feat(cli): add browser install cmd

feat: cow cli in terminal and chat

.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

.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 +33,15 @@ plugins/banwords/lib/__pycache__
 !plugins/keyword
 !plugins/linkai
 !plugins/agent
+!plugins/cow_cli
 client_config.json
 ref/
 .cursor/
 local/
+node_modules/
+
+# cow cli
+dist/
+build/
+*.egg-info/
+.cow.pid

README.md

@@ -4,42 +4,43 @@
   <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/>
-  [中文] | [<a href="docs/en/README.md">English</a>]
+  [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>
+  <a href="https://docs.cowagent.ai/guide/quick-start">🚀 快速开始</a> &nbsp;·&nbsp;
+  <a href="https://link-ai.tech/cowagent/create">☁️ 在线体验</a>
 </p>
 
 
-
 # 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
+-  ✅  **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
 -  ✅  **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、Doubao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
+-  ✅  **多模型接入：** 支持 OpenAI, Claude, Gemini, DeepSeek, MiniMax、GLM、Qwen、Kimi、Doubao 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。
-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。
+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](https://docs.cowagent.ai/intro/features)
+- 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
 
@@ -51,11 +52,11 @@ 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：
 
@@ -65,15 +66,15 @@ DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4
 
 # 🏷 更新日志
 
+>**2026.03.22：** [2.0.4版本](https://github.com/zhayujie/chatgpt-on-wechat/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。
+
+>**2026.03.18：** [2.0.3版本](https://github.com/zhayujie/chatgpt-on-wechat/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。
+
 >**2026.02.27：** [2.0.2版本](https://github.com/zhayujie/chatgpt-on-wechat/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/chatgpt-on-wechat/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/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。
-
->**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
-
->**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
+>**2026.02.03：** [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。
 
 更多更新历史请查看: [更新日志](https://docs.cowagent.ai/releases)
 
@@ -86,7 +87,7 @@ 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：
 
 ```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://docs.cowagent.ai/guide/quick-start)
@@ -98,15 +99,15 @@ 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：MiniMax-M2.5、glm-5、kimi-k2.5、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-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-turbo、kimi-k2.5、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 ~ 3.12 之间，推荐使用3.9版本。
 
-> 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：**
 
@@ -128,45 +129,50 @@ 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。
 
 ## 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-M2.5",                                    # 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
+  "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
+  "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
+  "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
 }
 ```
 
@@ -175,25 +181,24 @@ 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）
 </details>
 
 <details>
 <summary>2. 其他配置</summary>
 
-+ `model`: 模型名称，Agent模式下推荐使用 `MiniMax-M2.5`、`glm-5`、`kimi-k2.5`、`qwen3.5-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/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-turbo`、`kimi-k2.5`、`qwen3.5-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/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。
 </details>
 
 <details>
-<summary>3. 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。
@@ -205,10 +210,10 @@ 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
+python3 app.py         # 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)。
 
@@ -223,19 +228,20 @@ 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` 脚本支持一键启动和管理服务，包括 `./run.sh start`、`./run.sh stop`、`./run.sh restart`、`./run.sh logs` 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。
@@ -248,23 +254,13 @@ 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
 
@@ -273,43 +269,42 @@ volumes:
 <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>
@@ -319,26 +314,26 @@ volumes:
 
 ```json
 {
-    "model": "MiniMax-M2.5",
+    "model": "MiniMax-M2.7",
     "minimax_api_key": ""
 }
 ```
- - `model`: 可填写 `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
+ - `model`: 可填写 `MiniMax-M2.7、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-M2.5",
+  "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-M2.5、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
+- `bot_type`: OpenAI 兼容方式
+- `model`: 可填 `MiniMax-M2.7、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>
@@ -348,32 +343,32 @@ volumes:
 
 ```json
 {
-  "model": "glm-5",
+  "model": "glm-5-turbo",
   "zhipu_ai_api_key": ""
 }
 ```
- - `model`: 可填 `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
+ - `model`: 可填 `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-5",
+  "bot_type": "openai",
+  "model": "glm-5-turbo",
   "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-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
+- `bot_type`: OpenAI 兼容方式
+- `model`: 可填 `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
 {
@@ -384,18 +379,18 @@ volumes:
  - `model`: 可填写 `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 [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",
+  "bot_type": "openai",
   "model": "qwen3.5-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>
 
@@ -411,27 +406,27 @@ volumes:
 }
 ```
  - `model`: 可填写 `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
+ - `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",
+  "bot_type": "openai",
   "model": "kimi-k2.5",
   "open_ai_api_base": "https://api.moonshot.cn/v1",
   "open_ai_api_key": ""
 }
 ```
-- `bot_type`: OpenAI兼容方式
+- `bot_type`: OpenAI 兼容方式
 - `model`: 可填写 `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
+- `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
+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
 
@@ -449,7 +444,7 @@ volumes:
 <details>
 <summary>Claude</summary>
 
-1. API Key创建：在 [Claude控制台](https://console.anthropic.com/settings/keys) 创建API Key
+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
 
