",
+        "wechatmp": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
+        "wechatmp_service": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
+        "wechatcom_app": "channel.wechatcom.wechatcomapp_channel.WechatComAppChannel",
+        const.FEISHU: "channel.feishu.feishu_channel.FeiShuChanel",
+        const.DINGTALK: "channel.dingtalk.dingtalk_channel.DingTalkChanel",
+        const.WECOM_BOT: "channel.wecom_bot.wecom_bot_channel.WecomBotChannel",
+        const.QQ: "channel.qq.qq_channel.QQChannel",
+        const.WEIXIN: "channel.weixin.weixin_channel.WeixinChannel",
+        "wx": "channel.weixin.weixin_channel.WeixinChannel",
+    }
+    module_path = cls_map.get(channel_name)
+    if not module_path:
+        return
+    try:
+        parts = module_path.rsplit(".", 1)
+        module_name, class_name = parts[0], parts[1]
+        import importlib
+        module = importlib.import_module(module_name)
+        wrapper = getattr(module, class_name, None)
+        if wrapper and hasattr(wrapper, '__closure__') and wrapper.__closure__:
+            for cell in wrapper.__closure__:
+                try:
+                    cell_contents = cell.cell_contents
+                    if isinstance(cell_contents, dict):
+                        cell_contents.clear()
+                        logger.debug(f"[ChannelManager] Cleared singleton cache for {class_name}")
+                        break
+                except ValueError:
+                    pass
+    except Exception as e:
+        logger.warning(f"[ChannelManager] Failed to clear singleton cache: {e}")
+
+
 def sigterm_handler_wrap(_signo):
     old_handler = signal.getsignal(_signo)
 
@@ -25,22 +274,41 @@ def sigterm_handler_wrap(_signo):
     signal.signal(_signo, func)
 
 
-def start_channel(channel_name: str):
-    channel = channel_factory.create_channel(channel_name)
-    if channel_name in ["wx", "wxy", "terminal", "wechatmp", "web", "wechatmp_service", "wechatcom_app", "wework",
-                        const.FEISHU, const.DINGTALK]:
-        PluginManager().load_plugins()
+def _sync_builtin_skills():
+    """Sync builtin skills from project skills/ to workspace skills/ on startup."""
+    import shutil
+    try:
+        workspace = conf().get("agent_workspace", "~/cow")
+        workspace = os.path.expanduser(workspace)
+        project_root = os.path.dirname(os.path.abspath(__file__))
+        builtin_dir = os.path.join(project_root, "skills")
+        custom_dir = os.path.join(workspace, "skills")
 
-    if conf().get("use_linkai"):
-        try:
-            from common import linkai_client
-            threading.Thread(target=linkai_client.start, args=(channel,)).start()
-        except Exception as e:
-            pass
-    channel.startup()
+        if not os.path.isdir(builtin_dir):
+            return
+
+        os.makedirs(custom_dir, exist_ok=True)
+        synced = 0
+        for name in os.listdir(builtin_dir):
+            src = os.path.join(builtin_dir, name)
+            if not os.path.isdir(src) or not os.path.isfile(os.path.join(src, "SKILL.md")):
+                continue
+            dst = os.path.join(custom_dir, name)
+            try:
+                if os.path.isdir(dst):
+                    shutil.rmtree(dst)
+                shutil.copytree(src, dst)
+                synced += 1
+            except Exception as e:
+                logger.warning(f"[App] Failed to sync builtin skill '{name}': {e}")
+        if synced:
+            logger.info(f"[App] Synced {synced} builtin skill(s) to workspace")
+    except Exception as e:
+        logger.warning(f"[App] Builtin skills sync failed: {e}")
 
 
 def run():
+    global _channel_mgr
     try:
         # load config
         load_config()
@@ -49,16 +317,28 @@ def run():
         # kill signal
         sigterm_handler_wrap(signal.SIGTERM)
 
-        # create channel
-        channel_name = conf().get("channel_type", "wx")
+        # Parse channel_type into a list
+        raw_channel = conf().get("channel_type", "web")
 
         if "--cmd" in sys.argv:
-            channel_name = "terminal"
+            channel_names = ["terminal"]
+        else:
+            channel_names = _parse_channel_type(raw_channel)
+            if not channel_names:
+                channel_names = ["web"]
 
-        if channel_name == "wxy":
-            os.environ["WECHATY_LOG"] = "warn"
+        # Auto-start web console unless explicitly disabled
+        web_console_enabled = conf().get("web_console", True)
+        if web_console_enabled and "web" not in channel_names:
+            channel_names.append("web")
 
-        start_channel(channel_name)
+        # Sync builtin skills to workspace before channels start
+        _sync_builtin_skills()
+
+        logger.info(f"[App] Starting channels: {channel_names}")
+
+        _channel_mgr = ChannelManager()
+        _channel_mgr.start(channel_names, first_start=True)
 
         while True:
             time.sleep(1)

bridge/agent_bridge.py

@@ -28,7 +28,7 @@ def add_openai_compatible_support(bot_instance):
     """
     if hasattr(bot_instance, 'call_with_tools'):
         # Bot already has tool calling support (e.g., ZHIPUAIBot)
-        logger.info(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
+        logger.debug(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
         return bot_instance
 
     # Create a temporary mixin class that combines the bot with OpenAI compatibility
@@ -65,30 +65,74 @@ class AgentLLMModel(LLMModel):
     LLM Model adapter that uses COW's existing bot infrastructure
     """
 
+    _MODEL_BOT_TYPE_MAP = {
+        "wenxin": const.BAIDU, "wenxin-4": const.BAIDU,
+        "xunfei": const.XUNFEI, const.QWEN: const.QWEN_DASHSCOPE,
+        const.MODELSCOPE: const.MODELSCOPE,
+    }
+    _MODEL_PREFIX_MAP = [
+        ("qwen", const.QWEN_DASHSCOPE), ("qwq", const.QWEN_DASHSCOPE), ("qvq", const.QWEN_DASHSCOPE),
+        ("gemini", const.GEMINI), ("glm", const.ZHIPU_AI), ("claude", const.CLAUDEAPI),
+        ("moonshot", const.MOONSHOT), ("kimi", const.MOONSHOT),
+        ("doubao", const.DOUBAO), ("deepseek", const.DEEPSEEK),
+    ]
+
     def __init__(self, bridge: Bridge, bot_type: str = "chat"):
-        # Get model name directly from config
         from config import conf
-        model_name = conf().get("model", const.GPT_41)
-        super().__init__(model=model_name)
+        super().__init__(model=conf().get("model", const.GPT_41))
         self.bridge = bridge
         self.bot_type = bot_type
         self._bot = None
-        self._use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
-    
+        self._bot_model = None
+
+    @property
+    def model(self):
+        from config import conf
+        return conf().get("model", const.GPT_41)
+
+    @model.setter
+    def model(self, value):
+        pass
+
+    def _resolve_bot_type(self, model_name: str) -> str:
+        """Resolve bot type from model name, matching Bridge.__init__ logic."""
+        from config import conf
+
+        if conf().get("use_linkai", False) and conf().get("linkai_api_key"):
+            return const.LINKAI
+        # Support custom bot type configuration
+        configured_bot_type = conf().get("bot_type")
+        if configured_bot_type:
+            return configured_bot_type
+       
+        if not model_name or not isinstance(model_name, str):
+            return const.OPENAI
+        if model_name in self._MODEL_BOT_TYPE_MAP:
+            return self._MODEL_BOT_TYPE_MAP[model_name]
+        if model_name.lower().startswith("minimax") or model_name in ["abab6.5-chat"]:
+            return const.MiniMax
+        if model_name in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
+            return const.QWEN_DASHSCOPE
+        if model_name in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
+            return const.MOONSHOT
+        if conf().get("bot_type") == "modelscope":
+            return const.MODELSCOPE
+        for prefix, btype in self._MODEL_PREFIX_MAP:
+            if model_name.startswith(prefix):
+                return btype
+        return const.OPENAI
+
     @property
     def bot(self):
-        """Lazy load the bot and enhance it with tool calling if needed"""
-        if self._bot is None:
-            # If use_linkai is enabled, use LinkAI bot directly
-            if self._use_linkai:
-                self._bot = self.bridge.find_chat_bot(const.LINKAI)
-            else:
-                self._bot = self.bridge.get_bot(self.bot_type)
-                # Automatically add tool calling support if not present
-                self._bot = add_openai_compatible_support(self._bot)
-            
-            # Log bot info
-            bot_name = type(self._bot).__name__
+        """Lazy load the bot, re-create when model or bot_type changes"""
+        from models.bot_factory import create_bot
+        cur_model = self.model
+        cur_bot_type = self._resolve_bot_type(cur_model)
+        if self._bot is None or self._bot_model != cur_model or getattr(self, '_bot_type', None) != cur_bot_type:
+            self._bot = create_bot(cur_bot_type)
+            self._bot = add_openai_compatible_support(self._bot)
+            self._bot_model = cur_model
+            self._bot_type = cur_bot_type
         return self._bot
 
     def call(self, request: LLMRequest):
@@ -109,12 +153,28 @@ class AgentLLMModel(LLMModel):
                 # Only pass max_tokens if it's explicitly set
                 if request.max_tokens is not None:
                     kwargs['max_tokens'] = request.max_tokens
-                
+
                 # Extract system prompt if present
                 system_prompt = getattr(request, 'system', None)
                 if system_prompt:
                     kwargs['system'] = system_prompt
-                
+
+                # Pass context metadata to bot
+                channel_type = getattr(self, 'channel_type', None) or ''
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
+
+                # Determine thinking: respect global config, then channel_type
+                from config import conf
+                global_thinking = conf().get("enable_thinking", False)
+                if not global_thinking:
+                    kwargs['thinking'] = {"type": "disabled"}
+                else:
+                    kwargs['thinking'] = {"type": "enabled"} if channel_type == "web" else {"type": "disabled"}
+
                 response = self.bot.call_with_tools(**kwargs)
                 return self._format_response(response)
             else:
@@ -135,7 +195,7 @@ class AgentLLMModel(LLMModel):
                 # Use tool-enabled streaming call if available
                 # Extract system prompt if present
                 system_prompt = getattr(request, 'system', None)
-                
+
                 # Build kwargs for call_with_tools
                 kwargs = {
                     'messages': request.messages,
@@ -143,15 +203,31 @@ class AgentLLMModel(LLMModel):
                     'stream': True,
                     'model': self.model  # Pass model parameter
                 }
-                
+
                 # Only pass max_tokens if explicitly set, let the bot use its default
                 if request.max_tokens is not None:
                     kwargs['max_tokens'] = request.max_tokens
-                
+
                 # Add system prompt if present
                 if system_prompt:
                     kwargs['system'] = system_prompt
-                
+
+                # Pass context metadata to bot
+                channel_type = getattr(self, 'channel_type', None) or ''
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
+
+                # Determine thinking: respect global config, then channel_type
+                from config import conf
+                global_thinking = conf().get("enable_thinking", False)
+                if not global_thinking:
+                    kwargs['thinking'] = {"type": "disabled"}
+                else:
+                    kwargs['thinking'] = {"type": "enabled"} if channel_type == "web" else {"type": "disabled"}
+
                 stream = self.bot.call_with_tools(**kwargs)
                 
                 # Convert stream format to our expected format
@@ -214,10 +290,13 @@ class AgentBridge:
             tool_manager.load_tools()
             
             tools = []
+            workspace_dir = kwargs.get("workspace_dir")
             for tool_name in tool_manager.tool_classes.keys():
                 try:
                     tool = tool_manager.create_tool(tool_name)
                     if tool:
+                        if workspace_dir and hasattr(tool, 'cwd'):
+                            tool.cwd = workspace_dir
                         tools.append(tool)
                 except Exception as e:
                     logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
@@ -230,12 +309,13 @@ class AgentBridge:
             tools=tools,
             max_steps=kwargs.get("max_steps", 15),
             output_mode=kwargs.get("output_mode", "logger"),
-            workspace_dir=kwargs.get("workspace_dir"),  # Pass workspace for skills loading
-            enable_skills=kwargs.get("enable_skills", True),  # Enable skills by default
-            memory_manager=kwargs.get("memory_manager"),  # Pass memory manager
+            workspace_dir=kwargs.get("workspace_dir"),
+            skill_manager=kwargs.get("skill_manager"),
+            enable_skills=kwargs.get("enable_skills", True),
+            memory_manager=kwargs.get("memory_manager"),
             max_context_tokens=kwargs.get("max_context_tokens"),
             context_reserve_tokens=kwargs.get("context_reserve_tokens"),
-            runtime_info=kwargs.get("runtime_info")  # Pass runtime_info for dynamic time updates
+            runtime_info=kwargs.get("runtime_info"),
         )
 
         # Log skill loading details
@@ -290,9 +370,10 @@ class AgentBridge:
         Returns:
             Reply object
         """
+        session_id = None
+        agent = None
         try:
             # Extract session_id from context for user isolation
-            session_id = None
             if context:
                 session_id = context.kwargs.get("session_id") or context.get("session_id")
             
@@ -325,6 +406,14 @@ class AgentBridge:
                                 logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                             break
             
+            # Pass context metadata to model for downstream API requests
+            if context and hasattr(agent, 'model'):
+                agent.model.channel_type = context.get("channel_type", "")
+                agent.model.session_id = session_id or ""
+
+            # Store session_id on agent so executor can clear DB on fatal errors
+            agent._current_session_id = session_id
+
             try:
                 # Use agent's run_stream method with event handler
                 response = agent.run_stream(
@@ -336,11 +425,28 @@ class AgentBridge:
                 # Restore original tools
                 if context and context.get("is_scheduled_task"):
                     agent.tools = original_tools
-                
+
                 # Log execution summary
                 event_handler.log_summary()
+
+            # Persist new messages generated during this run
+            if session_id:
+                channel_type = (context.get("channel_type") or "") if context else ""
+                new_messages = getattr(agent, '_last_run_new_messages', [])
+                if new_messages:
+                    self._persist_messages(session_id, list(new_messages), channel_type)
+                else:
+                    with agent.messages_lock:
+                        msg_count = len(agent.messages)
+                    if msg_count == 0:
+                        try:
+                            from agent.memory import get_conversation_store
+                            get_conversation_store().clear_session(session_id)
+                            logger.info(f"[AgentBridge] Cleared DB for recovered session: {session_id}")
+                        except Exception as e:
+                            logger.warning(f"[AgentBridge] Failed to clear DB after recovery: {e}")
             
-            # Check if there are files to send (from read tool)
+            # Check if there are files to send (from send/read tool)
             if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
                 files_to_send = agent.stream_executor.files_to_send
                 if files_to_send:
@@ -358,6 +464,18 @@ class AgentBridge:
             
         except Exception as e:
             logger.error(f"Agent reply error: {e}")
+            # If the agent cleared its messages due to format error / overflow,
+            # also purge the DB so the next request starts clean.
+            if session_id and agent:
+                try:
+                    with agent.messages_lock:
+                        msg_count = len(agent.messages)
+                    if msg_count == 0:
+                        from agent.memory import get_conversation_store
+                        get_conversation_store().clear_session(session_id)
+                        logger.info(f"[AgentBridge] Cleared DB for session after error: {session_id}")
+                except Exception as db_err:
+                    logger.warning(f"[AgentBridge] Failed to clear DB after error: {db_err}")
             return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
     
     def _create_file_reply(self, file_info: dict, text_response: str, context: Context = None) -> Reply:
@@ -397,22 +515,26 @@ class AgentBridge:
                 reply.text_content = text_response
             return reply
         
-        # For other unknown file types, return text with file info
-        message = text_response or file_info.get("message", "文件已准备")
-        message += f"\n\n[文件: {file_info.get('file_name', file_path)}]"
-        return Reply(ReplyType.TEXT, message)
+        # For all other file types (tar.gz, zip, etc.), also use FILE type
+        file_url = f"file://{file_path}"
+        logger.info(f"[AgentBridge] Sending generic file: {file_url}")
+        reply = Reply(ReplyType.FILE, file_url)
+        reply.file_name = file_info.get("file_name", os.path.basename(file_path))
+        if text_response:
+            reply.text_content = text_response
+        return reply
     
     def _migrate_config_to_env(self, workspace_root: str):
         """
-        Migrate API keys from config.json to .env file if not already set
-        
+        Sync API keys from config.json to .env file.
+        Adds new keys and updates changed values on each startup.
+
         Args:
             workspace_root: Workspace directory path (not used, kept for compatibility)
         """
         from config import conf
         import os
         
-        # Mapping from config.json keys to environment variable names
         key_mapping = {
             "open_ai_api_key": "OPENAI_API_KEY",
             "open_ai_api_base": "OPENAI_API_BASE",
@@ -421,10 +543,9 @@ class AgentBridge:
             "linkai_api_key": "LINKAI_API_KEY",
         }
         
-        # Use fixed secure location for .env file
         env_file = expand_path("~/.cow/.env")
         
-        # Read existing env vars from .env file
+        # Read existing env vars (key -> value)
         existing_env_vars = {}
         if os.path.exists(env_file):
             try:
@@ -432,49 +553,110 @@ class AgentBridge:
                     for line in f:
                         line = line.strip()
                         if line and not line.startswith('#') and '=' in line:
-                            key, _ = line.split('=', 1)
-                            existing_env_vars[key.strip()] = True
+                            key, val = line.split('=', 1)
+                            existing_env_vars[key.strip()] = val.strip()
             except Exception as e:
                 logger.warning(f"[AgentBridge] Failed to read .env file: {e}")
         
-        # Check which keys need to be migrated
-        keys_to_migrate = {}
+        # Sync config.json values into .env (add/update/remove)
+        updated = False
         for config_key, env_key in key_mapping.items():
-            # Skip if already in .env file
-            if env_key in existing_env_vars:
-                continue
-            
-            # Get value from config.json
-            value = conf().get(config_key, "")
-            if value and value.strip():  # Only migrate non-empty values
-                keys_to_migrate[env_key] = value.strip()
-        
-        # Log summary if there are keys to skip
-        if existing_env_vars:
-            logger.debug(f"[AgentBridge] {len(existing_env_vars)} env vars already in .env")
-        
-        # Write new keys to .env file
-        if keys_to_migrate:
+            raw = conf().get(config_key, "")
+            value = raw.strip() if raw else ""
+            old_value = existing_env_vars.get(env_key)
+
+            if value:
+                if old_value == value:
+                    continue
+                existing_env_vars[env_key] = value
+                os.environ[env_key] = value
+                updated = True
+            else:
+                if old_value is None:
+                    continue
+                existing_env_vars.pop(env_key, None)
+                os.environ.pop(env_key, None)
+                updated = True
+            updated = True
+
+        if updated:
             try:
-                # Ensure ~/.cow directory and .env file exist
                 env_dir = os.path.dirname(env_file)
-                if not os.path.exists(env_dir):
-                    os.makedirs(env_dir, exist_ok=True)
-                if not os.path.exists(env_file):
-                    open(env_file, 'a').close()
-                
-                # Append new keys
-                with open(env_file, 'a', encoding='utf-8') as f:
-                    f.write('\n# Auto-migrated from config.json\n')
-                    for key, value in keys_to_migrate.items():
+                os.makedirs(env_dir, exist_ok=True)
+
+                with open(env_file, 'w', encoding='utf-8') as f:
+                    f.write('# Environment variables for agent\n')
+                    f.write('# Auto-managed - synced from config.json on startup\n\n')
+                    for key, value in sorted(existing_env_vars.items()):
                         f.write(f'{key}={value}\n')
-                        # Also set in current process
-                        os.environ[key] = value
-                
-                logger.info(f"[AgentBridge] Migrated {len(keys_to_migrate)} API keys from config.json to .env: {list(keys_to_migrate.keys())}")
+
+                logger.info(f"[AgentBridge] Synced API keys from config.json to .env")
             except Exception as e:
-                logger.warning(f"[AgentBridge] Failed to migrate API keys: {e}")
+                logger.warning(f"[AgentBridge] Failed to sync API keys: {e}")
     
+    def _persist_messages(
+        self, session_id: str, new_messages: list, channel_type: str = ""
+    ) -> None:
+        """
+        Persist new messages to the conversation store after each agent run.
+
+        Failures are logged but never propagate — they must not interrupt replies.
+        """
+        if not new_messages:
+            return
+        try:
+            from config import conf
+            if not conf().get("conversation_persistence", True):
+                return
+            # When deep-thinking display is disabled, strip "thinking" content
+            # blocks before persisting so they don't resurface on history reload.
+            # The in-memory message list keeps them intact for this run's
+            # multi-turn LLM context.
+            thinking_enabled = bool(conf().get("enable_thinking", False))
+        except Exception:
+            thinking_enabled = False
+
+        messages_to_store = new_messages
+        if not thinking_enabled:
+            messages_to_store = self._strip_thinking_blocks(new_messages)
+
+        try:
+            from agent.memory import get_conversation_store
+            get_conversation_store().append_messages(
+                session_id, messages_to_store, channel_type=channel_type
+            )
+        except Exception as e:
+            logger.warning(
+                f"[AgentBridge] Failed to persist messages for session={session_id}: {e}"
+            )
+
+    @staticmethod
+    def _strip_thinking_blocks(messages: list) -> list:
+        """Return a shallow copy of messages with assistant "thinking" blocks removed."""
+        cleaned = []
+        for msg in messages:
+            if not isinstance(msg, dict):
+                cleaned.append(msg)
+                continue
+            if msg.get("role") != "assistant":
+                cleaned.append(msg)
+                continue
+            content = msg.get("content")
+            if not isinstance(content, list):
+                cleaned.append(msg)
+                continue
+            filtered_blocks = [
+                b for b in content
+                if not (isinstance(b, dict) and b.get("type") == "thinking")
+            ]
+            if len(filtered_blocks) == len(content):
+                cleaned.append(msg)
+            else:
+                new_msg = dict(msg)
+                new_msg["content"] = filtered_blocks
+                cleaned.append(new_msg)
+        return cleaned
+
     def clear_session(self, session_id: str):
         """
         Clear a specific session's agent and conversation history

bridge/agent_event_handler.py

@@ -26,8 +26,7 @@ class AgentEventHandler:
         if context:
             self.channel = context.kwargs.get("channel") if hasattr(context, "kwargs") else None
         
-        # Track current thinking for channel output
-        self.current_thinking = ""
+        self.current_content = ""
         self.turn_number = 0
     
     def handle_event(self, event):
@@ -47,6 +46,8 @@ class AgentEventHandler:
             self._handle_message_update(data)
         elif event_type == "message_end":
             self._handle_message_end(data)
+        elif event_type == "reasoning_update":
+            pass
         elif event_type == "tool_execution_start":
             self._handle_tool_execution_start(data)
         elif event_type == "tool_execution_end":
@@ -59,30 +60,26 @@ class AgentEventHandler:
     def _handle_turn_start(self, data):
         """Handle turn start event"""
         self.turn_number = data.get("turn", 0)
-        self.has_tool_calls_in_turn = False
-        self.current_thinking = ""
+        self.current_content = ""
     
     def _handle_message_update(self, data):
-        """Handle message update event (streaming text)"""
+        """Handle message update event (streaming content text)"""
         delta = data.get("delta", "")
-        self.current_thinking += delta
+        self.current_content += delta
     
     def _handle_message_end(self, data):
         """Handle message end event"""
         tool_calls = data.get("tool_calls", [])
         
-        # Only send thinking process if followed by tool calls
         if tool_calls:
-            if self.current_thinking.strip():
-                logger.debug(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
-                # Send thinking process to channel
-                self._send_to_channel(f"{self.current_thinking.strip()}")
+            if self.current_content.strip():
+                logger.info(f"💭 {self.current_content.strip()[:200]}{'...' if len(self.current_content) > 200 else ''}")
+                self._send_to_channel(self.current_content.strip())
         else:
-            # No tool calls = final response (logged at agent_stream level)
-            if self.current_thinking.strip():
-                logger.debug(f"💬 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
+            if self.current_content.strip():
+                logger.debug(f"💬 {self.current_content.strip()[:200]}{'...' if len(self.current_content) > 200 else ''}")
         
-        self.current_thinking = ""
+        self.current_content = ""
     
     def _handle_tool_execution_start(self, data):
         """Handle tool execution start event - logged by agent_stream.py"""
@@ -94,15 +91,15 @@ class AgentEventHandler:
     
     def _send_to_channel(self, message):
         """
-        Try to send message to channel
-        
-        Args:
-            message: Message to send
+        Try to send intermediate message to channel.
+        Skipped in SSE mode because thinking text is already streamed via on_event.
         """
+        if self.context and self.context.get("on_event"):
+            return
+
         if self.channel:
             try:
                 from bridge.reply import Reply, ReplyType
-                # Create a Reply object for the message
                 reply = Reply(ReplyType.TEXT, message)
                 self.channel._send(reply, self.context)
             except Exception as e:

bridge/agent_initializer.py

@@ -77,10 +77,6 @@ class AgentInitializer:
         # Initialize skill manager
         skill_manager = self._initialize_skill_manager(workspace_root, session_id)
         
-        # Check if first conversation
-        from agent.prompt.workspace import is_first_conversation, mark_conversation_started
-        is_first = is_first_conversation(workspace_root)
-        
         # Build system prompt
         prompt_builder = PromptBuilder(workspace_dir=workspace_root, language="zh")
         runtime_info = self._get_runtime_info(workspace_root)
@@ -91,12 +87,8 @@ class AgentInitializer:
             skill_manager=skill_manager,
             memory_manager=memory_manager,
             runtime_info=runtime_info,
-            is_first_conversation=is_first
         )
         
-        if is_first:
-            mark_conversation_started(workspace_root)
-        
         # Get cost control parameters
         from config import conf
         max_steps = conf().get("agent_max_steps", 20)
@@ -115,11 +107,135 @@ class AgentInitializer:
             runtime_info=runtime_info  # Pass runtime_info for dynamic time updates
         )
         
-        # Attach memory manager
+        # Attach memory manager and share LLM model for summarization
         if memory_manager:
             agent.memory_manager = memory_manager
-        
+            if hasattr(agent, 'model') and agent.model:
+                memory_manager.flush_manager.llm_model = agent.model
+
+        # Restore persisted conversation history for this session
+        if session_id:
+            self._restore_conversation_history(agent, session_id)
+
+        # Start daily memory flush timer (once, on first agent init regardless of session)
+        self._start_daily_flush_timer()
+
         return agent
+
+    def _restore_conversation_history(self, agent, session_id: str) -> None:
+        """
+        Load persisted conversation messages from SQLite and inject them
+        into the agent's in-memory message list.
+
+        Only user text and assistant text are restored. Tool call chains
+        (tool_use / tool_result) are stripped out because:
+        1. They are intermediate process, the value is already in the final
+           assistant text reply.
+        2. They consume massive context tokens (often 80%+ of history).
+        3. Different models have incompatible tool message formats, so
+           restoring tool chains across model switches causes 400 errors.
+        4. Eliminates the entire class of tool_use/tool_result pairing bugs.
+        """
+        from config import conf
+        if not conf().get("conversation_persistence", True):
+            return
+
+        try:
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            max_turns = conf().get("agent_max_context_turns", 20)
+            restore_turns = max(3, max_turns // 6)
+            saved = store.load_messages(session_id, max_turns=restore_turns)
+            if saved:
+                filtered = self._filter_text_only_messages(saved)
+                if filtered:
+                    with agent.messages_lock:
+                        agent.messages = filtered
+                    logger.debug(
+                        f"[AgentInitializer] Restored {len(filtered)} text messages "
+                        f"(from {len(saved)} total, {restore_turns} turns cap) "
+                        f"for session={session_id}"
+                    )
+        except Exception as e:
+            logger.warning(
+                f"[AgentInitializer] Failed to restore conversation history for "
+                f"session={session_id}: {e}"
+            )
+
+    @staticmethod
+    def _filter_text_only_messages(messages: list) -> list:
+        """
+        Extract clean user/assistant turn pairs from raw message history.
+
+        Groups messages into turns (each starting with a real user query),
+        then keeps only:
+        - The first user text in each turn (the actual user input)
+        - The last assistant text in each turn (the final answer)
+
+        All tool_use, tool_result, intermediate assistant thoughts, and
+        internal hint messages injected by the agent loop are discarded.
+        """
+
+        def _extract_text(content) -> str:
+            if isinstance(content, str):
+                return content.strip()
+            if isinstance(content, list):
+                parts = [
+                    b.get("text", "")
+                    for b in content
+                    if isinstance(b, dict) and b.get("type") == "text"
+                ]
+                return "\n".join(p for p in parts if p).strip()
+            return ""
+
+        def _is_real_user_msg(msg: dict) -> bool:
+            """True for actual user input, False for tool_result or internal hints."""
+            if msg.get("role") != "user":
+                return False
+            content = msg.get("content")
+            if isinstance(content, list):
+                has_tool_result = any(
+                    isinstance(b, dict) and b.get("type") == "tool_result"
+                    for b in content
+                )
+                if has_tool_result:
+                    return False
+            text = _extract_text(content)
+            return bool(text)
+
+        # Group into turns: each turn starts with a real user message
+        turns = []
+        current_turn = None
+        for msg in messages:
+            if _is_real_user_msg(msg):
+                if current_turn is not None:
+                    turns.append(current_turn)
+                current_turn = {"user": msg, "assistants": []}
+            elif current_turn is not None and msg.get("role") == "assistant":
+                text = _extract_text(msg.get("content"))
+                if text:
+                    current_turn["assistants"].append(text)
+        if current_turn is not None:
+            turns.append(current_turn)
+
+        # Build result: one user msg + one assistant msg per turn
+        filtered = []
+        for turn in turns:
+            user_text = _extract_text(turn["user"].get("content"))
+            if not user_text:
+                continue
+            filtered.append({
+                "role": "user",
+                "content": [{"type": "text", "text": user_text}]
+            })
+            if turn["assistants"]:
+                final_reply = turn["assistants"][-1]
+                filtered.append({
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": final_reply}]
+                })
+
+        return filtered
     
     def _load_env_file(self):
         """Load environment variables from .env file"""
@@ -148,12 +264,11 @@ class AgentInitializer:
             from agent.tools import MemorySearchTool, MemoryGetTool
             from config import conf
             
-            # Get OpenAI config
+            # Initialize embedding provider (prefer OpenAI, fallback to LinkAI)
+            embedding_provider = None
+
             openai_api_key = conf().get("open_ai_api_key", "")
             openai_api_base = conf().get("open_ai_api_base", "")
-            
-            # Initialize embedding provider
-            embedding_provider = None
             if openai_api_key and openai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
                 try:
                     embedding_provider = create_embedding_provider(
@@ -166,6 +281,22 @@ class AgentInitializer:
                         logger.info("[AgentInitializer] OpenAI embedding initialized")
                 except Exception as e:
                     logger.warning(f"[AgentInitializer] OpenAI embedding failed: {e}")
+
+            if embedding_provider is None:
+                linkai_api_key = conf().get("linkai_api_key", "") or os.environ.get("LINKAI_API_KEY", "")
+                linkai_api_base = conf().get("linkai_api_base", "https://api.link-ai.tech")
+                if linkai_api_key and linkai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+                    try:
+                        embedding_provider = create_embedding_provider(
+                            provider="linkai",
+                            model="text-embedding-3-small",
+                            api_key=linkai_api_key,
+                            api_base=f"{linkai_api_base}/v1"
+                        )
+                        if session_id is None:
+                            logger.info("[AgentInitializer] LinkAI embedding initialized (fallback)")
+                    except Exception as e:
+                        logger.warning(f"[AgentInitializer] LinkAI embedding failed: {e}")
             
             # Create memory manager
             memory_config = MemoryConfig(workspace_root=workspace_root)
@@ -235,7 +366,7 @@ class AgentInitializer:
 
                 if tool:
                     # Apply workspace config to file operation tools
-                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']:
+                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls', 'web_fetch', 'send', 'browser']:
                         tool.config = file_config
                         tool.cwd = file_config.get("cwd", getattr(tool, 'cwd', None))
                         if 'memory_manager' in file_config:
@@ -283,7 +414,14 @@ class AgentInitializer:
                         tool.scheduler_service = scheduler_service
                         if not tool.config:
                             tool.config = {}
-                        tool.config["channel_type"] = conf().get("channel_type", "unknown")
+                        raw_ct = conf().get("channel_type", "unknown")
+                        if isinstance(raw_ct, list):
+                            ct = raw_ct[0] if raw_ct else "unknown"
+                        elif isinstance(raw_ct, str) and "," in raw_ct:
+                            ct = raw_ct.split(",")[0].strip()
+                        else:
+                            ct = raw_ct
+                        tool.config["channel_type"] = ct
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to inject scheduler dependencies: {e}")
     
@@ -291,7 +429,7 @@ class AgentInitializer:
         """Initialize skill manager"""
         try:
             from agent.skills import SkillManager
-            skill_manager = SkillManager(workspace_dir=workspace_root)
+            skill_manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
             return skill_manager
         except Exception as e:
             logger.warning(f"[AgentInitializer] Failed to initialize SkillManager: {e}")
@@ -327,10 +465,14 @@ class AgentInitializer:
                 'timezone': timezone_name
             }
         
+        def get_model():
+            """Get current model name dynamically from config"""
+            return conf().get("model", "unknown")
+
         return {
-            "model": conf().get("model", "unknown"),
+            "_get_model": get_model,
             "workspace": workspace_root,
-            "channel": conf().get("channel_type", "unknown"),
+            "channel": ", ".join(conf().get("channel_type")) if isinstance(conf().get("channel_type"), list) else conf().get("channel_type", "unknown"),
             "_get_current_time": get_current_time  # Dynamic time function
         }
     
@@ -348,7 +490,7 @@ class AgentInitializer:
         
         env_file = expand_path("~/.cow/.env")
         
-        # Read existing env vars
+        # Read existing env vars (key -> value)
         existing_env_vars = {}
         if os.path.exists(env_file):
             try:
@@ -356,35 +498,123 @@ class AgentInitializer:
                     for line in f:
                         line = line.strip()
                         if line and not line.startswith('#') and '=' in line:
-                            key, _ = line.split('=', 1)
-                            existing_env_vars[key.strip()] = True
+                            key, val = line.split('=', 1)
+                            existing_env_vars[key.strip()] = val.strip()
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to read .env file: {e}")
         