@@ -465,43 +460,53 @@ volumes:
 <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
+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-pro-preview",
+    "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-pro-preview、gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
+ - `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 
+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",
-    "open_ai_api_key": "sk-xxxxxxxxxxx",
-    "open_ai_api_base": "https://api.deepseek.com/v1", 
-    "bot_type": "chatGPT"
-
+    "deepseek_api_key": "sk-xxxxxxxxxxx"
 }
 ```
 
- - `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>
+ - `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
 
@@ -518,15 +523,15 @@ 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
 {
@@ -539,19 +544,19 @@ 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>
 
@@ -575,16 +580,16 @@ 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
@@ -603,13 +608,30 @@ 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
 
@@ -618,9 +640,26 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 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>
 
-项目启动后会默认运行Web控制台，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
 {
@@ -635,7 +674,7 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 </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。
 
@@ -671,7 +710,7 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 </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`：
 
@@ -686,7 +725,39 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 </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：
 
@@ -706,7 +777,7 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 </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。
 
@@ -741,7 +812,7 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 </details>
 
 <details>
-<summary>6. Terminal - 终端</summary>
+<summary>9. Terminal - 终端</summary>
 
 修改 `config.json` 中的 `channel_type` 字段：
 
@@ -759,8 +830,8 @@ 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。
+- [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。
 
 
 

agent/chat/service.py

@@ -27,7 +27,8 @@ class ChatService:
         """
         self.agent_bridge = agent_bridge
 
-    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None]):
+    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*.
 
@@ -37,11 +38,17 @@ class ChatService:
         :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()
 
@@ -68,9 +75,24 @@ class ChatService:
                     # a new segment; collect tool results until turn_end.
                     state.pending_tool_results = []
 
-            elif event_type == "tool_execution_end":
+            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)
@@ -111,7 +133,7 @@ class ChatService:
         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", 30)
+        max_context_turns = conf().get("agent_max_context_turns", 20)
 
         # Get full system prompt with skills
         full_system_prompt = agent.get_full_system_prompt()
@@ -144,10 +166,61 @@ class ChatService:
                     logger.info("[ChatService] Cleared agent message history after executor recovery")
             raise
 
-        # Append only the NEW messages from this execution (thread-safe)
+        # 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:
-            new_messages = executor.messages[original_length:]
-            agent.messages.extend(new_messages)
+            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
@@ -159,6 +232,25 @@ class ChatService:
 
 
 
+    @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."""
 
@@ -167,3 +259,6 @@ class _StreamState:
         # 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/memory/__init__.py

@@ -9,6 +9,7 @@ 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',
@@ -18,4 +19,5 @@ __all__ = [
     '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/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
@@ -363,182 +383,35 @@ 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,
     ) -> 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)
+        
         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 content was written
         """
-        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,
         )
-        
         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 +441,37 @@ 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 +479,7 @@ 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 +501,6 @@ class MemoryManager:
                     'keyword_score': result.score
                 }
         
-        # Calculate combined scores
         merged_results = []
         for entry in merged_map.values():
             combined_score = (
@@ -606,7 +508,11 @@ 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 +523,5 @@ 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/summarizer.py

@@ -1,225 +1,324 @@
 """
 Memory flush manager
 
-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 key-information entries
+- Writes to daily memory files (lazy creation)
+- Deduplicates trim flushes to avoid repeated writes
+- Runs summarization asynchronously to avoid blocking normal replies
+- Provides daily summary interface for scheduler
 """
 
-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 = """你是一个记忆提取助手。你的任务是从对话记录中提取值得记住的信息，生成简洁的记忆摘要。
+
+输出要求：
+1. 以事件/关键信息为维度记录，每条一行，用 "- " 开头
+2. 记录有价值的关键信息，例如用户提出的要求及助手的解决方案，对话中涉及的事实信息，用户的偏好、决策或重要结论
+3. 每条摘要需要简明扼要，只保留关键信息
+4. 直接输出摘要内容，不要加任何前缀说明
+5. 当对话没有任何记录价值例如只是简单问候，可回复"无\""""
+
+SUMMARIZE_USER_PROMPT = """请从以下对话记录中提取关键信息，生成记忆摘要：
+
+{conversation}"""
 
 
 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
     
-    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,
+    ) -> 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.
+        
+        Args:
+            messages: Conversation message list (OpenAI/Claude format)
+            user_id: Optional user ID for user-scoped memory
+            reason: Why flush was triggered ("trim" | "overflow" | "daily_summary")
+            max_messages: Max recent messages to summarize (0 = all)
+        
+        Returns:
+            True if flush was dispatched
+        """
+        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),
+                daemon=True,
+            )
+            thread.start()
+            logger.info(f"[MemoryFlush] Async flush dispatched (reason={reason}, msgs={len(snapshot)})")
+            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,
+    ):
+        """Background worker: summarize with LLM and write to daily file."""
+        try:
+            summary = self._summarize_messages(messages, max_messages)
+            if not summary or not summary.strip() or summary.strip() == "无":
+                logger.info(f"[MemoryFlush] No valuable content to flush (reason={reason})")
+                return
+            
+            daily_file = ensure_daily_memory_file(self.workspace_dir, user_id)
+            
+            if reason == "overflow":
+                header = f"## Context Overflow Recovery ({datetime.now().strftime('%H:%M')})"
+                note = "The following conversation was trimmed due to context overflow:\n"
+            elif reason == "trim":
+                header = f"## Trimmed Context ({datetime.now().strftime('%H:%M')})"
+                note = ""
+            elif reason == "daily_summary":
+                header = f"## Daily Summary ({datetime.now().strftime('%H:%M')})"
+                note = ""
+            else:
+                header = f"## Session Notes ({datetime.now().strftime('%H:%M')})"
+                note = ""
+            
+            flush_entry = f"\n{header}\n\n{note}{summary}\n"
+            
+            with open(daily_file, "a", encoding="utf-8") as f:
+                f.write(flush_entry)
+            
+            self.last_flush_timestamp = datetime.now()
+            
+            logger.info(f"[MemoryFlush] Wrote to {daily_file.name} (reason={reason}, chars={len(summary)})")
+            
+        except Exception as e:
+            logger.warning(f"[MemoryFlush] Async flush failed (reason={reason}): {e}")
+    
+    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,
+        )
+    
+    # ---- Internal helpers ----
+    
+    def _summarize_messages(self, messages: List[Dict], max_messages: int = 0) -> str:
+        """
+        Summarize conversation messages using LLM, with rule-based fallback.
+        """
+        conversation_text = self._format_conversation_for_summary(messages, max_messages)
+        if not conversation_text.strip():
+            return ""
+        
+        # Try LLM summarization first
+        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()
+            except Exception as e:
+                logger.warning(f"[MemoryFlush] LLM summarization failed, using fallback: {e}")
+        
+        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)
+
+    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)
+        
+        if isinstance(response, dict):
+            if response.get("error"):
+                raise RuntimeError(response.get("message", "LLM call failed"))
+            # OpenAI format
+            choices = response.get("choices", [])
+            if choices:
+                return choices[0].get("message", {}).get("content", "")
+        
+        # Handle response object with attribute access (e.g. OpenAI SDK response)
+        if hasattr(response, "choices") and response.choices:
+            return response.choices[0].message.content or ""
+        
+        return ""
+
+    @staticmethod
+    def _extract_summary_fallback(messages: List[Dict], max_messages: int = 0) -> str:
+        """Rule-based fallback when LLM is unavailable."""
+        msgs = messages if max_messages == 0 else messages[-max_messages * 2:]
+        
+        items = []
+        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) <= 5:
+                    continue
+                items.append(f"- 用户请求: {text[:200]}")
+            elif role == "assistant":
+                first_line = text.split("\n")[0].strip()
+                if len(first_line) > 10:
+                    items.append(f"- 处理结果: {first_line[:200]}")
+        
+        return "\n".join(items[:15])
+    
+    @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 +327,7 @@ 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 +336,28 @@ 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 +365,6 @@ 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

@@ -42,7 +42,6 @@ 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 +51,10 @@ 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 +70,6 @@ class PromptBuilder:
             skill_manager=skill_manager,
             memory_manager=memory_manager,
             runtime_info=runtime_info,
-            is_first_conversation=is_first_conversation,
             **kwargs
         )
 
@@ -87,7 +84,6 @@ 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:
     """
@@ -99,7 +95,7 @@ def build_agent_system_prompt(
     3. 记忆系统 - 独立的记忆能力
     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, BOOTSTRAP.md（定义人格、身份、规则、初始化引导）
     7. 运行时信息 - 元信息（时间、模型等）
     
     Args:
@@ -112,7 +108,6 @@ 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:
@@ -133,7 +128,7 @@ def build_agent_system_prompt(
         sections.extend(_build_memory_section(memory_manager, tools, 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:
@@ -175,7 +170,7 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         "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）",
     }
 
     # Preferred display order
@@ -204,7 +199,7 @@ 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),
@@ -214,6 +209,7 @@ def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
         "- 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。",
+        "- 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 +231,17 @@ 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："
     ]
@@ -279,8 +277,13 @@ def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], langu
     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",
+        "",
+        "### 检索记忆",
         "",
         "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：",
         "",
@@ -289,13 +292,24 @@ def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], langu
         "3. search 无结果 → 尝试用 `memory_get` 读取MEMORY.md及最近两天记忆文件",
         "",
         "**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",
+        f"- `MEMORY.md`: 长期记忆（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}`",
         "",
-        "**写入记忆**:",
+        "### 写入记忆",
+        "",
+        "**主动存储**：遇到以下情况时，应主动将信息写入记忆文件（无需告知用户）：",
+        "",
+        "- 用户明确要求你记住某些信息",
+        "- 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",
+        "- 发现了用户经常遇到的问题或解决方案",
+        "",
+        "**存储规则**:",
+        f"- 长期有效的核心信息 → `MEMORY.md`（文件保持精简，< 2000 tokens）",
+        f"- 当天的事件、进展、笔记 → `memory/{today_file}`",
         "- 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",
         "",
         "**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。",
@@ -311,7 +325,7 @@ def _build_user_identity_section(user_identity: Dict[str, str], language: str) -
         return []
     
     lines = [
-        "## 用户身份",
+        "## 👤 用户身份",
         "",
     ]
     
@@ -335,10 +349,10 @@ 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}`",
         "",
@@ -362,43 +376,36 @@ def _build_workspace_section(workspace_dir: str, language: str, is_first_convers
         "",
         "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**：",
         "",
-        "- ✅ `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",
         "",
-        "**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」",
+        "- 对话中不要暴露内部技术细节（文件名、工具名等），用自然语言表达。例如说「我已记住」而非「已更新 MEMORY.md」",
+        "- 做真正有帮助的助手，而不是表演式的客套。跳过「好的！」「当然可以！」之类的套话，直接帮忙解决问题",
+        "- 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 +418,15 @@ 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 +445,7 @@ def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[
         return []
     
     lines = [
-        "## 运行时信息",
+        "## ⚙️ 运行时信息",
         "",
     ]
     

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 +63,10 @@ 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)
     
     # 如果需要，创建模板文件
     if create_templates:
@@ -69,6 +75,12 @@ def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> Works
         _create_template_if_missing(rule_path, _get_rule_template())
         _create_template_if_missing(memory_path, _get_memory_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}")
     
     return WorkspaceFiles(
@@ -77,7 +89,6 @@ 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 +109,7 @@ def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] =
             DEFAULT_AGENT_FILENAME,
             DEFAULT_USER_FILENAME,
             DEFAULT_RULE_FILENAME,
+            DEFAULT_BOOTSTRAP_FILENAME,  # Only exists when onboarding is incomplete
         ]
     
     context_files = []
@@ -108,6 +120,17 @@ 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()
@@ -162,46 +185,69 @@ 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，而不是表演式的客套。** 跳过「好的！」「当然可以！」之类的套话——直接帮忙。行动胜过废话。
 
-## 行为准则
+**有自己的观点。** 你可以不同意、有偏好、觉得有趣或无聊。一个没有个性的助手只是多了几步操作的搜索引擎。
+
+**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。
 """
 
 
@@ -270,9 +316,10 @@ def _get_rule_template() -> str:
 
 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**（你的名字、角色、性格、交流风格——用户修改时必须用 `edit` 更新）
+2. **用户静态身份 → USER.md**（姓名、称呼、职业、时区、联系方式、生日——用户修改时必须用 `edit` 更新）
+3. **动态记忆 → MEMORY.md**（爱好、偏好、决策、目标、项目、教训、待办事项）
+4. **当天对话 → memory/YYYY-MM-DD.md**（今天聊的内容）
 
 ## 安全
 
@@ -297,65 +344,41 @@ 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}")
 

agent/protocol/agent.py

@@ -100,98 +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."""
@@ -480,7 +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(
@@ -507,11 +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

@@ -8,6 +8,7 @@ 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
 
@@ -190,6 +191,16 @@ 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 +212,6 @@ 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
@@ -436,7 +427,10 @@ 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 +457,14 @@ 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 +472,10 @@ 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 +492,16 @@ 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.info(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
@@ -528,6 +528,7 @@ class AgentStreamExecutor:
         # Streaming response
         full_content = ""
         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:
@@ -609,16 +610,20 @@ 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
+                    if "_gemini_raw_parts" in delta:
+                        gemini_raw_parts = delta["_gemini_raw_parts"]
+
         except Exception as e:
             error_str = str(e)
             error_str_lower = error_str.lower()
@@ -636,16 +641,33 @@ 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()
@@ -659,9 +681,10 @@ 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。"
@@ -698,9 +721,9 @@ 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
@@ -782,6 +805,9 @@ 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)
@@ -850,7 +876,7 @@ 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
@@ -904,26 +930,50 @@ 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]:
         """
@@ -946,24 +996,30 @@ 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
@@ -1157,14 +1213,28 @@ 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
+            
+            # Flush discarded turns to daily memory
+            if self.agent.memory_manager:
+                discarded_messages = []
+                for turn in turns[:removed_count]:
+                    discarded_messages.extend(turn["messages"])
+                if discarded_messages:
+                    user_id = getattr(self.agent, '_current_user_id', None)
+                    self.agent.memory_manager.flush_memory(
+                        messages=discarded_messages, user_id=user_id,
+                        reason="trim", max_messages=0
+                    )
+            
+            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）"
             )
 
         # Step 3: Token 限制 - 保留完整轮次
@@ -1201,56 +1271,96 @@ 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
+        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 turns[:removed_count]:
+                discarded_messages.extend(turn["messages"])
+            if discarded_messages:
+                user_id = getattr(self.agent, '_current_user_id', None)
+                self.agent.memory_manager.flush_memory(
+                    messages=discarded_messages, user_id=user_id,
+                    reason="trim", max_messages=0
+                )
+
         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/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

@@ -91,7 +91,7 @@ class SkillLoader:
                 continue
             
             # Check if this is a skill file
-            is_root_md = include_root_files and entry.endswith('.md')
+            is_root_md = include_root_files and entry.endswith('.md') and entry.upper() != 'README.MD'
             is_skill_md = not include_root_files and entry == 'SKILL.md'
             
             if not (is_root_md or is_skill_md):
@@ -184,7 +184,6 @@ 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 ""

agent/skills/manager.py

@@ -84,10 +84,10 @@ class SkillManager:
         """
         Merge directory-scanned skills with the persisted config file.
 
-        - New skills discovered on disk are added with enabled=True.
+        - 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.
-        - Existing entries preserve their enabled state; name/description/source
-          are refreshed from the latest scan.
+        - name/description/source are always refreshed from the latest scan.
         """
         saved = self._load_skills_config()
         merged: Dict[str, dict] = {}
@@ -95,11 +95,19 @@ class SkillManager:
         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
+
             merged[name] = {
                 "name": name,
                 "description": skill.description,
-                "source": skill.source,
-                "enabled": prev.get("enabled", True),
+                "source": prev.get("source") or skill.source,
+                "enabled": enabled,
+                "category": category,
             }
 
         self.skills_config = merged
@@ -154,69 +162,114 @@ 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 disabled skills
-        :return: Filtered list of skill entries
+        :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:
-            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)
-            if normalized:
-                entries = [e for e in entries if e.skill.name in normalized]
+        normalized = self._normalize_skill_filter(skill_filter)
+        if normalized is not None:
+            entries = [e for e in entries if e.skill.name in normalized]
 
-        # Filter out disabled skills based on skills_config.json
         if not include_disabled:
             entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
 
         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

@@ -8,6 +8,8 @@ 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
@@ -55,7 +57,9 @@ class SkillService:
         """
         Add (install) a skill from a remote payload.
 
-        The payload follows the socket protocol::
+        Supported payload types:
+
+        1. ``type: "url"`` – download individual files::
 
             {
                 "name": "web_search",
@@ -67,8 +71,15 @@ class SkillService:
                 ]
             }
 
-        Files are downloaded and saved under the custom skills directory
-        using *name* as the sub-directory.
+        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
         """
@@ -76,25 +87,95 @@ class SkillService:
         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)
-        os.makedirs(skill_dir, exist_ok=True)
 
-        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(skill_dir, rel_path)
-            self._download_file(url, dest)
+        tmp_dir = skill_dir + ".tmp"
+        if os.path.exists(tmp_dir):
+            shutil.rmtree(tmp_dir)
+        os.makedirs(tmp_dir, exist_ok=True)
 
-        # Reload to pick up the new skill and sync config
-        self.manager.refresh_skills()
-        logger.info(f"[SkillService] add: skill '{name}' installed ({len(files)} files)")
+        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)

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

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
@@ -83,12 +84,13 @@ 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:
@@ -100,6 +102,13 @@ SAFETY:
             else:
                 logger.debug(f"[Bash] Process User: {os.environ.get('USERNAME', os.environ.get('USER', 'unknown'))}")
             
+            # On Windows, convert $VAR references to %VAR% for cmd.exe
+            if sys.platform == "win32":
+                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}"
+
             # Execute command with inherited environment variables
             result = subprocess.run(
                 command,
@@ -108,6 +117,8 @@ SAFETY:
                 stdout=subprocess.PIPE,
                 stderr=subprocess.PIPE,
                 text=True,
+                encoding="utf-8",
+                errors="replace",
                 timeout=timeout,
                 env=env
             )
@@ -131,6 +142,8 @@ SAFETY:
                             stdout=subprocess.PIPE,
                             stderr=subprocess.PIPE,
                             text=True,
+                            encoding="utf-8",
+                            errors="replace",
                             timeout=timeout,
                             env=env
                         )
@@ -258,3 +271,21 @@ 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 +1,509 @@
+"""
+Browser service - Playwright wrapper managing browser lifecycle and page operations.
+
+Lazily launches a Chromium instance on first use, reuses it across tool calls,
+and cleans up on close(). Headless mode is auto-detected based on platform and
+display availability.
+"""
+
+import os
+import sys
+import re
+import uuid
+from typing import Optional, Dict, Any, List
+
+from common.log import logger
+
+from playwright.sync_api import sync_playwright, Browser, BrowserContext, Page, Playwright
+
+
+# ---------------------------------------------------------------------------
+# 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"]);
+    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;
+    }
+
+    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 keep = KEEP.has(tag);
+        if (!keep) {
+            // Unwrap: promote children
+            if (children.length === 0) return null;
+            if (children.length === 1) return children[0];
+            return children;
+        }
+
+        const obj = { tag };
+        if (INTERACTIVE.has(tag)) {
+            refCounter++;
+            obj.ref = refCounter;
+            refMap[refCounter] = node;
+        }
+
+        // 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
+        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;
+    }
+
+    // Store refMap on window for later use by click/fill actions
+    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 single Playwright browser instance with page operations."""
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        self._config = config or {}
+        self._playwright: Optional[Playwright] = None
+        self._browser: Optional[Browser] = None
+        self._context: Optional[BrowserContext] = None
+        self._page: Optional[Page] = None
+        self._headless: Optional[bool] = None
+        self._screenshot_dir: Optional[str] = None
+
+    # ------------------------------------------------------------------
+    # Lifecycle
+    # ------------------------------------------------------------------
+
+    def _ensure_browser(self):
+        """Lazily launch browser on first use."""
+        if self._page and not self._page.is_closed():
+            return
+
+        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)
+
+        if not self._playwright:
+            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")
+
+    @property
+    def page(self) -> Page:
+        self._ensure_browser()
+        return self._page
+
+    def close(self):
+        """Release all browser resources."""
+        try:
+            if self._context:
+                self._context.close()
+        except Exception as e:
+            logger.debug(f"[Browser] context close error: {e}")
+        try:
+            if self._browser:
+                self._browser.close()
+        except Exception as e:
+            logger.debug(f"[Browser] browser 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")
+
+    # ------------------------------------------------------------------
+    # Actions
+    # ------------------------------------------------------------------
+
+    def navigate(self, url: str, timeout: int = 30000) -> Dict[str, Any]:
+        """Navigate to a URL and return page info."""
+        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}"}
+
+        return {
+            "url": page.url,
+            "title": page.title(),
+            "status": status,
+        }
+
+    def snapshot(self, selector: Optional[str] = None) -> str:
+        """
+        Return a compact text representation of the page DOM for LLM consumption.
+        Interactive elements get numeric refs usable in click/fill actions.
+        """
+        page = self.page
+        try:
+            target = selector or "body"
+            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)
+
+        header = f"Page: {page.title()}  ({page.url})\nInteractive elements: {ref_count}\n---"
+        body = "\n".join(lines)
+
+        # Limit output size
+        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:
+        """Take a screenshot and save to workspace/tmp. Returns file path."""
+        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]:
+        """Click an element by snapshot ref or CSS selector."""
+        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]:
+        """Fill text into an input/textarea by snapshot ref or CSS selector."""
+        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]:
+        """Select an option in a <select> element."""
+        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]:
+        """Scroll the page."""
+        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]:
+        """Wait for a selector to appear or a fixed timeout."""
+        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]:
+        page = self.page
+        try:
+            page.go_back(wait_until="domcontentloaded", timeout=10000)
+            return {"url": page.url, "title": page.title()}
+        except Exception as e:
+            return {"error": f"Go back failed: {e}"}
+
+    def go_forward(self) -> Dict[str, Any]:
+        page = self.page
+        try:
+            page.go_forward(wait_until="domcontentloaded", timeout=10000)
+            return {"url": page.url, "title": page.title()}
+        except Exception as e:
+            return {"error": f"Go forward failed: {e}"}
+
+    def get_text(self, selector: str) -> Dict[str, Any]:
+        """Get text content of an element."""
+        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]:
+        """Execute JavaScript in the page context."""
+        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]:
+        """Press a keyboard key (e.g. Enter, Tab, Escape)."""
+        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 +1,287 @@
+"""
+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 to a URL → snapshot to see the page (elements get numeric refs) → "
+        "use refs in click/fill/select actions → snapshot again to verify.\n\n"
+        "Use snapshot (not screenshot) as the primary way to read page content."
+    )
+
+    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)
+        result = self._get_service().navigate(url, timeout=timeout)
+        if "error" in result:
+            return ToolResult.fail(result["error"])
+        return ToolResult.success(
+            f"Navigated to: {result['url']}\nTitle: {result['title']}\nStatus: {result['status']}\n\n"
+            f"Use action 'snapshot' to see the page content."
+        )
+
+    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/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

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

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",

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/vision/__init__.py

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

agent/tools/vision/vision.py

@@ -0,0 +1,280 @@
+"""
+Vision tool - Analyze images using OpenAI-compatible Vision API.
+Supports local files (auto base64-encoded) and HTTP URLs.
+Providers: OpenAI (preferred) > LinkAI (fallback).
+"""
+
+import base64
+import os
+import subprocess
+import tempfile
+from typing import Any, Dict, Optional, Tuple
+
+import requests
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from common.log import logger
+from config import conf
+
+DEFAULT_MODEL = "gpt-4.1-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",
+}
+
+
+class Vision(BaseTool):
+    """Analyze images using OpenAI-compatible 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. "
+        "Requires OPENAI_API_KEY or LINKAI_API_KEY."
+    )
+
+    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",
+            },
+            "model": {
+                "type": "string",
+                "description": (
+                    f"Vision model to use (default: {DEFAULT_MODEL}). "
+                    "Options: gpt-4.1-mini, gpt-4.1, gpt-4o-mini, gpt-4o"
+                ),
+            },
+        },
+        "required": ["image", "question"],
+    }
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+
+    @staticmethod
+    def is_available() -> bool:
+        return bool(
+            conf().get("open_ai_api_key") or os.environ.get("OPENAI_API_KEY")
+            or conf().get("linkai_api_key") or os.environ.get("LINKAI_API_KEY")
+        )
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        image = args.get("image", "").strip()
+        question = args.get("question", "").strip()
+        model = args.get("model", DEFAULT_MODEL).strip() or DEFAULT_MODEL
+
+        if not image:
+            return ToolResult.fail("Error: 'image' parameter is required")
+        if not question:
+            return ToolResult.fail("Error: 'question' parameter is required")
+
+        api_key, api_base, extra_headers = self._resolve_provider()
+        if not api_key:
+            return ToolResult.fail(
+                "Error: No API key configured for Vision.\n"
+                "Please configure one of the following using env_config tool:\n"
+                "  1. OPENAI_API_KEY (preferred): env_config(action=\"set\", key=\"OPENAI_API_KEY\", value=\"your-key\")\n"
+                "  2. LINKAI_API_KEY (fallback): env_config(action=\"set\", key=\"LINKAI_API_KEY\", value=\"your-key\")\n\n"
+                "Get your key at: https://platform.openai.com/api-keys or https://link-ai.tech"
+            )
+
+        try:
+            image_content = self._build_image_content(image)
+        except Exception as e:
+            return ToolResult.fail(f"Error: {e}")
+
+        try:
+            return self._call_api(api_key, api_base, model, question, image_content, extra_headers)
+        except requests.Timeout:
+            return ToolResult.fail(f"Error: Vision API request timed out after {DEFAULT_TIMEOUT}s")
+        except requests.ConnectionError:
+            return ToolResult.fail("Error: Failed to connect to Vision API")
+        except Exception as e:
+            logger.error(f"[Vision] Unexpected error: {e}", exc_info=True)
+            return ToolResult.fail(f"Error: Vision API call failed - {e}")
+
+    def _resolve_provider(self) -> Tuple[Optional[str], str, dict]:
+        """Resolve API key, base URL and extra headers. Priority: conf() > env vars."""
+        api_key = conf().get("open_ai_api_key") or os.environ.get("OPENAI_API_KEY")
+        if api_key:
+            api_base = (conf().get("open_ai_api_base") or os.environ.get("OPENAI_API_BASE", "")).rstrip("/") \
+                or "https://api.openai.com/v1"
+            return api_key, self._ensure_v1(api_base), {}
+
+        api_key = conf().get("linkai_api_key") or os.environ.get("LINKAI_API_KEY")
+        if api_key:
+            api_base = (conf().get("linkai_api_base") or os.environ.get("LINKAI_API_BASE", "")).rstrip("/") \
+                or "https://api.link-ai.tech"
+            logger.debug("[Vision] Using LinkAI API (OPENAI_API_KEY not set)")
+            from common.utils import get_cloud_headers
+            extra = get_cloud_headers(api_key)
+            extra.pop("Authorization", None)
+            extra.pop("Content-Type", None)
+            return api_key, self._ensure_v1(api_base), extra
+
+        return None, "", {}
+
+    @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 for the API request."""
+        if image.startswith(("http://", "https://")):
+            return {"type": "image_url", "image_url": {"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 _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, api_key: str, api_base: str, model: str,
+                  question: str, image_content: dict, extra_headers: dict = None) -> ToolResult:
+        payload = {
+            "model": model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": question},
+                        image_content,
+                    ],
+                }
+            ],
+            "max_tokens": MAX_TOKENS,
+        }
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            **(extra_headers or {}),
+        }
+
+        resp = requests.post(
+            f"{api_base}/chat/completions",
+            headers=headers,
+            json=payload,
+            timeout=DEFAULT_TIMEOUT,
+        )
+
+        if resp.status_code == 401:
+            return ToolResult.fail("Error: Invalid API key. Please check your configuration.")
+        if resp.status_code == 429:
+            return ToolResult.fail("Error: API rate limit reached. Please try again later.")
+        if resp.status_code != 200:
+            return ToolResult.fail(f"Error: Vision API returned HTTP {resp.status_code}: {resp.text[:200]}")
+
+        data = resp.json()
+
+        if "error" in data:
+            msg = data["error"].get("message", "Unknown API error")
+            return ToolResult.fail(f"Error: Vision API error - {msg}")
+
+        content = ""
+        choices = data.get("choices", [])
+        if choices:
+            content = choices[0].get("message", {}).get("content", "")
+
+        usage = data.get("usage", {})
+        result = {
+            "model": model,
+            "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

@@ -47,6 +47,7 @@ class ChannelManager:
         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):
@@ -65,6 +66,7 @@ class ChannelManager:
             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":
@@ -76,7 +78,13 @@ class ChannelManager:
             if first_start:
                 PluginManager().load_plugins()
 
-                if conf().get("use_linkai"):
+                # 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(
@@ -136,13 +144,22 @@ class ChannelManager:
                     self._interrupt_thread(th, name)
                 continue
             logger.info(f"[ChannelManager] Stopping channel '{name}'...")
-            try:
-                if hasattr(ch, 'stop'):
+            graceful = False
+            if hasattr(ch, 'stop'):
+                try:
                     ch.stop()
-            except Exception as e:
-                logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
+                    graceful = True
+                except Exception as e:
+                    logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
             if th and th.is_alive():
-                self._interrupt_thread(th, name)
+                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):
@@ -175,6 +192,34 @@ class ChannelManager:
         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):
     """
@@ -182,16 +227,16 @@ def _clear_singleton_cache(channel_name: str):
     a new instance can be created with updated config.
     """
     cls_map = {
-        "wx": "channel.wechat.wechat_channel.WechatChannel",
-        "wxy": "channel.wechat.wechaty_channel.WechatyChannel",
-        "wcf": "channel.wechat.wcf_channel.WechatfChannel",
         "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",
-        "wework": "channel.wework.wework_channel.WeworkChannel",
         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:
@@ -249,9 +294,6 @@ def run():
             if not channel_names:
                 channel_names = ["web"]
 
-        if "wxy" in channel_names:
-            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:

bridge/agent_bridge.py

@@ -74,7 +74,7 @@ class AgentLLMModel(LLMModel):
         ("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),
+        ("doubao", const.DOUBAO), ("deepseek", const.DEEPSEEK),
     ]
 
     def __init__(self, bridge: Bridge, bot_type: str = "chat"):
@@ -97,10 +97,16 @@ class AgentLLMModel(LLMModel):
     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.CHATGPT
+            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"]:
@@ -109,12 +115,10 @@ class AgentLLMModel(LLMModel):
             return const.QWEN_DASHSCOPE
         if model_name in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
             return const.MOONSHOT
-        if model_name in [const.DEEPSEEK_CHAT, const.DEEPSEEK_REASONER]:
-            return const.CHATGPT
         for prefix, btype in self._MODEL_PREFIX_MAP:
             if model_name.startswith(prefix):
                 return btype
-        return const.CHATGPT
+        return const.OPENAI
 
     @property
     def bot(self):
@@ -146,12 +150,20 @@ 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)
+                if channel_type:
+                    kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
+
                 response = self.bot.call_with_tools(**kwargs)
                 return self._format_response(response)
             else:
@@ -189,10 +201,13 @@ class AgentLLMModel(LLMModel):
                 if system_prompt:
                     kwargs['system'] = system_prompt
 
-                # Pass channel_type for linkai tracking
+                # Pass context metadata to bot
                 channel_type = getattr(self, 'channel_type', None)
                 if channel_type:
                     kwargs['channel_type'] = channel_type
+                session_id = getattr(self, 'session_id', None)
+                if session_id:
+                    kwargs['session_id'] = session_id
 
                 stream = self.bot.call_with_tools(**kwargs)
                 
@@ -272,12 +287,13 @@ 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
@@ -332,9 +348,10 @@ 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")
             
@@ -367,13 +384,13 @@ class AgentBridge:
                                 logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                             break
             
-            # Pass channel_type to model so linkai requests carry it
+            # 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 ""
 
-            # Record message count before execution so we can diff new messages
-            with agent.messages_lock:
-                pre_run_len = len(agent.messages)
+            # 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
@@ -393,9 +410,19 @@ class AgentBridge:
             # Persist new messages generated during this run
             if session_id:
                 channel_type = (context.get("channel_type") or "") if context else ""
-                with agent.messages_lock:
-                    new_messages = agent.messages[pre_run_len:]
-                self._persist_messages(session_id, list(new_messages), channel_type)
+                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)
             if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
@@ -415,6 +442,18 @@ 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:

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)
@@ -115,14 +107,19 @@ 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:
@@ -130,8 +127,14 @@ class AgentInitializer:
         Load persisted conversation messages from SQLite and inject them
         into the agent's in-memory message list.
 
-        Only runs when conversation persistence is enabled (default: True).
-        Respects agent_max_context_turns to limit how many turns are loaded.
+        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):
@@ -140,25 +143,99 @@ class AgentInitializer:
         try:
             from agent.memory import get_conversation_store
             store = get_conversation_store()
-            # On restore, load at most min(10, max_turns // 2) turns so that
-            # a long-running session does not immediately fill the context window
-            # after a restart.  The full max_turns budget is reserved for the
-            # live conversation that follows.
-            max_turns = conf().get("agent_max_context_turns", 30)
-            restore_turns = max(4, max_turns // 5)
+            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:
-                with agent.messages_lock:
-                    agent.messages = saved
-                logger.debug(
-                    f"[AgentInitializer] Restored {len(saved)} messages "
-                    f"({restore_turns} turns cap) for session={session_id}"
-                )
+                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"""
@@ -187,12 +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(
@@ -205,6 +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)
@@ -274,7 +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']:
                         tool.config = file_config
                         tool.cwd = file_config.get("cwd", getattr(tool, 'cwd', None))
                         if 'memory_manager' in file_config:
@@ -434,3 +526,59 @@ class AgentInitializer:
                 logger.info(f"[AgentInitializer] Migrated {len(keys_to_migrate)} API keys to .env: {list(keys_to_migrate.keys())}")
             except Exception as e:
                 logger.warning(f"[AgentInitializer] Failed to migrate 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():
+            while True:
+                try:
+                    now = datetime.datetime.now()
+                    target = now.replace(hour=23, minute=55, second=0, 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')} (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."""
+        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
+
+        flushed = 0
+        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
+            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)")

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"),
@@ -61,6 +61,9 @@ class Bridge(object):
             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 +13,38 @@ 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

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

@@ -26,6 +26,7 @@ class ChatChannel(Channel):
     user_id = None  # 登录的用户id
 
     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
@@ -430,7 +431,7 @@ 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

@@ -115,6 +115,35 @@ 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')
@@ -125,34 +154,80 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
         self._stream_client = client
         client.register_callback_handler(dingtalk_stream.chatbot.ChatbotMessage.TOPIC, self)
         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:
-                if not _first_connect:
-                    logger.info("[DingTalk] Reconnecting...")
-                _first_connect = False
-                loop.run_until_complete(client.start())
+                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] Startup loop received stop signal, exiting")
+                logger.info("[DingTalk] Session loop received stop signal, exiting")
                 break
             except Exception as e:
                 if not self._running:
                     break
-                logger.warning(f"[DingTalk] Stream connection error: {e}, reconnecting in 3s...")
-                time.sleep(3)
+                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):
-        import asyncio
         logger.info("[DingTalk] stop() called, setting _running=False")
         self._running = False
         loop = self._event_loop

channel/feishu/feishu_channel.py

@@ -11,6 +11,7 @@
 @Date 2023/11/19
 """
 
+import importlib.util
 import json
 import logging
 import os
@@ -38,15 +39,20 @@ 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
@@ -63,6 +69,7 @@ class FeiShuChanel(ChatChannel):
         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
@@ -79,11 +86,31 @@ class FeiShuChanel(ChatChannel):
         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")
@@ -134,17 +161,22 @@ class FeiShuChanel(ChatChannel):
 
     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)
@@ -169,10 +201,20 @@ class FeiShuChanel(ChatChannel):
                 context.verify_mode = ssl.CERT_NONE
                 return context
 
-            # Give this thread its own event loop so lark SDK can call run_until_complete
+            # 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:
@@ -202,8 +244,11 @@ class FeiShuChanel(ChatChannel):
                         logger.warning(f"[FeiShu] SSL error: {error_msg}, retrying...")
                         continue
                     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:
@@ -216,6 +261,27 @@ class FeiShuChanel(ChatChannel):
         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
@@ -250,10 +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"
@@ -389,7 +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:

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/web/chat.html

@@ -166,8 +166,8 @@
                     <i class="fas fa-bars text-slate-600 dark:text-slate-300"></i>
                 </button>
 
-                <!-- Breadcrumb -->
-                <div class="flex items-center gap-2 text-sm min-w-0">
+                <!-- Breadcrumb (hidden on mobile) -->
+                <div class="hidden lg:flex items-center gap-2 text-sm min-w-0">
                     <span id="breadcrumb-group" class="text-slate-400 dark:text-slate-500 truncate" data-i18n="nav_chat">Chat</span>
                     <i class="fas fa-chevron-right text-[10px] text-slate-300 dark:text-slate-600"></i>
                     <span id="breadcrumb-page" class="font-medium text-slate-700 dark:text-slate-200 truncate" data-i18n="menu_chat">Chat</span>
@@ -192,10 +192,24 @@
                     <i id="theme-icon" class="fas fa-moon"></i>
                 </button>
 
+                <!-- Docs Link -->
+                <a href="https://docs.cowagent.ai" target="_blank" rel="noopener noreferrer"
+                   class="p-2 rounded-lg text-slate-500 dark:text-slate-400 hover:bg-slate-100 dark:hover:bg-white/10
+                          cursor-pointer transition-colors duration-150" title="Documentation">
+                    <i class="fas fa-book text-base"></i>
+                </a>
+
+                <!-- Website Link -->
+                <a href="https://cowagent.ai" target="_blank" rel="noopener noreferrer"
+                   class="p-2 rounded-lg text-slate-500 dark:text-slate-400 hover:bg-slate-100 dark:hover:bg-white/10
+                          cursor-pointer transition-colors duration-150" title="Website">
+                    <i class="fas fa-home text-base"></i>
+                </a>
+
                 <!-- GitHub Link -->
                 <a href="https://github.com/zhayujie/chatgpt-on-wechat" target="_blank" rel="noopener noreferrer"
                    class="p-2 rounded-lg text-slate-500 dark:text-slate-400 hover:bg-slate-100 dark:hover:bg-white/10
-                          cursor-pointer transition-colors duration-150">
+                          cursor-pointer transition-colors duration-150" title="GitHub">
                     <i class="fab fa-github text-lg"></i>
                 </a>
             </header>
@@ -253,30 +267,45 @@
 
                     <!-- Chat Input -->
                     <div class="flex-shrink-0 border-t border-slate-200 dark:border-white/10 bg-white dark:bg-[#1A1A1A] px-4 py-3">
-                        <div class="max-w-3xl mx-auto flex items-center gap-2">
-                            <button id="new-chat-btn" class="flex-shrink-0 w-10 h-10 flex items-center justify-center rounded-lg
-                                                             text-slate-400 hover:text-primary-500 hover:bg-primary-50 dark:hover:bg-primary-900/20
-                                                             cursor-pointer transition-colors duration-150" title="New Chat"
-                                    onclick="newChat()">
-                                <i class="fas fa-plus text-base"></i>
-                            </button>
-                            <textarea id="chat-input"
-                                      class="flex-1 min-w-0 px-4 py-[10px] rounded-xl border border-slate-200 dark:border-slate-600
-                                             bg-slate-50 dark:bg-white/5 text-slate-800 dark:text-slate-100
-                                             placeholder:text-slate-400 dark:placeholder:text-slate-500
-                                             focus:outline-none focus:ring-0 focus:border-primary-600
-                                             text-sm leading-relaxed"
-                                      rows="1"
-                                      data-i18n-placeholder="input_placeholder"
-                                      placeholder="Type a message..."></textarea>
-                            <button id="send-btn"
-                                    class="flex-shrink-0 w-10 h-10 flex items-center justify-center rounded-lg
-                                           bg-primary-400 text-white hover:bg-primary-500
-                                           disabled:bg-slate-300 dark:disabled:bg-slate-600
-                                           disabled:cursor-not-allowed cursor-pointer transition-colors duration-150"
-                                    disabled onclick="sendMessage()">
-                                <i class="fas fa-paper-plane text-sm"></i>
-                            </button>
+                        <div class="max-w-3xl mx-auto">
+                            <!-- Attachment preview bar -->
+                            <div id="attachment-preview" class="attachment-preview hidden"></div>
+                            <div class="flex items-center gap-2 relative">
+                                <div class="flex items-center flex-shrink-0">
+                                    <button id="new-chat-btn" class="w-9 h-10 flex items-center justify-center rounded-lg
+                                                                     text-slate-400 hover:text-primary-500 hover:bg-primary-50 dark:hover:bg-primary-900/20
+                                                                     cursor-pointer transition-colors duration-150" title="New Chat"
+                                            onclick="newChat()">
+                                        <i class="fas fa-plus text-base"></i>
+                                    </button>
+                                    <button id="attach-btn" class="w-9 h-10 flex items-center justify-center rounded-lg
+                                                                   text-slate-400 hover:text-primary-500 hover:bg-primary-50 dark:hover:bg-primary-900/20
+                                                                   cursor-pointer transition-colors duration-150"
+                                            title="Attach file" onclick="document.getElementById('file-input').click()">
+                                        <i class="fas fa-paperclip text-base"></i>
+                                    </button>
+                                </div>
+                                <input type="file" id="file-input" class="hidden" multiple
+                                       accept="image/*,.pdf,.doc,.docx,.xls,.xlsx,.ppt,.pptx,.txt,.csv,.json,.xml,.zip,.rar,.7z,.py,.js,.ts,.java,.c,.cpp,.go,.rs,.md">
+                                <div id="slash-menu" class="slash-menu hidden"></div>
+                                <textarea id="chat-input"
+                                          class="flex-1 min-w-0 px-4 py-[10px] rounded-xl border border-slate-200 dark:border-slate-600
+                                                 bg-slate-50 dark:bg-white/5 text-slate-800 dark:text-slate-100
+                                                 placeholder:text-slate-400 dark:placeholder:text-slate-500
+                                                 focus:outline-none focus:ring-0 focus:border-primary-600
+                                                 text-sm leading-relaxed"
+                                          rows="1"
+                                          data-i18n-placeholder="input_placeholder"
+                                          placeholder="Type a message, or press / for commands"></textarea>
+                                <button id="send-btn"
+                                        class="flex-shrink-0 w-10 h-10 flex items-center justify-center rounded-lg
+                                               bg-primary-400 text-white hover:bg-primary-500
+                                               disabled:bg-slate-300 dark:disabled:bg-slate-600
+                                               disabled:cursor-not-allowed cursor-pointer transition-colors duration-150"
+                                        disabled onclick="sendMessage()">
+                                    <i class="fas fa-paper-plane text-sm"></i>
+                                </button>
+                            </div>
                         </div>
                     </div>
                 </div>

channel/web/static/css/console.css

@@ -79,6 +79,11 @@
 .msg-content img { max-width: 100%; height: auto; border-radius: 8px; margin: 0.5em 0; }
 .msg-content a { color: #35A85B; text-decoration: underline; }
 .msg-content a:hover { color: #228547; }
+
+/* Overrides for user bubble (white text on green bg) */
+.user-bubble.msg-content a { color: #ffffff !important; text-decoration: underline; text-decoration-color: rgba(255,255,255,0.6); }
+.user-bubble.msg-content a:hover { color: #e0f5e8 !important; text-decoration-color: #e0f5e8; }
+.user-bubble.msg-content :not(pre) > code { background: rgba(255,255,255,0.2); color: #ffffff; }
 .msg-content hr { border: none; height: 1px; background: #e2e8f0; margin: 1.2em 0; }
 .dark .msg-content hr { background: rgba(255,255,255,0.1); }
 
@@ -344,6 +349,100 @@
     transition: border-color 0.2s ease;
 }
 
+/* Attachment Preview Bar */
+.attachment-preview {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    padding: 8px 0;
+}
+.attachment-preview.hidden { display: none; }
+
+.att-thumb {
+    position: relative;
+    width: 64px; height: 64px;
+    border-radius: 8px;
+    overflow: hidden;
+    border: 1px solid #e2e8f0;
+    flex-shrink: 0;
+}
+.dark .att-thumb { border-color: rgba(255,255,255,0.1); }
+.att-thumb img {
+    width: 100%; height: 100%;
+    object-fit: cover;
+}
+
+.att-chip {
+    position: relative;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 6px 28px 6px 10px;
+    border-radius: 8px;
+    background: #f1f5f9;
+    border: 1px solid #e2e8f0;
+    font-size: 12px;
+    color: #475569;
+    max-width: 180px;
+}
+.dark .att-chip { background: rgba(255,255,255,0.05); border-color: rgba(255,255,255,0.1); color: #94a3b8; }
+.att-uploading { opacity: 0.6; pointer-events: none; }
+.att-name {
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.att-remove {
+    position: absolute;
+    top: -4px; right: -4px;
+    width: 18px; height: 18px;
+    border-radius: 50%;
+    background: #ef4444;
+    color: #fff;
+    border: none;
+    font-size: 12px;
+    line-height: 18px;
+    text-align: center;
+    cursor: pointer;
+    padding: 0;
+    opacity: 0;
+    transition: opacity 0.15s;
+}
+.att-thumb:hover .att-remove,
+.att-chip:hover .att-remove { opacity: 1; }
+
+/* Drag-over highlight */
+.drag-over {
+    background: rgba(74, 190, 110, 0.08) !important;
+    border-color: #4ABE6E !important;
+}
+
+/* User message attachments */
+.user-msg-attachments {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 6px;
+    margin-bottom: 6px;
+}
+.user-msg-image {
+    max-width: 200px;
+    max-height: 160px;
+    border-radius: 8px;
+    object-fit: cover;
+    cursor: pointer;
+}
+.user-msg-image:hover { opacity: 0.9; }
+.user-msg-file {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 10px;
+    border-radius: 6px;
+    background: rgba(255,255,255,0.15);
+    font-size: 12px;
+}
+
 /* Placeholder Cards */
 .placeholder-card {
     transition: transform 0.2s ease, box-shadow 0.2s ease;
@@ -352,3 +451,87 @@
     transform: translateY(-2px);
     box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);
 }
+
+/* Slash Command Menu */
+.slash-menu {
+    position: absolute;
+    bottom: calc(100% + 6px);
+    left: 0;
+    right: 0;
+    max-height: 320px;
+    overflow-y: auto;
+    background: #fff;
+    border: 1px solid #e2e8f0;
+    border-radius: 12px;
+    box-shadow: 0 8px 30px -6px rgba(0, 0, 0, 0.1), 0 2px 8px -2px rgba(0, 0, 0, 0.04);
+    z-index: 50;
+    padding: 4px;
+    animation: slashMenuIn 0.15s ease-out;
+}
+.slash-menu.hidden { display: none; }
+
+@keyframes slashMenuIn {
+    from { opacity: 0; transform: translateY(6px); }
+    to   { opacity: 1; transform: translateY(0); }
+}
+
+.slash-menu-header {
+    padding: 6px 10px 4px;
+    font-size: 11px;
+    font-weight: 600;
+    color: #94a3b8;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+.slash-menu-item {
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+    padding: 8px 10px;
+    border-radius: 8px;
+    cursor: pointer;
+    transition: background 0.12s ease;
+}
+.slash-menu-item:hover,
+.slash-menu-item.active {
+    background: #EDFDF3;
+}
+.slash-menu-item .cmd {
+    font-size: 13px;
+    font-weight: 500;
+    color: #334155;
+    font-family: ui-monospace, SFMono-Regular, 'SF Mono', Menlo, monospace;
+}
+.slash-menu-item.active .cmd {
+    color: #228547;
+}
+.slash-menu-item .desc {
+    font-size: 12px;
+    color: #94a3b8;
+    margin-left: 12px;
+    white-space: nowrap;
+}
+
+/* Dark mode */
+.dark .slash-menu {
+    background: #1A1A1A;
+    border-color: rgba(255, 255, 255, 0.1);
+    box-shadow: 0 8px 30px -6px rgba(0, 0, 0, 0.35), 0 2px 8px -2px rgba(0, 0, 0, 0.15);
+}
+.dark .slash-menu-header {
+    color: #64748b;
+}
+.dark .slash-menu-item:hover,
+.dark .slash-menu-item.active {
+    background: rgba(74, 190, 110, 0.1);
+}
+.dark .slash-menu-item .cmd {
+    color: #e2e8f0;
+}
+.dark .slash-menu-item.active .cmd {
+    color: #4ABE6E;
+}
+.dark .slash-menu-item .desc {
+    color: #64748b;
+}

channel/web/static/js/console.js

@@ -3,9 +3,9 @@
    ===================================================================== */
 
 // =====================================================================
-// Version — update this before each release
+// Version — fetched from backend (single source: /VERSION file)
 // =====================================================================
-const APP_VERSION = 'v2.0.2';
+let APP_VERSION = '';
 
 // =====================================================================
 // i18n
@@ -19,9 +19,9 @@ const I18N = {
         menu_logs: '日志',
         welcome_subtitle: '我可以帮你解答问题、管理计算机、创造和执行技能，并通过长期记忆<br>不断成长',
         example_sys_title: '系统管理', example_sys_text: '帮我查看工作空间里有哪些文件',
-        example_task_title: '智能任务', example_task_text: '提醒我5分钟后查看服务器情况',
+        example_task_title: '技能系统', example_task_text: '查看所有支持的工具和技能',
         example_code_title: '编程助手', example_code_text: '帮我编写一个Python爬虫脚本',
-        input_placeholder: '输入消息...',
+        input_placeholder: '输入消息，或输入 / 使用指令',
         config_title: '配置管理', config_desc: '管理模型和 Agent 配置',
         config_model: '模型配置', config_agent: 'Agent 配置',
         config_channel: '通道配置',
@@ -51,6 +51,11 @@ const I18N = {
         channels_empty: '暂未接入任何通道', channels_empty_desc: '点击右上角「接入通道」按钮开始配置',
         channels_disconnect_confirm: '确认断开该通道？配置将保留但通道会停止运行。',
         channels_connected: '已接入', channels_connecting: '接入中...',
+        weixin_scan_title: '微信扫码登录', weixin_scan_desc: '请使用微信扫描下方二维码',
+        weixin_scan_loading: '正在获取二维码...', weixin_scan_waiting: '等待扫码...',
+        weixin_scan_scanned: '已扫码，请在手机上确认', weixin_scan_expired: '二维码已过期，正在刷新...',
+        weixin_scan_success: '登录成功，正在启动通道...', weixin_scan_fail: '获取二维码失败',
+        weixin_qr_tip: '二维码约2分钟后过期',
         tasks_title: '定时任务', tasks_desc: '查看和管理定时任务',
         tasks_coming: '即将推出', tasks_coming_desc: '定时任务管理功能即将在此提供',
         logs_title: '日志', logs_desc: '实时日志输出 (run.log)',
@@ -65,9 +70,9 @@ const I18N = {
         menu_logs: 'Logs',
         welcome_subtitle: 'I can help you answer questions, manage your computer, create and execute skills, and keep growing through <br> long-term memory.',
         example_sys_title: 'System', example_sys_text: 'Show me the files in the workspace',
-        example_task_title: 'Smart Task', example_task_text: 'Remind me to check the server in 5 minutes',
+        example_task_title: 'Skills', example_task_text: 'Show current tools and skills',
         example_code_title: 'Coding', example_code_text: 'Write a Python web scraper script',