-        # Check which keys need migration
-        keys_to_migrate = {}
+        # Sync config.json values into .env (add/update/remove)
+        updated = False
         for config_key, env_key in key_mapping.items():
-            if env_key in existing_env_vars:
-                continue
-            value = conf().get(config_key, "")
-            if value and value.strip():
-                keys_to_migrate[env_key] = value.strip()
-        
-        # Write new keys
-        if keys_to_migrate:
+            raw = conf().get(config_key, "")
+            value = raw.strip() if raw else ""
+            old_value = existing_env_vars.get(env_key)
+
+            if value:
+                if old_value == value:
+                    continue
+                existing_env_vars[env_key] = value
+                os.environ[env_key] = value
+                updated = True
+            else:
+                if old_value is None:
+                    continue
+                existing_env_vars.pop(env_key, None)
+                os.environ.pop(env_key, None)
+                updated = True
+
+        if updated:
             try:
                 env_dir = os.path.dirname(env_file)
-                if not os.path.exists(env_dir):
-                    os.makedirs(env_dir, exist_ok=True)
-                if not os.path.exists(env_file):
-                    open(env_file, 'a').close()
-                
-                with open(env_file, 'a', encoding='utf-8') as f:
-                    f.write('\n# Auto-migrated from config.json\n')
-                    for key, value in keys_to_migrate.items():
+                os.makedirs(env_dir, exist_ok=True)
+
+                # Rewrite the entire .env file to ensure consistency
+                with open(env_file, 'w', encoding='utf-8') as f:
+                    f.write('# Environment variables for agent\n')
+                    f.write('# Auto-managed - synced from config.json on startup\n\n')
+                    for key, value in sorted(existing_env_vars.items()):
                         f.write(f'{key}={value}\n')
-                        os.environ[key] = value
-                
-                logger.info(f"[AgentInitializer] Migrated {len(keys_to_migrate)} API keys to .env: {list(keys_to_migrate.keys())}")
+
+                logger.info(f"[AgentInitializer] Synced API keys from config.json to .env")
             except Exception as e:
-                logger.warning(f"[AgentInitializer] Failed to migrate API keys: {e}")
+                logger.warning(f"[AgentInitializer] Failed to sync API keys: {e}")
+
+    def _start_daily_flush_timer(self):
+        """Start a background thread that flushes all agents' memory daily at 23:55."""
+        if getattr(self.agent_bridge, '_daily_flush_started', False):
+            return
+        self.agent_bridge._daily_flush_started = True
+
+        import threading
+
+        def _daily_flush_loop():
+            import random
+            while True:
+                try:
+                    now = datetime.datetime.now()
+                    jitter_min = random.randint(50, 55)
+                    jitter_sec = random.randint(0, 59)
+                    target = now.replace(hour=23, minute=jitter_min, second=jitter_sec, microsecond=0)
+                    if target <= now:
+                        target += datetime.timedelta(days=1)
+                    wait_seconds = (target - now).total_seconds()
+                    logger.info(f"[DailyFlush] Next flush at {target.strftime('%Y-%m-%d %H:%M:%S')} (in {wait_seconds/3600:.1f}h)")
+                    time.sleep(wait_seconds)
+
+                    self._flush_all_agents()
+                except Exception as e:
+                    logger.warning(f"[DailyFlush] Error in daily flush loop: {e}")
+                    time.sleep(3600)
+
+        t = threading.Thread(target=_daily_flush_loop, daemon=True)
+        t.start()
+
+    def _flush_all_agents(self):
+        """Flush memory for all active agent sessions, then run Deep Dream."""
+        agents = []
+        if self.agent_bridge.default_agent:
+            agents.append(("default", self.agent_bridge.default_agent))
+        for sid, agent in self.agent_bridge.agents.items():
+            agents.append((sid, agent))
+
+        if not agents:
+            return
+
+        # Phase 1: flush daily summaries
+        flushed = 0
+        flush_threads = []
+        dream_candidate = None
+        for label, agent in agents:
+            try:
+                if not agent.memory_manager:
+                    continue
+                with agent.messages_lock:
+                    messages = list(agent.messages)
+                if not messages:
+                    continue
+                result = agent.memory_manager.flush_manager.create_daily_summary(messages)
+                if result:
+                    flushed += 1
+                    t = agent.memory_manager.flush_manager._last_flush_thread
+                    if t:
+                        flush_threads.append(t)
+                if dream_candidate is None:
+                    dream_candidate = agent.memory_manager.flush_manager
+            except Exception as e:
+                logger.warning(f"[DailyFlush] Failed for session {label}: {e}")
+
+        if flushed:
+            logger.info(f"[DailyFlush] Flushed {flushed}/{len(agents)} agent session(s)")
+
+        # Wait for all flush threads to finish before dreaming
+        for t in flush_threads:
+            t.join(timeout=60)
+
+        # Phase 2: Deep Dream — distill daily memories → MEMORY.md + dream diary
+        if dream_candidate:
+            try:
+                result = dream_candidate.deep_dream()
+                if result:
+                    logger.info("[DeepDream] Memory distillation completed successfully")
+            except Exception as e:
+                logger.warning(f"[DeepDream] Failed: {e}")

bridge/bridge.py

@@ -13,7 +13,7 @@ from voice.factory import create_voice
 class Bridge(object):
     def __init__(self):
         self.btype = {
-            "chat": const.CHATGPT,
+            "chat": const.OPENAI,
             "voice_to_text": conf().get("voice_to_text", "openai"),
             "text_to_voice": conf().get("text_to_voice", "google"),
             "translate": conf().get("translate", "baidu"),
@@ -39,11 +39,8 @@ class Bridge(object):
                 self.btype["chat"] = const.BAIDU
             if model_type in ["xunfei"]:
                 self.btype["chat"] = const.XUNFEI
-            if model_type in [const.QWEN]:
-                self.btype["chat"] = const.QWEN
-            if model_type in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
+            if model_type in [const.QWEN, const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
                 self.btype["chat"] = const.QWEN_DASHSCOPE
-            # Support Qwen3 and other DashScope models
             if model_type and (model_type.startswith("qwen") or model_type.startswith("qwq") or model_type.startswith("qvq")):
                 self.btype["chat"] = const.QWEN_DASHSCOPE
             if model_type and model_type.startswith("gemini"):
@@ -55,6 +52,14 @@ class Bridge(object):
 
             if model_type in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
                 self.btype["chat"] = const.MOONSHOT
+            if model_type and model_type.startswith("kimi"):
+                self.btype["chat"] = const.MOONSHOT
+
+            if model_type and model_type.startswith("doubao"):
+                self.btype["chat"] = const.DOUBAO
+
+            if model_type and model_type.startswith("deepseek"):
+                self.btype["chat"] = const.DEEPSEEK
 
             if model_type in [const.MODELSCOPE]:
                 self.btype["chat"] = const.MODELSCOPE

channel/channel.py

@@ -13,12 +13,44 @@ class Channel(object):
     channel_type = ""
     NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE, ReplyType.IMAGE]
 
+    def __init__(self):
+        import threading
+        self._startup_event = threading.Event()
+        self._startup_error = None
+        self.cloud_mode = False  # set to True by ChannelManager when running with cloud client
+
     def startup(self):
         """
         init channel
         """
         raise NotImplementedError
 
+    def report_startup_success(self):
+        self._startup_error = None
+        self._startup_event.set()
+
+    def report_startup_error(self, error: str):
+        self._startup_error = error
+        self._startup_event.set()
+
+    def wait_startup(self, timeout: float = 3) -> (bool, str):
+        """
+        Wait for channel startup result.
+        Returns (success: bool, error_msg: str).
+        """
+        ready = self._startup_event.wait(timeout=timeout)
+        if not ready:
+            return True, ""
+        if self._startup_error:
+            return False, self._startup_error
+        return True, ""
+
+    def stop(self):
+        """
+        stop channel gracefully, called before restart
+        """
+        pass
+
     def handle_text(self, msg):
         """
         process received msg
@@ -51,11 +83,14 @@ class Channel(object):
                 if context and "channel_type" not in context:
                     context["channel_type"] = self.channel_type
 
+                # Read on_event callback injected by the channel (e.g. web SSE)
+                on_event = context.get("on_event") if context else None
+
                 # Use agent bridge to handle the query
                 return Bridge().fetch_agent_reply(
                     query=query,
                     context=context,
-                    on_event=None,
+                    on_event=on_event,
                     clear_history=False
                 )
             except Exception as e:

channel/channel_factory.py

@@ -12,16 +12,7 @@ def create_channel(channel_type) -> Channel:
     :return: channel instance
     """
     ch = Channel()
-    if channel_type == "wx":
-        from channel.wechat.wechat_channel import WechatChannel
-        ch = WechatChannel()
-    elif channel_type == "wxy":
-        from channel.wechat.wechaty_channel import WechatyChannel
-        ch = WechatyChannel()
-    elif channel_type == "wcf":
-        from channel.wechat.wcf_channel import WechatfChannel
-        ch = WechatfChannel()
-    elif channel_type == "terminal":
+    if channel_type == "terminal":
         from channel.terminal.terminal_channel import TerminalChannel
         ch = TerminalChannel()
     elif channel_type == 'web':
@@ -36,15 +27,22 @@ def create_channel(channel_type) -> Channel:
     elif channel_type == "wechatcom_app":
         from channel.wechatcom.wechatcomapp_channel import WechatComAppChannel
         ch = WechatComAppChannel()
-    elif channel_type == "wework":
-        from channel.wework.wework_channel import WeworkChannel
-        ch = WeworkChannel()
     elif channel_type == const.FEISHU:
         from channel.feishu.feishu_channel import FeiShuChanel
         ch = FeiShuChanel()
     elif channel_type == const.DINGTALK:
         from channel.dingtalk.dingtalk_channel import DingTalkChanel
         ch = DingTalkChanel()
+    elif channel_type == const.WECOM_BOT:
+        from channel.wecom_bot.wecom_bot_channel import WecomBotChannel
+        ch = WecomBotChannel()
+    elif channel_type == const.QQ:
+        from channel.qq.qq_channel import QQChannel
+        ch = QQChannel()
+    elif channel_type in (const.WEIXIN, "wx"):
+        from channel.weixin.weixin_channel import WeixinChannel
+        ch = WeixinChannel()
+        channel_type = const.WEIXIN
     else:
         raise RuntimeError
     ch.channel_type = channel_type

channel/chat_channel.py

@@ -24,11 +24,17 @@ handler_pool = ThreadPoolExecutor(max_workers=8)  # 处理消息的线程池
 class ChatChannel(Channel):
     name = None  # 登录的用户名
     user_id = None  # 登录的用户id
-    futures = {}  # 记录每个session_id提交到线程池的future对象, 用于重置会话时把没执行的future取消掉，正在执行的不会被取消
-    sessions = {}  # 用于控制并发，每个session_id同时只能有一个context在处理
-    lock = threading.Lock()  # 用于控制对sessions的访问
 
     def __init__(self):
+        super().__init__()
+        # Instance-level attributes so each channel subclass has its own
+        # independent session queue and lock. Previously these were class-level,
+        # which caused contexts from one channel (e.g. Feishu) to be consumed
+        # by another channel's consume() thread (e.g. Web), leading to errors
+        # like "No request_id found in context".
+        self.futures = {}
+        self.sessions = {}
+        self.lock = threading.Lock()
         _thread = threading.Thread(target=self.consume)
         _thread.setDaemon(True)
         _thread.start()
@@ -37,9 +43,8 @@ class ChatChannel(Channel):
     def _compose_context(self, ctype: ContextType, content, **kwargs):
         context = Context(ctype, content)
         context.kwargs = kwargs
-        # context首次传入时，origin_ctype是None,
-        # 引入的起因是：当输入语音时，会嵌套生成两个context，第一步语音转文本，第二步通过文本生成文字回复。
-        # origin_ctype用于第二步文本回复时，判断是否需要匹配前缀，如果是私聊的语音，就不需要匹配前缀
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
         if "origin_ctype" not in context:
             context["origin_ctype"] = ctype
         # context首次传入时，receiver是None，根据类型设置receiver
@@ -292,8 +297,12 @@ class ChatChannel(Channel):
                 logger.debug("[chat_channel] sending reply: {}, context: {}".format(reply, context))
                 
                 # 如果是文本回复，尝试提取并发送图片
-                if reply.type == ReplyType.TEXT:
+                # Web channel renders images/videos inline via renderMarkdown,
+                # so skip the extract-and-send step to avoid duplicate media.
+                if reply.type == ReplyType.TEXT and context.get("channel_type") != "web":
                     self._extract_and_send_images(reply, context)
+                elif reply.type == ReplyType.TEXT:
+                    self._send(reply, context)
                 # 如果是图片回复但带有文本内容，先发文本再发图片
                 elif reply.type == ReplyType.IMAGE_URL and hasattr(reply, 'text_content') and reply.text_content:
                     # 先发送文本
@@ -342,38 +351,30 @@ class ChatChannel(Channel):
         if media_items:
             logger.info(f"[chat_channel] Extracted {len(media_items)} media item(s) from reply")
             
-            # 先发送文本（保持原文本不变）
+            # Send text first (the frontend will embed video players via renderMarkdown).
             logger.info(f"[chat_channel] Sending text content before media: {reply.content[:100]}...")
             self._send(reply, context)
             logger.info(f"[chat_channel] Text sent, now sending {len(media_items)} media item(s)")
             
-            # 然后逐个发送媒体文件
             for i, (url, media_type) in enumerate(media_items):
                 try:
-                    # 判断是本地文件还是URL
+                    # Determine whether it is a remote URL or a local file.
                     if url.startswith(('http://', 'https://')):
-                        # 网络资源
                         if media_type == 'video':
-                            # 视频使用 FILE 类型发送
                             media_reply = Reply(ReplyType.FILE, url)
                             media_reply.file_name = os.path.basename(url)
                         else:
-                            # 图片使用 IMAGE_URL 类型
                             media_reply = Reply(ReplyType.IMAGE_URL, url)
                     elif os.path.exists(url):
-                        # 本地文件
                         if media_type == 'video':
-                            # 视频使用 FILE 类型，转换为 file:// URL
                             media_reply = Reply(ReplyType.FILE, f"file://{url}")
                             media_reply.file_name = os.path.basename(url)
                         else:
-                            # 图片使用 IMAGE_URL 类型，转换为 file:// URL
                             media_reply = Reply(ReplyType.IMAGE_URL, f"file://{url}")
                     else:
                         logger.warning(f"[chat_channel] Media file not found or invalid URL: {url}")
                         continue
                     
-                    # 发送媒体文件（添加小延迟避免频率限制）
                     if i > 0:
                         time.sleep(0.5)
                     self._send(media_reply, context)
@@ -426,7 +427,7 @@ class ChatChannel(Channel):
             if session_id not in self.sessions:
                 self.sessions[session_id] = [
                     Dequeue(),
-                    threading.BoundedSemaphore(conf().get("concurrency_in_session", 4)),
+                    threading.BoundedSemaphore(conf().get("concurrency_in_session", 1)),
                 ]
             if context.type == ContextType.TEXT and context.content.startswith("#"):
                 self.sessions[session_id][0].putleft(context)  # 优先处理管理命令

channel/chat_message.py

@@ -1,5 +1,5 @@
 """
-本类表示聊天消息，用于对itchat和wechaty的消息进行统一的封装。
+Unified chat message class for different channel implementations.
 
 填好必填项(群聊6个，非群聊8个)，即可接入ChatChannel，并支持插件，参考TerminalChannel
 

channel/dingtalk/dingtalk_channel.py

@@ -90,13 +90,9 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
     dingtalk_client_secret = conf().get('dingtalk_client_secret')
 
     def setup_logger(self):
-        logger = logging.getLogger()
-        handler = logging.StreamHandler()
-        handler.setFormatter(
-            logging.Formatter('%(asctime)s %(name)-8s %(levelname)-8s %(message)s [%(filename)s:%(lineno)d]'))
-        logger.addHandler(handler)
-        logger.setLevel(logging.INFO)
-        return logger
+        # Suppress verbose logs from dingtalk_stream SDK
+        logging.getLogger("dingtalk_stream").setLevel(logging.WARNING)
+        return logging.getLogger("DingTalk")
 
     def __init__(self):
         super().__init__()
@@ -104,6 +100,9 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
         self.logger = self.setup_logger()
         # 历史消息id暂存，用于幂等控制
         self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
+        self._stream_client = None
+        self._running = False
+        self._event_loop = None
         logger.debug("[DingTalk] client_id={}, client_secret={} ".format(
             self.dingtalk_client_id, self.dingtalk_client_secret))
         # 无需群校验和前缀
@@ -116,12 +115,130 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
         # Robot code cache (extracted from incoming messages)
         self._robot_code = None
 
+    def _open_connection(self, client):
+        """
+        Open a DingTalk stream connection directly, bypassing SDK's internal error-swallowing.
+        Returns (connection_dict, error_str). On success error_str is empty; on failure
+        connection_dict is None and error_str contains a human-readable message.
+        """
+        try:
+            resp = requests.post(
+                "https://api.dingtalk.com/v1.0/gateway/connections/open",
+                headers={"Content-Type": "application/json", "Accept": "application/json"},
+                json={
+                    "clientId": client.credential.client_id,
+                    "clientSecret": client.credential.client_secret,
+                    "subscriptions": [{"type": "CALLBACK",
+                                       "topic": dingtalk_stream.chatbot.ChatbotMessage.TOPIC}],
+                    "ua": "dingtalk-sdk-python/cow",
+                    "localIp": "",
+                },
+                timeout=10,
+            )
+            body = resp.json()
+            if not resp.ok:
+                code = body.get("code", resp.status_code)
+                message = body.get("message", resp.reason)
+                return None, f"open connection failed: [{code}] {message}"
+            return body, ""
+        except Exception as e:
+            return None, f"open connection failed: {e}"
+
     def startup(self):
+        import asyncio
+        self.dingtalk_client_id = conf().get('dingtalk_client_id')
+        self.dingtalk_client_secret = conf().get('dingtalk_client_secret')
+        self._running = True
         credential = dingtalk_stream.Credential(self.dingtalk_client_id, self.dingtalk_client_secret)
         client = dingtalk_stream.DingTalkStreamClient(credential)
+        self._stream_client = client
         client.register_callback_handler(dingtalk_stream.chatbot.ChatbotMessage.TOPIC, self)
-        logger.info("[DingTalk] ✅ Stream connected, ready to receive messages")
-        client.start_forever()
+        logger.info("[DingTalk] ✅ Stream client initialized, ready to receive messages")
+
+        # Run the connection loop ourselves instead of delegating to client.start(),
+        # so we can get detailed error messages and respond to stop() quickly.
+        import urllib.parse as _urlparse
+        import websockets as _ws
+        import json as _json
+        client.pre_start()
+        _first_connect = True
+        while self._running:
+            # Open connection using our own request so we get detailed error info.
+            connection, err_msg = self._open_connection(client)
+
+            if connection is None:
+                if _first_connect:
+                    logger.warning(f"[DingTalk] {err_msg}")
+                    self.report_startup_error(err_msg)
+                    _first_connect = False
+                else:
+                    logger.warning(f"[DingTalk] {err_msg}, retrying in 10s...")
+
+                # Interruptible sleep: checks _running every 100ms.
+                for _ in range(100):
+                    if not self._running:
+                        break
+                    time.sleep(0.1)
+                continue
+
+            if _first_connect:
+                logger.info("[DingTalk] ✅ Connected to DingTalk stream")
+                self.report_startup_success()
+                _first_connect = False
+            else:
+                logger.info("[DingTalk] Reconnected to DingTalk stream")
+
+            # Run the WebSocket session in an asyncio loop.
+            uri = '%s?ticket=%s' % (
+                connection['endpoint'],
+                _urlparse.quote_plus(connection['ticket'])
+            )
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            self._event_loop = loop
+            try:
+                async def _session():
+                    async with _ws.connect(uri) as websocket:
+                        client.websocket = websocket
+                        async for raw_message in websocket:
+                            json_message = _json.loads(raw_message)
+                            result = await client.route_message(json_message)
+                            if result == dingtalk_stream.DingTalkStreamClient.TAG_DISCONNECT:
+                                break
+
+                loop.run_until_complete(_session())
+            except (KeyboardInterrupt, SystemExit):
+                logger.info("[DingTalk] Session loop received stop signal, exiting")
+                break
+            except Exception as e:
+                if not self._running:
+                    break
+                logger.warning(f"[DingTalk] Stream session error: {e}, reconnecting in 3s...")
+                for _ in range(30):
+                    if not self._running:
+                        break
+                    time.sleep(0.1)
+            finally:
+                self._event_loop = None
+                try:
+                    loop.close()
+                except Exception:
+                    pass
+
+        logger.info("[DingTalk] Startup loop exited")
+
+    def stop(self):
+        logger.info("[DingTalk] stop() called, setting _running=False")
+        self._running = False
+        loop = self._event_loop
+        if loop and not loop.is_closed():
+            try:
+                loop.call_soon_threadsafe(loop.stop)
+                logger.info("[DingTalk] Sent stop signal to event loop")
+            except Exception as e:
+                logger.warning(f"[DingTalk] Error stopping event loop: {e}")
+        self._stream_client = None
+        logger.info("[DingTalk] stop() completed")
     
     def get_access_token(self):
         """
@@ -458,23 +575,21 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
     async def process(self, callback: dingtalk_stream.CallbackMessage):
         try:
             incoming_message = dingtalk_stream.ChatbotMessage.from_dict(callback.data)
-            
+
             # 缓存 robot_code，用于后续图片下载
             if hasattr(incoming_message, 'robot_code'):
                 self._robot_code_cache = incoming_message.robot_code
-            
-            # Debug: 打印完整的 event 数据
-            logger.debug(f"[DingTalk] ===== Incoming Message Debug =====")
-            logger.debug(f"[DingTalk] callback.data keys: {callback.data.keys() if hasattr(callback.data, 'keys') else 'N/A'}")
-            logger.debug(f"[DingTalk] incoming_message attributes: {dir(incoming_message)}")
-            logger.debug(f"[DingTalk] robot_code: {getattr(incoming_message, 'robot_code', 'N/A')}")
-            logger.debug(f"[DingTalk] chatbot_corp_id: {getattr(incoming_message, 'chatbot_corp_id', 'N/A')}")
-            logger.debug(f"[DingTalk] chatbot_user_id: {getattr(incoming_message, 'chatbot_user_id', 'N/A')}")
-            logger.debug(f"[DingTalk] conversation_id: {getattr(incoming_message, 'conversation_id', 'N/A')}")
-            logger.debug(f"[DingTalk] Raw callback.data: {callback.data}")
-            logger.debug(f"[DingTalk] =====================================")
-            
-            image_download_handler = self  # 传入方法所在的类实例
+
+            # Filter out stale messages from before channel startup (offline backlog)
+            create_at = getattr(incoming_message, 'create_at', None)
+            if create_at:
+                msg_age_s = time.time() - int(create_at) / 1000
+                if msg_age_s > 60:
+                    logger.warning(f"[DingTalk] stale msg filtered (age={msg_age_s:.0f}s), "
+                                   f"msg_id={getattr(incoming_message, 'message_id', 'N/A')}")
+                    return AckMessage.STATUS_OK, 'OK'
+
+            image_download_handler = self
             dingtalk_msg = DingTalkMessage(incoming_message, image_download_handler)
 
             if dingtalk_msg.is_group:
@@ -483,8 +598,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
                 self.handle_single(dingtalk_msg)
             return AckMessage.STATUS_OK, 'OK'
         except Exception as e:
-            logger.error(f"[DingTalk] process error: {e}")
-            logger.exception(e)  # 打印完整堆栈跟踪
+            logger.error(f"[DingTalk] process error: {e}", exc_info=True)
             return AckMessage.STATUS_SYSTEM_EXCEPTION, 'ERROR'
 
     @time_checker

channel/feishu/feishu_channel.py

@@ -11,7 +11,9 @@
 @Date 2023/11/19
 """
 
+import importlib.util
 import json
+import logging
 import os
 import ssl
 import threading
@@ -32,17 +34,25 @@ from common.log import logger
 from common.singleton import singleton
 from config import conf
 
+# Suppress verbose logs from Lark SDK
+logging.getLogger("Lark").setLevel(logging.WARNING)
+
 URL_VERIFICATION = "url_verification"
 
-# 尝试导入飞书SDK,如果未安装则websocket模式不可用
-try:
-    import lark_oapi as lark
+# Lazy-check for lark_oapi SDK availability without importing it at module level.
+# The full `import lark_oapi` pulls in 10k+ files and takes 4-10s, so we defer
+# the actual import to _startup_websocket() where it is needed.
+LARK_SDK_AVAILABLE = importlib.util.find_spec("lark_oapi") is not None
+lark = None  # will be populated on first use via _ensure_lark_imported()
 
-    LARK_SDK_AVAILABLE = True
-except ImportError:
-    LARK_SDK_AVAILABLE = False
-    logger.warning(
-        "[FeiShu] lark_oapi not installed, websocket mode is not available. Install with: pip install lark-oapi")
+
+def _ensure_lark_imported():
+    """Import lark_oapi on first use (takes 4-10s due to 10k+ source files)."""
+    global lark
+    if lark is None:
+        import lark_oapi as _lark
+        lark = _lark
+    return lark
 
 
 @singleton
@@ -56,6 +66,10 @@ class FeiShuChanel(ChatChannel):
         super().__init__()
         # 历史消息id暂存，用于幂等控制
         self.receivedMsgs = ExpiredDict(60 * 60 * 7.1)
+        self._http_server = None
+        self._ws_client = None
+        self._ws_thread = None
+        self._bot_open_id = None  # cached bot open_id for @-mention matching
         logger.debug("[FeiShu] app_id={}, app_secret={}, verification_token={}, event_mode={}".format(
             self.feishu_app_id, self.feishu_app_secret, self.feishu_token, self.feishu_event_mode))
         # 无需群校验和前缀
@@ -68,11 +82,66 @@ class FeiShuChanel(ChatChannel):
             raise Exception("lark_oapi not installed")
 
     def startup(self):
+        self.feishu_app_id = conf().get('feishu_app_id')
+        self.feishu_app_secret = conf().get('feishu_app_secret')
+        self.feishu_token = conf().get('feishu_token')
+        self.feishu_event_mode = conf().get('feishu_event_mode', 'websocket')
+        self._fetch_bot_open_id()
         if self.feishu_event_mode == 'websocket':
             self._startup_websocket()
         else:
             self._startup_webhook()
 
+    def _fetch_bot_open_id(self):
+        """Fetch the bot's own open_id via API so we can match @-mentions without feishu_bot_name."""
+        try:
+            access_token = self.fetch_access_token()
+            if not access_token:
+                logger.warning("[FeiShu] Cannot fetch bot info: no access_token")
+                return
+            headers = {"Authorization": "Bearer " + access_token}
+            resp = requests.get("https://open.feishu.cn/open-apis/bot/v3/info/", headers=headers, timeout=5)
+            if resp.status_code == 200:
+                data = resp.json()
+                if data.get("code") == 0:
+                    self._bot_open_id = data.get("bot", {}).get("open_id")
+                    logger.info(f"[FeiShu] Bot open_id fetched: {self._bot_open_id}")
+                else:
+                    logger.warning(f"[FeiShu] Fetch bot info failed: code={data.get('code')}, msg={data.get('msg')}")
+        except Exception as e:
+            logger.warning(f"[FeiShu] Fetch bot open_id error: {e}")
+
+    def stop(self):
+        import ctypes
+        logger.info("[FeiShu] stop() called")
+        ws_client = self._ws_client
+        self._ws_client = None
+        ws_thread = self._ws_thread
+        self._ws_thread = None
+        # Interrupt the ws thread first so its blocking start() unblocks
+        if ws_thread and ws_thread.is_alive():
+            try:
+                tid = ws_thread.ident
+                if tid:
+                    res = ctypes.pythonapi.PyThreadState_SetAsyncExc(
+                        ctypes.c_ulong(tid), ctypes.py_object(SystemExit)
+                    )
+                    if res == 1:
+                        logger.info("[FeiShu] Interrupted ws thread via ctypes")
+                    elif res > 1:
+                        ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_ulong(tid), None)
+            except Exception as e:
+                logger.warning(f"[FeiShu] Error interrupting ws thread: {e}")
+        # lark.ws.Client has no stop() method; thread interruption above is sufficient
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[FeiShu] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[FeiShu] Error stopping HTTP server: {e}")
+            self._http_server = None
+        logger.info("[FeiShu] stop() completed")
+
     def _startup_webhook(self):
         """启动HTTP服务器接收事件(webhook模式)"""
         logger.debug("[FeiShu] Starting in webhook mode...")
@@ -81,21 +150,33 @@ class FeiShuChanel(ChatChannel):
         )
         app = web.application(urls, globals(), autoreload=False)
         port = conf().get("feishu_port", 9891)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
+        try:
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
 
     def _startup_websocket(self):
         """启动长连接接收事件(websocket模式)"""
+        _ensure_lark_imported()
         logger.debug("[FeiShu] Starting in websocket mode...")
 
         # 创建事件处理器
         def handle_message_event(data: lark.im.v1.P2ImMessageReceiveV1) -> None:
             """处理接收消息事件 v2.0"""
             try:
-                logger.debug(f"[FeiShu] websocket receive event: {lark.JSON.marshal(data, indent=2)}")
-
-                # 转换为标准的event格式
                 event_dict = json.loads(lark.JSON.marshal(data))
                 event = event_dict.get("event", {})
+                msg = event.get("message", {})
+
+                # Skip group messages that don't @-mention the bot (reduce log noise)
+                if msg.get("chat_type") == "group" and not msg.get("mentions") and msg.get("message_type") == "text":
+                    return
+
+                logger.debug(f"[FeiShu] websocket receive event: {lark.JSON.marshal(data, indent=2)}")
 
                 # 处理消息
                 self._handle_message_event(event)
@@ -108,29 +189,36 @@ class FeiShuChanel(ChatChannel):
             .register_p2_im_message_receive_v1(handle_message_event) \
             .build()
 
-        # 尝试连接，如果遇到SSL错误则自动禁用证书验证
         def start_client_with_retry():
-            """启动websocket客户端，自动处理SSL证书错误"""
-            # 全局禁用SSL证书验证（在导入lark_oapi之前设置）
+            """Run ws client in this thread with its own event loop to avoid conflicts."""
+            import asyncio
             import ssl as ssl_module
-
-            # 保存原始的SSL上下文创建方法
             original_create_default_context = ssl_module.create_default_context
 
             def create_unverified_context(*args, **kwargs):
-                """创建一个不验证证书的SSL上下文"""
                 context = original_create_default_context(*args, **kwargs)
                 context.check_hostname = False
                 context.verify_mode = ssl.CERT_NONE
                 return context
 
-            # 尝试正常连接，如果失败则禁用SSL验证
+            # lark_oapi.ws.client captures the event loop at module-import time as a module-
+            # level global variable.  When a previous ws thread is force-killed via ctypes its
+            # loop may still be marked as "running", which causes the next ws_client.start()
+            # call (in this new thread) to raise "This event loop is already running".
+            # Fix: replace the module-level loop with a brand-new, idle loop before starting.
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            try:
+                import lark_oapi.ws.client as _lark_ws_client_mod
+                _lark_ws_client_mod.loop = loop
+            except Exception:
+                pass
+
+            startup_error = None
             for attempt in range(2):
                 try:
                     if attempt == 1:
-                        # 第二次尝试：禁用SSL验证
-                        logger.warning("[FeiShu] SSL certificate verification disabled due to certificate error. "
-                                       "This may happen when using corporate proxy or self-signed certificates.")
+                        logger.warning("[FeiShu] Retrying with SSL verification disabled...")
                         ssl_module.create_default_context = create_unverified_context
                         ssl_module._create_unverified_context = create_unverified_context
 
@@ -138,41 +226,62 @@ class FeiShuChanel(ChatChannel):
                         self.feishu_app_id,
                         self.feishu_app_secret,
                         event_handler=event_handler,
-                        log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.INFO
+                        log_level=lark.LogLevel.WARNING
                     )
-
+                    self._ws_client = ws_client
                     logger.debug("[FeiShu] Websocket client starting...")
                     ws_client.start()
-                    # 如果成功启动，跳出循环
                     break
 
+                except (SystemExit, KeyboardInterrupt):
+                    logger.info("[FeiShu] Websocket thread received stop signal")
+                    break
                 except Exception as e:
                     error_msg = str(e)
-                    # 检查是否是SSL证书验证错误
-                    is_ssl_error = "CERTIFICATE_VERIFY_FAILED" in error_msg or "certificate verify failed" in error_msg.lower()
-
+                    is_ssl_error = ("CERTIFICATE_VERIFY_FAILED" in error_msg
+                                    or "certificate verify failed" in error_msg.lower())
                     if is_ssl_error and attempt == 0:
-                        # 第一次遇到SSL错误，记录日志并继续循环（下次会禁用验证）
-                        logger.warning(f"[FeiShu] SSL certificate verification failed: {error_msg}")
-                        logger.info("[FeiShu] Retrying connection with SSL verification disabled...")
+                        logger.warning(f"[FeiShu] SSL error: {error_msg}, retrying...")
                         continue
-                    else:
-                        # 其他错误或禁用验证后仍失败，抛出异常
-                        logger.error(f"[FeiShu] Websocket client error: {e}", exc_info=True)
-                        # 恢复原始方法
-                        ssl_module.create_default_context = original_create_default_context
-                        raise
+                    logger.error(f"[FeiShu] Websocket client error: {e}", exc_info=True)
+                    startup_error = error_msg
+                    ssl_module.create_default_context = original_create_default_context
+                    break
+            if startup_error:
+                self.report_startup_error(startup_error)
+            try:
+                loop.close()
+            except Exception:
+                pass
+            logger.info("[FeiShu] Websocket thread exited")
 
-            # 注意：不恢复原始方法，因为ws_client.start()会持续运行
-
-        # 在新线程中启动客户端，避免阻塞主线程
         ws_thread = threading.Thread(target=start_client_with_retry, daemon=True)
+        self._ws_thread = ws_thread
         ws_thread.start()
-
-        # 保持主线程运行
-        logger.info("[FeiShu] ✅ Websocket connected, ready to receive messages")
+        logger.info("[FeiShu] ✅ Websocket thread started, ready to receive messages")
         ws_thread.join()
 
+    def _is_mention_bot(self, mentions: list) -> bool:
+        """Check whether any mention in the list refers to this bot.
+
+        Priority:
+        1. Match by open_id (obtained from /bot/v3/info at startup, no config needed)
+        2. Fallback to feishu_bot_name config for backward compatibility
+        3. If neither is available, assume the first mention is the bot (Feishu only
+           delivers group messages that @-mention the bot, so this is usually correct)
+        """
+        if self._bot_open_id:
+            return any(
+                m.get("id", {}).get("open_id") == self._bot_open_id
+                for m in mentions
+            )
+        bot_name = conf().get("feishu_bot_name")
+        if bot_name:
+            return any(m.get("name") == bot_name for m in mentions)
+        # Feishu event subscription only delivers messages that @-mention the bot,
+        # so reaching here means the bot was indeed mentioned.
+        return True
+
     def _handle_message_event(self, event: dict):
         """
         处理消息事件的核心逻辑
@@ -191,6 +300,15 @@ class FeiShuChanel(ChatChannel):
             return
         self.receivedMsgs[msg_id] = True
 
+        # Filter out stale messages from before channel startup (offline backlog)
+        import time as _time
+        create_time_ms = msg.get("create_time")
+        if create_time_ms:
+            msg_age_s = _time.time() - int(create_time_ms) / 1000
+            if msg_age_s > 60:
+                logger.warning(f"[FeiShu] stale msg filtered (age={msg_age_s:.0f}s), msg_id={msg_id}")
+                return
+
         is_group = False
         chat_type = msg.get("chat_type")
 
@@ -198,10 +316,9 @@ class FeiShuChanel(ChatChannel):
             if not msg.get("mentions") and msg.get("message_type") == "text":
                 # 群聊中未@不响应
                 return
-            if msg.get("mentions") and msg.get("mentions")[0].get("name") != conf().get("feishu_bot_name") and msg.get(
-                    "message_type") == "text":
-                # 不是@机器人，不响应
-                return
+            if msg.get("mentions") and msg.get("message_type") == "text":
+                if not self._is_mention_bot(msg.get("mentions")):
+                    return
             # 群聊
             is_group = True
             receive_id_type = "chat_id"
@@ -337,7 +454,7 @@ class FeiShuChanel(ChatChannel):
         can_reply = is_group and msg and hasattr(msg, 'msg_id') and msg.msg_id
 
         # Build content JSON
-        content_json = json.dumps(reply_content) if content_key is None else json.dumps({content_key: reply_content})
+        content_json = json.dumps(reply_content, ensure_ascii=False) if content_key is None else json.dumps({content_key: reply_content}, ensure_ascii=False)
         logger.debug(f"[FeiShu] Sending message: msg_type={msg_type}, content={content_json[:200]}")
 
         if can_reply:
@@ -677,6 +794,8 @@ class FeiShuChanel(ChatChannel):
     def _compose_context(self, ctype: ContextType, content, **kwargs):
         context = Context(ctype, content)
         context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
         if "origin_ctype" not in context:
             context["origin_ctype"] = ctype
 

channel/qq/qq_channel.py

@@ -0,0 +1,736 @@
+"""
+QQ Bot channel via WebSocket long connection.
+
+Supports:
+- Group chat (@bot), single chat (C2C), guild channel, guild DM
+- Text / image / file message send & receive
+- Heartbeat keep-alive and auto-reconnect with session resume
+"""
+
+import base64
+import json
+import os
+import threading
+import time
+
+import requests
+import websocket
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.qq.qq_message import QQMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from common.ws_client_compat import websocket_app_run_forever
+from config import conf
+
+# Rich media file_type constants
+QQ_FILE_TYPE_IMAGE = 1
+QQ_FILE_TYPE_VIDEO = 2
+QQ_FILE_TYPE_VOICE = 3
+QQ_FILE_TYPE_FILE = 4
+
+QQ_API_BASE = "https://api.sgroup.qq.com"
+
+# Intents: GROUP_AND_C2C_EVENT(1<<25) | PUBLIC_GUILD_MESSAGES(1<<30)
+DEFAULT_INTENTS = (1 << 25) | (1 << 30)
+
+# OpCode constants
+OP_DISPATCH = 0
+OP_HEARTBEAT = 1
+OP_IDENTIFY = 2
+OP_RESUME = 6
+OP_RECONNECT = 7
+OP_INVALID_SESSION = 9
+OP_HELLO = 10
+OP_HEARTBEAT_ACK = 11
+
+# Resumable error codes
+RESUMABLE_CLOSE_CODES = {4008, 4009}
+
+
+@singleton
+class QQChannel(ChatChannel):
+
+    def __init__(self):
+        super().__init__()
+        self.app_id = ""
+        self.app_secret = ""
+
+        self._access_token = ""
+        self._token_expires_at = 0
+
+        self._ws = None
+        self._ws_thread = None
+        self._heartbeat_thread = None
+        self._connected = False
+        self._stop_event = threading.Event()
+        self._token_lock = threading.Lock()
+
+        self._session_id = None
+        self._last_seq = None
+        self._heartbeat_interval = 45000
+        self._can_resume = False
+
+        self.received_msgs = ExpiredDict(60 * 60 * 7.1)
+        self._msg_seq_counter = {}
+
+        conf()["group_name_white_list"] = ["ALL_GROUP"]
+        conf()["single_chat_prefix"] = [""]
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def startup(self):
+        self.app_id = conf().get("qq_app_id", "")
+        self.app_secret = conf().get("qq_app_secret", "")
+
+        if not self.app_id or not self.app_secret:
+            err = "[QQ] qq_app_id and qq_app_secret are required"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        self._refresh_access_token()
+        if not self._access_token:
+            err = "[QQ] Failed to get initial access_token"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        self._stop_event.clear()
+        self._start_ws()
+
+    def stop(self):
+        logger.info("[QQ] stop() called")
+        self._stop_event.set()
+        if self._ws:
+            try:
+                self._ws.close()
+            except Exception:
+                pass
+        self._ws = None
+        self._connected = False
+
+    # ------------------------------------------------------------------
+    # Access Token
+    # ------------------------------------------------------------------
+
+    def _refresh_access_token(self):
+        try:
+            resp = requests.post(
+                "https://bots.qq.com/app/getAppAccessToken",
+                json={"appId": self.app_id, "clientSecret": self.app_secret},
+                timeout=10,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            self._access_token = data.get("access_token", "")
+            expires_in = int(data.get("expires_in", 7200))
+            self._token_expires_at = time.time() + expires_in - 60
+            logger.debug(f"[QQ] Access token refreshed, expires_in={expires_in}s")
+        except Exception as e:
+            logger.error(f"[QQ] Failed to refresh access_token: {e}")
+
+    def _get_access_token(self) -> str:
+        with self._token_lock:
+            if time.time() >= self._token_expires_at:
+                self._refresh_access_token()
+            return self._access_token
+
+    def _get_auth_headers(self) -> dict:
+        return {
+            "Authorization": f"QQBot {self._get_access_token()}",
+            "Content-Type": "application/json",
+        }
+
+    # ------------------------------------------------------------------
+    # WebSocket connection
+    # ------------------------------------------------------------------
+
+    def _get_ws_url(self) -> str:
+        try:
+            resp = requests.get(
+                f"{QQ_API_BASE}/gateway",
+                headers=self._get_auth_headers(),
+                timeout=10,
+            )
+            resp.raise_for_status()
+            url = resp.json().get("url", "")
+            logger.debug(f"[QQ] Gateway URL: {url}")
+            return url
+        except Exception as e:
+            logger.error(f"[QQ] Failed to get gateway URL: {e}")
+            return ""
+
+    def _start_ws(self):
+        ws_url = self._get_ws_url()
+        if not ws_url:
+            logger.error("[QQ] Cannot start WebSocket without gateway URL")
+            self.report_startup_error("Failed to get gateway URL")
+            return
+
+        def _on_open(ws):
+            logger.debug("[QQ] WebSocket connected, waiting for Hello...")
+
+        def _on_message(ws, raw):
+            try:
+                data = json.loads(raw)
+                self._handle_ws_message(data)
+            except Exception as e:
+                logger.error(f"[QQ] Failed to handle ws message: {e}", exc_info=True)
+
+        def _on_error(ws, error):
+            logger.error(f"[QQ] WebSocket error: {error}")
+
+        def _on_close(ws, close_status_code, close_msg):
+            logger.warning(f"[QQ] WebSocket closed: status={close_status_code}, msg={close_msg}")
+            self._connected = False
+            if not self._stop_event.is_set():
+                if close_status_code in RESUMABLE_CLOSE_CODES and self._session_id:
+                    self._can_resume = True
+                    logger.info("[QQ] Will attempt resume in 3s...")
+                    time.sleep(3)
+                else:
+                    self._can_resume = False
+                    logger.info("[QQ] Will reconnect in 5s...")
+                    time.sleep(5)
+                if not self._stop_event.is_set():
+                    self._start_ws()
+
+        self._ws = websocket.WebSocketApp(
+            ws_url,
+            on_open=_on_open,
+            on_message=_on_message,
+            on_error=_on_error,
+            on_close=_on_close,
+        )
+
+        def run_forever():
+            try:
+                websocket_app_run_forever(self._ws, ping_interval=0, reconnect=0)
+            except (SystemExit, KeyboardInterrupt):
+                logger.info("[QQ] WebSocket thread interrupted")
+            except Exception as e:
+                logger.error(f"[QQ] WebSocket run_forever error: {e}")
+
+        self._ws_thread = threading.Thread(target=run_forever, daemon=True)
+        self._ws_thread.start()
+        self._ws_thread.join()
+
+    def _ws_send(self, data: dict):
+        if self._ws:
+            self._ws.send(json.dumps(data, ensure_ascii=False))
+
+    # ------------------------------------------------------------------
+    # Identify & Resume & Heartbeat
+    # ------------------------------------------------------------------
+
+    def _send_identify(self):
+        self._ws_send({
+            "op": OP_IDENTIFY,
+            "d": {
+                "token": f"QQBot {self._get_access_token()}",
+                "intents": DEFAULT_INTENTS,
+                "shard": [0, 1],
+                "properties": {
+                    "$os": "linux",
+                    "$browser": "chatgpt-on-wechat",
+                    "$device": "chatgpt-on-wechat",
+                },
+            },
+        })
+        logger.debug(f"[QQ] Identify sent with intents={DEFAULT_INTENTS}")
+
+    def _send_resume(self):
+        self._ws_send({
+            "op": OP_RESUME,
+            "d": {
+                "token": f"QQBot {self._get_access_token()}",
+                "session_id": self._session_id,
+                "seq": self._last_seq,
+            },
+        })
+        logger.debug(f"[QQ] Resume sent: session_id={self._session_id}, seq={self._last_seq}")
+
+    def _start_heartbeat(self, interval_ms: int):
+        if self._heartbeat_thread and self._heartbeat_thread.is_alive():
+            return
+        self._heartbeat_interval = interval_ms
+        interval_sec = interval_ms / 1000.0
+
+        def heartbeat_loop():
+            while not self._stop_event.is_set() and self._connected:
+                try:
+                    self._ws_send({
+                        "op": OP_HEARTBEAT,
+                        "d": self._last_seq,
+                    })
+                except Exception as e:
+                    logger.warning(f"[QQ] Heartbeat send failed: {e}")
+                    break
+                self._stop_event.wait(interval_sec)
+
+        self._heartbeat_thread = threading.Thread(target=heartbeat_loop, daemon=True)
+        self._heartbeat_thread.start()
+
+    # ------------------------------------------------------------------
+    # Incoming message dispatch
+    # ------------------------------------------------------------------
+
+    def _handle_ws_message(self, data: dict):
+        op = data.get("op")
+        d = data.get("d")
+        t = data.get("t")
+        s = data.get("s")
+
+        if s is not None:
+            self._last_seq = s
+
+        if op == OP_HELLO:
+            heartbeat_interval = d.get("heartbeat_interval", 45000) if d else 45000
+            logger.debug(f"[QQ] Received Hello, heartbeat_interval={heartbeat_interval}ms")
+            self._heartbeat_interval = heartbeat_interval
+            if self._can_resume and self._session_id:
+                self._send_resume()
+            else:
+                self._send_identify()
+
+        elif op == OP_HEARTBEAT_ACK:
+            pass
+
+        elif op == OP_HEARTBEAT:
+            self._ws_send({"op": OP_HEARTBEAT, "d": self._last_seq})
+
+        elif op == OP_RECONNECT:
+            logger.warning("[QQ] Server requested reconnect")
+            self._can_resume = True
+            if self._ws:
+                self._ws.close()
+
+        elif op == OP_INVALID_SESSION:
+            logger.warning("[QQ] Invalid session, re-identifying...")
+            self._session_id = None
+            self._can_resume = False
+            time.sleep(2)
+            self._send_identify()
+
+        elif op == OP_DISPATCH:
+            if t == "READY":
+                self._session_id = d.get("session_id", "")
+                user = d.get("user", {})
+                bot_name = user.get('username', '')
+                logger.info(f"[QQ] ✅ Connected successfully (bot={bot_name})")
+                self._connected = True
+                self._can_resume = False
+                self._start_heartbeat(self._heartbeat_interval)
+                self.report_startup_success()
+
+            elif t == "RESUMED":
+                logger.info("[QQ] Session resumed successfully")
+                self._connected = True
+                self._can_resume = False
+                self._start_heartbeat(self._heartbeat_interval)
+
+            elif t in ("GROUP_AT_MESSAGE_CREATE", "C2C_MESSAGE_CREATE",
+                        "AT_MESSAGE_CREATE", "DIRECT_MESSAGE_CREATE"):
+                self._handle_msg_event(d, t)
+
+            elif t in ("GROUP_ADD_ROBOT", "FRIEND_ADD"):
+                logger.info(f"[QQ] Event: {t}")
+
+            else:
+                logger.debug(f"[QQ] Dispatch event: {t}")
+
+    # ------------------------------------------------------------------
+    # Message event handling
+    # ------------------------------------------------------------------
+
+    def _handle_msg_event(self, event_data: dict, event_type: str):
+        msg_id = event_data.get("id", "")
+        if self.received_msgs.get(msg_id):
+            logger.debug(f"[QQ] Duplicate msg filtered: {msg_id}")
+            return
+        self.received_msgs[msg_id] = True
+
+        try:
+            qq_msg = QQMessage(event_data, event_type)
+        except NotImplementedError as e:
+            logger.warning(f"[QQ] {e}")
+            return
+        except Exception as e:
+            logger.error(f"[QQ] Failed to parse message: {e}", exc_info=True)
+            return
+
+        is_group = qq_msg.is_group
+
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+
+        if is_group:
+            session_id = qq_msg.other_user_id
+        else:
+            session_id = qq_msg.from_user_id
+
+        if qq_msg.ctype == ContextType.IMAGE:
+            if hasattr(qq_msg, "image_path") and qq_msg.image_path:
+                file_cache.add(session_id, qq_msg.image_path, file_type="image")
+                logger.info(f"[QQ] Image cached for session {session_id}")
+            return
+
+        if qq_msg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                file_refs = []
+                for fi in cached_files:
+                    ftype = fi["type"]
+                    fpath = fi["path"]
+                    if ftype == "image":
+                        file_refs.append(f"[图片: {fpath}]")
+                    elif ftype == "video":
+                        file_refs.append(f"[视频: {fpath}]")
+                    else:
+                        file_refs.append(f"[文件: {fpath}]")
+                qq_msg.content = qq_msg.content + "\n" + "\n".join(file_refs)
+                logger.info(f"[QQ] Attached {len(cached_files)} cached file(s)")
+                file_cache.clear(session_id)
+
+        context = self._compose_context(
+            qq_msg.ctype,
+            qq_msg.content,
+            isgroup=is_group,
+            msg=qq_msg,
+            no_need_at=True,
+        )
+        if context:
+            self.produce(context)
+
+    # ------------------------------------------------------------------
+    # _compose_context
+    # ------------------------------------------------------------------
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+
+        if cmsg.is_group:
+            context["session_id"] = cmsg.other_user_id
+        else:
+            context["session_id"] = cmsg.from_user_id
+
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = content.strip()
+
+        return context
+
+    # ------------------------------------------------------------------
+    # Send reply
+    # ------------------------------------------------------------------
+
+    def send(self, reply: Reply, context: Context):
+        msg = context.get("msg")
+        is_group = context.get("isgroup", False)
+        receiver = context.get("receiver", "")
+
+        if not msg:
+            # Active send (e.g. scheduled tasks), no original message to reply to
+            self._active_send_text(reply.content if reply.type == ReplyType.TEXT else str(reply.content),
+                                   receiver, is_group)
+            return
+
+        event_type = getattr(msg, "event_type", "")
+        msg_id = getattr(msg, "msg_id", "")
+
+        if reply.type == ReplyType.TEXT:
+            self._send_text(reply.content, msg, event_type, msg_id)
+        elif reply.type in (ReplyType.IMAGE_URL, ReplyType.IMAGE):
+            self._send_image(reply.content, msg, event_type, msg_id)
+        elif reply.type == ReplyType.FILE:
+            if hasattr(reply, "text_content") and reply.text_content:
+                self._send_text(reply.text_content, msg, event_type, msg_id)
+                time.sleep(0.3)
+            self._send_file(reply.content, msg, event_type, msg_id)
+        elif reply.type in (ReplyType.VIDEO, ReplyType.VIDEO_URL):
+            self._send_media(reply.content, msg, event_type, msg_id, QQ_FILE_TYPE_VIDEO)
+        else:
+            logger.warning(f"[QQ] Unsupported reply type: {reply.type}, falling back to text")
+            self._send_text(str(reply.content), msg, event_type, msg_id)
+
+    # ------------------------------------------------------------------
+    # Send helpers
+    # ------------------------------------------------------------------
+
+    def _get_next_msg_seq(self, msg_id: str) -> int:
+        seq = self._msg_seq_counter.get(msg_id, 1)
+        self._msg_seq_counter[msg_id] = seq + 1
+        return seq
+
+    def _build_msg_url_and_base_body(self, msg: QQMessage, event_type: str, msg_id: str):
+        """Build the API URL and base body dict for sending a message."""
+        if event_type == "GROUP_AT_MESSAGE_CREATE":
+            group_openid = msg._rawmsg.get("group_openid", "")
+            url = f"{QQ_API_BASE}/v2/groups/{group_openid}/messages"
+            body = {
+                "msg_id": msg_id,
+                "msg_seq": self._get_next_msg_seq(msg_id),
+            }
+            return url, body, "group", group_openid
+
+        elif event_type == "C2C_MESSAGE_CREATE":
+            user_openid = msg._rawmsg.get("author", {}).get("user_openid", "") or msg.from_user_id
+            url = f"{QQ_API_BASE}/v2/users/{user_openid}/messages"
+            body = {
+                "msg_id": msg_id,
+                "msg_seq": self._get_next_msg_seq(msg_id),
+            }
+            return url, body, "c2c", user_openid
+
+        elif event_type == "AT_MESSAGE_CREATE":
+            channel_id = msg._rawmsg.get("channel_id", "")
+            url = f"{QQ_API_BASE}/channels/{channel_id}/messages"
+            body = {"msg_id": msg_id}
+            return url, body, "channel", channel_id
+
+        elif event_type == "DIRECT_MESSAGE_CREATE":
+            guild_id = msg._rawmsg.get("guild_id", "")
+            url = f"{QQ_API_BASE}/dms/{guild_id}/messages"
+            body = {"msg_id": msg_id}
+            return url, body, "dm", guild_id
+
+        return None, None, None, None
+
+    def _post_message(self, url: str, body: dict, event_type: str):
+        try:
+            resp = requests.post(url, json=body, headers=self._get_auth_headers(), timeout=10)
+            if resp.status_code in (200, 201, 202, 204):
+                logger.info(f"[QQ] Message sent successfully: event_type={event_type}")
+            else:
+                logger.error(f"[QQ] Failed to send message: status={resp.status_code}, "
+                             f"body={resp.text}")
+        except Exception as e:
+            logger.error(f"[QQ] Send message error: {e}")
+
+    # ------------------------------------------------------------------
+    # Active send (no original message, e.g. scheduled tasks)
+    # ------------------------------------------------------------------
+
+    def _active_send_text(self, content: str, receiver: str, is_group: bool):
+        """Send text without an original message (active push). QQ limits active messages to 4/month per user."""
+        if not receiver:
+            logger.warning("[QQ] No receiver for active send")
+            return
+        if is_group:
+            url = f"{QQ_API_BASE}/v2/groups/{receiver}/messages"
+        else:
+            url = f"{QQ_API_BASE}/v2/users/{receiver}/messages"
+        body = {
+            "content": content,
+            "msg_type": 0,
+        }
+        event_label = "GROUP_ACTIVE" if is_group else "C2C_ACTIVE"
+        self._post_message(url, body, event_label)
+
+    # ------------------------------------------------------------------
+    # Send text
+    # ------------------------------------------------------------------
+
+    def _send_text(self, content: str, msg: QQMessage, event_type: str, msg_id: str):
+        url, body, _, _ = self._build_msg_url_and_base_body(msg, event_type, msg_id)
+        if not url:
+            logger.warning(f"[QQ] Cannot send reply for event_type: {event_type}")
+            return
+        body["content"] = content
+        body["msg_type"] = 0
+        self._post_message(url, body, event_type)
+
+    # ------------------------------------------------------------------
+    # Rich media upload & send (image / video / file)
+    # ------------------------------------------------------------------
+
+    def _upload_rich_media(self, file_url: str, file_type: int, msg: QQMessage,
+                           event_type: str) -> str:
+        """
+        Upload media via QQ rich media API and return file_info.
+        For group: POST /v2/groups/{group_openid}/files
+        For c2c:   POST /v2/users/{openid}/files
+        """
+        if event_type == "GROUP_AT_MESSAGE_CREATE":
+            group_openid = msg._rawmsg.get("group_openid", "")
+            upload_url = f"{QQ_API_BASE}/v2/groups/{group_openid}/files"
+        elif event_type == "C2C_MESSAGE_CREATE":
+            user_openid = (msg._rawmsg.get("author", {}).get("user_openid", "")
+                           or msg.from_user_id)
+            upload_url = f"{QQ_API_BASE}/v2/users/{user_openid}/files"
+        else:
+            logger.warning(f"[QQ] Rich media upload not supported for event_type: {event_type}")
+            return ""
+
+        upload_body = {
+            "file_type": file_type,
+            "url": file_url,
+            "srv_send_msg": False,
+        }
+
+        try:
+            resp = requests.post(
+                upload_url, json=upload_body,
+                headers=self._get_auth_headers(), timeout=30,
+            )
+            if resp.status_code in (200, 201):
+                data = resp.json()
+                file_info = data.get("file_info", "")
+                logger.info(f"[QQ] Rich media uploaded: file_type={file_type}, "
+                            f"file_uuid={data.get('file_uuid', '')}")
+                return file_info
+            else:
+                logger.error(f"[QQ] Rich media upload failed: status={resp.status_code}, "
+                             f"body={resp.text}")
+                return ""
+        except Exception as e:
+            logger.error(f"[QQ] Rich media upload error: {e}")
+            return ""
+
+    def _upload_rich_media_base64(self, file_path: str, file_type: int, msg: QQMessage,
+                                  event_type: str) -> str:
+        """Upload local file via base64 file_data field."""
+        if event_type == "GROUP_AT_MESSAGE_CREATE":
+            group_openid = msg._rawmsg.get("group_openid", "")
+            upload_url = f"{QQ_API_BASE}/v2/groups/{group_openid}/files"
+        elif event_type == "C2C_MESSAGE_CREATE":
+            user_openid = (msg._rawmsg.get("author", {}).get("user_openid", "")
+                           or msg.from_user_id)
+            upload_url = f"{QQ_API_BASE}/v2/users/{user_openid}/files"
+        else:
+            logger.warning(f"[QQ] Rich media upload not supported for event_type: {event_type}")
+            return ""
+
+        try:
+            with open(file_path, "rb") as f:
+                file_data = base64.b64encode(f.read()).decode("utf-8")
+        except Exception as e:
+            logger.error(f"[QQ] Failed to read file for upload: {e}")
+            return ""
+
+        upload_body = {
+            "file_type": file_type,
+            "file_data": file_data,
+            "srv_send_msg": False,
+        }
+
+        try:
+            resp = requests.post(
+                upload_url, json=upload_body,
+                headers=self._get_auth_headers(), timeout=30,
+            )
+            if resp.status_code in (200, 201):
+                data = resp.json()
+                file_info = data.get("file_info", "")
+                logger.info(f"[QQ] Rich media uploaded (base64): file_type={file_type}, "
+                            f"file_uuid={data.get('file_uuid', '')}")
+                return file_info
+            else:
+                logger.error(f"[QQ] Rich media upload (base64) failed: status={resp.status_code}, "
+                             f"body={resp.text}")
+                return ""
+        except Exception as e:
+            logger.error(f"[QQ] Rich media upload (base64) error: {e}")
+            return ""
+
+    def _send_media_msg(self, file_info: str, msg: QQMessage, event_type: str, msg_id: str):
+        """Send a message with msg_type=7 (rich media) using file_info."""
+        url, body, _, _ = self._build_msg_url_and_base_body(msg, event_type, msg_id)
+        if not url:
+            return
+        body["msg_type"] = 7
+        body["media"] = {"file_info": file_info}
+        self._post_message(url, body, event_type)
+
+    def _send_image(self, img_path_or_url: str, msg: QQMessage, event_type: str, msg_id: str):
+        """Send image reply. Supports URL and local file path."""
+        if event_type not in ("GROUP_AT_MESSAGE_CREATE", "C2C_MESSAGE_CREATE"):
+            self._send_text(str(img_path_or_url), msg, event_type, msg_id)
+            return
+
+        if img_path_or_url.startswith("file://"):
+            img_path_or_url = img_path_or_url[7:]
+
+        if img_path_or_url.startswith(("http://", "https://")):
+            file_info = self._upload_rich_media(
+                img_path_or_url, QQ_FILE_TYPE_IMAGE, msg, event_type)
+        elif os.path.exists(img_path_or_url):
+            file_info = self._upload_rich_media_base64(
+                img_path_or_url, QQ_FILE_TYPE_IMAGE, msg, event_type)
+        else:
+            logger.error(f"[QQ] Image not found: {img_path_or_url}")
+            self._send_text("[Image send failed]", msg, event_type, msg_id)
+            return
+
+        if file_info:
+            self._send_media_msg(file_info, msg, event_type, msg_id)
+        else:
+            self._send_text("[Image upload failed]", msg, event_type, msg_id)
+
+    def _send_file(self, file_path_or_url: str, msg: QQMessage, event_type: str, msg_id: str):
+        """Send file reply."""
+        if event_type not in ("GROUP_AT_MESSAGE_CREATE", "C2C_MESSAGE_CREATE"):
+            self._send_text(str(file_path_or_url), msg, event_type, msg_id)
+            return
+
+        if file_path_or_url.startswith("file://"):
+            file_path_or_url = file_path_or_url[7:]
+
+        if file_path_or_url.startswith(("http://", "https://")):
+            file_info = self._upload_rich_media(
+                file_path_or_url, QQ_FILE_TYPE_FILE, msg, event_type)
+        elif os.path.exists(file_path_or_url):
+            file_info = self._upload_rich_media_base64(
+                file_path_or_url, QQ_FILE_TYPE_FILE, msg, event_type)
+        else:
+            logger.error(f"[QQ] File not found: {file_path_or_url}")
+            self._send_text("[File send failed]", msg, event_type, msg_id)
+            return
+
+        if file_info:
+            self._send_media_msg(file_info, msg, event_type, msg_id)
+        else:
+            self._send_text("[File upload failed]", msg, event_type, msg_id)
+
+    def _send_media(self, path_or_url: str, msg: QQMessage, event_type: str,
+                    msg_id: str, file_type: int):
+        """Generic media send for video/voice etc."""
+        if event_type not in ("GROUP_AT_MESSAGE_CREATE", "C2C_MESSAGE_CREATE"):
+            self._send_text(str(path_or_url), msg, event_type, msg_id)
+            return
+
+        if path_or_url.startswith("file://"):
+            path_or_url = path_or_url[7:]
+
+        if path_or_url.startswith(("http://", "https://")):
+            file_info = self._upload_rich_media(path_or_url, file_type, msg, event_type)
+        elif os.path.exists(path_or_url):
+            file_info = self._upload_rich_media_base64(path_or_url, file_type, msg, event_type)
+        else:
+            logger.error(f"[QQ] Media not found: {path_or_url}")
+            return
+
+        if file_info:
+            self._send_media_msg(file_info, msg, event_type, msg_id)
+        else:
+            logger.error(f"[QQ] Media upload failed: {path_or_url}")

channel/qq/qq_message.py

@@ -0,0 +1,123 @@
+import os
+import requests
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.log import logger
+from common.utils import expand_path
+from config import conf
+
+
+def _get_tmp_dir() -> str:
+    """Return the workspace tmp directory (absolute path), creating it if needed."""
+    ws_root = expand_path(conf().get("agent_workspace", "~/cow"))
+    tmp_dir = os.path.join(ws_root, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    return tmp_dir
+
+
+class QQMessage(ChatMessage):
+    """Message wrapper for QQ Bot (websocket long-connection mode)."""
+
+    def __init__(self, event_data: dict, event_type: str):
+        super().__init__(event_data)
+        self.msg_id = event_data.get("id", "")
+        self.create_time = event_data.get("timestamp", "")
+        self.is_group = event_type in ("GROUP_AT_MESSAGE_CREATE",)
+        self.event_type = event_type
+
+        author = event_data.get("author", {})
+        from_user_id = author.get("member_openid", "") or author.get("id", "")
+        group_openid = event_data.get("group_openid", "")
+
+        content = event_data.get("content", "").strip()
+
+        attachments = event_data.get("attachments", [])
+        has_image = any(
+            a.get("content_type", "").startswith("image/") for a in attachments
+        ) if attachments else False
+
+        if has_image and not content:
+            self.ctype = ContextType.IMAGE
+            img_attachment = next(
+                a for a in attachments if a.get("content_type", "").startswith("image/")
+            )
+            img_url = img_attachment.get("url", "")
+            if img_url and not img_url.startswith("http"):
+                img_url = "https://" + img_url
+            tmp_dir = _get_tmp_dir()
+            image_path = os.path.join(tmp_dir, f"qq_{self.msg_id}.png")
+            try:
+                resp = requests.get(img_url, timeout=30)
+                resp.raise_for_status()
+                with open(image_path, "wb") as f:
+                    f.write(resp.content)
+                self.content = image_path
+                self.image_path = image_path
+                logger.info(f"[QQ] Image downloaded: {image_path}")
+            except Exception as e:
+                logger.error(f"[QQ] Failed to download image: {e}")
+                self.content = "[Image download failed]"
+                self.image_path = None
+        elif has_image and content:
+            self.ctype = ContextType.TEXT
+            image_paths = []
+            tmp_dir = _get_tmp_dir()
+            for idx, att in enumerate(attachments):
+                if not att.get("content_type", "").startswith("image/"):
+                    continue
+                img_url = att.get("url", "")
+                if img_url and not img_url.startswith("http"):
+                    img_url = "https://" + img_url
+                img_path = os.path.join(tmp_dir, f"qq_{self.msg_id}_{idx}.png")
+                try:
+                    resp = requests.get(img_url, timeout=30)
+                    resp.raise_for_status()
+                    with open(img_path, "wb") as f:
+                        f.write(resp.content)
+                    image_paths.append(img_path)
+                except Exception as e:
+                    logger.error(f"[QQ] Failed to download mixed image: {e}")
+            content_parts = [content]
+            for p in image_paths:
+                content_parts.append(f"[图片: {p}]")
+            self.content = "\n".join(content_parts)
+        else:
+            self.ctype = ContextType.TEXT
+            self.content = content
+
+        if event_type == "GROUP_AT_MESSAGE_CREATE":
+            self.from_user_id = from_user_id
+            self.to_user_id = ""
+            self.other_user_id = group_openid
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = from_user_id
+
+        elif event_type == "C2C_MESSAGE_CREATE":
+            user_openid = author.get("user_openid", "") or from_user_id
+            self.from_user_id = user_openid
+            self.to_user_id = ""
+            self.other_user_id = user_openid
+            self.actual_user_id = user_openid
+
+        elif event_type == "AT_MESSAGE_CREATE":
+            self.from_user_id = from_user_id
+            self.to_user_id = ""
+            channel_id = event_data.get("channel_id", "")
+            self.other_user_id = channel_id
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = author.get("username", from_user_id)
+
+        elif event_type == "DIRECT_MESSAGE_CREATE":
+            self.from_user_id = from_user_id
+            self.to_user_id = ""
+            guild_id = event_data.get("guild_id", "")
+            self.other_user_id = f"dm_{guild_id}_{from_user_id}"
+            self.actual_user_id = from_user_id
+            self.actual_user_nickname = author.get("username", from_user_id)
+
+        else:
+            raise NotImplementedError(f"Unsupported QQ event type: {event_type}")
+
+        logger.debug(f"[QQ] Message parsed: type={event_type}, ctype={self.ctype}, "
+                     f"from={self.from_user_id}, content_len={len(self.content)}")