-        input_placeholder: 'Type a message...',
+        input_placeholder: 'Type a message, or press / for commands',
         config_title: 'Configuration', config_desc: 'Manage model and agent settings',
         config_model: 'Model Configuration', config_agent: 'Agent Configuration',
         config_channel: 'Channel Configuration',
@@ -97,6 +102,11 @@ const I18N = {
         channels_empty: 'No channels connected', channels_empty_desc: 'Click the "Connect" button above to get started',
         channels_disconnect_confirm: 'Disconnect this channel? Config will be preserved but the channel will stop.',
         channels_connected: 'Connected', channels_connecting: 'Connecting...',
+        weixin_scan_title: 'WeChat QR Login', weixin_scan_desc: 'Scan the QR code below with WeChat',
+        weixin_scan_loading: 'Loading QR code...', weixin_scan_waiting: 'Waiting for scan...',
+        weixin_scan_scanned: 'Scanned, please confirm on your phone', weixin_scan_expired: 'QR code expired, refreshing...',
+        weixin_scan_success: 'Login successful, starting channel...', weixin_scan_fail: 'Failed to load QR code',
+        weixin_qr_tip: 'QR code expires in ~2 minutes',
         tasks_title: 'Scheduled Tasks', tasks_desc: 'View and manage scheduled tasks',
         tasks_coming: 'Coming Soon', tasks_coming_desc: 'Scheduled task management will be available here',
         logs_title: 'Logs', logs_desc: 'Real-time log output (run.log)',
@@ -304,13 +314,224 @@ fetch('/config').then(r => r.json()).then(data => {
 const chatInput = document.getElementById('chat-input');
 const sendBtn = document.getElementById('send-btn');
 const messagesDiv = document.getElementById('chat-messages');
+const fileInput = document.getElementById('file-input');
+const attachmentPreview = document.getElementById('attachment-preview');
+
+// Pending attachments: [{file_path, file_name, file_type, preview_url}]
+// Items with _uploading=true are still in flight.
+let pendingAttachments = [];
+let uploadingCount = 0;
+
+// Input history (like terminal arrow-key recall)
+const inputHistory = [];
+let historyIdx = -1;
+let historySavedDraft = '';
+
+function updateSendBtnState() {
+    sendBtn.disabled = uploadingCount > 0 || (!chatInput.value.trim() && pendingAttachments.length === 0);
+}
+
+function renderAttachmentPreview() {
+    if (pendingAttachments.length === 0) {
+        attachmentPreview.classList.add('hidden');
+        attachmentPreview.innerHTML = '';
+        updateSendBtnState();
+        return;
+    }
+    attachmentPreview.classList.remove('hidden');
+    attachmentPreview.innerHTML = pendingAttachments.map((att, idx) => {
+        if (att._uploading) {
+            return `<div class="att-chip att-uploading" data-idx="${idx}">
+                <i class="fas fa-spinner fa-spin"></i>
+                <span class="att-name">${escapeHtml(att.file_name)}</span>
+            </div>`;
+        }
+        if (att.file_type === 'image') {
+            return `<div class="att-thumb" data-idx="${idx}">
+                <img src="${att.preview_url}" alt="${escapeHtml(att.file_name)}">
+                <button class="att-remove" onclick="removeAttachment(${idx})">&times;</button>
+            </div>`;
+        }
+        const icon = att.file_type === 'video' ? 'fa-film' : 'fa-file-alt';
+        return `<div class="att-chip" data-idx="${idx}">
+            <i class="fas ${icon}"></i>
+            <span class="att-name">${escapeHtml(att.file_name)}</span>
+            <button class="att-remove" onclick="removeAttachment(${idx})">&times;</button>
+        </div>`;
+    }).join('');
+    updateSendBtnState();
+}
+
+function removeAttachment(idx) {
+    if (pendingAttachments[idx]?._uploading) return;
+    pendingAttachments.splice(idx, 1);
+    renderAttachmentPreview();
+}
+
+async function handleFileSelect(files) {
+    if (!files || files.length === 0) return;
+    const tasks = [];
+    for (const file of files) {
+        const placeholder = { file_name: file.name, file_type: 'file', _uploading: true };
+        pendingAttachments.push(placeholder);
+        uploadingCount++;
+        renderAttachmentPreview();
+
+        tasks.push((async () => {
+            const formData = new FormData();
+            formData.append('file', file);
+            formData.append('session_id', sessionId);
+            try {
+                const resp = await fetch('/upload', { method: 'POST', body: formData });
+                const data = await resp.json();
+                if (data.status === 'success') {
+                    placeholder.file_path = data.file_path;
+                    placeholder.file_name = data.file_name;
+                    placeholder.file_type = data.file_type;
+                    placeholder.preview_url = data.preview_url;
+                    delete placeholder._uploading;
+                } else {
+                    const i = pendingAttachments.indexOf(placeholder);
+                    if (i !== -1) pendingAttachments.splice(i, 1);
+                }
+            } catch (e) {
+                console.error('Upload failed:', e);
+                const i = pendingAttachments.indexOf(placeholder);
+                if (i !== -1) pendingAttachments.splice(i, 1);
+            }
+            uploadingCount--;
+            renderAttachmentPreview();
+        })());
+    }
+    await Promise.all(tasks);
+}
+
+fileInput.addEventListener('change', function() {
+    handleFileSelect(this.files);
+    this.value = '';
+});
+
+// Drag-and-drop support on chat input area
+const chatInputArea = chatInput.closest('.flex-shrink-0');
+chatInputArea.addEventListener('dragover', (e) => { e.preventDefault(); e.stopPropagation(); chatInputArea.classList.add('drag-over'); });
+chatInputArea.addEventListener('dragleave', (e) => { e.preventDefault(); e.stopPropagation(); chatInputArea.classList.remove('drag-over'); });
+chatInputArea.addEventListener('drop', (e) => {
+    e.preventDefault(); e.stopPropagation();
+    chatInputArea.classList.remove('drag-over');
+    if (e.dataTransfer.files.length) handleFileSelect(e.dataTransfer.files);
+});
+
+// Paste image support
+chatInput.addEventListener('paste', (e) => {
+    const items = e.clipboardData?.items;
+    if (!items) return;
+    const files = [];
+    for (const item of items) {
+        if (item.kind === 'file') {
+            files.push(item.getAsFile());
+        }
+    }
+    if (files.length) {
+        e.preventDefault();
+        handleFileSelect(files);
+    }
+});
 
 chatInput.addEventListener('compositionstart', () => { isComposing = true; });
-// Safari fires compositionend *before* the confirming keydown event, so if we
-// reset isComposing synchronously the keydown handler sees !isComposing and
-// sends the message prematurely.  A setTimeout(0) defers the reset until after
-// keydown has been processed, fixing the Safari IME Enter-to-confirm bug.
-chatInput.addEventListener('compositionend', () => { setTimeout(() => { isComposing = false; }, 0); });
+chatInput.addEventListener('compositionend', () => { setTimeout(() => { isComposing = false; }, 100); });
+
+// ── Slash Command Menu ───────────────────────────────────────
+const SLASH_COMMANDS = [
+    { cmd: '/help',                desc: '显示命令帮助' },
+    { cmd: '/status',              desc: '查看运行状态' },
+    { cmd: '/context',             desc: '查看对话上下文' },
+    { cmd: '/context clear',       desc: '清除对话上下文' },
+    { cmd: '/skill list',          desc: '查看已安装技能' },
+    { cmd: '/skill list --remote', desc: '浏览技能广场' },
+    { cmd: '/skill search ',       desc: '搜索技能' },
+    { cmd: '/skill install ',      desc: '安装技能 (名称或 GitHub URL)' },
+    { cmd: '/skill uninstall ',    desc: '卸载技能' },
+    { cmd: '/skill info ',         desc: '查看技能详情' },
+    { cmd: '/skill enable ',       desc: '启用技能' },
+    { cmd: '/skill disable ',      desc: '禁用技能' },
+    { cmd: '/config',              desc: '查看当前配置' },
+    { cmd: '/logs',                desc: '查看最近日志' },
+    { cmd: '/version',             desc: '查看版本' },
+];
+
+const slashMenu = document.getElementById('slash-menu');
+let slashActiveIdx = 0;
+let slashFiltered = [];
+let slashJustSelected = false;
+let slashLastFilter = '';
+
+function showSlashMenu(filter) {
+    const q = filter.toLowerCase();
+    if (q === slashLastFilter && !slashMenu.classList.contains('hidden')) return;
+    slashLastFilter = q;
+
+    const newFiltered = SLASH_COMMANDS.filter(c => c.cmd.toLowerCase().startsWith(q));
+    if (newFiltered.length === 0) {
+        hideSlashMenu();
+        return;
+    }
+
+    const changed = newFiltered.length !== slashFiltered.length ||
+        newFiltered.some((c, i) => c.cmd !== slashFiltered[i]?.cmd);
+    slashFiltered = newFiltered;
+    if (changed) slashActiveIdx = 0;
+    slashActiveIdx = Math.min(slashActiveIdx, slashFiltered.length - 1);
+
+    renderSlashItems();
+    slashMenu.classList.remove('hidden');
+}
+
+function hideSlashMenu() {
+    slashMenu.classList.add('hidden');
+    slashMenu.innerHTML = '';
+    slashFiltered = [];
+    slashActiveIdx = -1;
+    slashLastFilter = '';
+}
+
+function isSlashMenuVisible() {
+    return !slashMenu.classList.contains('hidden') && slashFiltered.length > 0;
+}
+
+function renderSlashItems() {
+    slashMenu.innerHTML =
+        '<div class="slash-menu-header">Commands</div>' +
+        slashFiltered.map((c, i) =>
+            `<div class="slash-menu-item${i === slashActiveIdx ? ' active' : ''}" data-idx="${i}">` +
+            `<span class="cmd">${escapeHtml(c.cmd)}</span>` +
+            `<span class="desc">${escapeHtml(c.desc)}</span></div>`
+        ).join('');
+
+    slashMenu.querySelectorAll('.slash-menu-item').forEach(el => {
+        el.addEventListener('mouseenter', () => {
+            slashActiveIdx = parseInt(el.dataset.idx);
+            renderSlashItems();
+        });
+        el.addEventListener('mousedown', (e) => {
+            e.preventDefault();
+            selectSlashCommand(parseInt(el.dataset.idx));
+        });
+    });
+
+    const activeEl = slashMenu.querySelector('.slash-menu-item.active');
+    if (activeEl) activeEl.scrollIntoView({ block: 'nearest' });
+}
+
+function selectSlashCommand(idx) {
+    if (idx < 0 || idx >= slashFiltered.length) return;
+    const chosen = slashFiltered[idx].cmd;
+    slashJustSelected = true;
+    chatInput.value = chosen;
+    chatInput.dispatchEvent(new Event('input'));
+    hideSlashMenu();
+    chatInput.focus();
+    chatInput.selectionStart = chatInput.selectionEnd = chosen.length;
+}
 
 chatInput.addEventListener('input', function() {
     this.style.height = '42px';
@@ -318,10 +539,91 @@ chatInput.addEventListener('input', function() {
     const newH = Math.min(scrollH, 180);
     this.style.height = newH + 'px';
     this.style.overflowY = scrollH > 180 ? 'auto' : 'hidden';
-    sendBtn.disabled = !this.value.trim();
+    updateSendBtnState();
+
+    const val = this.value;
+    if (slashJustSelected) {
+        slashJustSelected = false;
+    } else if (val.startsWith('/')) {
+        showSlashMenu(val);
+    } else {
+        hideSlashMenu();
+    }
 });
 
 chatInput.addEventListener('keydown', function(e) {
+    if (e.keyCode === 229 || e.isComposing || isComposing) return;
+
+    if (isSlashMenuVisible()) {
+        if (e.key === 'ArrowDown') {
+            e.preventDefault();
+            slashActiveIdx = Math.min(slashActiveIdx + 1, slashFiltered.length - 1);
+            renderSlashItems();
+            return;
+        }
+        if (e.key === 'ArrowUp') {
+            e.preventDefault();
+            slashActiveIdx = Math.max(slashActiveIdx - 1, 0);
+            renderSlashItems();
+            return;
+        }
+        if (e.key === 'Enter' && !e.shiftKey && !e.ctrlKey) {
+            e.preventDefault();
+            selectSlashCommand(slashActiveIdx);
+            return;
+        }
+        if (e.key === 'Escape') {
+            e.preventDefault();
+            hideSlashMenu();
+            return;
+        }
+        if (e.key === 'Tab') {
+            e.preventDefault();
+            selectSlashCommand(slashActiveIdx);
+            return;
+        }
+    }
+
+    // Arrow-key history recall (only when input is empty or already browsing history)
+    if (e.key === 'ArrowUp' && inputHistory.length > 0 && !isSlashMenuVisible()) {
+        const curVal = this.value.trim();
+        const isSingleLine = !this.value.includes('\n');
+        if (isSingleLine && (curVal === '' || historyIdx >= 0)) {
+            e.preventDefault();
+            if (historyIdx < 0) {
+                historySavedDraft = this.value;
+                historyIdx = inputHistory.length - 1;
+            } else if (historyIdx > 0) {
+                historyIdx--;
+            }
+            this.value = inputHistory[historyIdx];
+            slashJustSelected = true;
+            this.dispatchEvent(new Event('input'));
+            hideSlashMenu();
+            this.selectionStart = this.selectionEnd = this.value.length;
+            return;
+        }
+    }
+    if (e.key === 'ArrowDown' && historyIdx >= 0 && !isSlashMenuVisible()) {
+        const isSingleLine = !this.value.includes('\n');
+        if (isSingleLine) {
+            e.preventDefault();
+            if (historyIdx < inputHistory.length - 1) {
+                historyIdx++;
+                this.value = inputHistory[historyIdx];
+            } else {
+                historyIdx = -1;
+                this.value = historySavedDraft;
+                historySavedDraft = '';
+            }
+            slashJustSelected = true;
+            this.dispatchEvent(new Event('input'));
+            hideSlashMenu();
+            this.selectionStart = this.selectionEnd = this.value.length;
+            return;
+        }
+    }
+
     if ((e.ctrlKey || e.shiftKey) && e.key === 'Enter') {
         const start = this.selectionStart;
         const end = this.selectionEnd;
@@ -329,12 +631,16 @@ chatInput.addEventListener('keydown', function(e) {
         this.selectionStart = this.selectionEnd = start + 1;
         this.dispatchEvent(new Event('input'));
         e.preventDefault();
-    } else if (e.key === 'Enter' && !e.shiftKey && !e.ctrlKey && !isComposing) {
+    } else if (e.key === 'Enter' && !e.shiftKey && !e.ctrlKey) {
         sendMessage();
         e.preventDefault();
     }
 });
 
+chatInput.addEventListener('blur', () => {
+    setTimeout(hideSlashMenu, 150);
+});
+
 document.querySelectorAll('.example-card').forEach(card => {
     card.addEventListener('click', () => {
         const textEl = card.querySelector('[data-i18n*="text"]');
@@ -348,25 +654,43 @@ document.querySelectorAll('.example-card').forEach(card => {
 
 function sendMessage() {
     const text = chatInput.value.trim();
-    if (!text) return;
+    if (!text && pendingAttachments.length === 0) return;
+
+    if (text) {
+        inputHistory.push(text);
+        historyIdx = -1;
+        historySavedDraft = '';
+    }
 
     const ws = document.getElementById('welcome-screen');
     if (ws) ws.remove();
 
     const timestamp = new Date();
-    addUserMessage(text, timestamp);
+    const attachments = [...pendingAttachments];
+    addUserMessage(text, timestamp, attachments);
 
     const loadingEl = addLoadingIndicator();
 
     chatInput.value = '';
     chatInput.style.height = '42px';
     chatInput.style.overflowY = 'hidden';
+    pendingAttachments = [];
+    renderAttachmentPreview();
     sendBtn.disabled = true;
 
+    const body = { session_id: sessionId, message: text, stream: true, timestamp: timestamp.toISOString() };
+    if (attachments.length > 0) {
+        body.attachments = attachments.map(a => ({
+            file_path: a.file_path,
+            file_name: a.file_name,
+            file_type: a.file_type,
+        }));
+    }
+
     fetch('/message', {
         method: 'POST',
         headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ session_id: sessionId, message: text, stream: true, timestamp: timestamp.toISOString() })
+        body: JSON.stringify(body)
     })
     .then(r => r.json())
     .then(data => {
@@ -576,13 +900,27 @@ function startPolling() {
     poll();
 }
 
-function createUserMessageEl(content, timestamp) {
+function createUserMessageEl(content, timestamp, attachments) {
     const el = document.createElement('div');
     el.className = 'flex justify-end px-4 sm:px-6 py-3';
+
+    let attachHtml = '';
+    if (attachments && attachments.length > 0) {
+        const items = attachments.map(a => {
+            if (a.file_type === 'image') {
+                return `<img src="${a.preview_url}" alt="${escapeHtml(a.file_name)}" class="user-msg-image">`;
+            }
+            const icon = a.file_type === 'video' ? 'fa-film' : 'fa-file-alt';
+            return `<div class="user-msg-file"><i class="fas ${icon}"></i> ${escapeHtml(a.file_name)}</div>`;
+        }).join('');
+        attachHtml = `<div class="user-msg-attachments">${items}</div>`;
+    }
+
+    const textHtml = content ? renderMarkdown(content) : '';
     el.innerHTML = `
         <div class="max-w-[75%] sm:max-w-[60%]">
-            <div class="bg-primary-400 text-white rounded-2xl px-4 py-2.5 text-sm leading-relaxed msg-content">
-                ${renderMarkdown(content)}
+            <div class="bg-primary-400 text-white rounded-2xl px-4 py-2.5 text-sm leading-relaxed msg-content user-bubble">
+                ${attachHtml}${textHtml}
             </div>
             <div class="text-xs text-slate-400 dark:text-slate-500 mt-1.5 text-right">${formatTime(timestamp)}</div>
         </div>
@@ -637,8 +975,8 @@ function createBotMessageEl(content, timestamp, requestId, toolCalls) {
     return el;
 }
 
-function addUserMessage(content, timestamp) {
-    const el = createUserMessageEl(content, timestamp);
+function addUserMessage(content, timestamp, attachments) {
+    const el = createUserMessageEl(content, timestamp, attachments);
     messagesDiv.appendChild(el);
     scrollChatToBottom();
 }
@@ -815,6 +1153,11 @@ function escapeHtml(str) {
     return div.innerHTML;
 }
 
+function ChannelsHandler_maskSecret(val) {
+    if (!val || val.length <= 8) return val;
+    return val.slice(0, 4) + '*'.repeat(val.length - 8) + val.slice(-4);
+}
+
 function formatToolArgs(args) {
     if (!args || Object.keys(args).length === 0) return '(none)';
     try {
@@ -909,7 +1252,10 @@ function initConfigView(data) {
     const providerEl = document.getElementById('cfg-provider');
     const providerOpts = Object.entries(configProviders).map(([pid, p]) => ({ value: pid, label: p.label }));
 
-    const detected = detectProvider(configCurrentModel);
+    // if use_linkai is enabled, always select linkai as the provider
+    // Otherwise prefer bot_type from config, fall back to model-based detection
+    const detected = data.use_linkai ? 'linkai'
+        : (data.bot_type && configProviders[data.bot_type] ? data.bot_type : detectProvider(configCurrentModel));
     cfgProviderValue = detected || (providerOpts[0] ? providerOpts[0].value : '');
 
     initDropdown(providerEl, providerOpts, cfgProviderValue, onProviderChange);
@@ -1063,6 +1409,11 @@ function saveModelConfig() {
     const updates = { model: model };
     const p = configProviders[cfgProviderValue];
     updates.use_linkai = (cfgProviderValue === 'linkai');
+    if (cfgProviderValue === 'linkai') {
+        updates.bot_type = '';
+    } else {
+        updates.bot_type = cfgProviderValue;
+    }
     if (p && p.api_base_key) {
         const base = document.getElementById('cfg-api-base').value.trim();
         if (base) updates[p.api_base_key] = base;
@@ -1429,6 +1780,8 @@ function loadChannelsView() {
 }
 
 function renderActiveChannels() {
+    stopWeixinQrPoll();
+    stopWeixinStatusPoll();
     const container = document.getElementById('channels-content');
     container.innerHTML = '';
     closeAddChannelPanel();
@@ -1454,17 +1807,30 @@ function renderActiveChannels() {
         card.id = `channel-card-${ch.name}`;
 
         const fieldsHtml = buildChannelFieldsHtml(ch.name, ch.fields || []);
+        const hasFields = (ch.fields || []).length > 0;
+
+        const weixinWaiting = ch.name === 'weixin' && ch.login_status && ch.login_status !== 'logged_in';
+        let statusDot, statusText;
+        if (weixinWaiting) {
+            statusDot = 'bg-amber-400 animate-pulse';
+            statusText = ch.login_status === 'scanned'
+                ? `<span class="text-xs text-primary-500">${t('weixin_scan_scanned')}</span>`
+                : `<span class="text-xs text-amber-500">${t('weixin_scan_waiting')}</span>`;
+        } else {
+            statusDot = 'bg-primary-400';
+            statusText = `<span class="text-xs text-primary-500">${t('channels_connected')}</span>`;
+        }
 
         card.innerHTML = `
-            <div class="flex items-center gap-4 mb-5">
+            <div class="flex items-center gap-4${hasFields || weixinWaiting ? ' mb-5' : ''}">
                 <div class="w-10 h-10 rounded-xl bg-${ch.color}-50 dark:bg-${ch.color}-900/20 flex items-center justify-center flex-shrink-0">
                     <i class="fas ${ch.icon} text-${ch.color}-500 text-base"></i>
                 </div>
                 <div class="flex-1 min-w-0">
                     <div class="flex items-center gap-2">
                         <span class="font-semibold text-slate-800 dark:text-slate-100">${escapeHtml(label)}</span>
-                        <span class="w-2 h-2 rounded-full bg-primary-400"></span>