channel/wechat/wcf_channel.py

@@ -1,179 +0,0 @@
-# encoding:utf-8
-
-"""
-wechat channel
-"""
-
-import io
-import json
-import os
-import threading
-import time
-from queue import Empty
-from typing import Any
-
-from bridge.context import *
-from bridge.reply import *
-from channel.chat_channel import ChatChannel
-from channel.wechat.wcf_message import WechatfMessage
-from common.log import logger
-from common.singleton import singleton
-from common.utils import *
-from config import conf, get_appdata_dir
-from wcferry import Wcf, WxMsg
-
-
-@singleton
-class WechatfChannel(ChatChannel):
-    NOT_SUPPORT_REPLYTYPE = []
-
-    def __init__(self):
-        super().__init__()
-        self.NOT_SUPPORT_REPLYTYPE = []
-        # 使用字典存储最近消息，用于去重
-        self.received_msgs = {}
-        # 初始化wcferry客户端
-        self.wcf = Wcf()
-        self.wxid = None  # 登录后会被设置为当前登录用户的wxid
-
-    def startup(self):
-        """
-        启动通道
-        """
-        try:
-            # wcferry会自动唤起微信并登录
-            self.wxid = self.wcf.get_self_wxid()
-            self.name = self.wcf.get_user_info().get("name")
-            logger.info(f"微信登录成功，当前用户ID: {self.wxid}, 用户名：{self.name}")
-            self.contact_cache = ContactCache(self.wcf)
-            self.contact_cache.update()
-            # 启动消息接收
-            self.wcf.enable_receiving_msg()
-            # 创建消息处理线程
-            t = threading.Thread(target=self._process_messages, name="WeChatThread", daemon=True)
-            t.start()
-
-
-        except Exception as e:
-            logger.error(f"微信通道启动失败: {e}")
-            raise e
-
-    def _process_messages(self):
-        """
-        处理消息队列
-        """
-        while True:
-            try:
-                msg = self.wcf.get_msg()
-                if msg:
-                    self._handle_message(msg)
-            except Empty:
-                continue
-            except Exception as e:
-                logger.error(f"处理消息失败: {e}")
-                continue
-
-    def _handle_message(self, msg: WxMsg):
-        """
-        处理单条消息
-        """
-        try:
-            # 构造消息对象
-            cmsg = WechatfMessage(self, msg)
-            # 消息去重
-            if cmsg.msg_id in self.received_msgs:
-                return
-            self.received_msgs[cmsg.msg_id] = time.time()
-            # 清理过期消息ID
-            self._clean_expired_msgs()
-
-            logger.debug(f"收到消息: {msg}")
-            context = self._compose_context(cmsg.ctype, cmsg.content,
-                                            isgroup=cmsg.is_group,
-                                            msg=cmsg)
-            if context:
-                self.produce(context)
-        except Exception as e:
-            logger.error(f"处理消息失败: {e}")
-
-    def _clean_expired_msgs(self, expire_time: float = 60):
-        """
-        清理过期的消息ID
-        """
-        now = time.time()
-        for msg_id in list(self.received_msgs.keys()):
-            if now - self.received_msgs[msg_id] > expire_time:
-                del self.received_msgs[msg_id]
-
-    def send(self, reply: Reply, context: Context):
-        """
-        发送消息
-        """
-        receiver = context["receiver"]
-        if not receiver:
-            logger.error("receiver is empty")
-            return
-
-        try:
-            if reply.type == ReplyType.TEXT:
-                # 处理@信息
-                at_list = []
-                if context.get("isgroup"):
-                    if context["msg"].actual_user_id:
-                        at_list = [context["msg"].actual_user_id]
-                at_str = ",".join(at_list) if at_list else ""
-                self.wcf.send_text(reply.content, receiver, at_str)
-
-            elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
-                self.wcf.send_text(reply.content, receiver)
-            else:
-                logger.error(f"暂不支持的消息类型: {reply.type}")
-
-        except Exception as e:
-            logger.error(f"发送消息失败: {e}")
-
-    def close(self):
-        """
-        关闭通道
-        """
-        try:
-            self.wcf.cleanup()
-        except Exception as e:
-            logger.error(f"关闭通道失败: {e}")
-
-
-class ContactCache:
-    def __init__(self, wcf):
-        """
-        wcf: 一个 wcfferry.client.Wcf 实例
-        """
-        self.wcf = wcf
-        self._contact_map = {}  # 形如 {wxid: {完整联系人信息}}
-
-    def update(self):
-        """
-        更新缓存：调用 get_contacts()，
-        再把 wcf.contacts 构建成 {wxid: {完整信息}} 的字典
-        """
-        self.wcf.get_contacts()
-        self._contact_map.clear()
-        for item in self.wcf.contacts:
-            wxid = item.get('wxid')
-            if wxid:  # 确保有 wxid 字段
-                self._contact_map[wxid] = item
-
-    def get_contact(self, wxid: str) -> dict:
-        """
-        返回该 wxid 对应的完整联系人 dict，
-        如果没找到就返回 None
-        """
-        return self._contact_map.get(wxid)
-
-    def get_name_by_wxid(self, wxid: str) -> str:
-        """
-        通过wxid，获取成员/群名称
-        """
-        contact = self.get_contact(wxid)
-        if contact:
-            return contact.get('name', '')
-        return ''

channel/wechat/wcf_message.py

@@ -1,58 +0,0 @@
-# encoding:utf-8
-
-"""
-wechat channel message
-"""
-
-from bridge.context import ContextType
-from channel.chat_message import ChatMessage
-from common.log import logger
-from wcferry import WxMsg
-
-
-class WechatfMessage(ChatMessage):
-    """
-    微信消息封装类
-    """
-
-    def __init__(self, channel, wcf_msg: WxMsg, is_group=False):
-        """
-        初始化消息对象
-        :param wcf_msg: wcferry消息对象
-        :param is_group: 是否是群消息
-        """
-        super().__init__(wcf_msg)
-        self.msg_id = wcf_msg.id
-        self.create_time = wcf_msg.ts  # 使用消息时间戳
-        self.is_group = is_group or wcf_msg._is_group
-        self.wxid = channel.wxid
-        self.name = channel.name
-
-        # 解析消息类型
-        if wcf_msg.is_text():
-            self.ctype = ContextType.TEXT
-            self.content = wcf_msg.content
-        else:
-            raise NotImplementedError(f"Unsupported message type: {wcf_msg.type}")
-
-        # 设置发送者和接收者信息
-        self.from_user_id = self.wxid if wcf_msg.sender == self.wxid else wcf_msg.sender
-        self.from_user_nickname = self.name if wcf_msg.sender == self.wxid else channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
-        self.to_user_id = self.wxid
-        self.to_user_nickname = self.name
-        self.other_user_id = wcf_msg.sender
-        self.other_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
-
-        # 群消息特殊处理
-        if self.is_group:
-            self.other_user_id = wcf_msg.roomid
-            self.other_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.roomid)
-            self.actual_user_id = wcf_msg.sender
-            self.actual_user_nickname = channel.wcf.get_alias_in_chatroom(wcf_msg.sender, wcf_msg.roomid)
-            if not self.actual_user_nickname:  # 群聊获取不到企微号成员昵称，这里尝试从联系人缓存去获取
-                self.actual_user_nickname = channel.contact_cache.get_name_by_wxid(wcf_msg.sender)
-            self.room_id = wcf_msg.roomid
-            self.is_at = wcf_msg.is_at(self.wxid)  # 是否被@当前登录用户
-
-        # 判断是否是自己发送的消息
-        self.my_msg = wcf_msg.from_self()

channel/wechat/wechat_channel.py

@@ -1,309 +0,0 @@
-# encoding:utf-8
-
-"""
-wechat channel
-"""
-
-import io
-import json
-import os
-import threading
-import time
-import requests
-
-from bridge.context import *
-from bridge.reply import *
-from channel.chat_channel import ChatChannel
-from channel import chat_channel
-from channel.wechat.wechat_message import *
-from common.expired_dict import ExpiredDict
-from common.log import logger
-from common.singleton import singleton
-from common.time_check import time_checker
-from common.utils import convert_webp_to_png, remove_markdown_symbol
-from config import conf, get_appdata_dir
-from lib import itchat
-from lib.itchat.content import *
-
-
-@itchat.msg_register([TEXT, VOICE, PICTURE, NOTE, ATTACHMENT, SHARING])
-def handler_single_msg(msg):
-    try:
-        cmsg = WechatMessage(msg, False)
-    except NotImplementedError as e:
-        logger.debug("[WX]single message {} skipped: {}".format(msg["MsgId"], e))
-        return None
-    WechatChannel().handle_single(cmsg)
-    return None
-
-
-@itchat.msg_register([TEXT, VOICE, PICTURE, NOTE, ATTACHMENT, SHARING], isGroupChat=True)
-def handler_group_msg(msg):
-    try:
-        cmsg = WechatMessage(msg, True)
-    except NotImplementedError as e:
-        logger.debug("[WX]group message {} skipped: {}".format(msg["MsgId"], e))
-        return None
-    WechatChannel().handle_group(cmsg)
-    return None
-
-
-def _check(func):
-    def wrapper(self, cmsg: ChatMessage):
-        msgId = cmsg.msg_id
-        if msgId in self.receivedMsgs:
-            logger.info("Wechat message {} already received, ignore".format(msgId))
-            return
-        self.receivedMsgs[msgId] = True
-        create_time = cmsg.create_time  # 消息时间戳
-        if conf().get("hot_reload") == True and int(create_time) < int(time.time()) - 60:  # 跳过1分钟前的历史消息
-            logger.debug("[WX]history message {} skipped".format(msgId))
-            return
-        if cmsg.my_msg and not cmsg.is_group:
-            logger.debug("[WX]my message {} skipped".format(msgId))
-            return
-        return func(self, cmsg)
-
-    return wrapper
-
-
-# 可用的二维码生成接口
-# https://api.qrserver.com/v1/create-qr-code/?size=400×400&data=https://www.abc.com
-# https://api.isoyu.com/qr/?m=1&e=L&p=20&url=https://www.abc.com
-def qrCallback(uuid, status, qrcode):
-    # logger.debug("qrCallback: {} {}".format(uuid,status))
-    if status == "0":
-        try:
-            from PIL import Image
-
-            img = Image.open(io.BytesIO(qrcode))
-            _thread = threading.Thread(target=img.show, args=("QRCode",))
-            _thread.setDaemon(True)
-            _thread.start()
-        except Exception as e:
-            pass
-
-        import qrcode
-
-        url = f"https://login.weixin.qq.com/l/{uuid}"
-
-        qr_api1 = "https://api.isoyu.com/qr/?m=1&e=L&p=20&url={}".format(url)
-        qr_api2 = "https://api.qrserver.com/v1/create-qr-code/?size=400×400&data={}".format(url)
-        qr_api3 = "https://api.pwmqr.com/qrcode/create/?url={}".format(url)
-        qr_api4 = "https://my.tv.sohu.com/user/a/wvideo/getQRCode.do?text={}".format(url)
-        print("You can also scan QRCode in any website below:")
-        print(qr_api3)
-        print(qr_api4)
-        print(qr_api2)
-        print(qr_api1)
-        _send_qr_code([qr_api3, qr_api4, qr_api2, qr_api1])
-        qr = qrcode.QRCode(border=1)
-        qr.add_data(url)
-        qr.make(fit=True)
-        try:
-            qr.print_ascii(invert=True)
-        except UnicodeEncodeError:
-            print("ASCII QR code printing failed due to encoding issues.")
-
-
-@singleton
-class WechatChannel(ChatChannel):
-    NOT_SUPPORT_REPLYTYPE = []
-
-    def __init__(self):
-        super().__init__()
-        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
-        self.auto_login_times = 0
-
-    def startup(self):
-        try:
-            time.sleep(3)
-            logger.error("""[WechatChannel] 当前channel暂不可用，目前支持的channel有:
-                1. terminal: 终端
-                2. wechatmp: 个人公众号
-                3. wechatmp_service: 企业公众号
-                4. wechatcom_app: 企微自建应用
-                5. dingtalk: 钉钉
-                6. feishu: 飞书
-                7. web: 网页
-                8. wcf: wechat (需Windows环境，参考 https://github.com/zhayujie/chatgpt-on-wechat/pull/2562 )
-            可修改 config.json 配置文件的 channel_type 字段进行切换""")
-
-            # itchat.instance.receivingRetryCount = 600  # 修改断线超时时间
-            # # login by scan QRCode
-            # hotReload = conf().get("hot_reload", False)
-            # status_path = os.path.join(get_appdata_dir(), "itchat.pkl")
-            # itchat.auto_login(
-            #     enableCmdQR=2,
-            #     hotReload=hotReload,
-            #     statusStorageDir=status_path,
-            #     qrCallback=qrCallback,
-            #     exitCallback=self.exitCallback,
-            #     loginCallback=self.loginCallback
-            # )
-            # self.user_id = itchat.instance.storageClass.userName
-            # self.name = itchat.instance.storageClass.nickName
-            # logger.info("Wechat login success, user_id: {}, nickname: {}".format(self.user_id, self.name))
-            # # start message listener
-            # itchat.run()
-        except Exception as e:
-            logger.exception(e)
-
-    def exitCallback(self):
-        try:
-            from common.linkai_client import chat_client
-            if chat_client.client_id and conf().get("use_linkai"):
-                _send_logout()
-                time.sleep(2)
-                self.auto_login_times += 1
-                if self.auto_login_times < 100:
-                    chat_channel.handler_pool._shutdown = False
-                    self.startup()
-        except Exception as e:
-            pass
-
-    def loginCallback(self):
-        logger.debug("Login success")
-        _send_login_success()
-
-    # handle_* 系列函数处理收到的消息后构造Context，然后传入produce函数中处理Context和发送回复
-    # Context包含了消息的所有信息，包括以下属性
-    #   type 消息类型, 包括TEXT、VOICE、IMAGE_CREATE
-    #   content 消息内容，如果是TEXT类型，content就是文本内容，如果是VOICE类型，content就是语音文件名，如果是IMAGE_CREATE类型，content就是图片生成命令
-    #   kwargs 附加参数字典，包含以下的key：
-    #        session_id: 会话id
-    #        isgroup: 是否是群聊
-    #        receiver: 需要回复的对象
-    #        msg: ChatMessage消息对象
-    #        origin_ctype: 原始消息类型，语音转文字后，私聊时如果匹配前缀失败，会根据初始消息是否是语音来放宽触发规则
-    #        desire_rtype: 希望回复类型，默认是文本回复，设置为ReplyType.VOICE是语音回复
-    @time_checker
-    @_check
-    def handle_single(self, cmsg: ChatMessage):
-        # filter system message
-        if cmsg.other_user_id in ["weixin"]:
-            return
-        if cmsg.ctype == ContextType.VOICE:
-            if conf().get("speech_recognition") != True:
-                return
-            logger.debug("[WX]receive voice msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.IMAGE:
-            logger.debug("[WX]receive image msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.PATPAT:
-            logger.debug("[WX]receive patpat msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.TEXT:
-            logger.debug("[WX]receive text msg: {}, cmsg={}".format(json.dumps(cmsg._rawmsg, ensure_ascii=False), cmsg))
-        else:
-            logger.debug("[WX]receive msg: {}, cmsg={}".format(cmsg.content, cmsg))
-        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=False, msg=cmsg)
-        if context:
-            self.produce(context)
-
-    @time_checker
-    @_check
-    def handle_group(self, cmsg: ChatMessage):
-        if cmsg.ctype == ContextType.VOICE:
-            if conf().get("group_speech_recognition") != True:
-                return
-            logger.debug("[WX]receive voice for group msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.IMAGE:
-            logger.debug("[WX]receive image for group msg: {}".format(cmsg.content))
-        elif cmsg.ctype in [ContextType.JOIN_GROUP, ContextType.PATPAT, ContextType.ACCEPT_FRIEND, ContextType.EXIT_GROUP]:
-            logger.debug("[WX]receive note msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.TEXT:
-            # logger.debug("[WX]receive group msg: {}, cmsg={}".format(json.dumps(cmsg._rawmsg, ensure_ascii=False), cmsg))
-            pass
-        elif cmsg.ctype == ContextType.FILE:
-            logger.debug(f"[WX]receive attachment msg, file_name={cmsg.content}")
-        else:
-            logger.debug("[WX]receive group msg: {}".format(cmsg.content))
-        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=True, msg=cmsg, no_need_at=conf().get("no_need_at", False))
-        if context:
-            self.produce(context)
-
-    # 统一的发送函数，每个Channel自行实现，根据reply的type字段发送不同类型的消息
-    def send(self, reply: Reply, context: Context):
-        receiver = context["receiver"]
-        if reply.type == ReplyType.TEXT:
-            reply.content = remove_markdown_symbol(reply.content)
-            itchat.send(reply.content, toUserName=receiver)
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
-            reply.content = remove_markdown_symbol(reply.content)
-            itchat.send(reply.content, toUserName=receiver)
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.VOICE:
-            itchat.send_file(reply.content, toUserName=receiver)
-            logger.info("[WX] sendFile={}, receiver={}".format(reply.content, receiver))
-        elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
-            img_url = reply.content
-            logger.debug(f"[WX] start download image, img_url={img_url}")
-            pic_res = requests.get(img_url, stream=True)
-            image_storage = io.BytesIO()
-            size = 0
-            for block in pic_res.iter_content(1024):
-                size += len(block)
-                image_storage.write(block)
-            logger.info(f"[WX] download image success, size={size}, img_url={img_url}")
-            image_storage.seek(0)
-            if ".webp" in img_url:
-                try:
-                    image_storage = convert_webp_to_png(image_storage)
-                except Exception as e:
-                    logger.error(f"Failed to convert image: {e}")
-                    return
-            itchat.send_image(image_storage, toUserName=receiver)
-            logger.info("[WX] sendImage url={}, receiver={}".format(img_url, receiver))
-        elif reply.type == ReplyType.IMAGE:  # 从文件读取图片
-            image_storage = reply.content
-            image_storage.seek(0)
-            itchat.send_image(image_storage, toUserName=receiver)
-            logger.info("[WX] sendImage, receiver={}".format(receiver))
-        elif reply.type == ReplyType.FILE:  # 新增文件回复类型
-            file_storage = reply.content
-            itchat.send_file(file_storage, toUserName=receiver)
-            logger.info("[WX] sendFile, receiver={}".format(receiver))
-        elif reply.type == ReplyType.VIDEO:  # 新增视频回复类型
-            video_storage = reply.content
-            itchat.send_video(video_storage, toUserName=receiver)
-            logger.info("[WX] sendFile, receiver={}".format(receiver))
-        elif reply.type == ReplyType.VIDEO_URL:  # 新增视频URL回复类型
-            video_url = reply.content
-            logger.debug(f"[WX] start download video, video_url={video_url}")
-            video_res = requests.get(video_url, stream=True)
-            video_storage = io.BytesIO()
-            size = 0
-            for block in video_res.iter_content(1024):
-                size += len(block)
-                video_storage.write(block)
-            logger.info(f"[WX] download video success, size={size}, video_url={video_url}")
-            video_storage.seek(0)
-            itchat.send_video(video_storage, toUserName=receiver)
-            logger.info("[WX] sendVideo url={}, receiver={}".format(video_url, receiver))
-
-def _send_login_success():
-    try:
-        from common.linkai_client import chat_client
-        if chat_client.client_id:
-            chat_client.send_login_success()
-    except Exception as e:
-        pass
-
-
-def _send_logout():
-    try:
-        from common.linkai_client import chat_client
-        if chat_client.client_id:
-            chat_client.send_logout()
-    except Exception as e:
-        pass
-
-
-def _send_qr_code(qrcode_list: list):
-    try:
-        from common.linkai_client import chat_client
-        if chat_client.client_id:
-            chat_client.send_qrcode(qrcode_list)
-    except Exception as e:
-        pass
-

channel/wechat/wechat_message.py

@@ -1,124 +0,0 @@
-import re
-
-from bridge.context import ContextType
-from channel.chat_message import ChatMessage
-from common.log import logger
-from common.tmp_dir import TmpDir
-from lib import itchat
-from lib.itchat.content import *
-
-class WechatMessage(ChatMessage):
-    def __init__(self, itchat_msg, is_group=False):
-        super().__init__(itchat_msg)
-        self.msg_id = itchat_msg["MsgId"]
-        self.create_time = itchat_msg["CreateTime"]
-        self.is_group = is_group
-
-        notes_join_group = ["加入群聊", "加入了群聊", "invited", "joined"]  # 可通过添加对应语言的加入群聊通知中的关键词适配更多
-        notes_bot_join_group = ["邀请你", "invited you", "You've joined", "你通过扫描"]
-        notes_exit_group = ["移出了群聊", "removed"]  # 可通过添加对应语言的踢出群聊通知中的关键词适配更多
-        notes_patpat = ["拍了拍我", "tickled my", "tickled me"] # 可通过添加对应语言的拍一拍通知中的关键词适配更多
-
-        if itchat_msg["Type"] == TEXT:
-            self.ctype = ContextType.TEXT
-            self.content = itchat_msg["Text"]
-        elif itchat_msg["Type"] == VOICE:
-            self.ctype = ContextType.VOICE
-            self.content = TmpDir().path() + itchat_msg["FileName"]  # content直接存临时目录路径
-            self._prepare_fn = lambda: itchat_msg.download(self.content)
-        elif itchat_msg["Type"] == PICTURE and itchat_msg["MsgType"] == 3:
-            self.ctype = ContextType.IMAGE
-            self.content = TmpDir().path() + itchat_msg["FileName"]  # content直接存临时目录路径
-            self._prepare_fn = lambda: itchat_msg.download(self.content)
-        elif itchat_msg["Type"] == NOTE and itchat_msg["MsgType"] == 10000:
-            if is_group:
-                if any(note_bot_join_group in itchat_msg["Content"] for note_bot_join_group in notes_bot_join_group):  # 邀请机器人加入群聊
-                    logger.warn("机器人加入群聊消息，不处理~")
-                    pass
-                elif any(note_join_group in itchat_msg["Content"] for note_join_group in notes_join_group): # 若有任何在notes_join_group列表中的字符串出现在NOTE中
-                # 这里只能得到nickname， actual_user_id还是机器人的id
-                    if "加入群聊" not in itchat_msg["Content"]:
-                        self.ctype = ContextType.JOIN_GROUP
-                        self.content = itchat_msg["Content"]
-                        if "invited" in itchat_msg["Content"]: # 匹配英文信息
-                            self.actual_user_nickname = re.findall(r'invited\s+(.+?)\s+to\s+the\s+group\s+chat', itchat_msg["Content"])[0]
-                        elif "joined" in itchat_msg["Content"]: # 匹配通过二维码加入的英文信息
-                            self.actual_user_nickname = re.findall(r'"(.*?)" joined the group chat via the QR Code shared by', itchat_msg["Content"])[0]
-                        elif "加入了群聊" in itchat_msg["Content"]:
-                            self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[-1]
-                    elif "加入群聊" in itchat_msg["Content"]:
-                        self.ctype = ContextType.JOIN_GROUP
-                        self.content = itchat_msg["Content"]
-                        self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[0]
-
-                elif any(note_exit_group in itchat_msg["Content"] for note_exit_group in notes_exit_group):  # 若有任何在notes_exit_group列表中的字符串出现在NOTE中
-                    self.ctype = ContextType.EXIT_GROUP
-                    self.content = itchat_msg["Content"]
-                    self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[0]
-
-                elif any(note_patpat in itchat_msg["Content"] for note_patpat in notes_patpat):  # 若有任何在notes_patpat列表中的字符串出现在NOTE中:
-                    self.ctype = ContextType.PATPAT
-                    self.content = itchat_msg["Content"]
-                    if "拍了拍我" in itchat_msg["Content"]:  # 识别中文
-                        self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[0]
-                    elif "tickled my" in itchat_msg["Content"] or "tickled me" in itchat_msg["Content"]:
-                        self.actual_user_nickname = re.findall(r'^(.*?)(?:tickled my|tickled me)', itchat_msg["Content"])[0]
-                else:
-                    raise NotImplementedError("Unsupported note message: " + itchat_msg["Content"])
-                    
-            elif "你已添加了" in itchat_msg["Content"]:  #通过好友请求
-                self.ctype = ContextType.ACCEPT_FRIEND
-                self.content = itchat_msg["Content"]
-            elif any(note_patpat in itchat_msg["Content"] for note_patpat in notes_patpat):  # 若有任何在notes_patpat列表中的字符串出现在NOTE中:
-                self.ctype = ContextType.PATPAT
-                self.content = itchat_msg["Content"]
-            else:
-                raise NotImplementedError("Unsupported note message: " + itchat_msg["Content"])
-        elif itchat_msg["Type"] == ATTACHMENT:
-            self.ctype = ContextType.FILE
-            self.content = TmpDir().path() + itchat_msg["FileName"]  # content直接存临时目录路径
-            self._prepare_fn = lambda: itchat_msg.download(self.content)
-        elif itchat_msg["Type"] == SHARING:
-            self.ctype = ContextType.SHARING
-            self.content = itchat_msg.get("Url")
-
-        else:
-            raise NotImplementedError("Unsupported message type: Type:{} MsgType:{}".format(itchat_msg["Type"], itchat_msg["MsgType"]))
-
-        self.from_user_id = itchat_msg["FromUserName"]
-        self.to_user_id = itchat_msg["ToUserName"]
-
-        user_id = itchat.instance.storageClass.userName
-        nickname = itchat.instance.storageClass.nickName
-
-        # 虽然from_user_id和to_user_id用的少，但是为了保持一致性，还是要填充一下
-        # 以下很繁琐，一句话总结：能填的都填了。
-        if self.from_user_id == user_id:
-            self.from_user_nickname = nickname
-        if self.to_user_id == user_id:
-            self.to_user_nickname = nickname
-        try:  # 陌生人时候, User字段可能不存在
-            # my_msg 为True是表示是自己发送的消息
-            self.my_msg = itchat_msg["ToUserName"] == itchat_msg["User"]["UserName"] and \
-                          itchat_msg["ToUserName"] != itchat_msg["FromUserName"]
-            self.other_user_id = itchat_msg["User"]["UserName"]
-            self.other_user_nickname = itchat_msg["User"]["NickName"]
-            if self.other_user_id == self.from_user_id:
-                self.from_user_nickname = self.other_user_nickname
-            if self.other_user_id == self.to_user_id:
-                self.to_user_nickname = self.other_user_nickname
-            if itchat_msg["User"].get("Self"):
-                # 自身的展示名，当设置了群昵称时，该字段表示群昵称
-                self.self_display_name = itchat_msg["User"].get("Self").get("DisplayName")
-        except KeyError as e:  # 处理偶尔没有对方信息的情况
-            logger.warn("[WX]get other_user_id failed: " + str(e))
-            if self.from_user_id == user_id:
-                self.other_user_id = self.to_user_id
-            else:
-                self.other_user_id = self.from_user_id
-
-        if self.is_group:
-            self.is_at = itchat_msg["IsAt"]
-            self.actual_user_id = itchat_msg["ActualUserName"]
-            if self.ctype not in [ContextType.JOIN_GROUP, ContextType.PATPAT, ContextType.EXIT_GROUP]:
-                self.actual_user_nickname = itchat_msg["ActualNickName"]

channel/wechat/wechaty_channel.py

@@ -1,129 +0,0 @@
-# encoding:utf-8
-
-"""
-wechaty channel
-Python Wechaty - https://github.com/wechaty/python-wechaty
-"""
-import asyncio
-import base64
-import os
-import time
-
-from wechaty import Contact, Wechaty
-from wechaty.user import Message
-from wechaty_puppet import FileBox
-
-from bridge.context import *
-from bridge.context import Context
-from bridge.reply import *
-from channel.chat_channel import ChatChannel
-from channel.wechat.wechaty_message import WechatyMessage
-from common.log import logger
-from common.singleton import singleton
-from config import conf
-
-try:
-    from voice.audio_convert import any_to_sil
-except Exception as e:
-    pass
-
-
-@singleton
-class WechatyChannel(ChatChannel):
-    NOT_SUPPORT_REPLYTYPE = []
-
-    def __init__(self):
-        super().__init__()
-
-    def startup(self):
-        config = conf()
-        token = config.get("wechaty_puppet_service_token")
-        os.environ["WECHATY_PUPPET_SERVICE_TOKEN"] = token
-        asyncio.run(self.main())
-
-    async def main(self):
-        loop = asyncio.get_event_loop()
-        # 将asyncio的loop传入处理线程
-        self.handler_pool._initializer = lambda: asyncio.set_event_loop(loop)
-        self.bot = Wechaty()
-        self.bot.on("login", self.on_login)
-        self.bot.on("message", self.on_message)
-        await self.bot.start()
-
-    async def on_login(self, contact: Contact):
-        self.user_id = contact.contact_id
-        self.name = contact.name
-        logger.info("[WX] login user={}".format(contact))
-
-    # 统一的发送函数，每个Channel自行实现，根据reply的type字段发送不同类型的消息
-    def send(self, reply: Reply, context: Context):
-        receiver_id = context["receiver"]
-        loop = asyncio.get_event_loop()
-        if context["isgroup"]:
-            receiver = asyncio.run_coroutine_threadsafe(self.bot.Room.find(receiver_id), loop).result()
-        else:
-            receiver = asyncio.run_coroutine_threadsafe(self.bot.Contact.find(receiver_id), loop).result()
-        msg = None
-        if reply.type == ReplyType.TEXT:
-            msg = reply.content
-            asyncio.run_coroutine_threadsafe(receiver.say(msg), loop).result()
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
-            msg = reply.content
-            asyncio.run_coroutine_threadsafe(receiver.say(msg), loop).result()
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.VOICE:
-            voiceLength = None
-            file_path = reply.content
-            sil_file = os.path.splitext(file_path)[0] + ".sil"
-            voiceLength = int(any_to_sil(file_path, sil_file))
-            if voiceLength >= 60000:
-                voiceLength = 60000
-                logger.info("[WX] voice too long, length={}, set to 60s".format(voiceLength))
-            # 发送语音
-            t = int(time.time())
-            msg = FileBox.from_file(sil_file, name=str(t) + ".sil")
-            if voiceLength is not None:
-                msg.metadata["voiceLength"] = voiceLength
-            asyncio.run_coroutine_threadsafe(receiver.say(msg), loop).result()
-            try:
-                os.remove(file_path)
-                if sil_file != file_path:
-                    os.remove(sil_file)
-            except Exception as e:
-                pass
-            logger.info("[WX] sendVoice={}, receiver={}".format(reply.content, receiver))
-        elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
-            img_url = reply.content
-            t = int(time.time())
-            msg = FileBox.from_url(url=img_url, name=str(t) + ".png")
-            asyncio.run_coroutine_threadsafe(receiver.say(msg), loop).result()
-            logger.info("[WX] sendImage url={}, receiver={}".format(img_url, receiver))
-        elif reply.type == ReplyType.IMAGE:  # 从文件读取图片
-            image_storage = reply.content
-            image_storage.seek(0)
-            t = int(time.time())
-            msg = FileBox.from_base64(base64.b64encode(image_storage.read()), str(t) + ".png")
-            asyncio.run_coroutine_threadsafe(receiver.say(msg), loop).result()
-            logger.info("[WX] sendImage, receiver={}".format(receiver))
-
-    async def on_message(self, msg: Message):
-        """
-        listen for message event
-        """
-        try:
-            cmsg = await WechatyMessage(msg)
-        except NotImplementedError as e:
-            logger.debug("[WX] {}".format(e))
-            return
-        except Exception as e:
-            logger.exception("[WX] {}".format(e))
-            return
-        logger.debug("[WX] message:{}".format(cmsg))
-        room = msg.room()  # 获取消息来自的群聊. 如果消息不是来自群聊, 则返回None
-        isgroup = room is not None
-        ctype = cmsg.ctype
-        context = self._compose_context(ctype, cmsg.content, isgroup=isgroup, msg=cmsg)
-        if context:
-            logger.info("[WX] receiveMsg={}, context={}".format(cmsg, context))
-            self.produce(context)

channel/wechat/wechaty_message.py

@@ -1,89 +0,0 @@
-import asyncio
-import re
-
-from wechaty import MessageType
-from wechaty.user import Message
-
-from bridge.context import ContextType
-from channel.chat_message import ChatMessage
-from common.log import logger
-from common.tmp_dir import TmpDir
-
-
-class aobject(object):
-    """Inheriting this class allows you to define an async __init__.
-
-    So you can create objects by doing something like `await MyClass(params)`
-    """
-
-    async def __new__(cls, *a, **kw):
-        instance = super().__new__(cls)
-        await instance.__init__(*a, **kw)
-        return instance
-
-    async def __init__(self):
-        pass
-
-
-class WechatyMessage(ChatMessage, aobject):
-    async def __init__(self, wechaty_msg: Message):
-        super().__init__(wechaty_msg)
-
-        room = wechaty_msg.room()
-
-        self.msg_id = wechaty_msg.message_id
-        self.create_time = wechaty_msg.payload.timestamp
-        self.is_group = room is not None
-
-        if wechaty_msg.type() == MessageType.MESSAGE_TYPE_TEXT:
-            self.ctype = ContextType.TEXT
-            self.content = wechaty_msg.text()
-        elif wechaty_msg.type() == MessageType.MESSAGE_TYPE_AUDIO:
-            self.ctype = ContextType.VOICE
-            voice_file = await wechaty_msg.to_file_box()
-            self.content = TmpDir().path() + voice_file.name  # content直接存临时目录路径
-
-            def func():
-                loop = asyncio.get_event_loop()
-                asyncio.run_coroutine_threadsafe(voice_file.to_file(self.content), loop).result()
-
-            self._prepare_fn = func
-
-        else:
-            raise NotImplementedError("Unsupported message type: {}".format(wechaty_msg.type()))
-
-        from_contact = wechaty_msg.talker()  # 获取消息的发送者
-        self.from_user_id = from_contact.contact_id
-        self.from_user_nickname = from_contact.name
-
-        # group中的from和to，wechaty跟itchat含义不一样
-        # wecahty: from是消息实际发送者, to:所在群
-        # itchat: 如果是你发送群消息，from和to是你自己和所在群，如果是别人发群消息，from和to是所在群和你自己
-        # 但这个差别不影响逻辑，group中只使用到：1.用from来判断是否是自己发的，2.actual_user_id来判断实际发送用户
-
-        if self.is_group:
-            self.to_user_id = room.room_id
-            self.to_user_nickname = await room.topic()
-        else:
-            to_contact = wechaty_msg.to()
-            self.to_user_id = to_contact.contact_id
-            self.to_user_nickname = to_contact.name
-
-        if self.is_group or wechaty_msg.is_self():  # 如果是群消息，other_user设置为群，如果是私聊消息，而且自己发的，就设置成对方。
-            self.other_user_id = self.to_user_id
-            self.other_user_nickname = self.to_user_nickname
-        else:
-            self.other_user_id = self.from_user_id
-            self.other_user_nickname = self.from_user_nickname
-
-        if self.is_group:  # wechaty群聊中，实际发送用户就是from_user
-            self.is_at = await wechaty_msg.mention_self()
-            if not self.is_at:  # 有时候复制粘贴的消息，不算做@，但是内容里面会有@xxx，这里做一下兼容
-                name = wechaty_msg.wechaty.user_self().name
-                pattern = f"@{re.escape(name)}(\u2005|\u0020)"
-                if re.search(pattern, self.content):
-                    logger.debug(f"wechaty message {self.msg_id} include at")
-                    self.is_at = True
-
-            self.actual_user_id = self.from_user_id
-            self.actual_user_nickname = self.from_user_nickname

channel/wechatcom/wechatcomapp_channel.py

@@ -36,6 +36,7 @@ class WechatComAppChannel(ChatChannel):
         self.agent_id = conf().get("wechatcomapp_agent_id")
         self.token = conf().get("wechatcomapp_token")
         self.aes_key = conf().get("wechatcomapp_aes_key")
+        self._http_server = None
         logger.info(
             "[wechatcom] Initializing WeCom app channel, corp_id: {}, agent_id: {}".format(self.corp_id, self.agent_id)
         )
@@ -51,13 +52,24 @@ class WechatComAppChannel(ChatChannel):
         logger.info("[wechatcom] 📡 Listening on http://0.0.0.0:{}/wxcomapp/".format(port))
         logger.info("[wechatcom] 🤖 Ready to receive messages")
         
-        # Suppress web.py's default server startup message
-        old_stdout = sys.stdout
-        sys.stdout = io.StringIO()
+        # Build WSGI app with middleware (same as runsimple but without print)
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
         try:
-            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
-        finally:
-            sys.stdout = old_stdout
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[wechatcom] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[wechatcom] Error stopping HTTP server: {e}")
+            self._http_server = None
 
     def send(self, reply: Reply, context: Context):
         receiver = context["receiver"]

channel/wechatmp/README.md

@@ -1,6 +1,6 @@
 # 微信公众号channel
 
-鉴于个人微信号在服务器上通过itchat登录有封号风险，这里新增了微信公众号channel，提供无风险的服务。
+微信公众号channel，提供稳定的服务。
 目前支持订阅号和服务号两种类型的公众号，它们都支持文本交互，语音和图片输入。其中个人主体的微信订阅号由于无法通过微信认证，存在回复时间限制，每天的图片和声音回复次数也有限制。
 
 ## 使用方法（订阅号，服务号类似）

channel/wechatmp/wechatmp_channel.py

@@ -41,6 +41,7 @@ class WechatMPChannel(ChatChannel):
         super().__init__()
         self.passive_reply = passive_reply
         self.NOT_SUPPORT_REPLYTYPE = []
+        self._http_server = None
         appid = conf().get("wechatmp_app_id")
         secret = conf().get("wechatmp_app_secret")
         token = conf().get("wechatmp_token")
@@ -69,7 +70,23 @@ class WechatMPChannel(ChatChannel):
             urls = ("/wx", "channel.wechatmp.active_reply.Query")
         app = web.application(urls, globals(), autoreload=False)
         port = conf().get("wechatmp_port", 8080)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
+        try:
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[wechatmp] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[wechatmp] Error stopping HTTP server: {e}")
+            self._http_server = None
 
     def start_loop(self, loop):
         asyncio.set_event_loop(loop)

channel/wecom_bot/wecom_bot_channel.py

@@ -0,0 +1,788 @@
+"""
+WeCom (企业微信) AI Bot channel via WebSocket long connection.
+
+Supports:
+- Single chat and group chat (text / image / file input & output)
+- Scheduled task push via aibot_send_msg
+- Heartbeat keep-alive and auto-reconnect
+"""
+
+import base64
+import hashlib
+import json
+import math
+import os
+import threading
+import time
+import uuid
+
+import requests
+import websocket
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.wecom_bot.wecom_bot_message import WecomBotMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from common.ws_client_compat import websocket_app_run_forever
+from config import conf
+
+WECOM_WS_URL = "wss://openws.work.weixin.qq.com"
+HEARTBEAT_INTERVAL = 30
+MEDIA_CHUNK_SIZE = 512 * 1024  # 512KB per chunk (before base64 encoding)
+
+
+@singleton
+class WecomBotChannel(ChatChannel):
+
+    def __init__(self):
+        super().__init__()
+        self.bot_id = ""
+        self.bot_secret = ""
+        self.received_msgs = ExpiredDict(60 * 60 * 7.1)
+        self._ws = None
+        self._ws_thread = None
+        self._heartbeat_thread = None
+        self._connected = False
+        self._stop_event = threading.Event()
+        self._pending_responses = {}  # req_id -> (threading.Event, result_holder)
+        self._pending_lock = threading.Lock()
+        self._stream_states = {}  # req_id -> {"stream_id": str, "content": str}
+
+        conf()["group_name_white_list"] = ["ALL_GROUP"]
+        conf()["single_chat_prefix"] = [""]
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def startup(self):
+        self.bot_id = conf().get("wecom_bot_id", "")
+        self.bot_secret = conf().get("wecom_bot_secret", "")
+
+        if not self.bot_id or not self.bot_secret:
+            err = "[WecomBot] wecom_bot_id and wecom_bot_secret are required"
+            logger.error(err)
+            self.report_startup_error(err)
+            return
+
+        self._stop_event.clear()
+        self._start_ws()
+
+    def stop(self):
+        logger.info("[WecomBot] stop() called")
+        self._stop_event.set()
+        if self._ws:
+            try:
+                self._ws.close()
+            except Exception:
+                pass
+        self._ws = None
+        self._connected = False
+
+    # ------------------------------------------------------------------
+    # WebSocket connection
+    # ------------------------------------------------------------------
+
+    def _start_ws(self):
+        def _on_open(ws):
+            logger.info("[WecomBot] WebSocket connected, sending subscribe...")
+            self._send_subscribe()
+
+        def _on_message(ws, raw):
+            try:
+                data = json.loads(raw)
+                self._handle_ws_message(data)
+            except Exception as e:
+                logger.error(f"[WecomBot] Failed to handle ws message: {e}", exc_info=True)
+
+        def _on_error(ws, error):
+            logger.error(f"[WecomBot] WebSocket error: {error}")
+
+        def _on_close(ws, close_status_code, close_msg):
+            logger.warning(f"[WecomBot] WebSocket closed: status={close_status_code}, msg={close_msg}")
+            self._connected = False
+            if not self._stop_event.is_set():
+                logger.info("[WecomBot] Will reconnect in 5s...")
+                time.sleep(5)
+                if not self._stop_event.is_set():
+                    self._start_ws()
+
+        self._ws = websocket.WebSocketApp(
+            WECOM_WS_URL,
+            on_open=_on_open,
+            on_message=_on_message,
+            on_error=_on_error,
+            on_close=_on_close,
+        )
+
+        def run_forever():
+            try:
+                websocket_app_run_forever(self._ws, ping_interval=0, reconnect=0)
+            except (SystemExit, KeyboardInterrupt):
+                logger.info("[WecomBot] WebSocket thread interrupted")
+            except Exception as e:
+                logger.error(f"[WecomBot] WebSocket run_forever error: {e}")
+
+        self._ws_thread = threading.Thread(target=run_forever, daemon=True)
+        self._ws_thread.start()
+        self._ws_thread.join()
+
+    def _ws_send(self, data: dict):
+        if self._ws:
+            self._ws.send(json.dumps(data, ensure_ascii=False))
+
+    def _gen_req_id(self) -> str:
+        return uuid.uuid4().hex[:16]
+
+    # ------------------------------------------------------------------
+    # Subscribe & heartbeat
+    # ------------------------------------------------------------------
+
+    def _send_subscribe(self):
+        self._ws_send({
+            "cmd": "aibot_subscribe",
+            "headers": {"req_id": self._gen_req_id()},
+            "body": {
+                "bot_id": self.bot_id,
+                "secret": self.bot_secret,
+            },
+        })
+
+    def _start_heartbeat(self):
+        if self._heartbeat_thread and self._heartbeat_thread.is_alive():
+            return
+
+        def heartbeat_loop():
+            while not self._stop_event.is_set() and self._connected:
+                try:
+                    self._ws_send({
+                        "cmd": "ping",
+                        "headers": {"req_id": self._gen_req_id()},
+                    })
+                except Exception as e:
+                    logger.warning(f"[WecomBot] Heartbeat send failed: {e}")
+                    break
+                self._stop_event.wait(HEARTBEAT_INTERVAL)
+
+        self._heartbeat_thread = threading.Thread(target=heartbeat_loop, daemon=True)
+        self._heartbeat_thread.start()
+
+    # ------------------------------------------------------------------
+    # Incoming message dispatch
+    # ------------------------------------------------------------------
+
+    def _send_and_wait(self, data: dict, timeout: float = 15) -> dict:
+        """Send a ws message and wait for the matching response by req_id."""
+        req_id = data.get("headers", {}).get("req_id", "")
+        event = threading.Event()
+        holder = {"data": None}
+        with self._pending_lock:
+            self._pending_responses[req_id] = (event, holder)
+        self._ws_send(data)
+        event.wait(timeout=timeout)
+        with self._pending_lock:
+            self._pending_responses.pop(req_id, None)
+        return holder["data"] or {}
+
+    def _handle_ws_message(self, data: dict):
+        cmd = data.get("cmd", "")
+        errcode = data.get("errcode")
+        req_id = data.get("headers", {}).get("req_id", "")
+
+        # Check if this is a response to a pending request
+        if req_id:
+            with self._pending_lock:
+                pending = self._pending_responses.get(req_id)
+            if pending:
+                event, holder = pending
+                holder["data"] = data
+                event.set()
+                return
+
+        # Subscribe response (only handle once before connected)
+        if errcode is not None and cmd == "":
+            if not self._connected:
+                if errcode == 0:
+                    logger.info("[WecomBot] ✅ Subscribe success")
+                    self._connected = True
+                    self._start_heartbeat()
+                    self.report_startup_success()
+                else:
+                    errmsg = data.get("errmsg", "unknown error")
+                    logger.error(f"[WecomBot] Subscribe failed: errcode={errcode}, errmsg={errmsg}")
+                    self.report_startup_error(errmsg)
+            return
+
+        if cmd == "aibot_msg_callback":
+            self._handle_msg_callback(data)
+        elif cmd == "aibot_event_callback":
+            self._handle_event_callback(data)
+        elif cmd == "":
+            if errcode and errcode != 0:
+                logger.warning(f"[WecomBot] Response error: {data}")
+
+    # ------------------------------------------------------------------
+    # Message callback
+    # ------------------------------------------------------------------
+
+    def _handle_msg_callback(self, data: dict):
+        body = data.get("body", {})
+        req_id = data.get("headers", {}).get("req_id", "")
+        msg_id = body.get("msgid", "")
+
+        if self.received_msgs.get(msg_id):
+            logger.debug(f"[WecomBot] Duplicate msg filtered: {msg_id}")
+            return
+        self.received_msgs[msg_id] = True
+
+        chattype = body.get("chattype", "single")
+        is_group = chattype == "group"
+
+        try:
+            wecom_msg = WecomBotMessage(body, is_group=is_group)
+        except NotImplementedError as e:
+            logger.warning(f"[WecomBot] {e}")
+            return
+        except Exception as e:
+            logger.error(f"[WecomBot] Failed to parse message: {e}", exc_info=True)
+            return
+
+        wecom_msg.req_id = req_id
+
+        # File cache logic (same pattern as feishu)
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+
+        if is_group:
+            if conf().get("group_shared_session", True):
+                session_id = body.get("chatid", "")
+            else:
+                session_id = wecom_msg.from_user_id + "_" + body.get("chatid", "")
+        else:
+            session_id = wecom_msg.from_user_id
+
+        if wecom_msg.ctype == ContextType.IMAGE:
+            if hasattr(wecom_msg, "image_path") and wecom_msg.image_path:
+                file_cache.add(session_id, wecom_msg.image_path, file_type="image")
+                logger.info(f"[WecomBot] Image cached for session {session_id}")
+            return
+
+        if wecom_msg.ctype == ContextType.FILE:
+            wecom_msg.prepare()
+            file_cache.add(session_id, wecom_msg.content, file_type="file")
+            logger.info(f"[WecomBot] File cached for session {session_id}: {wecom_msg.content}")
+            return
+
+        if wecom_msg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                file_refs = []
+                for fi in cached_files:
+                    ftype = fi["type"]
+                    fpath = fi["path"]
+                    if ftype == "image":
+                        file_refs.append(f"[图片: {fpath}]")
+                    elif ftype == "video":
+                        file_refs.append(f"[视频: {fpath}]")
+                    else:
+                        file_refs.append(f"[文件: {fpath}]")
+                wecom_msg.content = wecom_msg.content + "\n" + "\n".join(file_refs)
+                logger.info(f"[WecomBot] Attached {len(cached_files)} cached file(s)")
+                file_cache.clear(session_id)
+
+        context = self._compose_context(
+            wecom_msg.ctype,
+            wecom_msg.content,
+            isgroup=is_group,
+            msg=wecom_msg,
+            no_need_at=True,
+        )
+        if context:
+            if req_id:
+                context["on_event"] = self._make_stream_callback(req_id)
+            self.produce(context)
+
+    # ------------------------------------------------------------------
+    # Event callback
+    # ------------------------------------------------------------------
+
+    def _handle_event_callback(self, data: dict):
+        body = data.get("body", {})
+        event = body.get("event", {})
+        event_type = event.get("eventtype", "")
+
+        if event_type == "enter_chat":
+            logger.info(f"[WecomBot] User entered chat: {body.get('from', {}).get('userid')}")
+        elif event_type == "disconnected_event":
+            logger.warning("[WecomBot] Received disconnected_event, another connection took over")
+        else:
+            logger.debug(f"[WecomBot] Event: {event_type}")
+
+    # ------------------------------------------------------------------
+    # Stream callback (for agent on_event)
+    # ------------------------------------------------------------------
+
+    def _make_stream_callback(self, req_id: str):
+        """Build an on_event callback that pushes agent stream deltas to wecom via stream message.
+
+        All intermediate segments (thinking before tool calls) and the final answer
+        are accumulated into a single stream message, separated by '---'.
+        Throttles push to at most once per 100ms to avoid WebSocket congestion.
+        """
+        stream_id = uuid.uuid4().hex[:16]
+        self._stream_states[req_id] = {
+            "stream_id": stream_id,
+            "committed": "",
+            "current": "",
+            "last_push_time": 0,
+            "last_push_len": 0,
+        }
+
+        def _push_stream(state: dict, force: bool = False):
+            """Push current stream content to wecom (throttled unless forced)."""
+            now = time.time()
+            if not force and now - state["last_push_time"] < 0.1:
+                return
+            content = state["committed"] + state["current"]
+            if len(content) == state["last_push_len"]:
+                return
+            state["last_push_time"] = now
+            state["last_push_len"] = len(content)
+            try:
+                self._ws_send({
+                    "cmd": "aibot_respond_msg",
+                    "headers": {"req_id": req_id},
+                    "body": {
+                        "msgtype": "stream",
+                        "stream": {
+                            "id": state["stream_id"],
+                            "finish": False,
+                            "content": content,
+                        },
+                    },
+                })
+            except Exception as e:
+                logger.warning(f"[WecomBot] Stream push failed: {e}")
+
+        def on_event(event: dict):
+            event_type = event.get("type")
+            data = event.get("data", {})
+            state = self._stream_states.get(req_id)
+            if not state:
+                return
+
+            if event_type == "turn_start":
+                state["current"] = ""
+
+            elif event_type == "message_update":
+                delta = data.get("delta", "")
+                if delta:
+                    state["current"] += delta
+                    _push_stream(state)
+
+            elif event_type == "message_end":
+                tool_calls = data.get("tool_calls", [])
+                if tool_calls:
+                    if state["current"].strip():
+                        state["committed"] += state["current"].strip() + "\n\n---\n\n"
+                        state["current"] = ""
+                else:
+                    state["committed"] += state["current"]
+                    state["current"] = ""
+                _push_stream(state, force=True)
+
+        return on_event
+
+    # ------------------------------------------------------------------
+    # _compose_context (same pattern as feishu)
+    # ------------------------------------------------------------------
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+
+        if cmsg.is_group:
+            if conf().get("group_shared_session", True):
+                context["session_id"] = cmsg.other_user_id
+            else:
+                context["session_id"] = f"{cmsg.from_user_id}:{cmsg.other_user_id}"
+        else:
+            context["session_id"] = cmsg.from_user_id
+
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = content.strip()
+
+        return context
+
+    # ------------------------------------------------------------------
+    # Send reply
+    # ------------------------------------------------------------------
+
+    def send(self, reply: Reply, context: Context):
+        msg = context.get("msg")
+        is_group = context.get("isgroup", False)
+        receiver = context.get("receiver", "")
+
+        # Determine req_id for responding or use send_msg for scheduled push
+        req_id = getattr(msg, "req_id", None) if msg else None
+
+        if reply.type == ReplyType.TEXT:
+            self._send_text(reply.content, receiver, is_group, req_id)
+        elif reply.type in (ReplyType.IMAGE_URL, ReplyType.IMAGE):
+            self._send_image(reply.content, receiver, is_group, req_id)
+        elif reply.type == ReplyType.FILE:
+            if hasattr(reply, "text_content") and reply.text_content:
+                self._send_text(reply.text_content, receiver, is_group, req_id)
+                time.sleep(0.3)
+            self._send_file(reply.content, receiver, is_group, req_id)
+        elif reply.type == ReplyType.VIDEO or reply.type == ReplyType.VIDEO_URL:
+            self._send_file(reply.content, receiver, is_group, req_id, media_type="video")
+        else:
+            logger.warning(f"[WecomBot] Unsupported reply type: {reply.type}, falling back to text")
+            self._send_text(str(reply.content), receiver, is_group, req_id)
+
+    # ------------------------------------------------------------------
+    # Respond message (via websocket)
+    # ------------------------------------------------------------------
+
+    def _send_text(self, content: str, receiver: str, is_group: bool, req_id: str = None):
+        """Send text/markdown reply. Reuses stream state if available (streaming mode)."""
+        if req_id:
+            state = self._stream_states.pop(req_id, None)
+            if state:
+                final_content = state["committed"] if state["committed"] else content
+                stream_id = state["stream_id"]
+            else:
+                final_content = content
+                stream_id = uuid.uuid4().hex[:16]
+
+            # Brief pause so the server finishes processing the last intermediate chunk
+            # before receiving the finish packet
+            time.sleep(0.15)
+
+            self._ws_send({
+                "cmd": "aibot_respond_msg",
+                "headers": {"req_id": req_id},
+                "body": {
+                    "msgtype": "stream",
+                    "stream": {
+                        "id": stream_id,
+                        "finish": True,
+                        "content": final_content,
+                    },
+                },
+            })
+        else:
+            self._active_send_markdown(content, receiver, is_group)
+
+    def _send_image(self, img_path_or_url: str, receiver: str, is_group: bool, req_id: str = None):
+        """Send image reply. Converts to JPG/PNG and compresses if >2MB."""
+        local_path = img_path_or_url
+        if local_path.startswith("file://"):
+            local_path = local_path[7:]
+
+        if local_path.startswith(("http://", "https://")):
+            try:
+                resp = requests.get(local_path, timeout=30)
+                resp.raise_for_status()
+                ct = resp.headers.get("Content-Type", "")
+                if "jpeg" in ct or "jpg" in ct:
+                    ext = ".jpg"
+                elif "webp" in ct:
+                    ext = ".webp"
+                elif "gif" in ct:
+                    ext = ".gif"
+                else:
+                    ext = ".png"
+                tmp_path = f"/tmp/wecom_img_{uuid.uuid4().hex[:8]}{ext}"
+                with open(tmp_path, "wb") as f:
+                    f.write(resp.content)
+                logger.info(f"[WecomBot] Image downloaded: size={len(resp.content)}, "
+                            f"content-type={ct}, path={tmp_path}")
+                local_path = tmp_path
+            except Exception as e:
+                logger.error(f"[WecomBot] Failed to download image for sending: {e}")
+                self._send_text("[Image send failed]", receiver, is_group, req_id)
+                return
+
+        if not os.path.exists(local_path):
+            logger.error(f"[WecomBot] Image file not found: {local_path}")
+            return
+
+        max_image_size = 2 * 1024 * 1024  # 2MB limit for image upload
+        local_path = self._ensure_image_format(local_path)
+        if not local_path:
+            self._send_text("[Image format conversion failed]", receiver, is_group, req_id)
+            return
+
+        if os.path.getsize(local_path) > max_image_size:
+            local_path = self._compress_image(local_path, max_image_size)
+            if not local_path:
+                self._send_text("[Image too large]", receiver, is_group, req_id)
+                return
+
+        file_size = os.path.getsize(local_path)
+        logger.info(f"[WecomBot] Uploading image: path={local_path}, size={file_size} bytes")
+        media_id = self._upload_media(local_path, "image")
+        if not media_id:
+            logger.error("[WecomBot] Failed to upload image")
+            self._send_text("[Image upload failed]", receiver, is_group, req_id)
+            return
+
+        if req_id:
+            self._ws_send({
+                "cmd": "aibot_respond_msg",
+                "headers": {"req_id": req_id},
+                "body": {
+                    "msgtype": "image",
+                    "image": {"media_id": media_id},
+                },
+            })
+        else:
+            self._ws_send({
+                "cmd": "aibot_send_msg",
+                "headers": {"req_id": self._gen_req_id()},
+                "body": {
+                    "chatid": receiver,
+                    "chat_type": 2 if is_group else 1,
+                    "msgtype": "image",
+                    "image": {"media_id": media_id},
+                },
+            })
+
+    @staticmethod
+    def _ensure_image_format(file_path: str) -> str:
+        """Ensure image is JPG or PNG (the only formats wecom supports). Convert if needed."""
+        try:
+            from PIL import Image
+            img = Image.open(file_path)
+            fmt = (img.format or "").upper()
+            if fmt in ("JPEG", "PNG"):
+                # Already a supported format, but make sure the filename extension matches
+                ext = os.path.splitext(file_path)[1].lower()
+                if fmt == "JPEG" and ext in (".jpg", ".jpeg"):
+                    return file_path
+                if fmt == "PNG" and ext == ".png":
+                    return file_path
+                # Extension doesn't match — rename/copy with correct extension
+                correct_ext = ".jpg" if fmt == "JPEG" else ".png"
+                out_path = f"/tmp/wecom_fmt_{uuid.uuid4().hex[:8]}{correct_ext}"
+                img.save(out_path, fmt)
+                logger.info(f"[WecomBot] Image renamed: {file_path} -> {out_path} ({fmt})")
+                return out_path
+
+            # Unsupported format (WebP, GIF, BMP, etc.) — convert to PNG
+            if img.mode == "RGBA":
+                out_path = f"/tmp/wecom_fmt_{uuid.uuid4().hex[:8]}.png"
+                img.save(out_path, "PNG")
+            else:
+                out_path = f"/tmp/wecom_fmt_{uuid.uuid4().hex[:8]}.jpg"
+                img.convert("RGB").save(out_path, "JPEG", quality=90)
+            logger.info(f"[WecomBot] Image converted from {fmt} -> {out_path}")
+            return out_path
+        except Exception as e:
+            logger.error(f"[WecomBot] Image format check failed: {e}")
+            return file_path
+
+    @staticmethod
+    def _compress_image(file_path: str, max_bytes: int) -> str:
+        """Compress image to fit within max_bytes. Returns new path or empty string."""
+        try:
+            from PIL import Image
+            img = Image.open(file_path)
+            if img.mode == "RGBA":
+                img = img.convert("RGB")
+
+            out_path = f"/tmp/wecom_compressed_{uuid.uuid4().hex[:8]}.jpg"
+            quality = 85
+            while quality >= 30:
+                img.save(out_path, "JPEG", quality=quality, optimize=True)
+                if os.path.getsize(out_path) <= max_bytes:
+                    logger.info(f"[WecomBot] Image compressed: quality={quality}, "
+                                f"size={os.path.getsize(out_path)} bytes")
+                    return out_path
+                quality -= 10
+
+            # Still too large — resize
+            ratio = (max_bytes / os.path.getsize(out_path)) ** 0.5
+            new_size = (int(img.width * ratio), int(img.height * ratio))
+            img = img.resize(new_size, Image.LANCZOS)
+            img.save(out_path, "JPEG", quality=70, optimize=True)
+            if os.path.getsize(out_path) <= max_bytes:
+                logger.info(f"[WecomBot] Image compressed with resize: {new_size}, "
+                            f"size={os.path.getsize(out_path)} bytes")
+                return out_path
+
+            logger.error(f"[WecomBot] Cannot compress image below {max_bytes} bytes")
+            return ""
+        except Exception as e:
+            logger.error(f"[WecomBot] Image compression failed: {e}")
+            return ""
+
+    def _send_file(self, file_path: str, receiver: str, is_group: bool,
+                   req_id: str = None, media_type: str = "file"):
+        """Send file/video reply by uploading media first."""
+        local_path = file_path
+        if local_path.startswith("file://"):
+            local_path = local_path[7:]
+
+        if local_path.startswith(("http://", "https://")):
+            try:
+                resp = requests.get(local_path, timeout=60)
+                resp.raise_for_status()
+                ext = os.path.splitext(local_path)[1] or ".bin"
+                tmp_path = f"/tmp/wecom_file_{uuid.uuid4().hex[:8]}{ext}"
+                with open(tmp_path, "wb") as f:
+                    f.write(resp.content)
+                local_path = tmp_path
+            except Exception as e:
+                logger.error(f"[WecomBot] Failed to download file for sending: {e}")
+                return
+
+        if not os.path.exists(local_path):
+            logger.error(f"[WecomBot] File not found: {local_path}")
+            return
+
+        media_id = self._upload_media(local_path, media_type)
+        if not media_id:
+            logger.error(f"[WecomBot] Failed to upload {media_type}")
+            return
+
+        if req_id:
+            self._ws_send({
+                "cmd": "aibot_respond_msg",
+                "headers": {"req_id": req_id},
+                "body": {
+                    "msgtype": media_type,
+                    media_type: {"media_id": media_id},
+                },
+            })
+        else:
+            self._ws_send({
+                "cmd": "aibot_send_msg",
+                "headers": {"req_id": self._gen_req_id()},
+                "body": {
+                    "chatid": receiver,
+                    "chat_type": 2 if is_group else 1,
+                    "msgtype": media_type,
+                    media_type: {"media_id": media_id},
+                },
+            })
+
+    def _active_send_markdown(self, content: str, receiver: str, is_group: bool):
+        """Proactively send markdown message (for scheduled tasks, no req_id)."""
+        self._ws_send({
+            "cmd": "aibot_send_msg",
+            "headers": {"req_id": self._gen_req_id()},
+            "body": {
+                "chatid": receiver,
+                "chat_type": 2 if is_group else 1,
+                "msgtype": "markdown",
+                "markdown": {"content": content},
+            },
+        })
+
+    # ------------------------------------------------------------------
+    # Media upload (chunked)
+    # ------------------------------------------------------------------
+
+    def _upload_media(self, file_path: str, media_type: str = "file") -> str:
+        """
+        Upload a local file to wecom bot via chunked upload protocol.
+        Returns media_id on success, empty string on failure.
+        """
+        if not os.path.exists(file_path):
+            logger.error(f"[WecomBot] Upload file not found: {file_path}")
+            return ""
+
+        file_size = os.path.getsize(file_path)
+        if file_size < 5:
+            logger.error(f"[WecomBot] File too small: {file_size} bytes")
+            return ""
+
+        filename = os.path.basename(file_path)
+        total_chunks = math.ceil(file_size / MEDIA_CHUNK_SIZE)
+        if total_chunks > 100:
+            logger.error(f"[WecomBot] Too many chunks: {total_chunks} > 100")
+            return ""
+
+        file_md5 = hashlib.md5()
+        with open(file_path, "rb") as f:
+            for block in iter(lambda: f.read(8192), b""):
+                file_md5.update(block)
+        md5_hex = file_md5.hexdigest()
+
+        # 1. Init upload
+        init_resp = self._send_and_wait({
+            "cmd": "aibot_upload_media_init",
+            "headers": {"req_id": self._gen_req_id()},
+            "body": {
+                "type": media_type,
+                "filename": filename,
+                "total_size": file_size,
+                "total_chunks": total_chunks,
+                "md5": md5_hex,
+            },
+        }, timeout=15)
+
+        if init_resp.get("errcode") != 0:
+            logger.error(f"[WecomBot] Upload init failed: {init_resp}")
+            return ""
+
+        upload_id = init_resp.get("body", {}).get("upload_id")
+        if not upload_id:
+            logger.error("[WecomBot] Failed to get upload_id")
+            return ""
+
+        # 2. Upload chunks
+        with open(file_path, "rb") as f:
+            for idx in range(total_chunks):
+                chunk = f.read(MEDIA_CHUNK_SIZE)
+                b64_data = base64.b64encode(chunk).decode("utf-8")
+                chunk_resp = self._send_and_wait({
+                    "cmd": "aibot_upload_media_chunk",
+                    "headers": {"req_id": self._gen_req_id()},
+                    "body": {
+                        "upload_id": upload_id,
+                        "chunk_index": idx,
+                        "base64_data": b64_data,
+                    },
+                }, timeout=30)
+                if chunk_resp.get("errcode") != 0:
+                    logger.error(f"[WecomBot] Chunk {idx} upload failed: {chunk_resp}")
+                    return ""
+
+        # 3. Finish upload
+        finish_resp = self._send_and_wait({
+            "cmd": "aibot_upload_media_finish",
+            "headers": {"req_id": self._gen_req_id()},
+            "body": {"upload_id": upload_id},
+        }, timeout=30)
+
+        if finish_resp.get("errcode") != 0:
+            logger.error(f"[WecomBot] Upload finish failed: {finish_resp}")
+            return ""
+
+        media_id = finish_resp.get("body", {}).get("media_id", "")
+        if media_id:
+            logger.info(f"[WecomBot] Media uploaded: media_id={media_id}")
+        else:
+            logger.error("[WecomBot] Failed to get media_id from finish response")
+        return media_id