-                        <span class="text-xs text-primary-500">${t('channels_connected')}</span>
+                        <span class="w-2 h-2 rounded-full ${statusDot}"></span>
+                        ${statusText}
                     </div>
                     <p class="text-xs text-slate-500 dark:text-slate-400 mt-0.5 font-mono">${escapeHtml(ch.name)}</p>
                 </div>
@@ -1476,7 +1842,14 @@ function renderActiveChannels() {
                     ${t('channels_disconnect')}
                 </button>
             </div>
-            <div class="space-y-4">
+            ${weixinWaiting ? `<div id="weixin-active-qr" class="flex flex-col items-center py-2">
+                <button onclick="showWeixinActiveQr()"
+                    class="px-4 py-2 rounded-lg bg-primary-500 hover:bg-primary-600 text-white text-sm font-medium
+                           cursor-pointer transition-colors duration-150">
+                    ${t('weixin_scan_title')}
+                </button>
+            </div>` : ''}
+            ${hasFields ? `<div class="space-y-4">
                 ${fieldsHtml}
                 <div class="flex items-center justify-end gap-3 pt-1">
                     <span id="ch-status-${ch.name}" class="text-xs text-primary-500 opacity-0 transition-opacity duration-300"></span>
@@ -1485,10 +1858,14 @@ function renderActiveChannels() {
                                cursor-pointer transition-colors duration-150 disabled:opacity-50 disabled:cursor-not-allowed"
                         id="ch-save-${ch.name}">${t('channels_save')}</button>
                 </div>
-            </div>`;
+            </div>` : ''}`;
 
         container.appendChild(card);
         bindSecretFieldEvents(card);
+
+        if (weixinWaiting) {
+            startWeixinActiveStatusPoll();
+        }
     });
 }
 
@@ -1620,6 +1997,9 @@ function openAddChannelPanel() {
     const activeNames = new Set(channelsData.filter(c => c.active).map(c => c.name));
     const available = channelsData.filter(c => !activeNames.has(c.name));
 
+    const content = document.getElementById('channels-content');
+    if (activeNames.size === 0 && content) content.classList.add('hidden');
+
     if (available.length === 0) {
         panel.innerHTML = `<div class="bg-white dark:bg-[#1A1A1A] rounded-xl border border-slate-200 dark:border-white/10 p-6 text-center">
             <p class="text-sm text-slate-500 dark:text-slate-400">${currentLang === 'zh' ? '所有通道均已接入' : 'All channels are already connected'}</p>
@@ -1674,14 +2054,18 @@ function openAddChannelPanel() {
 }
 
 function closeAddChannelPanel() {
+    stopWeixinQrPoll();
     const panel = document.getElementById('channels-add-panel');
     if (panel) {
         panel.classList.add('hidden');
         panel.innerHTML = '';
     }
+    const content = document.getElementById('channels-content');
+    if (content) content.classList.remove('hidden');
 }
 
 function onAddChannelSelect(chName) {
+    stopWeixinQrPoll();
     const fieldsContainer = document.getElementById('add-channel-fields');
     const actions = document.getElementById('add-channel-actions');
 
@@ -1691,6 +2075,16 @@ function onAddChannelSelect(chName) {
         return;
     }
 
+    if (chName === 'weixin') {
+        actions.classList.add('hidden');
+        fieldsContainer.innerHTML = `
+            <div id="weixin-qr-panel" class="flex flex-col items-center py-4">
+                <p class="text-sm text-slate-500 dark:text-slate-400 mb-4">${t('weixin_scan_loading')}</p>
+            </div>`;
+        startWeixinQrLogin();
+        return;
+    }
+
     const ch = channelsData.find(c => c.name === chName);
     if (!ch) return;
 
@@ -1728,7 +2122,14 @@ function submitAddChannel() {
     .then(data => {
         if (data.status === 'success') {
             const ch = channelsData.find(c => c.name === chName);
-            if (ch) ch.active = true;
+            if (ch) {
+                ch.active = true;
+                (ch.fields || []).forEach(f => {
+                    if (updates[f.key] !== undefined) {
+                        f.value = f.type === 'secret' ? ChannelsHandler_maskSecret(updates[f.key]) : updates[f.key];
+                    }
+                });
+            }
             renderActiveChannels();
         } else {
             if (btn) { btn.disabled = false; btn.textContent = t('channels_connect_btn'); }
@@ -1739,6 +2140,172 @@ function submitAddChannel() {
     });
 }
 
+// =====================================================================
+// WeChat QR Login
+// =====================================================================
+let _weixinQrPollTimer = null;
+let _weixinStatusPollTimer = null;
+
+function stopWeixinStatusPoll() {
+    if (_weixinStatusPollTimer) {
+        clearTimeout(_weixinStatusPollTimer);
+        _weixinStatusPollTimer = null;
+    }
+}
+
+function startWeixinActiveStatusPoll() {
+    stopWeixinStatusPoll();
+    _weixinStatusPollTimer = setTimeout(() => {
+        fetch('/api/channels').then(r => r.json()).then(data => {
+            if (data.status !== 'success') return;
+            const wx = (data.channels || []).find(c => c.name === 'weixin');
+            if (!wx || !wx.active) return;
+            if (wx.login_status === 'logged_in') {
+                channelsData = data.channels;
+                renderActiveChannels();
+            } else {
+                const ch = channelsData.find(c => c.name === 'weixin');
+                if (ch) ch.login_status = wx.login_status;
+                startWeixinActiveStatusPoll();
+            }
+        }).catch(() => { startWeixinActiveStatusPoll(); });
+    }, 3000);
+}
+
+function showWeixinActiveQr() {
+    const container = document.getElementById('weixin-active-qr');
+    if (!container) return;
+    container.innerHTML = `
+        <div id="weixin-qr-panel" class="flex flex-col items-center py-2">
+            <p class="text-sm text-slate-500 dark:text-slate-400 mb-4">${t('weixin_scan_loading')}</p>
+        </div>`;
+    stopWeixinStatusPoll();
+    startWeixinQrLogin();
+}
+
+function stopWeixinQrPoll() {
+    if (_weixinQrPollTimer) {
+        clearTimeout(_weixinQrPollTimer);
+        _weixinQrPollTimer = null;
+    }
+}
+
+function startWeixinQrLogin() {
+    stopWeixinQrPoll();
+    fetch('/api/weixin/qrlogin')
+        .then(r => r.json())
+        .then(data => {
+            const panel = document.getElementById('weixin-qr-panel');
+            if (!panel) return;
+            if (data.status !== 'success') {
+                panel.innerHTML = `<p class="text-sm text-red-500">${t('weixin_scan_fail')}: ${data.message || ''}</p>`;
+                return;
+            }
+            renderWeixinQr(data.qr_image || data.qrcode_url, 'waiting');
+            if (data.source === 'channel') {
+                startWeixinActiveStatusPoll();
+            } else {
+                pollWeixinQrStatus();
+            }
+        })
+        .catch(() => {
+            const panel = document.getElementById('weixin-qr-panel');
+            if (panel) panel.innerHTML = `<p class="text-sm text-red-500">${t('weixin_scan_fail')}</p>`;
+        });
+}
+
+function renderWeixinQr(qrcodeUrl, status) {
+    const panel = document.getElementById('weixin-qr-panel');
+    if (!panel) return;
+
+    let statusText = t('weixin_scan_waiting');
+    let statusColor = 'text-slate-500 dark:text-slate-400';
+    if (status === 'scanned') {
+        statusText = t('weixin_scan_scanned');
+        statusColor = 'text-primary-500';
+    } else if (status === 'expired') {
+        statusText = t('weixin_scan_expired');
+        statusColor = 'text-amber-500';
+    } else if (status === 'confirmed') {
+        statusText = t('weixin_scan_success');
+        statusColor = 'text-primary-500';
+    }
+
+    panel.innerHTML = `
+        <div class="flex flex-col items-center">
+            <p class="text-sm font-medium text-slate-700 dark:text-slate-200 mb-1">${t('weixin_scan_title')}</p>
+            <p class="text-xs text-slate-400 dark:text-slate-500 mb-4">${t('weixin_scan_desc')}</p>
+            <div class="bg-white p-3 rounded-xl shadow-sm border border-slate-100 dark:border-slate-700 mb-3">
+                <img src="${escapeHtml(qrcodeUrl)}" alt="QR Code" class="w-52 h-52" style="image-rendering: pixelated;"/>
+            </div>
+            <p class="text-xs ${statusColor} mb-1">${statusText}</p>
+            <p class="text-xs text-slate-400 dark:text-slate-500">${t('weixin_qr_tip')}</p>
+        </div>`;
+}
+
+function pollWeixinQrStatus() {
+    _weixinQrPollTimer = setTimeout(() => {
+        fetch('/api/weixin/qrlogin', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ action: 'poll' })
+        })
+        .then(r => r.json())
+        .then(data => {
+            const panel = document.getElementById('weixin-qr-panel');
+            if (!panel) { stopWeixinQrPoll(); return; }
+
+            if (data.status !== 'success') {
+                pollWeixinQrStatus();
+                return;
+            }
+
+            const qrStatus = data.qr_status;
+            if (qrStatus === 'confirmed') {
+                renderWeixinQr('', 'confirmed');
+                panel.innerHTML = `
+                    <div class="flex flex-col items-center py-4">
+                        <div class="w-12 h-12 rounded-full bg-primary-50 dark:bg-primary-900/30 flex items-center justify-center mb-3">
+                            <i class="fas fa-check text-primary-500 text-lg"></i>
+                        </div>
+                        <p class="text-sm font-medium text-primary-600 dark:text-primary-400">${t('weixin_scan_success')}</p>
+                    </div>`;
+                connectWeixinAfterQr();
+            } else if (qrStatus === 'expired' && (data.qr_image || data.qrcode_url)) {
+                renderWeixinQr(data.qr_image || data.qrcode_url, 'waiting');
+                pollWeixinQrStatus();
+            } else if (qrStatus === 'scaned') {
+                const img = panel.querySelector('img');
+                const currentSrc = img ? img.src : '';
+                renderWeixinQr(currentSrc, 'scanned');
+                pollWeixinQrStatus();
+            } else {
+                pollWeixinQrStatus();
+            }
+        })
+        .catch(() => {
+            pollWeixinQrStatus();
+        });
+    }, 2000);
+}
+
+function connectWeixinAfterQr() {
+    fetch('/api/channels', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ action: 'connect', channel: 'weixin', config: {} })
+    })
+    .then(r => r.json())
+    .then(data => {
+        if (data.status === 'success') {
+            const ch = channelsData.find(c => c.name === 'weixin');
+            if (ch) ch.active = true;
+            setTimeout(() => renderActiveChannels(), 1500);
+        }
+    })
+    .catch(() => {});
+}
+
 // =====================================================================
 // Scheduler View
 // =====================================================================
@@ -1856,7 +2423,12 @@ navigateTo = function(viewId) {
 // =====================================================================
 applyTheme();
 applyI18n();
-document.getElementById('sidebar-version').textContent = `CowAgent ${APP_VERSION}`;
+fetch('/api/version').then(r => r.json()).then(data => {
+    APP_VERSION = `v${data.version}`;
+    document.getElementById('sidebar-version').textContent = `CowAgent ${APP_VERSION}`;
+}).catch(() => {
+    document.getElementById('sidebar-version').textContent = 'CowAgent';
+});
 chatInput.focus();
 
 // Re-enable color transition AFTER first paint so the theme applied in <head>

channel/web/web_channel.py

@@ -20,6 +20,17 @@ from common.log import logger
 from common.singleton import singleton
 from config import conf
 
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".svg"}
+VIDEO_EXTENSIONS = {".mp4", ".webm", ".avi", ".mov", ".mkv"}
+
+
+def _get_upload_dir() -> str:
+    from common.utils import expand_path
+    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 WebMessage(ChatMessage):
     def __init__(
@@ -152,10 +163,53 @@ class WebChannel(ChatChannel):
 
         return on_event
 
+    def upload_file(self):
+        """Handle file upload via multipart/form-data. Save to workspace/tmp/ and return metadata."""
+        try:
+            params = web.input(file={}, session_id="")
+            file_obj = params.get("file")
+            session_id = params.get("session_id", "")
+            if file_obj is None or not hasattr(file_obj, "filename") or not file_obj.filename:
+                return json.dumps({"status": "error", "message": "No file uploaded"})
+
+            upload_dir = _get_upload_dir()
+
+            original_name = file_obj.filename
+            ext = os.path.splitext(original_name)[1].lower()
+            safe_name = f"web_{uuid.uuid4().hex[:8]}{ext}"
+            save_path = os.path.join(upload_dir, safe_name)
+
+            with open(save_path, "wb") as f:
+                f.write(file_obj.read() if hasattr(file_obj, "read") else file_obj.value)
+
+            if ext in IMAGE_EXTENSIONS:
+                file_type = "image"
+            elif ext in VIDEO_EXTENSIONS:
+                file_type = "video"
+            else:
+                file_type = "file"
+
+            preview_url = f"/uploads/{safe_name}"
+
+            logger.info(f"[WebChannel] File uploaded: {original_name} -> {save_path} ({file_type})")
+
+            return json.dumps({
+                "status": "success",
+                "file_path": save_path,
+                "file_name": original_name,
+                "file_type": file_type,
+                "preview_url": preview_url,
+            }, ensure_ascii=False)
+
+        except Exception as e:
+            logger.error(f"[WebChannel] File upload error: {e}", exc_info=True)
+            return json.dumps({"status": "error", "message": str(e)})
+
     def post_message(self):
         """
         Handle incoming messages from users via POST request.
         Returns a request_id for tracking this specific request.
+        Supports optional attachments (file paths from /upload).
         """
         try:
             data = web.data()
@@ -163,6 +217,25 @@ class WebChannel(ChatChannel):
             session_id = json_data.get('session_id', f'session_{int(time.time())}')
             prompt = json_data.get('message', '')
             use_sse = json_data.get('stream', True)
+            attachments = json_data.get('attachments', [])
+
+            # Append file references to the prompt (same format as QQ channel)
+            if attachments:
+                file_refs = []
+                for att in attachments:
+                    ftype = att.get("file_type", "file")
+                    fpath = att.get("file_path", "")
+                    if not fpath:
+                        continue
+                    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}]")
+                if file_refs:
+                    prompt = prompt + "\n" + "\n".join(file_refs)
+                    logger.info(f"[WebChannel] Attached {len(file_refs)} file(s) to message")
 
             request_id = self._generate_request_id()
             self.request_to_session[request_id] = session_id
@@ -280,13 +353,15 @@ class WebChannel(ChatChannel):
         # 打印可用渠道类型提示
         logger.info(
             "[WebChannel] 全部可用通道如下，可修改 config.json 配置文件中的 channel_type 字段进行切换，多个通道用逗号分隔：")
-        logger.info("[WebChannel]   1. web              - 网页")
-        logger.info("[WebChannel]   2. terminal         - 终端")
-        logger.info("[WebChannel]   3. feishu           - 飞书")
-        logger.info("[WebChannel]   4. dingtalk         - 钉钉")
-        logger.info("[WebChannel]   5. wechatcom_app    - 企微自建应用")
-        logger.info("[WebChannel]   6. wechatmp         - 个人公众号")
-        logger.info("[WebChannel]   7. wechatmp_service - 企业公众号")
+        logger.info("[WebChannel]   1. weixin           - 微信")
+        logger.info("[WebChannel]   2. web              - 网页")
+        logger.info("[WebChannel]   3. terminal         - 终端")
+        logger.info("[WebChannel]   4. feishu           - 飞书")
+        logger.info("[WebChannel]   5. dingtalk         - 钉钉")
+        logger.info("[WebChannel]   6. wecom_bot        - 企微智能机器人")
+        logger.info("[WebChannel]   7. wechatcom_app    - 企微自建应用")
+        logger.info("[WebChannel]   8. wechatmp         - 个人公众号")
+        logger.info("[WebChannel]   9. wechatmp_service - 企业公众号")
         logger.info("[WebChannel] ✅ Web控制台已运行")
         logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}")
         logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port} (请将YOUR_IP替换为服务器IP)")
@@ -300,11 +375,14 @@ class WebChannel(ChatChannel):
         urls = (
             '/', 'RootHandler',
             '/message', 'MessageHandler',
+            '/upload', 'UploadHandler',
+            '/uploads/(.*)', 'UploadsHandler',
             '/poll', 'PollHandler',
             '/stream', 'StreamHandler',
             '/chat', 'ChatHandler',
             '/config', 'ConfigHandler',
             '/api/channels', 'ChannelsHandler',
+            '/api/weixin/qrlogin', 'WeixinQrHandler',
             '/api/tools', 'ToolsHandler',
             '/api/skills', 'SkillsHandler',
             '/api/memory', 'MemoryHandler',
@@ -312,6 +390,7 @@ class WebChannel(ChatChannel):
             '/api/scheduler', 'SchedulerHandler',
             '/api/history', 'HistoryHandler',
             '/api/logs', 'LogsHandler',
+            '/api/version', 'VersionHandler',
             '/assets/(.*)', 'AssetsHandler',
         )
         app = web.application(urls, globals(), autoreload=False)
@@ -356,6 +435,34 @@ class MessageHandler:
         return WebChannel().post_message()
 
 
+class UploadHandler:
+    def POST(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        return WebChannel().upload_file()
+
+
+class UploadsHandler:
+    def GET(self, file_name):
+        """Serve uploaded files from workspace/tmp/ for preview."""
+        try:
+            upload_dir = _get_upload_dir()
+            full_path = os.path.normpath(os.path.join(upload_dir, file_name))
+            if not os.path.abspath(full_path).startswith(os.path.abspath(upload_dir)):
+                raise web.notfound()
+            if not os.path.isfile(full_path):
+                raise web.notfound()
+            content_type = mimetypes.guess_type(full_path)[0] or "application/octet-stream"
+            web.header('Content-Type', content_type)
+            web.header('Cache-Control', 'public, max-age=86400')
+            with open(full_path, 'rb') as f:
+                return f.read()
+        except web.HTTPError:
+            raise
+        except Exception as e:
+            logger.error(f"[WebChannel] Error serving upload: {e}")
+            raise web.notfound()
+
+
 class PollHandler:
     def POST(self):
         return WebChannel().poll_response()
@@ -387,14 +494,14 @@ class ChatHandler:
 class ConfigHandler:
 
     _RECOMMENDED_MODELS = [
-        const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING,
-        const.GLM_5, const.GLM_4_7,
+        const.MINIMAX_M2_7, const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING,
+        const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7,
         const.QWEN3_MAX, const.QWEN35_PLUS,
         const.KIMI_K2_5, const.KIMI_K2,
         const.DOUBAO_SEED_2_PRO, const.DOUBAO_SEED_2_CODE,
         const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET,
-        const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE,
-        const.GPT_5, const.GPT_41, const.GPT_4o,
+        const.GEMINI_31_FLASH_LITE_PRE, const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE,
+        const.GPT_54, const.GPT_54_MINI, const.GPT_54_NANO, const.GPT_5, const.GPT_41, const.GPT_4o,
         const.DEEPSEEK_CHAT, const.DEEPSEEK_REASONER,
     ]
 
@@ -404,14 +511,14 @@ class ConfigHandler:
             "api_key_field": "minimax_api_key",
             "api_base_key": None,
             "api_base_default": None,
-            "models": [const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING],
+            "models": [const.MINIMAX_M2_7, const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING],
         }),
-        ("glm-4", {
+        ("zhipu", {
             "label": "智谱AI",
             "api_key_field": "zhipu_ai_api_key",
             "api_base_key": "zhipu_ai_api_base",
             "api_base_default": "https://open.bigmodel.cn/api/paas/v4",
-            "models": [const.GLM_5, const.GLM_4_7],
+            "models": [const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7],
         }),
         ("dashscope", {
             "label": "通义千问",
@@ -446,20 +553,20 @@ class ConfigHandler:
             "api_key_field": "gemini_api_key",
             "api_base_key": "gemini_api_base",
             "api_base_default": "https://generativelanguage.googleapis.com",
-            "models": [const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE],
+            "models": [const.GEMINI_31_FLASH_LITE_PRE, const.GEMINI_31_PRO_PRE, const.GEMINI_3_FLASH_PRE],
         }),
-        ("openAI", {
+        ("openai", {
             "label": "OpenAI",
             "api_key_field": "open_ai_api_key",
             "api_base_key": "open_ai_api_base",
             "api_base_default": "https://api.openai.com/v1",
-            "models": [const.GPT_5, const.GPT_41, const.GPT_4o],
+            "models": [const.GPT_54, const.GPT_54_MINI, const.GPT_54_NANO, const.GPT_5, const.GPT_41, const.GPT_4o],