channel/wecom_bot/wecom_bot_message.py

@@ -0,0 +1,216 @@
+import os
+import re
+import base64
+import requests
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from common.log import logger
+from common.utils import expand_path
+from config import conf
+from Crypto.Cipher import AES
+
+
+MAGIC_SIGNATURES = [
+    (b"%PDF", ".pdf"),
+    (b"\x89PNG\r\n\x1a\n", ".png"),
+    (b"\xff\xd8\xff", ".jpg"),
+    (b"GIF87a", ".gif"),
+    (b"GIF89a", ".gif"),
+    (b"RIFF", ".webp"),  # RIFF....WEBP, further checked below
+    (b"PK\x03\x04", ".zip"),  # zip / docx / xlsx / pptx
+    (b"\x1f\x8b", ".gz"),
+    (b"Rar!\x1a\x07", ".rar"),
+    (b"7z\xbc\xaf\x27\x1c", ".7z"),
+    (b"\x00\x00\x00", ".mp4"),  # ftyp box, further checked below
+    (b"#!AMR", ".amr"),
+]
+
+OFFICE_ZIP_MARKERS = {
+    b"word/": ".docx",
+    b"xl/": ".xlsx",
+    b"ppt/": ".pptx",
+}
+
+
+def _guess_ext_from_bytes(data: bytes) -> str:
+    """Guess file extension from file content magic bytes."""
+    if not data or len(data) < 8:
+        return ""
+    for sig, ext in MAGIC_SIGNATURES:
+        if data[:len(sig)] == sig:
+            if ext == ".webp" and data[8:12] != b"WEBP":
+                continue
+            if ext == ".mp4":
+                if b"ftyp" not in data[4:12]:
+                    continue
+            if ext == ".zip":
+                for marker, office_ext in OFFICE_ZIP_MARKERS.items():
+                    if marker in data[:2000]:
+                        return office_ext
+                return ".zip"
+            return ext
+    return ""
+
+
+def _decrypt_media(url: str, aeskey: str) -> bytes:
+    """
+    Download and decrypt AES-256-CBC encrypted media from wecom bot.
+    Returns decrypted bytes.
+    """
+    resp = requests.get(url, timeout=30)
+    resp.raise_for_status()
+    encrypted = resp.content
+
+    key = base64.b64decode(aeskey + "=" * (-len(aeskey) % 4))
+    if len(key) != 32:
+        raise ValueError(f"Invalid AES key length: {len(key)}, expected 32")
+
+    iv = key[:16]
+    cipher = AES.new(key, AES.MODE_CBC, iv)
+    decrypted = cipher.decrypt(encrypted)
+
+    pad_len = decrypted[-1]
+    if pad_len > 32:
+        raise ValueError(f"Invalid PKCS7 padding length: {pad_len}")
+    return decrypted[:-pad_len]
+
+
+def _get_tmp_dir() -> str:
+    """Return the workspace tmp directory (absolute path), creating it if needed."""
+    ws_root = expand_path(conf().get("agent_workspace", "~/cow"))
+    tmp_dir = os.path.join(ws_root, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    return tmp_dir
+
+
+class WecomBotMessage(ChatMessage):
+    """Message wrapper for wecom bot (websocket long-connection mode)."""
+
+    def __init__(self, msg_body: dict, is_group: bool = False):
+        super().__init__(msg_body)
+        self.msg_id = msg_body.get("msgid")
+        self.create_time = msg_body.get("create_time")
+        self.is_group = is_group
+
+        msg_type = msg_body.get("msgtype")
+        from_userid = msg_body.get("from", {}).get("userid", "")
+        chat_id = msg_body.get("chatid", "")
+        bot_id = msg_body.get("aibotid", "")
+
+        if msg_type == "text":
+            self.ctype = ContextType.TEXT
+            content = msg_body.get("text", {}).get("content", "")
+            if is_group:
+                content = re.sub(r"@\S+\s*", "", content).strip()
+            self.content = content
+
+        elif msg_type == "voice":
+            self.ctype = ContextType.TEXT
+            self.content = msg_body.get("voice", {}).get("content", "")
+
+        elif msg_type == "image":
+            self.ctype = ContextType.IMAGE
+            image_info = msg_body.get("image", {})
+            image_url = image_info.get("url", "")
+            aeskey = image_info.get("aeskey", "")
+            tmp_dir = _get_tmp_dir()
+            image_path = os.path.join(tmp_dir, f"wecom_{self.msg_id}.png")
+
+            try:
+                data = _decrypt_media(image_url, aeskey)
+                with open(image_path, "wb") as f:
+                    f.write(data)
+                self.content = image_path
+                self.image_path = image_path
+                logger.info(f"[WecomBot] Image downloaded: {image_path}")
+            except Exception as e:
+                logger.error(f"[WecomBot] Failed to download image: {e}")
+                self.content = "[Image download failed]"
+                self.image_path = None
+
+        elif msg_type == "mixed":
+            self.ctype = ContextType.TEXT
+            text_parts = []
+            image_paths = []
+            mixed_items = msg_body.get("mixed", {}).get("msg_item", [])
+            tmp_dir = _get_tmp_dir()
+
+            for idx, item in enumerate(mixed_items):
+                item_type = item.get("msgtype")
+                if item_type == "text":
+                    txt = item.get("text", {}).get("content", "")
+                    if is_group:
+                        txt = re.sub(r"@\S+\s*", "", txt).strip()
+                    if txt:
+                        text_parts.append(txt)
+                elif item_type == "image":
+                    img_info = item.get("image", {})
+                    img_url = img_info.get("url", "")
+                    img_aeskey = img_info.get("aeskey", "")
+                    img_path = os.path.join(tmp_dir, f"wecom_{self.msg_id}_{idx}.png")
+                    try:
+                        img_data = _decrypt_media(img_url, img_aeskey)
+                        with open(img_path, "wb") as f:
+                            f.write(img_data)
+                        image_paths.append(img_path)
+                    except Exception as e:
+                        logger.error(f"[WecomBot] Failed to download mixed image: {e}")
+
+            content_parts = text_parts[:]
+            for p in image_paths:
+                content_parts.append(f"[图片: {p}]")
+            self.content = "\n".join(content_parts) if content_parts else "[Mixed message]"
+
+        elif msg_type == "file":
+            self.ctype = ContextType.FILE
+            file_info = msg_body.get("file", {})
+            file_url = file_info.get("url", "")
+            aeskey = file_info.get("aeskey", "")
+            tmp_dir = _get_tmp_dir()
+            base_path = os.path.join(tmp_dir, f"wecom_{self.msg_id}")
+            self.content = base_path
+
+            def _download_file():
+                try:
+                    data = _decrypt_media(file_url, aeskey)
+                    ext = _guess_ext_from_bytes(data)
+                    final_path = base_path + ext
+                    with open(final_path, "wb") as f:
+                        f.write(data)
+                    self.content = final_path
+                    logger.info(f"[WecomBot] File downloaded: {final_path}")
+                except Exception as e:
+                    logger.error(f"[WecomBot] Failed to download file: {e}")
+            self._prepare_fn = _download_file
+
+        elif msg_type == "video":
+            self.ctype = ContextType.FILE
+            video_info = msg_body.get("video", {})
+            video_url = video_info.get("url", "")
+            aeskey = video_info.get("aeskey", "")
+            tmp_dir = _get_tmp_dir()
+            self.content = os.path.join(tmp_dir, f"wecom_{self.msg_id}.mp4")
+
+            def _download_video():
+                try:
+                    data = _decrypt_media(video_url, aeskey)
+                    with open(self.content, "wb") as f:
+                        f.write(data)
+                    logger.info(f"[WecomBot] Video downloaded: {self.content}")
+                except Exception as e:
+                    logger.error(f"[WecomBot] Failed to download video: {e}")
+            self._prepare_fn = _download_video
+
+        else:
+            raise NotImplementedError(f"Unsupported message type: {msg_type}")
+
+        self.from_user_id = from_userid
+        self.to_user_id = bot_id
+        if is_group:
+            self.other_user_id = chat_id
+            self.actual_user_id = from_userid
+            self.actual_user_nickname = from_userid
+        else:
+            self.other_user_id = from_userid
+            self.actual_user_id = from_userid

channel/weixin/weixin_api.py

@@ -0,0 +1,412 @@
+"""
+Weixin HTTP JSON API client.
+
+Implements the ilink bot protocol:
+  - getUpdates (long-poll)
+  - sendMessage
+  - getUploadUrl
+  - getConfig
+  - sendTyping
+  - QR login (get_bot_qrcode / get_qrcode_status)
+
+CDN media upload with AES-128-ECB encryption.
+"""
+
+import base64
+import hashlib
+import os
+import random
+import struct
+import time
+import uuid
+
+import requests
+
+from common.log import logger
+
+DEFAULT_BASE_URL = "https://ilinkai.weixin.qq.com"
+CDN_BASE_URL = "https://novac2c.cdn.weixin.qq.com/c2c"
+DEFAULT_LONG_POLL_TIMEOUT = 35
+DEFAULT_API_TIMEOUT = 15
+QR_POLL_TIMEOUT = 35
+BOT_TYPE = "3"
+
+
+def _random_wechat_uin() -> str:
+    val = random.randint(0, 0xFFFFFFFF)
+    return base64.b64encode(str(val).encode("utf-8")).decode("utf-8")
+
+
+CHANNEL_VERSION = "2.0.0"
+# iLink-App-ClientVersion: uint32 encoded as major<<16 | minor<<8 | patch
+# 2.0.0 → 0x00020000 = 131072
+CLIENT_VERSION = "131072"
+
+
+def _build_headers(token: str = "") -> dict:
+    headers = {
+        "Content-Type": "application/json",
+        "AuthorizationType": "ilink_bot_token",
+        "X-WECHAT-UIN": _random_wechat_uin(),
+        "iLink-App-Id": "bot",
+        "iLink-App-ClientVersion": CLIENT_VERSION,
+    }
+    if token:
+        headers["Authorization"] = f"Bearer {token}"
+    return headers
+
+
+def _ensure_trailing_slash(url: str) -> str:
+    return url if url.endswith("/") else url + "/"
+
+
+class WeixinApi:
+    """Stateless HTTP client for the Weixin ilink bot API."""
+
+    def __init__(self, base_url: str = DEFAULT_BASE_URL, token: str = "",
+                 cdn_base_url: str = CDN_BASE_URL):
+        self.base_url = base_url
+        self.token = token
+        self.cdn_base_url = cdn_base_url
+
+    def _post(self, endpoint: str, body: dict, timeout: int = DEFAULT_API_TIMEOUT) -> dict:
+        url = _ensure_trailing_slash(self.base_url) + endpoint
+        headers = _build_headers(self.token)
+        body.setdefault("base_info", {}).setdefault("channel_version", CHANNEL_VERSION)
+        try:
+            resp = requests.post(url, json=body, headers=headers, timeout=timeout)
+            resp.raise_for_status()
+            return resp.json()
+        except requests.exceptions.Timeout:
+            logger.debug(f"[Weixin] API timeout: {endpoint}")
+            return {"ret": 0, "msgs": []}
+        except Exception as e:
+            logger.error(f"[Weixin] API error {endpoint}: {e}")
+            raise
+
+    # ── getUpdates (long-poll) ─────────────────────────────────────────
+
+    def get_updates(self, get_updates_buf: str = "", timeout: int = DEFAULT_LONG_POLL_TIMEOUT) -> dict:
+        return self._post("ilink/bot/getupdates", {
+            "get_updates_buf": get_updates_buf,
+        }, timeout=timeout + 5)
+
+    # ── sendMessage ────────────────────────────────────────────────────
+
+    def send_text(self, to: str, text: str, context_token: str) -> dict:
+        return self._post("ilink/bot/sendmessage", {
+            "msg": {
+                "from_user_id": "",
+                "to_user_id": to,
+                "client_id": uuid.uuid4().hex[:16],
+                "message_type": 2,  # BOT
+                "message_state": 2,  # FINISH
+                "item_list": [{"type": 1, "text_item": {"text": text}}],
+                "context_token": context_token,
+            }
+        })
+
+    def send_image_item(self, to: str, context_token: str,
+                        encrypt_query_param: str, aes_key_b64: str,
+                        ciphertext_size: int, text: str = "") -> dict:
+        items = []
+        if text:
+            items.append({"type": 1, "text_item": {"text": text}})
+        items.append({
+            "type": 2,
+            "image_item": {
+                "media": {
+                    "encrypt_query_param": encrypt_query_param,
+                    "aes_key": aes_key_b64,
+                    "encrypt_type": 1,
+                },
+                "mid_size": ciphertext_size,
+            }
+        })
+        return self._send_items(to, context_token, items)
+
+    def send_file_item(self, to: str, context_token: str,
+                       encrypt_query_param: str, aes_key_b64: str,
+                       file_name: str, file_size: int, text: str = "") -> dict:
+        items = []
+        if text:
+            items.append({"type": 1, "text_item": {"text": text}})
+        items.append({
+            "type": 4,
+            "file_item": {
+                "media": {
+                    "encrypt_query_param": encrypt_query_param,
+                    "aes_key": aes_key_b64,
+                    "encrypt_type": 1,
+                },
+                "file_name": file_name,
+                "len": str(file_size),
+            }
+        })
+        return self._send_items(to, context_token, items)
+
+    def send_video_item(self, to: str, context_token: str,
+                        encrypt_query_param: str, aes_key_b64: str,
+                        ciphertext_size: int, text: str = "") -> dict:
+        items = []
+        if text:
+            items.append({"type": 1, "text_item": {"text": text}})
+        items.append({
+            "type": 5,
+            "video_item": {
+                "media": {
+                    "encrypt_query_param": encrypt_query_param,
+                    "aes_key": aes_key_b64,
+                    "encrypt_type": 1,
+                },
+                "video_size": ciphertext_size,
+            }
+        })
+        return self._send_items(to, context_token, items)
+
+    def _send_items(self, to: str, context_token: str, items: list) -> dict:
+        return self._post("ilink/bot/sendmessage", {
+            "msg": {
+                "from_user_id": "",
+                "to_user_id": to,
+                "client_id": uuid.uuid4().hex[:16],
+                "message_type": 2,
+                "message_state": 2,
+                "item_list": items,
+                "context_token": context_token,
+            }
+        })
+
+    # ── getUploadUrl ───────────────────────────────────────────────────
+
+    def get_upload_url(self, filekey: str, media_type: int, to_user_id: str,
+                       rawsize: int, rawfilemd5: str, filesize: int,
+                       aeskey: str) -> dict:
+        return self._post("ilink/bot/getuploadurl", {
+            "filekey": filekey,
+            "media_type": media_type,
+            "to_user_id": to_user_id,
+            "rawsize": rawsize,
+            "rawfilemd5": rawfilemd5,
+            "filesize": filesize,
+            "aeskey": aeskey,
+            "no_need_thumb": True,
+        })
+
+    # ── getConfig / sendTyping ─────────────────────────────────────────
+
+    def get_config(self, user_id: str, context_token: str = "") -> dict:
+        return self._post("ilink/bot/getconfig", {
+            "ilink_user_id": user_id,
+            "context_token": context_token,
+        }, timeout=10)
+
+    def send_typing(self, user_id: str, typing_ticket: str, status: int = 1) -> dict:
+        return self._post("ilink/bot/sendtyping", {
+            "ilink_user_id": user_id,
+            "typing_ticket": typing_ticket,
+            "status": status,
+        }, timeout=10)
+
+    # ── QR Login ───────────────────────────────────────────────────────
+
+    def fetch_qr_code(self) -> dict:
+        url = _ensure_trailing_slash(self.base_url) + f"ilink/bot/get_bot_qrcode?bot_type={BOT_TYPE}"
+        resp = requests.get(url, timeout=15)
+        resp.raise_for_status()
+        return resp.json()
+
+    def poll_qr_status(self, qrcode: str, timeout: int = QR_POLL_TIMEOUT) -> dict:
+        url = (_ensure_trailing_slash(self.base_url) +
+               f"ilink/bot/get_qrcode_status?qrcode={requests.utils.quote(qrcode)}")
+        headers = {
+            "iLink-App-Id": "bot",
+            "iLink-App-ClientVersion": CLIENT_VERSION,
+        }
+        try:
+            resp = requests.get(url, headers=headers, timeout=timeout)
+            resp.raise_for_status()
+            return resp.json()
+        except requests.exceptions.Timeout:
+            return {"status": "wait"}
+
+
+# ── AES-128-ECB helpers ─────────────────────────────────────────────
+
+def _aes_ecb_encrypt(data: bytes, key: bytes) -> bytes:
+    from Crypto.Cipher import AES
+    pad_len = 16 - (len(data) % 16)
+    padded = data + bytes([pad_len] * pad_len)
+    cipher = AES.new(key, AES.MODE_ECB)
+    return cipher.encrypt(padded)
+
+
+def _aes_ecb_decrypt(data: bytes, key: bytes) -> bytes:
+    from Crypto.Cipher import AES
+    cipher = AES.new(key, AES.MODE_ECB)
+    decrypted = cipher.decrypt(data)
+    pad_len = decrypted[-1]
+    if pad_len > 16:
+        return decrypted
+    return decrypted[:-pad_len]
+
+
+def _file_md5(file_path: str) -> str:
+    h = hashlib.md5()
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(8192), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def _md5_bytes(data: bytes) -> str:
+    return hashlib.md5(data).hexdigest()
+
+
+def _aes_ecb_padded_size(plaintext_size: int) -> int:
+    """PKCS7 padded size for AES-128-ECB."""
+    return ((plaintext_size + 1 + 15) // 16) * 16
+
+
+UPLOAD_MAX_RETRIES = 3
+
+
+def upload_media_to_cdn(api: WeixinApi, file_path: str, to_user_id: str,
+                        media_type: int) -> dict:
+    """
+    Upload a local file to the Weixin CDN (matching official plugin protocol).
+
+    Args:
+        api: WeixinApi instance
+        file_path: local file path
+        to_user_id: target user id
+        media_type: 1=IMAGE, 2=VIDEO, 3=FILE
+
+    Returns:
+        dict with keys: encrypt_query_param, aes_key_b64, ciphertext_size, raw_size
+    """
+    aes_key = os.urandom(16)
+    aes_key_hex = aes_key.hex()
+    filekey = uuid.uuid4().hex
+
+    with open(file_path, "rb") as f:
+        raw_data = f.read()
+
+    raw_size = len(raw_data)
+    raw_md5 = _md5_bytes(raw_data)
+    cipher_size = _aes_ecb_padded_size(raw_size)
+
+    encrypted = _aes_ecb_encrypt(raw_data, aes_key)
+
+    from urllib.parse import quote
+
+    download_param = None
+    last_error = None
+    for attempt in range(1, UPLOAD_MAX_RETRIES + 1):
+        try:
+            if attempt > 1:
+                filekey = uuid.uuid4().hex
+            resp = api.get_upload_url(
+                filekey=filekey,
+                media_type=media_type,
+                to_user_id=to_user_id,
+                rawsize=raw_size,
+                rawfilemd5=raw_md5,
+                filesize=cipher_size,
+                aeskey=aes_key_hex,
+            )
+
+            # API may return either upload_full_url (new) or upload_param (legacy)
+            upload_full_url = resp.get("upload_full_url", "")
+            upload_param = resp.get("upload_param", "")
+            if upload_full_url:
+                cdn_url = upload_full_url
+            elif upload_param:
+                cdn_url = (f"{api.cdn_base_url}/upload"
+                           f"?encrypted_query_param={quote(upload_param)}"
+                           f"&filekey={quote(filekey)}")
+            else:
+                raise RuntimeError(f"[Weixin] getUploadUrl returned neither upload_full_url nor upload_param: {resp}")
+
+            cdn_resp = requests.post(cdn_url, data=encrypted, headers={
+                "Content-Type": "application/octet-stream",
+                "Content-Length": str(len(encrypted)),
+            }, timeout=120)
+            if 400 <= cdn_resp.status_code < 500:
+                err_msg = cdn_resp.headers.get("x-error-message", cdn_resp.text[:200])
+                raise RuntimeError(f"CDN client error {cdn_resp.status_code}: {err_msg}")
+            cdn_resp.raise_for_status()
+            download_param = cdn_resp.headers.get("x-encrypted-param", "")
+            if not download_param:
+                raise RuntimeError("CDN response missing x-encrypted-param header")
+            logger.debug(f"[Weixin] CDN upload success attempt={attempt} filekey={filekey}")
+            break
+        except Exception as e:
+            last_error = e
+            if "client error" in str(e):
+                raise
+            if attempt < UPLOAD_MAX_RETRIES:
+                backoff = 2 ** attempt
+                logger.warning(f"[Weixin] CDN upload attempt {attempt} failed, retrying in {backoff}s: {e}")
+                time.sleep(backoff)
+            else:
+                logger.error(f"[Weixin] CDN upload failed after {UPLOAD_MAX_RETRIES} attempts: {e}")
+
+    if not download_param:
+        raise last_error or RuntimeError("CDN upload failed")
+
+    aes_key_b64 = base64.b64encode(aes_key_hex.encode("utf-8")).decode("utf-8")
+
+    return {
+        "encrypt_query_param": download_param,
+        "aes_key_b64": aes_key_b64,
+        "ciphertext_size": cipher_size,
+        "raw_size": raw_size,
+    }
+
+
+def download_media_from_cdn(cdn_base_url: str, encrypt_query_param: str,
+                            aes_key: str, save_path: str) -> str:
+    """
+    Download and decrypt a media file from Weixin CDN.
+
+    Args:
+        cdn_base_url: CDN base URL
+        encrypt_query_param: encrypted query parameter from message
+        aes_key: hex or base64 encoded AES key
+        save_path: path to save decrypted file
+
+    Returns:
+        save_path on success
+    """
+    from urllib.parse import quote
+    url = f"{cdn_base_url}/download?encrypted_query_param={quote(encrypt_query_param)}"
+    resp = requests.get(url, timeout=60)
+    resp.raise_for_status()
+
+    # Determine key format:
+    # 1) 32-char hex string → 16 raw bytes
+    # 2) base64 string → decode → if 32 bytes, treat as hex-encoded → 16 raw bytes
+    # 3) base64 string → decode → 16 raw bytes directly
+    try:
+        key_bytes = bytes.fromhex(aes_key)
+        if len(key_bytes) != 16:
+            raise ValueError()
+    except (ValueError, TypeError):
+        decoded = base64.b64decode(aes_key)
+        if len(decoded) == 32:
+            try:
+                key_bytes = bytes.fromhex(decoded.decode("ascii"))
+            except (ValueError, UnicodeDecodeError):
+                raise ValueError(f"Invalid AES key: 32 bytes but not valid hex")
+        elif len(decoded) == 16:
+            key_bytes = decoded
+        else:
+            raise ValueError(f"Invalid AES key length after base64 decode: {len(decoded)}")
+
+    decrypted = _aes_ecb_decrypt(resp.content, key_bytes)
+
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    with open(save_path, "wb") as f:
+        f.write(decrypted)
+    return save_path

channel/weixin/weixin_channel.py

@@ -0,0 +1,637 @@
+"""
+Weixin channel implementation.
+
+Uses HTTP long-poll (getUpdates) to receive messages and sendMessage to reply.
+Login via QR code scan through the ilink bot API.
+"""
+
+import json
+import os
+import threading
+import time
+import uuid
+
+import requests
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.weixin.weixin_api import (
+    WeixinApi, upload_media_to_cdn,
+    DEFAULT_BASE_URL, CDN_BASE_URL,
+)
+from channel.weixin.weixin_message import WeixinMessage
+from common.expired_dict import ExpiredDict
+from common.log import logger
+from common.singleton import singleton
+from config import conf
+
+MAX_CONSECUTIVE_FAILURES = 3
+BACKOFF_DELAY = 30
+RETRY_DELAY = 2
+SESSION_EXPIRED_ERRCODE = -14
+TEXT_CHUNK_LIMIT = 4000
+QR_LOGIN_TIMEOUT_S = 480
+QR_MAX_REFRESHES = 10
+
+
+def _load_credentials(cred_path: str) -> dict:
+    """Load saved credentials from JSON file."""
+    try:
+        if os.path.exists(cred_path):
+            with open(cred_path, "r") as f:
+                return json.load(f)
+    except Exception as e:
+        logger.warning(f"[Weixin] Failed to load credentials: {e}")
+    return {}
+
+
+def _save_credentials(cred_path: str, data: dict):
+    """Save credentials to JSON file."""
+    os.makedirs(os.path.dirname(cred_path), exist_ok=True)
+    with open(cred_path, "w") as f:
+        json.dump(data, f, indent=2)
+    try:
+        os.chmod(cred_path, 0o600)
+    except Exception:
+        pass
+
+
+@singleton
+class WeixinChannel(ChatChannel):
+
+    LOGIN_STATUS_IDLE = "idle"
+    LOGIN_STATUS_WAITING = "waiting_scan"
+    LOGIN_STATUS_SCANNED = "scanned"
+    LOGIN_STATUS_OK = "logged_in"
+
+    def __init__(self):
+        super().__init__()
+        self.api = None
+        self._stop_event = threading.Event()
+        self._poll_thread = None
+        self._context_tokens = {}  # user_id -> context_token
+        self._received_msgs = ExpiredDict(60 * 60 * 7.1)
+        self._get_updates_buf = ""
+        self._credentials_path = ""
+        self.login_status = self.LOGIN_STATUS_IDLE
+        self._current_qr_url = ""
+
+        conf()["single_chat_prefix"] = [""]
+
+    # ── Lifecycle ──────────────────────────────────────────────────────
+
+    def startup(self):
+        self._stop_event.clear()
+
+        base_url = conf().get("weixin_base_url", DEFAULT_BASE_URL)
+        cdn_base_url = conf().get("weixin_cdn_base_url", CDN_BASE_URL)
+        token = conf().get("weixin_token", "")
+
+        self._credentials_path = os.path.expanduser(
+            conf().get("weixin_credentials_path", "~/.weixin_cow_credentials.json")
+        )
+
+        if not token:
+            creds = _load_credentials(self._credentials_path)
+            token = creds.get("token", "")
+            if creds.get("base_url"):
+                base_url = creds["base_url"]
+
+        if not token:
+            token, base_url = self._login_with_retry(base_url)
+            if not token:
+                return
+
+        self.api = WeixinApi(base_url=base_url, token=token, cdn_base_url=cdn_base_url)
+        self.login_status = self.LOGIN_STATUS_OK
+
+        logger.info(f"[Weixin] 微信通道已启动，凭证保存在 {self._credentials_path}，"
+                     f"如需重新扫码登录请删除该文件后重启")
+        self.report_startup_success()
+
+        self._poll_loop()
+
+    def _login_with_retry(self, base_url: str) -> tuple:
+        """Attempt QR login, then wait for stop if failed.
+        Returns (token, base_url) on success, or ("", "") if stopped."""
+        logger.info("[Weixin] No token found, starting QR login...")
+        self.login_status = self.LOGIN_STATUS_WAITING
+        login_result = self._qr_login(base_url)
+        if login_result:
+            return login_result["token"], login_result.get("base_url", base_url)
+
+        self.login_status = self.LOGIN_STATUS_IDLE
+        if not self._stop_event.is_set():
+            logger.info("[Weixin] QR login timed out, waiting for stop or reconnect...")
+            print("  二维码登录超时，请通过控制台重新接入\n")
+            self._stop_event.wait()
+
+        logger.info("[Weixin] Login cancelled by stop event")
+        return "", ""
+
+    def stop(self):
+        logger.info("[Weixin] stop() called")
+        self._stop_event.set()
+
+    def _relogin(self) -> bool:
+        """Re-login after session expiry. Returns True on success."""
+        base_url = self.api.base_url if self.api else DEFAULT_BASE_URL
+        if os.path.exists(self._credentials_path):
+            try:
+                os.remove(self._credentials_path)
+            except Exception:
+                pass
+        self.login_status = self.LOGIN_STATUS_WAITING
+        result = self._qr_login(base_url)
+        if not result:
+            self.login_status = self.LOGIN_STATUS_IDLE
+            return False
+        self.api = WeixinApi(
+            base_url=result.get("base_url", base_url),
+            token=result["token"],
+            cdn_base_url=self.api.cdn_base_url if self.api else CDN_BASE_URL,
+        )
+        self.login_status = self.LOGIN_STATUS_OK
+        self._context_tokens.clear()
+        return True
+
+    # ── QR Login ───────────────────────────────────────────────────────
+
+    @staticmethod
+    def _print_qr(qrcode_url: str):
+        """Print QR code to terminal for scanning."""
+        print("\n" + "=" * 60)
+        print("  请使用微信扫描二维码登录 (二维码约2分钟后过期)")
+        print("=" * 60)
+        try:
+            import qrcode as qr_lib
+            import io
+            qr = qr_lib.QRCode(error_correction=qr_lib.constants.ERROR_CORRECT_L, box_size=1, border=1)
+            qr.add_data(qrcode_url)
+            qr.make(fit=True)
+            buf = io.StringIO()
+            qr.print_ascii(out=buf, invert=True)
+            try:
+                print(buf.getvalue())
+            except UnicodeEncodeError:
+                # Windows GBK terminals cannot render Unicode block characters
+                print(f"\n  (终端不支持显示二维码，请使用链接扫码)")
+                print(f"  二维码链接: {qrcode_url}\n")
+        except ImportError:
+            print(f"\n  二维码链接: {qrcode_url}")
+            print("  (安装 'qrcode' 包可在终端显示二维码)\n")
+
+    def _notify_cloud_qrcode(self, qrcode_url: str):
+        """Send QR code URL to cloud console when running in cloud mode."""
+        if not self.cloud_mode:
+            return
+        try:
+            from common import cloud_client
+            client = getattr(cloud_client, "chat_client", None)
+            if client and getattr(client, "client_id", None):
+                client.send_channel_qrcode("weixin", qrcode_url)
+        except Exception as e:
+            logger.warning(f"[Weixin] Failed to notify cloud QR code: {e}")
+
+    def _notify_cloud_connected(self):
+        """Send connected status to cloud console when login succeeds."""
+        if not self.cloud_mode:
+            return
+        try:
+            from common import cloud_client
+            client = getattr(cloud_client, "chat_client", None)
+            if client and getattr(client, "client_id", None):
+                client.send_channel_status("weixin", "connected")
+        except Exception as e:
+            logger.warning(f"[Weixin] Failed to notify cloud connected: {e}")
+
+    def _qr_login(self, base_url: str) -> dict:
+        """Perform interactive QR code login. Returns dict with token/base_url or empty dict."""
+        api = WeixinApi(base_url=base_url)
+        try:
+            qr_resp = api.fetch_qr_code()
+        except Exception as e:
+            logger.error(f"[Weixin] Failed to fetch QR code: {e}")
+            return {}
+
+        qrcode = qr_resp.get("qrcode", "")
+        qrcode_url = qr_resp.get("qrcode_img_content", "")
+
+        if not qrcode:
+            logger.error("[Weixin] No QR code returned from server")
+            return {}
+
+        self._current_qr_url = qrcode_url
+        logger.info(f"[Weixin] 微信二维码链接: {qrcode_url}")
+        self._print_qr(qrcode_url)
+        self._notify_cloud_qrcode(qrcode_url)
+        print("  等待扫码...\n")
+
+        scanned_printed = False
+        refresh_count = 0
+        deadline = time.time() + QR_LOGIN_TIMEOUT_S
+
+        while not self._stop_event.is_set():
+            if time.time() >= deadline:
+                logger.warning(f"[Weixin] QR login timed out after {QR_LOGIN_TIMEOUT_S}s")
+                print(f"\n  二维码登录超时（{QR_LOGIN_TIMEOUT_S}s），请重启后重试")
+                break
+
+            try:
+                status_resp = api.poll_qr_status(qrcode)
+            except Exception as e:
+                logger.error(f"[Weixin] QR status poll error: {e}")
+                return {}
+
+            status = status_resp.get("status", "wait")
+
+            if status == "wait":
+                pass
+            elif status == "scaned":
+                self.login_status = self.LOGIN_STATUS_SCANNED
+                if not scanned_printed:
+                    print("  已扫码，请在手机上确认...")
+                    scanned_printed = True
+            elif status == "expired":
+                refresh_count += 1
+                if refresh_count >= QR_MAX_REFRESHES:
+                    logger.warning(f"[Weixin] QR code refreshed {QR_MAX_REFRESHES} times, giving up")
+                    print(f"\n  二维码已刷新 {QR_MAX_REFRESHES} 次仍未扫码，请重启后重试")
+                    break
+                print(f"  二维码已过期，正在刷新（{refresh_count}/{QR_MAX_REFRESHES}）...")
+                try:
+                    qr_resp = api.fetch_qr_code()
+                    qrcode = qr_resp.get("qrcode", "")
+                    qrcode_url = qr_resp.get("qrcode_img_content", "")
+                    scanned_printed = False
+                    self._current_qr_url = qrcode_url
+                    logger.info(f"[Weixin] 微信二维码链接 ({refresh_count}/{QR_MAX_REFRESHES}): {qrcode_url}")
+                    self._print_qr(qrcode_url)
+                    self._notify_cloud_qrcode(qrcode_url)
+                except Exception as e:
+                    logger.error(f"[Weixin] QR refresh failed: {e}")
+                    return {}
+            elif status == "confirmed":
+                bot_token = status_resp.get("bot_token", "")
+                bot_id = status_resp.get("ilink_bot_id", "")
+                result_base_url = status_resp.get("baseurl", base_url)
+                user_id = status_resp.get("ilink_user_id", "")
+
+                if not bot_token or not bot_id:
+                    logger.error("[Weixin] Login confirmed but missing token/bot_id")
+                    return {}
+
+                self._current_qr_url = ""
+                print(f"\n  ✅ 微信登录成功！bot_id={bot_id}")
+                logger.info(f"[Weixin] Login confirmed: bot_id={bot_id}")
+                self._notify_cloud_connected()
+
+                creds = {
+                    "token": bot_token,
+                    "base_url": result_base_url,
+                    "bot_id": bot_id,
+                    "user_id": user_id,
+                }
+                _save_credentials(self._credentials_path, creds)
+                logger.info(f"[Weixin] Credentials saved to {self._credentials_path}")
+
+                return {"token": bot_token, "base_url": result_base_url}
+
+            self._stop_event.wait(1)
+
+        self._current_qr_url = ""
+        if self._stop_event.is_set():
+            logger.info("[Weixin] QR login cancelled by stop event")
+        return {}
+
+    # ── Long-poll loop ─────────────────────────────────────────────────
+
+    def _poll_loop(self):
+        """Main long-poll loop: getUpdates -> parse -> produce."""
+        logger.info("[Weixin] Starting long-poll loop")
+        consecutive_failures = 0
+
+        while not self._stop_event.is_set():
+            try:
+                resp = self.api.get_updates(self._get_updates_buf)
+
+                ret = resp.get("ret", 0)
+                errcode = resp.get("errcode", 0)
+
+                is_error = (ret != 0) or (errcode != 0)
+                if is_error:
+                    if errcode == SESSION_EXPIRED_ERRCODE or ret == SESSION_EXPIRED_ERRCODE:
+                        logger.error("[Weixin] Session expired (errcode -14), starting re-login...")
+                        if self._relogin():
+                            logger.info("[Weixin] Re-login successful, resuming long-poll")
+                            self._get_updates_buf = ""
+                            consecutive_failures = 0
+                            continue
+                        else:
+                            logger.error("[Weixin] Re-login failed, will retry in 5 minutes")
+                            self._stop_event.wait(300)
+                            continue
+
+                    consecutive_failures += 1
+                    errmsg = resp.get("errmsg", "")
+                    logger.error(f"[Weixin] getUpdates error: ret={ret} errcode={errcode} "
+                                 f"errmsg={errmsg} ({consecutive_failures}/{MAX_CONSECUTIVE_FAILURES})")
+                    if consecutive_failures >= MAX_CONSECUTIVE_FAILURES:
+                        consecutive_failures = 0
+                        self._stop_event.wait(BACKOFF_DELAY)
+                    else:
+                        self._stop_event.wait(RETRY_DELAY)
+                    continue
+
+                consecutive_failures = 0
+
+                # Update sync cursor
+                new_buf = resp.get("get_updates_buf", "")
+                if new_buf:
+                    self._get_updates_buf = new_buf
+
+                # Process messages
+                msgs = resp.get("msgs", [])
+                for raw_msg in msgs:
+                    try:
+                        self._process_message(raw_msg)
+                    except Exception as e:
+                        logger.error(f"[Weixin] Failed to process message: {e}", exc_info=True)
+
+            except Exception as e:
+                if self._stop_event.is_set():
+                    break
+                consecutive_failures += 1
+                logger.error(f"[Weixin] getUpdates exception: {e} "
+                             f"({consecutive_failures}/{MAX_CONSECUTIVE_FAILURES})")
+                if consecutive_failures >= MAX_CONSECUTIVE_FAILURES:
+                    consecutive_failures = 0
+                    self._stop_event.wait(BACKOFF_DELAY)
+                else:
+                    self._stop_event.wait(RETRY_DELAY)
+
+        logger.info("[Weixin] Long-poll loop ended")
+
+    def _process_message(self, raw_msg: dict):
+        """Parse a single inbound message and produce to the handling queue."""
+        msg_type = raw_msg.get("message_type", 0)
+        if msg_type != 1:  # Only process USER messages (type=1)
+            return
+
+        msg_id = str(raw_msg.get("message_id", raw_msg.get("seq", "")))
+        if self._received_msgs.get(msg_id):
+            return
+        self._received_msgs[msg_id] = True
+
+        from_user = raw_msg.get("from_user_id", "")
+        context_token = raw_msg.get("context_token", "")
+
+        if context_token and from_user:
+            self._context_tokens[from_user] = context_token
+
+        cdn_base_url = self.api.cdn_base_url if self.api else CDN_BASE_URL
+        try:
+            wx_msg = WeixinMessage(raw_msg, cdn_base_url=cdn_base_url)
+        except Exception as e:
+            logger.error(f"[Weixin] Failed to parse WeixinMessage: {e}", exc_info=True)
+            return
+
+        logger.info(f"[Weixin] Received: from={from_user} ctype={wx_msg.ctype} "
+                     f"content={str(wx_msg.content)[:50]}")
+
+        # File cache logic
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+        session_id = from_user
+
+        if wx_msg.ctype == ContextType.IMAGE:
+            if hasattr(wx_msg, "image_path") and wx_msg.image_path:
+                file_cache.add(session_id, wx_msg.image_path, file_type="image")
+                logger.info(f"[Weixin] Image cached for session {session_id}")
+            return
+
+        if wx_msg.ctype == ContextType.FILE:
+            wx_msg.prepare()
+            file_cache.add(session_id, wx_msg.content, file_type="file")
+            logger.info(f"[Weixin] File cached for session {session_id}: {wx_msg.content}")
+            return
+
+        if wx_msg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                refs = []
+                for fi in cached_files:
+                    ftype, fpath = fi["type"], fi["path"]
+                    if ftype == "image":
+                        refs.append(f"[图片: {fpath}]")
+                    elif ftype == "video":
+                        refs.append(f"[视频: {fpath}]")
+                    else:
+                        refs.append(f"[文件: {fpath}]")
+                wx_msg.content = wx_msg.content + "\n" + "\n".join(refs)
+                file_cache.clear(session_id)
+
+        context = self._compose_context(
+            wx_msg.ctype,
+            wx_msg.content,
+            isgroup=False,
+            msg=wx_msg,
+            no_need_at=True,
+        )
+        if context:
+            self.produce(context)
+
+    # ── _compose_context ───────────────────────────────────────────────
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "channel_type" not in context:
+            context["channel_type"] = self.channel_type
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+        context["session_id"] = cmsg.from_user_id
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            img_match_prefix = check_prefix(content, conf().get("image_create_prefix"))
+            if img_match_prefix:
+                content = content.replace(img_match_prefix, "", 1)
+                context.type = ContextType.IMAGE_CREATE
+            else:
+                context.type = ContextType.TEXT
+            context.content = content.strip()
+
+        return context
+
+    # ── Send reply ─────────────────────────────────────────────────────
+
+    def send(self, reply: Reply, context: Context):
+        receiver = context.get("receiver", "")
+        msg = context.get("msg")
+        context_token = self._get_context_token(receiver, msg)
+
+        if not context_token:
+            logger.error(f"[Weixin] No context_token for receiver={receiver}, cannot send")
+            return
+
+        if reply.type == ReplyType.TEXT:
+            self._send_text(reply.content, receiver, context_token)
+        elif reply.type in (ReplyType.IMAGE_URL, ReplyType.IMAGE):
+            self._send_image(reply.content, receiver, context_token)
+        elif reply.type == ReplyType.FILE:
+            self._send_file(reply.content, receiver, context_token)
+        elif reply.type in (ReplyType.VIDEO, ReplyType.VIDEO_URL):
+            self._send_video(reply.content, receiver, context_token)
+        else:
+            logger.warning(f"[Weixin] Unsupported reply type: {reply.type}, fallback to text")
+            self._send_text(str(reply.content), receiver, context_token)
+
+    def _get_context_token(self, receiver: str, msg=None) -> str:
+        """Get the context_token for a receiver, required for all sends."""
+        if msg and hasattr(msg, "context_token") and msg.context_token:
+            return msg.context_token
+        return self._context_tokens.get(receiver, "")
+
+    def _send_text(self, text: str, receiver: str, context_token: str):
+        if len(text) <= TEXT_CHUNK_LIMIT:
+            try:
+                self.api.send_text(receiver, text, context_token)
+                logger.debug(f"[Weixin] Text sent to {receiver}, len={len(text)}")
+            except Exception as e:
+                logger.error(f"[Weixin] Failed to send text: {e}")
+            return
+
+        chunks = self._split_text(text, TEXT_CHUNK_LIMIT)
+        for i, chunk in enumerate(chunks):
+            try:
+                self.api.send_text(receiver, chunk, context_token)
+                logger.debug(f"[Weixin] Text chunk {i+1}/{len(chunks)} sent to {receiver}, len={len(chunk)}")
+            except Exception as e:
+                logger.error(f"[Weixin] Failed to send text chunk {i+1}/{len(chunks)}: {e}")
+                break
+            if i < len(chunks) - 1:
+                time.sleep(0.5)
+
+    @staticmethod
+    def _split_text(text: str, limit: int) -> list:
+        """Split text into chunks, preferring to break at paragraph or line boundaries."""
+        if len(text) <= limit:
+            return [text]
+        chunks = []
+        while text:
+            if len(text) <= limit:
+                chunks.append(text)
+                break
+            cut = text.rfind("\n\n", 0, limit)
+            if cut <= 0:
+                cut = text.rfind("\n", 0, limit)
+            if cut <= 0:
+                cut = limit
+            chunks.append(text[:cut])
+            text = text[cut:].lstrip("\n")
+        return chunks
+
+    def _send_image(self, img_path_or_url: str, receiver: str, context_token: str):
+        local_path = self._resolve_media_path(img_path_or_url)
+        if not local_path:
+            self._send_text("[Image send failed: file not found]", receiver, context_token)
+            return
+        try:
+            result = upload_media_to_cdn(self.api, local_path, receiver, media_type=1)
+            self.api.send_image_item(
+                to=receiver,
+                context_token=context_token,
+                encrypt_query_param=result["encrypt_query_param"],
+                aes_key_b64=result["aes_key_b64"],
+                ciphertext_size=result["ciphertext_size"],
+            )
+            logger.info(f"[Weixin] Image sent to {receiver}")
+        except Exception as e:
+            logger.error(f"[Weixin] Image send failed: {e}")
+            self._send_text("[Image send failed]", receiver, context_token)
+
+    def _send_file(self, file_path_or_url: str, receiver: str, context_token: str):
+        local_path = self._resolve_media_path(file_path_or_url)
+        if not local_path:
+            self._send_text("[File send failed: file not found]", receiver, context_token)
+            return
+        try:
+            result = upload_media_to_cdn(self.api, local_path, receiver, media_type=3)
+            self.api.send_file_item(
+                to=receiver,
+                context_token=context_token,
+                encrypt_query_param=result["encrypt_query_param"],
+                aes_key_b64=result["aes_key_b64"],
+                file_name=os.path.basename(local_path),
+                file_size=result["raw_size"],
+            )
+            logger.info(f"[Weixin] File sent to {receiver}")
+        except Exception as e:
+            logger.error(f"[Weixin] File send failed: {e}")
+            self._send_text("[File send failed]", receiver, context_token)
+
+    def _send_video(self, video_path_or_url: str, receiver: str, context_token: str):
+        local_path = self._resolve_media_path(video_path_or_url)
+        if not local_path:
+            self._send_text("[Video send failed: file not found]", receiver, context_token)
+            return
+        try:
+            result = upload_media_to_cdn(self.api, local_path, receiver, media_type=2)
+            self.api.send_video_item(
+                to=receiver,
+                context_token=context_token,
+                encrypt_query_param=result["encrypt_query_param"],
+                aes_key_b64=result["aes_key_b64"],
+                ciphertext_size=result["ciphertext_size"],
+            )
+            logger.info(f"[Weixin] Video sent to {receiver}")
+        except Exception as e:
+            logger.error(f"[Weixin] Video send failed: {e}")
+            self._send_text("[Video send failed]", receiver, context_token)
+
+    @staticmethod
+    def _resolve_media_path(path_or_url: str) -> str:
+        """Resolve a file path or URL to a local file path. Downloads if needed."""
+        if not path_or_url:
+            return ""
+
+        local_path = path_or_url
+        if local_path.startswith("file://"):
+            local_path = local_path[7:]
+
+        if local_path.startswith(("http://", "https://")):
+            try:
+                resp = requests.get(local_path, timeout=60)
+                resp.raise_for_status()
+                ct = resp.headers.get("Content-Type", "")
+                ext = ".bin"
+                if "jpeg" in ct or "jpg" in ct:
+                    ext = ".jpg"
+                elif "png" in ct:
+                    ext = ".png"
+                elif "gif" in ct:
+                    ext = ".gif"
+                elif "webp" in ct:
+                    ext = ".webp"
+                elif "mp4" in ct:
+                    ext = ".mp4"
+                elif "pdf" in ct:
+                    ext = ".pdf"
+
+                tmp_path = f"/tmp/wx_media_{uuid.uuid4().hex[:8]}{ext}"
+                with open(tmp_path, "wb") as f:
+                    f.write(resp.content)
+                return tmp_path
+            except Exception as e:
+                logger.error(f"[Weixin] Failed to download media: {e}")
+                return ""
+
+        if os.path.exists(local_path):
+            return local_path
+
+        logger.warning(f"[Weixin] Media file not found: {local_path}")
+        return ""

channel/weixin/weixin_message.py

@@ -0,0 +1,204 @@
+"""
+Weixin ChatMessage implementation.
+
+Parses WeixinMessage from the getUpdates API into the unified ChatMessage format.
+"""
+
+import os
+import uuid
+
+from bridge.context import ContextType
+from channel.chat_message import ChatMessage
+from channel.weixin.weixin_api import download_media_from_cdn, CDN_BASE_URL
+from common.log import logger
+from common.utils import expand_path
+from config import conf
+
+
+# MessageItemType constants from the Weixin protocol
+ITEM_TEXT = 1
+ITEM_IMAGE = 2
+ITEM_VOICE = 3
+ITEM_FILE = 4
+ITEM_VIDEO = 5
+
+
+def _get_tmp_dir() -> str:
+    ws_root = expand_path(conf().get("agent_workspace", "~/cow"))
+    tmp_dir = os.path.join(ws_root, "tmp")
+    os.makedirs(tmp_dir, exist_ok=True)
+    return tmp_dir
+
+
+class WeixinMessage(ChatMessage):
+    """Message wrapper for Weixin channel."""
+
+    def __init__(self, msg: dict, cdn_base_url: str = CDN_BASE_URL):
+        super().__init__(msg)
+
+        self.msg_id = str(msg.get("message_id", msg.get("seq", uuid.uuid4().hex[:8])))
+        self.create_time = msg.get("create_time_ms", 0)
+        self.context_token = msg.get("context_token", "")
+        self.is_group = False  # Weixin plugin only supports direct chat
+        self.is_at = False
+
+        from_user_id = msg.get("from_user_id", "")
+        to_user_id = msg.get("to_user_id", "")
+
+        self.from_user_id = from_user_id
+        self.from_user_nickname = from_user_id
+        self.to_user_id = to_user_id
+        self.to_user_nickname = to_user_id
+        self.other_user_id = from_user_id
+        self.other_user_nickname = from_user_id
+        self.actual_user_id = from_user_id
+        self.actual_user_nickname = from_user_id
+
+        item_list = msg.get("item_list", [])
+
+        # Parse items: find text and media
+        text_body = ""
+        media_item = None
+        media_type = None
+        ref_text = ""
+
+        for item in item_list:
+            itype = item.get("type", 0)
+
+            if itype == ITEM_TEXT:
+                text_item = item.get("text_item", {})
+                text_body = text_item.get("text", "")
+
+                ref = item.get("ref_msg")
+                if ref:
+                    ref_title = ref.get("title", "")
+                    ref_mi = ref.get("message_item", {})
+                    ref_body = ""
+                    if ref_mi.get("type") == ITEM_TEXT:
+                        ref_body = ref_mi.get("text_item", {}).get("text", "")
+                    if ref_title or ref_body:
+                        parts = [p for p in [ref_title, ref_body] if p]
+                        ref_text = f"[引用: {' | '.join(parts)}]\n"
+                    # If ref is a media item, treat it as the media to download
+                    if ref_mi.get("type") in (ITEM_IMAGE, ITEM_VIDEO, ITEM_FILE):
+                        media_item = ref_mi
+                        media_type = ref_mi.get("type")
+
+            elif itype == ITEM_VOICE:
+                voice_item = item.get("voice_item", {})
+                voice_text = voice_item.get("text", "")
+                if voice_text:
+                    text_body = voice_text
+                else:
+                    # Voice without transcription - download the audio
+                    media_item = item
+                    media_type = ITEM_VOICE
+
+            elif itype in (ITEM_IMAGE, ITEM_VIDEO, ITEM_FILE):
+                if not media_item:
+                    media_item = item
+                    media_type = itype
+
+        # Determine ctype and content
+        if media_item and not text_body:
+            self._setup_media(media_item, media_type, cdn_base_url)
+        elif media_item and text_body:
+            # Text + media: download media, attach as file ref in text
+            self.ctype = ContextType.TEXT
+            media_path = self._download_media(media_item, media_type, cdn_base_url)
+            if media_path:
+                if media_type == ITEM_IMAGE:
+                    text_body += f"\n[图片: {media_path}]"
+                elif media_type == ITEM_VIDEO:
+                    text_body += f"\n[视频: {media_path}]"
+                else:
+                    text_body += f"\n[文件: {media_path}]"
+            self.content = ref_text + text_body
+        else:
+            self.ctype = ContextType.TEXT
+            self.content = ref_text + text_body
+
+    def _setup_media(self, item: dict, media_type: int, cdn_base_url: str):
+        """Set up message as a media type, with lazy download via _prepare_fn."""
+        if media_type == ITEM_IMAGE:
+            self.ctype = ContextType.IMAGE
+            image_path = self._download_media(item, ITEM_IMAGE, cdn_base_url)
+            if image_path:
+                self.content = image_path
+                self.image_path = image_path
+            else:
+                self.ctype = ContextType.TEXT
+                self.content = "[Image download failed]"
+
+        elif media_type == ITEM_VIDEO:
+            self.ctype = ContextType.FILE
+            save_path = os.path.join(_get_tmp_dir(), f"wx_{self.msg_id}.mp4")
+            self.content = save_path
+
+            def _download():
+                path = self._download_media(item, ITEM_VIDEO, cdn_base_url)
+                if path:
+                    self.content = path
+            self._prepare_fn = _download
+
+        elif media_type == ITEM_FILE:
+            self.ctype = ContextType.FILE
+            file_name = item.get("file_item", {}).get("file_name", f"wx_{self.msg_id}")
+            save_path = os.path.join(_get_tmp_dir(), file_name)
+            self.content = save_path
+
+            def _download():
+                path = self._download_media(item, ITEM_FILE, cdn_base_url)
+                if path:
+                    self.content = path
+            self._prepare_fn = _download
+
+        elif media_type == ITEM_VOICE:
+            self.ctype = ContextType.VOICE
+            save_path = os.path.join(_get_tmp_dir(), f"wx_{self.msg_id}.silk")
+            self.content = save_path
+
+            def _download():
+                path = self._download_media(item, ITEM_VOICE, cdn_base_url)
+                if path:
+                    self.content = path
+            self._prepare_fn = _download
+
+    def _download_media(self, item: dict, media_type: int, cdn_base_url: str) -> str:
+        """Download media from CDN, returns local file path or empty string."""
+        type_key_map = {
+            ITEM_IMAGE: "image_item",
+            ITEM_VIDEO: "video_item",
+            ITEM_FILE: "file_item",
+            ITEM_VOICE: "voice_item",
+        }
+        key = type_key_map.get(media_type, "")
+        info = item.get(key, {})
+        media = info.get("media", {})
+
+        encrypt_param = media.get("encrypt_query_param", "")
+        # aes_key can be in image_item.aeskey (hex) or media.aes_key (b64)
+        aes_key = info.get("aeskey", "") or media.get("aes_key", "")
+
+        if not encrypt_param or not aes_key:
+            logger.warning(f"[Weixin] Missing CDN params for media download (type={media_type})")
+            return ""
+
+        if media_type == ITEM_FILE:
+            original_name = info.get("file_name", "")
+            if original_name:
+                save_path = os.path.join(_get_tmp_dir(), original_name)
+            else:
+                save_path = os.path.join(_get_tmp_dir(), f"wx_{self.msg_id}.bin")
+        else:
+            ext_map = {ITEM_IMAGE: ".jpg", ITEM_VIDEO: ".mp4", ITEM_VOICE: ".silk"}
+            ext = ext_map.get(media_type, "")
+            save_path = os.path.join(_get_tmp_dir(), f"wx_{self.msg_id}{ext}")
+
+        try:
+            download_media_from_cdn(cdn_base_url, encrypt_param, aes_key, save_path)
+            logger.info(f"[Weixin] Media downloaded: {save_path}")
+            return save_path
+        except Exception as e:
+            logger.error(f"[Weixin] Media download failed: {e}")
+            return ""

channel/wework/run.py

@@ -1,17 +0,0 @@
-import os
-import time
-os.environ['ntwork_LOG'] = "ERROR"
-import ntwork
-
-wework = ntwork.WeWork()
-
-
-def forever():
-    try:
-        while True:
-            time.sleep(0.1)
-    except KeyboardInterrupt:
-        ntwork.exit_()
-        os._exit(0)
-
-

channel/wework/wework_channel.py

@@ -1,326 +0,0 @@
-import io
-import os
-import random
-import tempfile
-import threading
-os.environ['ntwork_LOG'] = "ERROR"
-import ntwork
-import requests
-import uuid
-
-from bridge.context import *
-from bridge.reply import *
-from channel.chat_channel import ChatChannel
-from channel.wework.wework_message import *
-from channel.wework.wework_message import WeworkMessage
-from common.singleton import singleton
-from common.log import logger
-from common.time_check import time_checker
-from common.utils import compress_imgfile, fsize
-from config import conf
-from channel.wework.run import wework
-from channel.wework import run
-from PIL import Image
-
-
-def get_wxid_by_name(room_members, group_wxid, name):
-    if group_wxid in room_members:
-        for member in room_members[group_wxid]['member_list']:
-            if member['room_nickname'] == name or member['username'] == name:
-                return member['user_id']
-    return None  # 如果没有找到对应的group_wxid或name，则返回None
-
-
-def download_and_compress_image(url, filename, quality=30):
-    # 确定保存图片的目录
-    directory = os.path.join(os.getcwd(), "tmp")
-    # 如果目录不存在，则创建目录
-    if not os.path.exists(directory):
-        os.makedirs(directory)
-
-    # 下载图片
-    pic_res = requests.get(url, stream=True)
-    image_storage = io.BytesIO()
-    for block in pic_res.iter_content(1024):
-        image_storage.write(block)
-
-    # 检查图片大小并可能进行压缩
-    sz = fsize(image_storage)
-    if sz >= 10 * 1024 * 1024:  # 如果图片大于 10 MB
-        logger.info("[wework] image too large, ready to compress, sz={}".format(sz))
-        image_storage = compress_imgfile(image_storage, 10 * 1024 * 1024 - 1)
-        logger.info("[wework] image compressed, sz={}".format(fsize(image_storage)))
-
-    # 将内存缓冲区的指针重置到起始位置
-    image_storage.seek(0)
-
-    # 读取并保存图片
-    image = Image.open(image_storage)
-    image_path = os.path.join(directory, f"{filename}.png")
-    image.save(image_path, "png")
-
-    return image_path
-
-
-def download_video(url, filename):
-    # 确定保存视频的目录
-    directory = os.path.join(os.getcwd(), "tmp")
-    # 如果目录不存在，则创建目录
-    if not os.path.exists(directory):
-        os.makedirs(directory)
-
-    # 下载视频
-    response = requests.get(url, stream=True)
-    total_size = 0
-
-    video_path = os.path.join(directory, f"{filename}.mp4")
-
-    with open(video_path, 'wb') as f:
-        for block in response.iter_content(1024):
-            total_size += len(block)
-
-            # 如果视频的总大小超过30MB (30 * 1024 * 1024 bytes)，则停止下载并返回
-            if total_size > 30 * 1024 * 1024:
-                logger.info("[WX] Video is larger than 30MB, skipping...")
-                return None
-
-            f.write(block)
-
-    return video_path
-
-
-def create_message(wework_instance, message, is_group):
-    logger.debug(f"正在为{'群聊' if is_group else '单聊'}创建 WeworkMessage")
-    cmsg = WeworkMessage(message, wework=wework_instance, is_group=is_group)
-    logger.debug(f"cmsg:{cmsg}")
-    return cmsg
-
-
-def handle_message(cmsg, is_group):
-    logger.debug(f"准备用 WeworkChannel 处理{'群聊' if is_group else '单聊'}消息")
-    if is_group:
-        WeworkChannel().handle_group(cmsg)
-    else:
-        WeworkChannel().handle_single(cmsg)
-    logger.debug(f"已用 WeworkChannel 处理完{'群聊' if is_group else '单聊'}消息")
-
-
-def _check(func):
-    def wrapper(self, cmsg: ChatMessage):
-        msgId = cmsg.msg_id
-        create_time = cmsg.create_time  # 消息时间戳
-        if create_time is None:
-            return func(self, cmsg)
-        if int(create_time) < int(time.time()) - 60:  # 跳过1分钟前的历史消息
-            logger.debug("[WX]history message {} skipped".format(msgId))
-            return
-        return func(self, cmsg)
-
-    return wrapper
-
-
-@wework.msg_register(
-    [ntwork.MT_RECV_TEXT_MSG, ntwork.MT_RECV_IMAGE_MSG, 11072, ntwork.MT_RECV_LINK_CARD_MSG,ntwork.MT_RECV_FILE_MSG, ntwork.MT_RECV_VOICE_MSG])
-def all_msg_handler(wework_instance: ntwork.WeWork, message):
-    logger.debug(f"收到消息: {message}")
-    if 'data' in message:
-        # 首先查找conversation_id，如果没有找到，则查找room_conversation_id
-        conversation_id = message['data'].get('conversation_id', message['data'].get('room_conversation_id'))
-        if conversation_id is not None:
-            is_group = "R:" in conversation_id
-            try:
-                cmsg = create_message(wework_instance=wework_instance, message=message, is_group=is_group)
-            except NotImplementedError as e:
-                logger.error(f"[WX]{message.get('MsgId', 'unknown')} 跳过: {e}")
-                return None
-            delay = random.randint(1, 2)
-            timer = threading.Timer(delay, handle_message, args=(cmsg, is_group))
-            timer.start()
-        else:
-            logger.debug("消息数据中无 conversation_id")
-            return None
-    return None
-
-
-def accept_friend_with_retries(wework_instance, user_id, corp_id):
-    result = wework_instance.accept_friend(user_id, corp_id)
-    logger.debug(f'result:{result}')
-
-
-# @wework.msg_register(ntwork.MT_RECV_FRIEND_MSG)
-# def friend(wework_instance: ntwork.WeWork, message):
-#     data = message["data"]
-#     user_id = data["user_id"]
-#     corp_id = data["corp_id"]
-#     logger.info(f"接收到好友请求，消息内容：{data}")
-#     delay = random.randint(1, 180)
-#     threading.Timer(delay, accept_friend_with_retries, args=(wework_instance, user_id, corp_id)).start()
-#
-#     return None
-
-
-def get_with_retry(get_func, max_retries=5, delay=5):
-    retries = 0
-    result = None
-    while retries < max_retries:
-        result = get_func()
-        if result:
-            break
-        logger.warning(f"获取数据失败，重试第{retries + 1}次······")
-        retries += 1
-        time.sleep(delay)  # 等待一段时间后重试
-    return result
-
-
-@singleton
-class WeworkChannel(ChatChannel):
-    NOT_SUPPORT_REPLYTYPE = []
-
-    def __init__(self):
-        super().__init__()
-
-    def startup(self):
-        smart = conf().get("wework_smart", True)
-        wework.open(smart)
-        logger.info("等待登录······")
-        wework.wait_login()
-        login_info = wework.get_login_info()
-        self.user_id = login_info['user_id']
-        self.name = login_info['nickname']
-        logger.info(f"登录信息:>>>user_id:{self.user_id}>>>>>>>>name:{self.name}")
-        logger.info("静默延迟60s，等待客户端刷新数据，请勿进行任何操作······")
-        time.sleep(60)
-        contacts = get_with_retry(wework.get_external_contacts)
-        rooms = get_with_retry(wework.get_rooms)
-        directory = os.path.join(os.getcwd(), "tmp")
-        if not contacts or not rooms:
-            logger.error("获取contacts或rooms失败，程序退出")
-            ntwork.exit_()
-            os.exit(0)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        # 将contacts保存到json文件中
-        with open(os.path.join(directory, 'wework_contacts.json'), 'w', encoding='utf-8') as f:
-            json.dump(contacts, f, ensure_ascii=False, indent=4)
-        with open(os.path.join(directory, 'wework_rooms.json'), 'w', encoding='utf-8') as f:
-            json.dump(rooms, f, ensure_ascii=False, indent=4)
-        # 创建一个空字典来保存结果
-        result = {}
-
-        # 遍历列表中的每个字典
-        for room in rooms['room_list']:
-            # 获取聊天室ID
-            room_wxid = room['conversation_id']
-
-            # 获取聊天室成员
-            room_members = wework.get_room_members(room_wxid)
-
-            # 将聊天室成员保存到结果字典中
-            result[room_wxid] = room_members
-
-        # 将结果保存到json文件中
-        with open(os.path.join(directory, 'wework_room_members.json'), 'w', encoding='utf-8') as f:
-            json.dump(result, f, ensure_ascii=False, indent=4)
-        logger.info("wework程序初始化完成········")
-        run.forever()
-
-    @time_checker
-    @_check
-    def handle_single(self, cmsg: ChatMessage):
-        if cmsg.from_user_id == cmsg.to_user_id:
-            # ignore self reply
-            return
-        if cmsg.ctype == ContextType.VOICE:
-            if not conf().get("speech_recognition"):
-                return
-            logger.debug("[WX]receive voice msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.IMAGE:
-            logger.debug("[WX]receive image msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.PATPAT:
-            logger.debug("[WX]receive patpat msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.TEXT:
-            logger.debug("[WX]receive text msg: {}, cmsg={}".format(json.dumps(cmsg._rawmsg, ensure_ascii=False), cmsg))
-        else:
-            logger.debug("[WX]receive msg: {}, cmsg={}".format(cmsg.content, cmsg))
-        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=False, msg=cmsg)
-        if context:
-            self.produce(context)
-
-    @time_checker
-    @_check
-    def handle_group(self, cmsg: ChatMessage):
-        if cmsg.ctype == ContextType.VOICE:
-            if not conf().get("speech_recognition"):
-                return
-            logger.debug("[WX]receive voice for group msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.IMAGE:
-            logger.debug("[WX]receive image for group msg: {}".format(cmsg.content))
-        elif cmsg.ctype in [ContextType.JOIN_GROUP, ContextType.PATPAT]:
-            logger.debug("[WX]receive note msg: {}".format(cmsg.content))
-        elif cmsg.ctype == ContextType.TEXT:
-            pass
-        else:
-            logger.debug("[WX]receive group msg: {}".format(cmsg.content))
-        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=True, msg=cmsg)
-        if context:
-            self.produce(context)
-
-    # 统一的发送函数，每个Channel自行实现，根据reply的type字段发送不同类型的消息
-    def send(self, reply: Reply, context: Context):
-        logger.debug(f"context: {context}")
-        receiver = context["receiver"]
-        actual_user_id = context["msg"].actual_user_id
-        if reply.type == ReplyType.TEXT or reply.type == ReplyType.TEXT_:
-            match = re.search(r"^@(.*?)\n", reply.content)
-            logger.debug(f"match: {match}")
-            if match:
-                new_content = re.sub(r"^@(.*?)\n", "\n", reply.content)
-                at_list = [actual_user_id]
-                logger.debug(f"new_content: {new_content}")
-                wework.send_room_at_msg(receiver, new_content, at_list)
-            else:
-                wework.send_text(receiver, reply.content)
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
-            wework.send_text(receiver, reply.content)
-            logger.info("[WX] sendMsg={}, receiver={}".format(reply, receiver))
-        elif reply.type == ReplyType.IMAGE:  # 从文件读取图片
-            image_storage = reply.content
-            image_storage.seek(0)
-            # Read data from image_storage
-            data = image_storage.read()
-            # Create a temporary file
-            with tempfile.NamedTemporaryFile(delete=False) as temp:
-                temp_path = temp.name
-                temp.write(data)
-            # Send the image
-            wework.send_image(receiver, temp_path)
-            logger.info("[WX] sendImage, receiver={}".format(receiver))
-            # Remove the temporary file
-            os.remove(temp_path)
-        elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
-            img_url = reply.content
-            filename = str(uuid.uuid4())
-
-            # 调用你的函数，下载图片并保存为本地文件
-            image_path = download_and_compress_image(img_url, filename)
-
-            wework.send_image(receiver, file_path=image_path)
-            logger.info("[WX] sendImage url={}, receiver={}".format(img_url, receiver))
-        elif reply.type == ReplyType.VIDEO_URL:
-            video_url = reply.content
-            filename = str(uuid.uuid4())
-            video_path = download_video(video_url, filename)
-
-            if video_path is None:
-                # 如果视频太大，下载可能会被跳过，此时 video_path 将为 None
-                wework.send_text(receiver, "抱歉，视频太大了！！！")
-            else:
-                wework.send_video(receiver, video_path)
-            logger.info("[WX] sendVideo, receiver={}".format(receiver))
-        elif reply.type == ReplyType.VOICE:
-            current_dir = os.getcwd()
-            voice_file = reply.content.split("/")[-1]
-            reply.content = os.path.join(current_dir, "tmp", voice_file)
-            wework.send_file(receiver, reply.content)
-            logger.info("[WX] sendFile={}, receiver={}".format(reply.content, receiver))