         }),
         ("deepseek", {
             "label": "DeepSeek",
-            "api_key_field": "open_ai_api_key",
-            "api_base_key": None,
-            "api_base_default": None,
+            "api_key_field": "deepseek_api_key",
+            "api_base_key": "deepseek_api_base",
+            "api_base_default": "https://api.deepseek.com/v1",
             "models": [const.DEEPSEEK_CHAT, const.DEEPSEEK_REASONER],
         }),
         ("linkai", {
@@ -472,10 +579,10 @@ class ConfigHandler:
     ])
 
     EDITABLE_KEYS = {
-        "model", "use_linkai",
-        "open_ai_api_base", "claude_api_base", "gemini_api_base",
+        "model", "bot_type", "use_linkai",
+        "open_ai_api_base", "deepseek_api_base", "claude_api_base", "gemini_api_base",
         "zhipu_ai_api_base", "moonshot_base_url", "ark_base_url",
-        "open_ai_api_key", "claude_api_key", "gemini_api_key",
+        "open_ai_api_key", "deepseek_api_key", "claude_api_key", "gemini_api_key",
         "zhipu_ai_api_key", "dashscope_api_key", "moonshot_api_key",
         "ark_api_key", "minimax_api_key", "linkai_api_key",
         "agent_max_context_tokens", "agent_max_context_turns", "agent_max_steps",
@@ -522,9 +629,11 @@ class ConfigHandler:
                 "use_agent": use_agent,
                 "title": title,
                 "model": local_config.get("model", ""),
+                "bot_type": "openai" if local_config.get("bot_type") == "chatGPT" else local_config.get("bot_type", ""),
+                "use_linkai": bool(local_config.get("use_linkai", False)),
                 "channel_type": local_config.get("channel_type", ""),
                 "agent_max_context_tokens": local_config.get("agent_max_context_tokens", 50000),
-                "agent_max_context_turns": local_config.get("agent_max_context_turns", 30),
+                "agent_max_context_turns": local_config.get("agent_max_context_turns", 20),
                 "agent_max_steps": local_config.get("agent_max_steps", 15),
                 "api_bases": api_bases,
                 "api_keys": api_keys_masked,
@@ -580,6 +689,12 @@ class ChannelsHandler:
     """API for managing external channel configurations (feishu, dingtalk, etc)."""
 
     CHANNEL_DEFS = OrderedDict([
+        ("weixin", {
+            "label": {"zh": "微信", "en": "WeChat"},
+            "icon": "fa-comment",
+            "color": "emerald",
+            "fields": [],
+        }),
         ("feishu", {
             "label": {"zh": "飞书", "en": "Feishu"},
             "icon": "fa-paper-plane",
@@ -600,6 +715,24 @@ class ChannelsHandler:
                 {"key": "dingtalk_client_secret", "label": "Client Secret", "type": "secret"},
             ],
         }),
+        ("wecom_bot", {
+            "label": {"zh": "企微智能机器人", "en": "WeCom Bot"},
+            "icon": "fa-robot",
+            "color": "emerald",
+            "fields": [
+                {"key": "wecom_bot_id", "label": "Bot ID", "type": "text"},
+                {"key": "wecom_bot_secret", "label": "Secret", "type": "secret"},
+            ],
+        }),
+        ("qq", {
+            "label": {"zh": "QQ 机器人", "en": "QQ Bot"},
+            "icon": "fa-comment",
+            "color": "blue",
+            "fields": [
+                {"key": "qq_app_id", "label": "App ID", "type": "text"},
+                {"key": "qq_app_secret", "label": "App Secret", "type": "secret"},
+            ],
+        }),
         ("wechatcom_app", {
             "label": {"zh": "企微自建应用", "en": "WeCom App"},
             "icon": "fa-building",
@@ -627,6 +760,20 @@ class ChannelsHandler:
         }),
     ])
 
+    @staticmethod
+    def _get_weixin_login_status() -> str:
+        try:
+            import sys
+            app_module = sys.modules.get('__main__') or sys.modules.get('app')
+            mgr = getattr(app_module, '_channel_mgr', None) if app_module else None
+            if mgr:
+                ch = mgr.get_channel("weixin")
+                if ch and hasattr(ch, 'login_status'):
+                    return ch.login_status
+        except Exception:
+            pass
+        return "unknown"
+
     @staticmethod
     def _mask_secret(value: str) -> str:
         if not value or len(value) <= 8:
@@ -666,14 +813,17 @@ class ChannelsHandler:
                         "value": display_val,
                         "default": f.get("default", ""),
                     })
-                channels.append({
+                ch_info = {
                     "name": ch_name,
                     "label": ch_def["label"],
                     "icon": ch_def["icon"],
                     "color": ch_def["color"],
                     "active": ch_name in active_channels,
                     "fields": fields_out,
-                })
+                }
+                if ch_name == "weixin" and ch_name in active_channels:
+                    ch_info["login_status"] = self._get_weixin_login_status()
+                channels.append(ch_info)
             return json.dumps({"status": "success", "channels": channels}, ensure_ascii=False)
         except Exception as e:
             logger.error(f"[WebChannel] Channels API error: {e}")
@@ -893,6 +1043,157 @@ class ChannelsHandler:
         }, ensure_ascii=False)
 
 
+class WeixinQrHandler:
+    """Handle WeChat QR code login from the web console.
+
+    GET  /api/weixin/qrlogin          → fetch a new QR code
+    POST /api/weixin/qrlogin          → poll QR status or start channel after login
+    """
+
+    _qr_state = {}
+
+    @staticmethod
+    def _qr_to_data_uri(data: str) -> str:
+        """Generate a QR code as a PNG data URI."""
+        try:
+            import qrcode as qr_lib
+            import io
+            import base64
+            qr = qr_lib.QRCode(error_correction=qr_lib.constants.ERROR_CORRECT_L, box_size=6, border=2)
+            qr.add_data(data)
+            qr.make(fit=True)
+            img = qr.make_image(fill_color="black", back_color="white")
+            buf = io.BytesIO()
+            img.save(buf, format="PNG")
+            b64 = base64.b64encode(buf.getvalue()).decode("ascii")
+            return f"data:image/png;base64,{b64}"
+        except ImportError:
+            return ""
+
+    @staticmethod
+    def _get_running_channel():
+        try:
+            import sys
+            app_module = sys.modules.get('__main__') or sys.modules.get('app')
+            mgr = getattr(app_module, '_channel_mgr', None) if app_module else None
+            if mgr:
+                return mgr.get_channel("weixin")
+        except Exception:
+            pass
+        return None
+
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            running_ch = self._get_running_channel()
+            if running_ch and hasattr(running_ch, '_current_qr_url') and running_ch._current_qr_url:
+                qr_image = self._qr_to_data_uri(running_ch._current_qr_url)
+                return json.dumps({
+                    "status": "success",
+                    "qrcode_url": running_ch._current_qr_url,
+                    "qr_image": qr_image,
+                    "source": "channel",
+                })
+
+            from channel.weixin.weixin_api import WeixinApi, DEFAULT_BASE_URL
+            base_url = conf().get("weixin_base_url", DEFAULT_BASE_URL)
+            api = WeixinApi(base_url=base_url)
+            qr_resp = api.fetch_qr_code()
+            qrcode = qr_resp.get("qrcode", "")
+            qrcode_url = qr_resp.get("qrcode_img_content", "")
+            if not qrcode:
+                return json.dumps({"status": "error", "message": "No QR code returned"})
+            qr_image = self._qr_to_data_uri(qrcode_url)
+            WeixinQrHandler._qr_state = {
+                "qrcode": qrcode,
+                "qrcode_url": qrcode_url,
+                "base_url": base_url,
+            }
+            return json.dumps({"status": "success", "qrcode_url": qrcode_url, "qr_image": qr_image})
+        except Exception as e:
+            logger.error(f"[WebChannel] WeixinQr GET error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+    def POST(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            body = json.loads(web.data())
+            action = body.get("action", "poll")
+
+            if action == "poll":
+                return self._poll_status()
+            elif action == "refresh":
+                return self.GET()
+            else:
+                return json.dumps({"status": "error", "message": f"unknown action: {action}"})
+        except Exception as e:
+            logger.error(f"[WebChannel] WeixinQr POST error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+    def _poll_status(self):
+        state = WeixinQrHandler._qr_state
+        qrcode = state.get("qrcode", "")
+        base_url = state.get("base_url", "")
+        if not qrcode:
+            return json.dumps({"status": "error", "message": "No active QR session"})
+
+        from channel.weixin.weixin_api import WeixinApi, DEFAULT_BASE_URL
+        api = WeixinApi(base_url=base_url or DEFAULT_BASE_URL)
+        try:
+            status_resp = api.poll_qr_status(qrcode, timeout=10)
+        except Exception as e:
+            return json.dumps({"status": "error", "message": str(e)})
+
+        qr_status = status_resp.get("status", "wait")
+
+        if qr_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:
+                return json.dumps({"status": "error", "message": "Login confirmed but missing token"})
+
+            cred_path = os.path.expanduser(
+                conf().get("weixin_credentials_path", "~/.weixin_cow_credentials.json")
+            )
+            from channel.weixin.weixin_channel import _save_credentials
+            _save_credentials(cred_path, {
+                "token": bot_token,
+                "base_url": result_base_url,
+                "bot_id": bot_id,
+                "user_id": user_id,
+            })
+            conf()["weixin_token"] = bot_token
+            conf()["weixin_base_url"] = result_base_url
+
+            WeixinQrHandler._qr_state = {}
+            logger.info(f"[WebChannel] WeChat QR login confirmed: bot_id={bot_id}")
+
+            return json.dumps({
+                "status": "success",
+                "qr_status": "confirmed",
+                "bot_id": bot_id,
+            })
+
+        if qr_status == "expired":
+            new_resp = api.fetch_qr_code()
+            new_qrcode = new_resp.get("qrcode", "")
+            new_qrcode_url = new_resp.get("qrcode_img_content", "")
+            new_qr_image = self._qr_to_data_uri(new_qrcode_url)
+            WeixinQrHandler._qr_state["qrcode"] = new_qrcode
+            WeixinQrHandler._qr_state["qrcode_url"] = new_qrcode_url
+            return json.dumps({
+                "status": "success",
+                "qr_status": "expired",
+                "qrcode_url": new_qrcode_url,
+                "qr_image": new_qr_image,
+            })
+
+        return json.dumps({"status": "success", "qr_status": qr_status})
+
+
 def _get_workspace_root():
     """Resolve the agent workspace directory."""
     from common.utils import expand_path
@@ -1129,3 +1430,10 @@ class AssetsHandler:
         except Exception as e:
             logger.error(f"Error serving static file: {e}", exc_info=True)  # 添加更详细的错误信息
             raise web.notfound()
+
+
+class VersionHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        from cli import __version__
+        return json.dumps({"version": __version__})

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.cloud_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.cloud_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.cloud_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.cloud_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/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/wecom_bot/wecom_bot_channel.py

@@ -0,0 +1,768 @@
+"""
+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 '---'.
+        """
+        stream_id = uuid.uuid4().hex[:16]
+        self._stream_states[req_id] = {
+            "stream_id": stream_id,
+            "committed": "",  # finalized content from previous segments
+            "current": "",    # current segment being streamed
+        }
+
+        def _push_stream(state: dict):
+            """Push current stream content to wecom."""
+            self._ws_send({
+                "cmd": "aibot_respond_msg",
+                "headers": {"req_id": req_id},
+                "body": {
+                    "msgtype": "stream",
+                    "stream": {
+                        "id": state["stream_id"],
+                        "finish": False,
+                        "content": state["committed"] + state["current"],
+                    },
+                },
+            })
+
+        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"] = ""
+
+        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"] or content
+                stream_id = state["stream_id"]
+            else:
+                final_content = content
+                stream_id = uuid.uuid4().hex[:16]
+            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 +1,395 @@
+"""
+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")
+
+
+def _build_headers(token: str = "") -> dict:
+    headers = {
+        "Content-Type": "application/json",
+        "AuthorizationType": "ilink_bot_token",
+        "X-WECHAT-UIN": _random_wechat_uin(),
+    }
+    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)
+        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-ClientVersion": "1"}
+        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,
+            )
+            upload_param = resp.get("upload_param", "")
+            if not upload_param:
+                raise RuntimeError(f"[Weixin] getUploadUrl returned no upload_param: {resp}")
+
+            cdn_url = (f"{api.cdn_base_url}/upload"
+                       f"?encrypted_query_param={quote(upload_param)}"
+                       f"&filekey={quote(filekey)}")
+
+            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 +1,629 @@
+"""
+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
+            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)
+            qr.print_ascii(invert=True)
+        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
-
-
-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
-    from PIL import Image
-    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 @@
+2.0.4

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 +1,76 @@
+"""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
+
+
+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.
+  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(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 +1,63 @@
+"""cow install-browser - Install Playwright + Chromium for the browser tool."""
+
+import os
+import sys
+import subprocess
+
+import click
+
+
+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:
+    """True when running on a Linux server without a display."""
+    return sys.platform == "linux" and not _has_display()
+
+
+@click.command("install-browser")
+def install_browser():
+    """Install browser tool dependencies (Playwright + Chromium)."""
+    python = sys.executable
+
+    # Step 1: Install playwright package
+    click.echo(click.style("[1/3] Installing playwright Python package...", fg="yellow"))
+    ret = subprocess.call([python, "-m", "pip", "install", "playwright"])
+    if ret != 0:
+        click.echo(click.style("Failed to install playwright package.", fg="red"))
+        raise SystemExit(1)
+    click.echo(click.style("playwright package installed.", fg="green"))
+    click.echo()
+
+    # Step 2: System dependencies (Linux only)
+    if sys.platform == "linux":
+        click.echo(click.style("[2/3] Installing system dependencies (Linux)...", fg="yellow"))
+        ret = subprocess.call([python, "-m", "playwright", "install-deps", "chromium"])
+        if ret != 0:
+            click.echo(click.style(
+                "Could not auto-install system deps (may need sudo).\n"
+                f"  Run manually: sudo {python} -m playwright install-deps chromium",
+                fg="yellow",
+            ))
+    else:
+        click.echo(click.style(f"[2/3] Skipping system deps (not needed on {sys.platform}).", fg="yellow"))
+    click.echo()
+
+    # Step 3: Install Chromium (headless shell on Linux servers, full elsewhere)
+    click.echo(click.style("[3/3] Installing Chromium browser...", fg="yellow"))
+    cmd = [python, "-m", "playwright", "install", "chromium"]
+    if _is_headless_linux():
+        cmd.append("--only-shell")
+        click.echo("  (headless-only mode for Linux server)")
+    elif sys.platform == "linux":
+        click.echo("  (full browser for Linux desktop)")
+
+    ret = subprocess.call(cmd)
+    if ret != 0:
+        click.echo(click.style("Failed to install Chromium.", fg="red"))
+        raise SystemExit(1)
+
+    click.echo()
+    click.echo(click.style("Browser tool ready! Restart CowAgent to enable it.", fg="green"))

cli/commands/process.py

@@ -0,0 +1,281 @@
+"""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. Git pull while service is still running
+    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.")
+
+    # 2. Stop service
+    ctx.invoke(stop)
+
+    # 3. Install dependencies
+    python = sys.executable
+    req_file = os.path.join(root, "requirements.txt")
+    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=True)
+
+
+@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

cli/utils.py

@@ -0,0 +1,62 @@
+"""Shared utilities for cow CLI."""
+
+import os
+import sys
+import json
+
+
+def get_project_root() -> str:
+    """Get the CowAgent project root directory."""
+    # cli/ is directly under the project root
+    return os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def get_workspace_dir() -> str:
+    """Get the agent workspace directory from config, defaulting to ~/cow."""
+    config = load_config_json()
+    workspace = config.get("agent_workspace", "~/cow")
+    return os.path.expanduser(workspace)
+
+
+def get_skills_dir() -> str:
+    """Get the custom skills directory."""
+    return os.path.join(get_workspace_dir(), "skills")
+
+
+def get_builtin_skills_dir() -> str:
+    """Get the builtin skills directory."""
+    return os.path.join(get_project_root(), "skills")
+
+
+def load_config_json() -> dict:
+    """Load config.json from project root."""
+    config_path = os.path.join(get_project_root(), "config.json")
+    if not os.path.exists(config_path):
+        return {}
+    try:
+        with open(config_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except Exception:
+        return {}
+
+
+def load_skills_config() -> dict:
+    """Load skills_config.json from the custom skills directory."""
+    path = os.path.join(get_skills_dir(), "skills_config.json")
+    if not os.path.exists(path):
+        return {}
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except Exception:
+        return {}
+
+
+def ensure_sys_path():
+    """Add project root to sys.path so we can import agent modules."""
+    root = get_project_root()
+    if root not in sys.path:
+        sys.path.insert(0, root)
+
+
+SKILL_HUB_API = "https://skills.cowagent.ai/api"

common/cloud_client.py

@@ -3,6 +3,18 @@ Cloud management client for connecting to the LinkAI control console.
 
 Handles remote configuration sync, message push, and skill management
 via the LinkAI socket protocol.
+
+NOTE: By default, no cloud-related config is enabled. The application runs
+entirely locally without connecting to any remote service. The cloud client
+is only activated when BOTH of the following conditions are met:
+
+  1. ``use_linkai`` is set to True in config (checked in app.py before
+     importing this module).
+  2. ``cloud_deployment_id`` (or env CLOUD_DEPLOYMENT_ID) is non-empty
+     (checked in app.py and again in the ``start()`` function below).
+
+If either condition is missing, this module is never loaded and the
+program continues as a purely local application.
 """
 
 from bridge.context import Context, ContextType
@@ -20,6 +32,20 @@ import os
 chat_client: LinkAIClient
 
 
+CHANNEL_ACTIONS = {"channel_create", "channel_update", "channel_delete"}
+
+# channelType -> config key mapping for app credentials
+CREDENTIAL_MAP = {
+    "feishu":            ("feishu_app_id",          "feishu_app_secret"),
+    "dingtalk":          ("dingtalk_client_id",      "dingtalk_client_secret"),
+    "wecom_bot":         ("wecom_bot_id",            "wecom_bot_secret"),
+    "qq":                ("qq_app_id",               "qq_app_secret"),
+    "wechatmp":          ("wechatmp_app_id",         "wechatmp_app_secret"),
+    "wechatmp_service":  ("wechatmp_app_id",         "wechatmp_app_secret"),
+    "wechatcom_app":     ("wechatcomapp_agent_id",   "wechatcomapp_secret"),
+}
+
+
 class CloudClient(LinkAIClient):
     def __init__(self, api_key: str, channel, host: str = ""):
         super().__init__(api_key, host)
@@ -96,6 +122,12 @@ class CloudClient(LinkAIClient):
         if not self.client_id:
             return
         logger.info(f"[CloudClient] Loading remote config: {config}")
+
+        action = config.get("action")
+        if action in CHANNEL_ACTIONS:
+            self._dispatch_channel_action(action, config.get("data", {}))
+            return
+
         if config.get("enabled") != "Y":
             return
 