channel/wework/wework_message.py

@@ -1,227 +0,0 @@
-import datetime
-import json
-import os
-import re
-import time
-import pilk
-
-from bridge.context import ContextType
-from channel.chat_message import ChatMessage
-from common.log import logger
-from ntwork.const import send_type
-
-
-def get_with_retry(get_func, max_retries=5, delay=5):
-    retries = 0
-    result = None
-    while retries < max_retries:
-        result = get_func()
-        if result:
-            break
-        logger.warning(f"获取数据失败，重试第{retries + 1}次······")
-        retries += 1
-        time.sleep(delay)  # 等待一段时间后重试
-    return result
-
-
-def get_room_info(wework, conversation_id):
-    logger.debug(f"传入的 conversation_id: {conversation_id}")
-    rooms = wework.get_rooms()
-    if not rooms or 'room_list' not in rooms:
-        logger.error(f"获取群聊信息失败: {rooms}")
-        return None
-    time.sleep(1)
-    logger.debug(f"获取到的群聊信息: {rooms}")
-    for room in rooms['room_list']:
-        if room['conversation_id'] == conversation_id:
-            return room
-    return None
-
-
-def cdn_download(wework, message, file_name):
-    data = message["data"]
-    aes_key = data["cdn"]["aes_key"]
-    file_size = data["cdn"]["size"]
-
-    # 获取当前工作目录，然后与文件名拼接得到保存路径
-    current_dir = os.getcwd()
-    save_path = os.path.join(current_dir, "tmp", file_name)
-
-    # 下载保存图片到本地
-    if "url" in data["cdn"].keys() and "auth_key" in data["cdn"].keys():
-        url = data["cdn"]["url"]
-        auth_key = data["cdn"]["auth_key"]
-        # result = wework.wx_cdn_download(url, auth_key, aes_key, file_size, save_path)  # ntwork库本身接口有问题，缺失了aes_key这个参数
-        """
-        下载wx类型的cdn文件，以https开头
-        """
-        data = {
-            'url': url,
-            'auth_key': auth_key,
-            'aes_key': aes_key,
-            'size': file_size,
-            'save_path': save_path
-        }
-        result = wework._WeWork__send_sync(send_type.MT_WXCDN_DOWNLOAD_MSG, data)  # 直接用wx_cdn_download的接口内部实现来调用
-    elif "file_id" in data["cdn"].keys():
-        if message["type"] == 11042:
-            file_type = 2
-        elif message["type"] == 11045:
-            file_type = 5
-        file_id = data["cdn"]["file_id"]
-        result = wework.c2c_cdn_download(file_id, aes_key, file_size, file_type, save_path)
-    else:
-        logger.error(f"something is wrong, data: {data}")
-        return
-
-    # 输出下载结果
-    logger.debug(f"result: {result}")
-
-
-def c2c_download_and_convert(wework, message, file_name):
-    data = message["data"]
-    aes_key = data["cdn"]["aes_key"]
-    file_size = data["cdn"]["size"]
-    file_type = 5
-    file_id = data["cdn"]["file_id"]
-
-    current_dir = os.getcwd()
-    save_path = os.path.join(current_dir, "tmp", file_name)
-    result = wework.c2c_cdn_download(file_id, aes_key, file_size, file_type, save_path)
-    logger.debug(result)
-
-    # 在下载完SILK文件之后，立即将其转换为WAV文件
-    base_name, _ = os.path.splitext(save_path)
-    wav_file = base_name + ".wav"
-    pilk.silk_to_wav(save_path, wav_file, rate=24000)
-
-    # 删除SILK文件
-    try:
-        os.remove(save_path)
-    except Exception as e:
-        pass
-
-
-class WeworkMessage(ChatMessage):
-    def __init__(self, wework_msg, wework, is_group=False):
-        try:
-            super().__init__(wework_msg)
-            self.msg_id = wework_msg['data'].get('conversation_id', wework_msg['data'].get('room_conversation_id'))
-            # 使用.get()防止 'send_time' 键不存在时抛出错误
-            self.create_time = wework_msg['data'].get("send_time")
-            self.is_group = is_group
-            self.wework = wework
-
-            if wework_msg["type"] == 11041:  # 文本消息类型
-                if any(substring in wework_msg['data']['content'] for substring in ("该消息类型暂不能展示", "不支持的消息类型")):
-                    return
-                self.ctype = ContextType.TEXT
-                self.content = wework_msg['data']['content']
-            elif wework_msg["type"] == 11044:  # 语音消息类型，需要缓存文件
-                file_name = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + ".silk"
-                base_name, _ = os.path.splitext(file_name)
-                file_name_2 = base_name + ".wav"
-                current_dir = os.getcwd()
-                self.ctype = ContextType.VOICE
-                self.content = os.path.join(current_dir, "tmp", file_name_2)
-                self._prepare_fn = lambda: c2c_download_and_convert(wework, wework_msg, file_name)
-            elif wework_msg["type"] == 11042:  # 图片消息类型，需要下载文件
-                file_name = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + ".jpg"
-                current_dir = os.getcwd()
-                self.ctype = ContextType.IMAGE
-                self.content = os.path.join(current_dir, "tmp", file_name)
-                self._prepare_fn = lambda: cdn_download(wework, wework_msg, file_name)
-            elif wework_msg["type"] == 11045:  # 文件消息
-                print("文件消息")
-                print(wework_msg)
-                file_name = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-                file_name = file_name + wework_msg['data']['cdn']['file_name']
-                current_dir = os.getcwd()
-                self.ctype = ContextType.FILE
-                self.content = os.path.join(current_dir, "tmp", file_name)
-                self._prepare_fn = lambda: cdn_download(wework, wework_msg, file_name)
-            elif wework_msg["type"] == 11047:  # 链接消息
-                self.ctype = ContextType.SHARING
-                self.content = wework_msg['data']['url']
-            elif wework_msg["type"] == 11072:  # 新成员入群通知
-                self.ctype = ContextType.JOIN_GROUP
-                member_list = wework_msg['data']['member_list']
-                self.actual_user_nickname = member_list[0]['name']
-                self.actual_user_id = member_list[0]['user_id']
-                self.content = f"{self.actual_user_nickname}加入了群聊！"
-                directory = os.path.join(os.getcwd(), "tmp")
-                rooms = get_with_retry(wework.get_rooms)
-                if not rooms:
-                    logger.error("更新群信息失败···")
-                else:
-                    result = {}
-                    for room in rooms['room_list']:
-                        # 获取聊天室ID
-                        room_wxid = room['conversation_id']
-
-                        # 获取聊天室成员
-                        room_members = wework.get_room_members(room_wxid)
-
-                        # 将聊天室成员保存到结果字典中
-                        result[room_wxid] = room_members
-                    with open(os.path.join(directory, 'wework_room_members.json'), 'w', encoding='utf-8') as f:
-                        json.dump(result, f, ensure_ascii=False, indent=4)
-                    logger.info("有新成员加入，已自动更新群成员列表缓存！")
-            else:
-                raise NotImplementedError(
-                    "Unsupported message type: Type:{} MsgType:{}".format(wework_msg["type"], wework_msg["MsgType"]))
-
-            data = wework_msg['data']
-            login_info = self.wework.get_login_info()
-            logger.debug(f"login_info: {login_info}")
-            nickname = f"{login_info['username']}({login_info['nickname']})" if login_info['nickname'] else login_info['username']
-            user_id = login_info['user_id']
-
-            sender_id = data.get('sender')
-            conversation_id = data.get('conversation_id')
-            sender_name = data.get("sender_name")
-
-            self.from_user_id = user_id if sender_id == user_id else conversation_id
-            self.from_user_nickname = nickname if sender_id == user_id else sender_name
-            self.to_user_id = user_id
-            self.to_user_nickname = nickname
-            self.other_user_nickname = sender_name
-            self.other_user_id = conversation_id
-
-            if self.is_group:
-                conversation_id = data.get('conversation_id') or data.get('room_conversation_id')
-                self.other_user_id = conversation_id
-                if conversation_id:
-                    room_info = get_room_info(wework=wework, conversation_id=conversation_id)
-                    self.other_user_nickname = room_info.get('nickname', None) if room_info else None
-                    self.from_user_nickname = room_info.get('nickname', None) if room_info else None
-                    at_list = data.get('at_list', [])
-                    tmp_list = []
-                    for at in at_list:
-                        tmp_list.append(at['nickname'])
-                    at_list = tmp_list
-                    logger.debug(f"at_list: {at_list}")
-                    logger.debug(f"nickname: {nickname}")
-                    self.is_at = False
-                    if nickname in at_list or login_info['nickname'] in at_list or login_info['username'] in at_list:
-                        self.is_at = True
-                    self.at_list = at_list
-
-                    # 检查消息内容是否包含@用户名。处理复制粘贴的消息，这类消息可能不会触发@通知，但内容中可能包含 "@用户名"。
-                    content = data.get('content', '')
-                    name = nickname
-                    pattern = f"@{re.escape(name)}(\u2005|\u0020)"
-                    if re.search(pattern, content):
-                        logger.debug(f"Wechaty message {self.msg_id} includes at")
-                        self.is_at = True
-
-                    if not self.actual_user_id:
-                        self.actual_user_id = data.get("sender")
-                    self.actual_user_nickname = sender_name if self.ctype != ContextType.JOIN_GROUP else self.actual_user_nickname
-                else:
-                    logger.error("群聊消息中没有找到 conversation_id 或 room_conversation_id")
-
-            logger.debug(f"WeworkMessage has been successfully instantiated with message id: {self.msg_id}")
-        except Exception as e:
-            logger.error(f"在 WeworkMessage 的初始化过程中出现错误：{e}")
-            raise e

cli/VERSION

@@ -0,0 +1 @@
+2.0.6

cli/__init__.py

@@ -0,0 +1,13 @@
+"""CowAgent CLI - Manage your CowAgent from the command line."""
+
+import os as _os
+
+def _read_version():
+    version_file = _os.path.join(_os.path.dirname(_os.path.abspath(__file__)), "VERSION")
+    try:
+        with open(version_file, "r") as f:
+            return f.read().strip()
+    except FileNotFoundError:
+        return "0.0.0"
+
+__version__ = _read_version()

cli/__main__.py

@@ -0,0 +1,4 @@
+"""Allow running as: python -m cli"""
+from cli.cli import main
+
+main()

cli/cli.py

@@ -0,0 +1,79 @@
+"""CowAgent CLI entry point."""
+
+import click
+from cli import __version__
+from cli.commands.skill import skill
+from cli.commands.process import start, stop, restart, update, status, logs
+from cli.commands.context import context
+from cli.commands.install import install_browser
+from cli.commands.knowledge import knowledge
+
+
+HELP_TEXT = """Usage: cow COMMAND [ARGS]...
+
+  CowAgent CLI - Manage your CowAgent instance.
+
+Commands:
+  help     Show this message.
+  version  Show the version.
+  start    Start CowAgent.
+  stop     Stop CowAgent.
+  restart  Restart CowAgent.
+  update   Update CowAgent and restart.
+  status   Show CowAgent running status.
+  logs     View CowAgent logs.
+  skill    Manage CowAgent skills.
+  knowledge  Manage knowledge base.
+  install-browser  Install browser tool (Playwright + Chromium).
+
+Tip: You can also send /help, /skill list, etc. in agent chat."""
+
+
+class CowCLI(click.Group):
+
+    def format_help(self, ctx, formatter):
+        formatter.write(HELP_TEXT.strip())
+        formatter.write("\n")
+
+    def parse_args(self, ctx, args):
+        if args and args[0] == 'help':
+            click.echo(HELP_TEXT.strip())
+            ctx.exit(0)
+        return super().parse_args(ctx, args)
+
+
+@click.group(cls=CowCLI, invoke_without_command=True, context_settings=dict(help_option_names=[]))
+@click.pass_context
+def main(ctx):
+    """CowAgent CLI - Manage your CowAgent instance."""
+    if ctx.invoked_subcommand is None:
+        click.echo(HELP_TEXT.strip())
+
+
+@main.command()
+def version():
+    """Show the version."""
+    click.echo(f"cow {__version__}")
+
+
+@main.command(name='help')
+@click.pass_context
+def help_cmd(ctx):
+    """Show this message."""
+    click.echo(HELP_TEXT.strip())
+
+
+main.add_command(skill)
+main.add_command(start)
+main.add_command(stop)
+main.add_command(restart)
+main.add_command(update)
+main.add_command(status)
+main.add_command(logs)
+main.add_command(context)
+main.add_command(knowledge)
+main.add_command(install_browser)
+
+
+if __name__ == '__main__':
+    main()

cli/commands/context.py

@@ -0,0 +1,29 @@
+"""cow context - Context management commands."""
+
+import click
+
+
+CHAT_HINT = (
+    "Context commands operate on the running agent's memory.\n"
+    "Please send the command in a chat conversation instead:\n\n"
+    "  /context        - View current context info\n"
+    "  /context clear  - Clear conversation context"
+)
+
+
+@click.group(invoke_without_command=True)
+@click.pass_context
+def context(ctx):
+    """View or manage conversation context.
+
+    Context commands need access to the running agent's memory.
+    Use them in chat conversations: /context or /context clear
+    """
+    if ctx.invoked_subcommand is None:
+        click.echo(f"\n  {CHAT_HINT}\n")
+
+
+@context.command()
+def clear():
+    """Clear conversation context (messages history)."""
+    click.echo(f"\n  {CHAT_HINT}\n")

cli/commands/install.py

@@ -0,0 +1,259 @@
+"""cow install-browser - Install Playwright + Chromium for the browser tool."""
+
+import os
+import sys
+import subprocess
+from typing import Callable, Optional
+
+import click
+
+PLAYWRIGHT_VERSION = "1.52.0"
+PLAYWRIGHT_LEGACY_VERSION = "1.28.0"
+GLIBC_THRESHOLD = (2, 28)
+CHINA_MIRROR = "https://registry.npmmirror.com/-/binary/playwright"
+
+# stream(msg, fg=None) — fg is "yellow" | "green" | "red" | None
+StreamFn = Callable[[str, Optional[str]], None]
+# on_phase(msg) — coarse-grained progress for chat channels (Chinese)
+PhaseFn = Callable[[str], None]
+
+
+def _phase(cb: Optional[PhaseFn], msg: str) -> None:
+    if cb:
+        cb(msg)
+
+
+def _has_display() -> bool:
+    """Check if a graphical display is available (Linux only)."""
+    return bool(os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"))
+
+
+def _is_headless_linux() -> bool:
+    return sys.platform == "linux" and not _has_display()
+
+
+def _get_installed_version() -> str:
+    try:
+        out = subprocess.check_output(
+            [sys.executable, "-c", "import playwright; print(playwright.__version__)"],
+            stderr=subprocess.DEVNULL,
+        )
+        return out.decode().strip()
+    except Exception:
+        return ""
+
+
+def _version_tuple(v: str):
+    try:
+        return tuple(int(x) for x in v.split(".")[:3])
+    except (ValueError, AttributeError):
+        return (0, 0, 0)
+
+
+def _get_glibc_version():
+    if sys.platform != "linux":
+        return None
+    try:
+        import ctypes
+        libc = ctypes.CDLL("libc.so.6")
+        gnu_get_libc_version = libc.gnu_get_libc_version
+        gnu_get_libc_version.restype = ctypes.c_char_p
+        ver = gnu_get_libc_version().decode()
+        parts = ver.split(".")
+        return (int(parts[0]), int(parts[1]))
+    except Exception:
+        return None
+
+
+def _is_china_network() -> bool:
+    try:
+        out = subprocess.check_output(
+            [sys.executable, "-m", "pip", "config", "get", "global.index-url"],
+            stderr=subprocess.DEVNULL,
+        )
+        url = out.decode().strip().lower()
+        return any(kw in url for kw in ("tsinghua", "aliyun", "npmmirror", "douban", "ustc", "huawei", "tencentyun"))
+    except Exception:
+        return False
+
+
+def _pip_install(package_spec: str, stream: StreamFn) -> int:
+    """Install a package, retrying with --user on permission failure."""
+    python = sys.executable
+    ret = subprocess.call([python, "-m", "pip", "install", package_spec])
+    if ret != 0:
+        stream("  Retrying with --user flag...", "yellow")
+        ret = subprocess.call([python, "-m", "pip", "install", "--user", package_spec])
+    return ret
+
+
+def _default_stream(msg: str, fg: Optional[str] = None) -> None:
+    """CLI: colored click output."""
+    if fg == "yellow":
+        click.echo(click.style(msg, fg="yellow"))
+    elif fg == "green":
+        click.echo(click.style(msg, fg="green"))
+    elif fg == "red":
+        click.echo(click.style(msg, fg="red"))
+    else:
+        click.echo(msg)
+
+
+def run_install_browser(
+    stream: Optional[StreamFn] = None,
+    on_phase: Optional[PhaseFn] = None,
+) -> int:
+    """
+    Install Playwright Python package, optional Linux deps, and Chromium.
+
+    Reused by ``cow install-browser`` CLI and chat ``/install-browser``.
+
+    Args:
+        stream: Optional callback ``(message, fg)`` for each line. ``fg`` is
+            ``yellow`` / ``green`` / ``red`` or None. Defaults to colored click output.
+        on_phase: Optional callback for coarse progress (e.g. push to chat);
+            messages are short Chinese status lines.
+
+    Returns:
+        0 on success, 1 on fatal failure (pip or chromium install failed).
+    """
+    stream = stream or _default_stream
+    python = sys.executable
+    legacy_mode = False
+
+    _phase(on_phase, "🔧 开始安装浏览器工具依赖（约几分钟，请耐心等待）…")
+
+    glibc = _get_glibc_version()
+    if glibc and glibc < GLIBC_THRESHOLD:
+        legacy_mode = True
+        glibc_str = f"{glibc[0]}.{glibc[1]}"
+        stream(
+            f"glibc {glibc_str} detected (< 2.28). "
+            f"Will install playwright {PLAYWRIGHT_LEGACY_VERSION} for compatibility.",
+            "yellow",
+        )
+        stream("  Note: upgrade your OS for full browser tool support.", "yellow")
+        stream("")
+        _phase(
+            on_phase,
+            f"ℹ️ 检测到 glibc {glibc_str}（较旧），将安装兼容版 Playwright {PLAYWRIGHT_LEGACY_VERSION}。",
+        )
+
+    target_version = PLAYWRIGHT_LEGACY_VERSION if legacy_mode else PLAYWRIGHT_VERSION
+
+    _phase(on_phase, "📦 [1/3] 正在安装 Playwright Python 包…")
+    stream("[1/3] Installing playwright Python package...", "yellow")
+    ret = _pip_install(f"playwright=={target_version}", stream)
+    if ret != 0:
+        stream("Failed to install playwright package.", "red")
+        _phase(on_phase, "❌ [1/3] Playwright Python 包安装失败。")
+        return 1
+
+    installed = _get_installed_version()
+    if installed:
+        stream(f"  playwright {installed} installed.", "green")
+    stream("")
+    _phase(on_phase, f"✅ [1/3] Playwright 包已安装（{installed or target_version}）。")
+
+    if sys.platform == "linux":
+        _phase(on_phase, "🔧 [2/3] 正在安装 Linux 系统依赖与轻量中文字体（文泉驿正黑，部分步骤可能需要 sudo）…")
+        stream("[2/3] Installing system dependencies (Linux)...", "yellow")
+        ret = subprocess.call([python, "-m", "playwright", "install-deps", "chromium"])
+        if ret != 0:
+            stream(
+                "  Could not auto-install system deps (may need sudo).\n"
+                f"  Run manually: sudo {python} -m playwright install-deps chromium",
+                "yellow",
+            )
+        # Prefer fonts-wqy-zenhei only (~few MB). fonts-noto-cjk is much larger (~150MB+).
+        stream("  Installing CJK font (fonts-wqy-zenhei, lightweight)...")
+        font_ret = subprocess.call(
+            ["sudo", "apt-get", "install", "-y", "--no-install-recommends", "fonts-wqy-zenhei"],
+            stderr=subprocess.DEVNULL,
+        )
+        if font_ret != 0:
+            stream(
+                "  Could not auto-install CJK font.\n"
+                "  Run manually: sudo apt-get install -y fonts-wqy-zenhei\n"
+                "  (Optional, larger full coverage: sudo apt-get install -y fonts-noto-cjk)",
+                "yellow",
+            )
+        else:
+            subprocess.call(["fc-cache", "-fv"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+            stream("  CJK font (wqy-zenhei) installed.", "green")
+        _phase(
+            on_phase,
+            "✅ [2/3] Linux 依赖与字体步骤已执行（若有权限问题请查看服务器日志或手动执行提示命令）。",
+        )
+    else:
+        stream(f"[2/3] Skipping system deps (not needed on {sys.platform}).", "yellow")
+        _phase(on_phase, f"ℹ️ [2/3] 当前系统（{sys.platform}）跳过 Linux 专用依赖。")
+    stream("")
+
+    _phase(on_phase, "🌐 [3/3] 正在下载并安装 Chromium（体积较大，请耐心等待）…")
+    stream("[3/3] Installing Chromium browser...", "yellow")
+    cmd = [python, "-m", "playwright", "install", "chromium"]
+
+    if _is_headless_linux() and not legacy_mode:
+        ver = _version_tuple(installed or "")
+        if ver >= (1, 57, 0):
+            cmd.append("--only-shell")
+            stream("  (headless shell for Linux server)", None)
+        else:
+            stream("  (full Chromium)", None)
+    elif sys.platform == "linux" and _has_display():
+        stream("  (full browser for Linux desktop)", None)
+
+    env = os.environ.copy()
+    use_mirror = _is_china_network()
+    if use_mirror:
+        env["PLAYWRIGHT_DOWNLOAD_HOST"] = CHINA_MIRROR
+        stream(f"  (using China mirror: {CHINA_MIRROR})", None)
+        _phase(on_phase, "📡 检测到国内 pip 源配置，Chromium 将优先走国内镜像下载。")
+
+    ret = subprocess.call(cmd, env=env)
+
+    if ret != 0 and use_mirror:
+        stream("  Mirror download failed, retrying with official CDN...", "yellow")
+        _phase(on_phase, "⚠️ 镜像下载失败，正在改用官方源重试…")
+        env_no_mirror = os.environ.copy()
+        env_no_mirror.pop("PLAYWRIGHT_DOWNLOAD_HOST", None)
+        ret = subprocess.call(cmd, env=env_no_mirror)
+
+    if ret != 0:
+        stream("Failed to install Chromium.", "red")
+        _phase(on_phase, "❌ [3/3] Chromium 安装失败。")
+        return 1
+
+    stream("")
+    _phase(on_phase, "✅ [3/3] Chromium 已安装。")
+
+    stream("Verifying browser installation...", None)
+    _phase(on_phase, "🔍 正在验证 Playwright 能否正常加载…")
+    ret = subprocess.call(
+        [python, "-c", "from playwright.sync_api import sync_playwright; print('OK')"],
+        stderr=subprocess.DEVNULL,
+    )
+    if ret != 0:
+        stream(
+            "  Warning: playwright import failed. Browser tool may not work on this system.\n"
+            "  Consider upgrading your OS or using Docker.",
+            "yellow",
+        )
+        _phase(on_phase, "⚠️ 验证未完全通过：本机可能仍无法使用浏览器工具，请查看日志或升级系统。")
+    else:
+        stream("  Verification passed.", "green")
+        _phase(on_phase, "✅ 验证通过。")
+
+    stream("")
+    stream("Browser tool ready! Restart CowAgent to enable it.", "green")
+    _phase(on_phase, "🎉 全部步骤结束。请重启 CowAgent 后使用 browser 工具。")
+    return 0
+
+
+@click.command("install-browser")
+def install_browser():
+    """Install browser tool dependencies (Playwright + Chromium)."""
+    code = run_install_browser()
+    if code != 0:
+        raise SystemExit(code)

cli/commands/knowledge.py

@@ -0,0 +1,121 @@
+"""cow knowledge - Knowledge base management commands."""
+
+import os
+
+import click
+
+from cli.utils import get_project_root
+
+
+def _get_knowledge_dir():
+    """Resolve the knowledge directory path from config or default."""
+    try:
+        import sys
+        sys.path.insert(0, get_project_root())
+        from config import conf
+        from common.utils import expand_path
+        workspace = expand_path(conf().get("agent_workspace", "~/cow"))
+    except Exception:
+        workspace = os.path.expanduser("~/cow")
+    return os.path.join(workspace, "knowledge")
+
+
+def _get_knowledge_enabled():
+    try:
+        import sys
+        sys.path.insert(0, get_project_root())
+        from config import conf
+        return conf().get("knowledge", True)
+    except Exception:
+        return True
+
+
+@click.group(invoke_without_command=True)
+@click.pass_context
+def knowledge(ctx):
+    """Manage CowAgent knowledge base."""
+    if ctx.invoked_subcommand is None:
+        click.echo(_stats())
+
+
+@knowledge.command("list")
+def knowledge_list():
+    """Display knowledge base file tree."""
+    click.echo(_tree())
+
+
+def _stats() -> str:
+    knowledge_dir = _get_knowledge_dir()
+    if not os.path.isdir(knowledge_dir):
+        return "Knowledge base directory not found."
+
+    enabled = _get_knowledge_enabled()
+    total_files = 0
+    total_bytes = 0
+    cat_count = {}
+
+    for root, dirs, files in os.walk(knowledge_dir):
+        dirs[:] = [d for d in dirs if not d.startswith(".")]
+        rel_root = os.path.relpath(root, knowledge_dir)
+        category = rel_root.split(os.sep)[0] if rel_root != "." else "root"
+        for f in files:
+            if f.endswith(".md") and f not in ("index.md", "log.md"):
+                total_files += 1
+                total_bytes += os.path.getsize(os.path.join(root, f))
+                cat_count[category] = cat_count.get(category, 0) + 1
+
+    status_icon = click.style("enabled", fg="green") if enabled else click.style("disabled", fg="red")
+    lines = [
+        f"\n  Knowledge Base  [{status_icon}]",
+        "",
+        f"  Pages:  {total_files}",
+        f"  Size:   {total_bytes / 1024:.1f} KB",
+        "",
+    ]
+    if cat_count:
+        lines.append("  Categories:")
+        for cat in sorted(cat_count.keys()):
+            lines.append(f"    {cat}/  ({cat_count[cat]} pages)")
+        lines.append("")
+
+    lines.append(f"  Path: {knowledge_dir}")
+    lines.append("")
+    return "\n".join(lines)
+
+
+def _tree() -> str:
+    knowledge_dir = _get_knowledge_dir()
+    if not os.path.isdir(knowledge_dir):
+        return "Knowledge base directory not found."
+
+    tree_lines = ["  knowledge/"]
+
+    subdirs = sorted([
+        d for d in os.listdir(knowledge_dir)
+        if os.path.isdir(os.path.join(knowledge_dir, d)) and not d.startswith(".")
+    ])
+
+    for i, subdir in enumerate(subdirs):
+        is_last_dir = (i == len(subdirs) - 1)
+        branch = "└── " if is_last_dir else "├── "
+        subdir_path = os.path.join(knowledge_dir, subdir)
+        md_files = sorted([
+            f for f in os.listdir(subdir_path)
+            if f.endswith(".md") and not f.startswith(".")
+        ])
+        tree_lines.append(f"  {branch}{subdir}/ ({len(md_files)})")
+
+        child_prefix = "      " if is_last_dir else "  │   "
+        max_show = 15
+        for j, fname in enumerate(md_files[:max_show]):
+            is_last_file = (j == len(md_files[:max_show]) - 1) and len(md_files) <= max_show
+            fb = "└── " if is_last_file else "├── "
+            name = fname.replace(".md", "")
+            tree_lines.append(f"{child_prefix}{fb}{name}")
+        if len(md_files) > max_show:
+            tree_lines.append(f"{child_prefix}└── ... +{len(md_files) - max_show} more")
+
+    if not subdirs:
+        tree_lines.append("  (empty)")
+
+    return "\n" + "\n".join(tree_lines) + "\n"

cli/commands/process.py

@@ -0,0 +1,317 @@
+"""cow start/stop/restart/status/logs - Process management commands."""
+
+import os
+import sys
+import subprocess
+import time
+from typing import Optional
+
+import click
+
+from cli.utils import get_project_root
+
+_IS_WIN = sys.platform == "win32"
+
+
+def _get_pid_file():
+    return os.path.join(get_project_root(), ".cow.pid")
+
+
+def _get_log_file():
+    return os.path.join(get_project_root(), "nohup.out")
+
+
+def _is_pid_alive(pid: int) -> bool:
+    """Check whether a process is still running (cross-platform)."""
+    if _IS_WIN:
+        try:
+            out = subprocess.check_output(
+                ["tasklist", "/FI", f"PID eq {pid}", "/NH"],
+                stderr=subprocess.DEVNULL,
+            )
+            return str(pid) in out.decode(errors="ignore")
+        except Exception:
+            return False
+    else:
+        try:
+            os.kill(pid, 0)
+            return True
+        except (ProcessLookupError, PermissionError):
+            return False
+
+
+def _kill_pid(pid: int, force: bool = False):
+    """Terminate a process by PID (cross-platform)."""
+    if _IS_WIN:
+        flag = "/F" if force else ""
+        cmd = ["taskkill"]
+        if force:
+            cmd.append("/F")
+        cmd.extend(["/PID", str(pid)])
+        subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    else:
+        import signal
+        sig = signal.SIGKILL if force else signal.SIGTERM
+        os.kill(pid, sig)
+
+
+def _read_pid() -> Optional[int]:
+    pid_file = _get_pid_file()
+    if not os.path.exists(pid_file):
+        return None
+    try:
+        with open(pid_file, "r") as f:
+            pid = int(f.read().strip())
+        if _is_pid_alive(pid):
+            return pid
+        os.remove(pid_file)
+        return None
+    except (ValueError, OSError):
+        try:
+            os.remove(pid_file)
+        except OSError:
+            pass
+        return None
+
+
+def _write_pid(pid: int):
+    with open(_get_pid_file(), "w") as f:
+        f.write(str(pid))
+
+
+def _remove_pid():
+    pid_file = _get_pid_file()
+    if os.path.exists(pid_file):
+        os.remove(pid_file)
+
+
+@click.command()
+@click.option("--foreground", "-f", is_flag=True, help="Run in foreground (don't daemonize)")
+@click.option("--no-logs", is_flag=True, help="Don't tail logs after starting")
+def start(foreground, no_logs):
+    """Start CowAgent."""
+    pid = _read_pid()
+    if pid:
+        click.echo(f"CowAgent is already running (PID: {pid}).")
+        return
+
+    root = get_project_root()
+    app_py = os.path.join(root, "app.py")
+    if not os.path.exists(app_py):
+        click.echo("Error: app.py not found in project root.", err=True)
+        sys.exit(1)
+
+    python = sys.executable
+
+    if foreground:
+        click.echo("Starting CowAgent in foreground...")
+        if _IS_WIN:
+            sys.exit(subprocess.call([python, app_py], cwd=root))
+        else:
+            os.execv(python, [python, app_py])
+    else:
+        log_file = _get_log_file()
+        click.echo("Starting CowAgent...")
+
+        popen_kwargs = dict(cwd=root)
+        if _IS_WIN:
+            CREATE_NO_WINDOW = 0x08000000
+            popen_kwargs["creationflags"] = (
+                subprocess.CREATE_NEW_PROCESS_GROUP | CREATE_NO_WINDOW
+            )
+        else:
+            popen_kwargs["start_new_session"] = True
+
+        with open(log_file, "a") as log:
+            proc = subprocess.Popen(
+                [python, app_py],
+                stdout=log,
+                stderr=log,
+                **popen_kwargs,
+            )
+        _write_pid(proc.pid)
+        click.echo(click.style(f"✓ CowAgent started (PID: {proc.pid})", fg="green"))
+        click.echo(f"  Logs: {log_file}")
+
+        if not no_logs:
+            click.echo("  Press Ctrl+C to stop tailing logs.\n")
+            _tail_log(log_file)
+
+
+@click.command()
+def stop():
+    """Stop CowAgent."""
+    pid = _read_pid()
+    if not pid:
+        click.echo("CowAgent is not running.")
+        return
+
+    click.echo(f"Stopping CowAgent (PID: {pid})...")
+    try:
+        _kill_pid(pid)
+        for _ in range(30):
+            time.sleep(0.1)
+            if not _is_pid_alive(pid):
+                break
+        else:
+            _kill_pid(pid, force=True)
+    except (ProcessLookupError, OSError):
+        pass
+
+    _remove_pid()
+    click.echo(click.style("✓ CowAgent stopped.", fg="green"))
+
+
+@click.command()
+@click.option("--no-logs", is_flag=True, help="Don't tail logs after restarting")
+@click.pass_context
+def restart(ctx, no_logs):
+    """Restart CowAgent."""
+    ctx.invoke(stop)
+    time.sleep(1)
+    ctx.invoke(start, no_logs=no_logs)
+
+
+@click.command()
+@click.pass_context
+def update(ctx):
+    """Update CowAgent and restart."""
+    root = get_project_root()
+
+    # 1. Stop service first so git pull won't conflict with running code
+    ctx.invoke(stop)
+
+    # 2. Git pull
+    if os.path.isdir(os.path.join(root, ".git")):
+        click.echo("Pulling latest code...")
+        ret = subprocess.call(["git", "pull"], cwd=root)
+        if ret != 0:
+            click.echo("Error: git pull failed.", err=True)
+            sys.exit(1)
+    else:
+        click.echo("Not a git repository, skipping code update.")
+
+    python = sys.executable
+    req_file = os.path.join(root, "requirements.txt")
+
+    if _IS_WIN:
+        # On Windows, `cow.exe` (this process) locks the exe file, so
+        # `pip install -e .` fails with WinError 5.  Write a small .bat
+        # helper that waits for cow.exe to exit, then installs & starts.
+        bat = os.path.join(root, "_cow_update.bat")
+        lines = [
+            "@echo off",
+            "chcp 65001 >nul",
+            "echo Waiting for cow.exe to exit...",
+            "timeout /t 3 /nobreak >nul",
+        ]
+        if os.path.exists(req_file):
+            lines.append(f'echo Installing dependencies...')
+            lines.append(f'"{python}" -m pip install -r requirements.txt -q')
+        lines += [
+            "echo Reinstalling cow CLI...",
+            f'"{python}" -m pip install -e . -q',
+            "echo Starting CowAgent...",
+            f'"{python}" -m cli.cli start --no-logs',
+            "echo.",
+            "echo Update complete. You can close this window.",
+            "pause >nul",
+            "del \"%~f0\"",
+        ]
+        with open(bat, "w", encoding="utf-8") as f:
+            f.write("\n".join(lines) + "\n")
+
+        subprocess.Popen(
+            ["cmd.exe", "/c", "start", "CowAgent Update", "/wait", bat],
+            cwd=root,
+        )
+        click.echo(click.style(
+            "✓ Update script launched. Please follow the new window for progress.",
+            fg="green"))
+    else:
+        # 3. Install dependencies
+        if os.path.exists(req_file):
+            click.echo("Installing dependencies...")
+            subprocess.call(
+                [python, "-m", "pip", "install", "-r", "requirements.txt", "-q"],
+                cwd=root,
+            )
+        click.echo("Reinstalling cow CLI...")
+        subprocess.call(
+            [python, "-m", "pip", "install", "-e", ".", "-q"],
+            cwd=root,
+        )
+
+        # 4. Start service
+        click.echo("")
+        time.sleep(1)
+        ctx.invoke(start, no_logs=False)
+
+
+@click.command()
+def status():
+    """Show CowAgent running status."""
+    from cli import __version__
+    from cli.utils import load_config_json
+
+    pid = _read_pid()
+    if pid:
+        click.echo(click.style(f"● CowAgent is running (PID: {pid})", fg="green"))
+    else:
+        click.echo(click.style("● CowAgent is not running", fg="red"))
+
+    click.echo(f"  版本: v{__version__}")
+
+    cfg = load_config_json()
+    if cfg:
+        channel = cfg.get("channel_type", "unknown")
+        if isinstance(channel, list):
+            channel = ", ".join(channel)
+        click.echo(f"  通道: {channel}")
+        click.echo(f"  模型: {cfg.get('model', 'unknown')}")
+        mode = "Agent" if cfg.get("agent") else "Chat"
+        click.echo(f"  模式: {mode}")
+
+
+@click.command()
+@click.option("--follow", "-f", is_flag=True, help="Follow log output")
+@click.option("--lines", "-n", default=50, help="Number of lines to show")
+def logs(follow, lines):
+    """View CowAgent logs."""
+    log_file = _get_log_file()
+    if not os.path.exists(log_file):
+        click.echo("No log file found.")
+        return
+
+    if follow:
+        _tail_log(log_file, lines)
+    else:
+        _print_last_lines(log_file, lines)
+
+
+def _print_last_lines(file_path: str, n: int = 50):
+    """Print the last N lines of a file (cross-platform)."""
+    try:
+        with open(file_path, "r", encoding="utf-8", errors="replace") as f:
+            all_lines = f.readlines()
+        for line in all_lines[-n:]:
+            click.echo(line, nl=False)
+    except Exception as e:
+        click.echo(f"Error reading log file: {e}", err=True)
+
+
+def _tail_log(log_file: str, lines: int = 50):
+    """Follow log file output. Blocks until Ctrl+C (cross-platform)."""
+    _print_last_lines(log_file, lines)
+
+    try:
+        with open(log_file, "r", encoding="utf-8", errors="replace") as f:
+            f.seek(0, 2)
+            while True:
+                line = f.readline()
+                if line:
+                    click.echo(line, nl=False)
+                else:
+                    time.sleep(0.3)
+    except KeyboardInterrupt:
+        pass