@@ -123,50 +155,17 @@ class CloudClient(LinkAIClient):
         if config.get("model"):
             local_config["model"] = config.get("model")
 
-        # Channel configuration
+        # Channel configuration (legacy single-channel path)
         if config.get("channelType"):
             if local_config.get("channel_type") != config.get("channelType"):
                 local_config["channel_type"] = config.get("channelType")
                 need_restart_channel = True
 
-        # Channel-specific app credentials
+        # Channel-specific app credentials (legacy single-channel path)
         current_channel_type = local_config.get("channel_type", "")
-
-        if config.get("app_id") is not None:
-            if current_channel_type == "feishu":
-                if local_config.get("feishu_app_id") != config.get("app_id"):
-                    local_config["feishu_app_id"] = config.get("app_id")
-                    need_restart_channel = True
-            elif current_channel_type == "dingtalk":
-                if local_config.get("dingtalk_client_id") != config.get("app_id"):
-                    local_config["dingtalk_client_id"] = config.get("app_id")
-                    need_restart_channel = True
-            elif current_channel_type in ("wechatmp", "wechatmp_service"):
-                if local_config.get("wechatmp_app_id") != config.get("app_id"):
-                    local_config["wechatmp_app_id"] = config.get("app_id")
-                    need_restart_channel = True
-            elif current_channel_type == "wechatcom_app":
-                if local_config.get("wechatcomapp_agent_id") != config.get("app_id"):
-                    local_config["wechatcomapp_agent_id"] = config.get("app_id")
-                    need_restart_channel = True
-
-        if config.get("app_secret"):
-            if current_channel_type == "feishu":
-                if local_config.get("feishu_app_secret") != config.get("app_secret"):
-                    local_config["feishu_app_secret"] = config.get("app_secret")
-                    need_restart_channel = True
-            elif current_channel_type == "dingtalk":
-                if local_config.get("dingtalk_client_secret") != config.get("app_secret"):
-                    local_config["dingtalk_client_secret"] = config.get("app_secret")
-                    need_restart_channel = True
-            elif current_channel_type in ("wechatmp", "wechatmp_service"):
-                if local_config.get("wechatmp_app_secret") != config.get("app_secret"):
-                    local_config["wechatmp_app_secret"] = config.get("app_secret")
-                    need_restart_channel = True
-            elif current_channel_type == "wechatcom_app":
-                if local_config.get("wechatcomapp_secret") != config.get("app_secret"):
-                    local_config["wechatcomapp_secret"] = config.get("app_secret")
-                    need_restart_channel = True
+        if self._set_channel_credentials(local_config, current_channel_type,
+                                         config.get("app_id"), config.get("app_secret")):
+            need_restart_channel = True
 
         if config.get("admin_password"):
             if not pconf("Godcmd"):
@@ -190,12 +189,243 @@ class CloudClient(LinkAIClient):
             if pconf("linkai")["midjourney"]:
                 pconf("linkai")["midjourney"]["use_image_create_prefix"] = False
 
-        # Save configuration to config.json file
         self._save_config_to_file(local_config)
 
         if need_restart_channel:
             self._restart_channel(local_config.get("channel_type", ""))
 
+    # ------------------------------------------------------------------
+    # channel CRUD operations
+    # ------------------------------------------------------------------
+    def _dispatch_channel_action(self, action: str, data: dict):
+        channel_type = data.get("channelType")
+        if not channel_type:
+            logger.warning(f"[CloudClient] Channel action '{action}' missing channelType, data={data}")
+            return
+        logger.info(f"[CloudClient] Channel action: {action}, channelType={channel_type}")
+
+        if action == "channel_create":
+            self._handle_channel_create(channel_type, data)
+        elif action == "channel_update":
+            self._handle_channel_update(channel_type, data)
+        elif action == "channel_delete":
+            self._handle_channel_delete(channel_type, data)
+
+    def _handle_channel_create(self, channel_type: str, data: dict):
+        local_config = conf()
+        cred_changed = self._set_channel_credentials(
+            local_config, channel_type, data.get("appId"), data.get("appSecret"))
+        self._add_channel_type(local_config, channel_type)
+        self._save_config_to_file(local_config)
+
+        if not self.channel_mgr:
+            return
+
+        existing_ch = self.channel_mgr.get_channel(channel_type)
+        skip_restart = existing_ch and not cred_changed
+        if skip_restart and channel_type in ("weixin", "wx"):
+            login_status = getattr(existing_ch, "login_status", "")
+            if login_status != "logged_in":
+                skip_restart = False
+                logger.info(f"[CloudClient] Channel '{channel_type}' not logged in "
+                            f"(status={login_status}), forcing restart")
+        if skip_restart:
+            logger.info(f"[CloudClient] Channel '{channel_type}' already running with same config, "
+                        "skip restart, reporting status only")
+            threading.Thread(
+                target=self._report_channel_startup, args=(channel_type,), daemon=True
+            ).start()
+            return
+
+        threading.Thread(
+            target=self._do_add_channel, args=(channel_type,), daemon=True
+        ).start()
+
+    def _handle_channel_update(self, channel_type: str, data: dict):
+        local_config = conf()
+        enabled = data.get("enabled", "Y")
+
+        cred_changed = self._set_channel_credentials(
+            local_config, channel_type, data.get("appId"), data.get("appSecret"))
+        if enabled == "N":
+            self._remove_channel_type(local_config, channel_type)
+        else:
+            self._add_channel_type(local_config, channel_type)
+        self._save_config_to_file(local_config)
+
+        if not self.channel_mgr:
+            return
+
+        if enabled == "N":
+            threading.Thread(
+                target=self._do_remove_channel, args=(channel_type,), daemon=True
+            ).start()
+        else:
+            existing_ch = self.channel_mgr.get_channel(channel_type)
+            needs_restart = cred_changed or not existing_ch
+            if not needs_restart and channel_type in ("weixin", "wx"):
+                login_status = getattr(existing_ch, "login_status", "")
+                if login_status != "logged_in":
+                    needs_restart = True
+                    logger.info(f"[CloudClient] Channel '{channel_type}' not logged in "
+                                f"(status={login_status}), forcing restart")
+            if existing_ch and not needs_restart:
+                logger.info(f"[CloudClient] Channel '{channel_type}' already running with same config, "
+                            "skip restart, reporting status only")
+                threading.Thread(
+                    target=self._report_channel_startup, args=(channel_type,), daemon=True
+                ).start()
+            else:
+                threading.Thread(
+                    target=self._do_restart_channel, args=(self.channel_mgr, channel_type), daemon=True
+                ).start()
+
+    def _handle_channel_delete(self, channel_type: str, data: dict):
+        local_config = conf()
+        self._clear_channel_credentials(local_config, channel_type)
+        self._remove_channel_type(local_config, channel_type)
+        self._save_config_to_file(local_config)
+
+        if channel_type in ("weixin", "wx"):
+            self._remove_weixin_credentials()
+
+        if self.channel_mgr:
+            threading.Thread(
+                target=self._do_remove_channel, args=(channel_type,), daemon=True
+            ).start()
+
+    @staticmethod
+    def _remove_weixin_credentials():
+        """Remove the weixin token credentials file so next connect triggers QR login."""
+        cred_path = os.path.expanduser(
+            conf().get("weixin_credentials_path", "~/.weixin_cow_credentials.json")
+        )
+        try:
+            if os.path.exists(cred_path):
+                os.remove(cred_path)
+                logger.info(f"[CloudClient] Removed weixin credentials: {cred_path}")
+        except Exception as e:
+            logger.warning(f"[CloudClient] Failed to remove weixin credentials: {e}")
+
+    # ------------------------------------------------------------------
+    # channel credentials helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _set_channel_credentials(local_config: dict, channel_type: str,
+                                 app_id, app_secret) -> bool:
+        """
+        Write app_id / app_secret into the correct config keys for *channel_type*.
+        Also syncs the values to environment variables (upper-cased key) so that
+        skills that rely on env-based checks (e.g. has_env_var) work immediately.
+        Returns True if any value actually changed.
+        """
+        cred = CREDENTIAL_MAP.get(channel_type)
+        if not cred:
+            return False
+        id_key, secret_key = cred
+        changed = False
+        if app_id is not None and local_config.get(id_key) != app_id:
+            local_config[id_key] = app_id
+            os.environ[id_key.upper()] = str(app_id)
+            changed = True
+        if app_secret is not None and local_config.get(secret_key) != app_secret:
+            local_config[secret_key] = app_secret
+            os.environ[secret_key.upper()] = str(app_secret)
+            changed = True
+        if changed:
+            logger.info(f"[CloudClient] Synced {channel_type} credentials to conf and env")
+        return changed
+
+    @staticmethod
+    def _clear_channel_credentials(local_config: dict, channel_type: str):
+        cred = CREDENTIAL_MAP.get(channel_type)
+        if not cred:
+            return
+        id_key, secret_key = cred
+        local_config.pop(id_key, None)
+        local_config.pop(secret_key, None)
+        os.environ.pop(id_key.upper(), None)
+        os.environ.pop(secret_key.upper(), None)
+
+    # ------------------------------------------------------------------
+    # channel_type list helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _parse_channel_types(local_config: dict) -> list:
+        raw = local_config.get("channel_type", "")
+        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 []
+
+    @staticmethod
+    def _add_channel_type(local_config: dict, channel_type: str):
+        types = CloudClient._parse_channel_types(local_config)
+        if channel_type not in types:
+            types.append(channel_type)
+            local_config["channel_type"] = ", ".join(types)
+
+    @staticmethod
+    def _remove_channel_type(local_config: dict, channel_type: str):
+        types = CloudClient._parse_channel_types(local_config)
+        if channel_type in types:
+            types.remove(channel_type)
+            local_config["channel_type"] = ", ".join(types)
+
+    # ------------------------------------------------------------------
+    # channel manager thread helpers
+    # ------------------------------------------------------------------
+    def _do_add_channel(self, channel_type: str):
+        try:
+            self.channel_mgr.add_channel(channel_type)
+            logger.info(f"[CloudClient] Channel '{channel_type}' added successfully")
+        except Exception as e:
+            logger.error(f"[CloudClient] Failed to add channel '{channel_type}': {e}", exc_info=True)
+            self.send_channel_status(channel_type, "error", str(e))
+            return
+        self._report_channel_startup(channel_type)
+
+    def _do_remove_channel(self, channel_type: str):
+        try:
+            self.channel_mgr.remove_channel(channel_type)
+            logger.info(f"[CloudClient] Channel '{channel_type}' removed successfully")
+        except Exception as e:
+            logger.error(f"[CloudClient] Failed to remove channel '{channel_type}': {e}")
+
+    def send_channel_qrcode(self, channel_type: str, qrcode_url: str):
+        """Report QR code URL for a channel that requires scan-to-login."""
+        if self.client_id:
+            from linkai.api.client.client import ClientMsgType
+            msg = self._build_package(ClientMsgType.CHANNEL_STATUS)
+            msg["data"]["channelType"] = channel_type
+            msg["data"]["status"] = "qrcode"
+            msg["data"]["qrcodeUrl"] = qrcode_url
+            self._send_package(msg)
+            logger.info(f"[CloudClient] Sent QR code status for '{channel_type}'")
+
+    def _report_channel_startup(self, channel_type: str):
+        """Wait for channel startup result and report to cloud."""
+        ch = self.channel_mgr.get_channel(channel_type)
+        if not ch:
+            self.send_channel_status(channel_type, "error", "channel instance not found")
+            return
+
+        if channel_type in ("weixin", "wx") and hasattr(ch, "login_status"):
+            login_status = getattr(ch, "login_status", "")
+            if login_status in ("waiting_scan", "scanned", "idle"):
+                logger.info(f"[CloudClient] Channel '{channel_type}' is waiting for QR login, "
+                            "skip reporting connected")
+                return
+
+        success, error = ch.wait_startup(timeout=3)
+        if success:
+            logger.info(f"[CloudClient] Channel '{channel_type}' connected, reporting status")
+            self.send_channel_status(channel_type, "connected")
+        else:
+            logger.warning(f"[CloudClient] Channel '{channel_type}' startup failed: {error}")
+            self.send_channel_status(channel_type, "error", error)
+
     # ------------------------------------------------------------------
     # skill callback
     # ------------------------------------------------------------------
@@ -252,13 +482,72 @@ class CloudClient(LinkAIClient):
         payload = data.get("payload", {})
         query = payload.get("query", "")
         session_id = payload.get("session_id", "cloud_console")
-        logger.info(f"[CloudClient] on_chat: session={session_id}, query={query[:80]}")
+        channel_type = payload.get("channel_type", "")
+        if not session_id.startswith("session_"):
+            session_id = f"session_{session_id}"
+        logger.info(f"[CloudClient] on_chat: session={session_id}, channel={channel_type}, query={query[:80]}")
 
         svc = self.chat_service
         if svc is None:
             raise RuntimeError("ChatService not available")
 
-        svc.run(query=query, session_id=session_id, send_chunk_fn=send_chunk_fn)
+        svc.run(query=query, session_id=session_id, channel_type=channel_type, send_chunk_fn=send_chunk_fn)
+
+    # ------------------------------------------------------------------
+    # history callback
+    # ------------------------------------------------------------------
+    def on_history(self, data: dict) -> dict:
+        """
+        Handle HISTORY messages from the cloud console.
+        Returns paginated conversation history for a session.
+
+        :param data: message data with 'action' and 'payload' (session_id, page, page_size)
+        :return: response dict
+        """
+        action = data.get("action", "query")
+        payload = data.get("payload", {})
+        logger.info(f"[CloudClient] on_history: action={action}")
+
+        if action == "query":
+            return self._query_history(payload)
+
+        return {"action": action, "code": 404, "message": f"unknown action: {action}", "payload": None}
+
+    def _query_history(self, payload: dict) -> dict:
+        """Query paginated conversation history using ConversationStore."""
+        session_id = payload.get("session_id", "")
+        page = int(payload.get("page", 1))
+        page_size = int(payload.get("page_size", 20))
+
+        if not session_id:
+            return {
+                "action": "query",
+                "payload": {"status": "error", "message": "session_id required"},
+            }
+
+        # Web channel stores sessions with a "session_" prefix
+        if not session_id.startswith("session_"):
+            session_id = f"session_{session_id}"
+        logger.info(f"[CloudClient] history query: session={session_id}, page={page}, page_size={page_size}")
+
+        try:
+            from agent.memory.conversation_store import get_conversation_store
+            store = get_conversation_store()
+            result = store.load_history_page(
+                session_id=session_id,
+                page=page,
+                page_size=page_size,
+            )
+            return {
+                "action": "query",
+                "payload": {"status": "success", **result},
+            }
+        except Exception as e:
+            logger.error(f"[CloudClient] History query error: {e}")
+            return {
+                "action": "query",
+                "payload": {"status": "error", "message": str(e)},
+            }
 
     # ------------------------------------------------------------------
     # channel restart helpers
@@ -279,13 +568,15 @@ class CloudClient(LinkAIClient):
         """
         try:
             mgr.restart(new_channel_type)
-            # Update the client's channel reference
             if mgr.channel:
                 self.channel = mgr.channel
                 self.client_type = mgr.channel.channel_type
                 logger.info(f"[CloudClient] Channel reference updated to '{new_channel_type}'")
         except Exception as e:
             logger.error(f"[CloudClient] Channel restart failed: {e}")
+            self.send_channel_status(new_channel_type, "error", str(e))
+            return
+        self._report_channel_startup(new_channel_type)
 
     # ------------------------------------------------------------------
     # config persistence
@@ -313,7 +604,85 @@ class CloudClient(LinkAIClient):
             logger.error(f"[CloudClient] Failed to save configuration to config.json: {e}")
 
 
+def get_root_domain(host: str = "") -> str:
+    """Extract root domain from a hostname.
+
+    If *host* is empty, reads CLOUD_HOST env var / cloud_host config.
+    """
+    if not host:
+        host = os.environ.get("CLOUD_HOST") or conf().get("cloud_host", "")
+    if not host:
+        return ""
+    host = host.strip().rstrip("/")
+    if "://" in host:
+        host = host.split("://", 1)[1]
+    host = host.split("/", 1)[0].split(":")[0]
+    parts = host.split(".")
+    if len(parts) >= 2:
+        return ".".join(parts[-2:])
+    return host
+
+
+def get_deployment_id() -> str:
+    """Return cloud deployment id from env var or config."""
+    return os.environ.get("CLOUD_DEPLOYMENT_ID") or conf().get("cloud_deployment_id", "")
+
+
+def get_website_base_url() -> str:
+    """Return the public URL prefix that maps to the workspace websites/ dir.
+
+    Returns empty string when cloud deployment is not configured.
+    """
+    deployment_id = get_deployment_id()
+    if not deployment_id:
+        return ""
+
+    websites_domain = os.environ.get("CLOUD_WEBSITES_DOMAIN") or conf().get("cloud_websites_domain", "")
+    if websites_domain:
+        websites_domain = websites_domain.strip().rstrip("/")
+        return f"https://{websites_domain}/{deployment_id}"
+
+    domain = get_root_domain()
+    if not domain:
+        return ""
+    return f"https://app.{domain}/{deployment_id}"
+
+
+def build_website_prompt(workspace_dir: str) -> list:
+    """Build system prompt lines for cloud website/file sharing rules.
+
+    Returns an empty list when cloud deployment is not configured,
+    so callers can safely do ``lines.extend(build_website_prompt(...))``.
+    """
+    base_url = get_website_base_url()
+    if not base_url:
+        return []
+
+    return [
+        "**文件分享与网页生成规则** (非常重要 — 当前为云部署模式):",
+        "",
+        f"云端已为工作空间的 `websites/` 目录配置好公网路由映射，访问地址前缀为: `{base_url}`",
+        "",
+        "1. **网页/网站**: 编写网页、H5页面等前端代码时，**必须**将文件放到 `websites/` 目录中",
+        f"   - 例如: `websites/index.html` → `{base_url}/index.html`",
+        f"   - 例如: `websites/my-app/index.html` → `{base_url}/my-app/index.html`",
+        "",
+        "2. **生成文件分享** (PPT、PDF、图片、音视频等): 当你为用户生成了需要下载或查看的文件时，**可以**将文件保存到 `websites/` 目录中",
+        f"   - 例如: 生成的PPT保存到 `websites/files/report.pptx` → 下载链接为 `{base_url}/files/report.pptx`",
+        "   - 你仍然可以同时使用 `send` 工具发送文件（在飞书、钉钉等IM渠道中有效），但**必须同时在回复文本中提供下载链接**作为兜底，因为部分渠道（如网页端）无法通过 send 接收本地文件",
+        "",
+        "3. **必须发送链接**: 无论是网页还是文件，生成后**必须将完整的访问/下载链接直接写在回复文本中发送给用户**",
+        "",
+        "4. **文件名和路径尽量使用英文/拼音/数字等**，不要使用中文，避免链接无法访问",
+        "",
+        "5. 建议为每个独立项目在 `websites/` 下创建子目录，保持结构清晰",
+        "",
+    ]
+
 def start(channel, channel_mgr=None):
+    if not get_deployment_id():
+        return
+
     global chat_client
     chat_client = CloudClient(api_key=conf().get("linkai_api_key"), host=conf().get("cloud_host", ""), channel=channel)
     chat_client.channel_mgr = channel_mgr
@@ -322,6 +691,21 @@ def start(channel, channel_mgr=None):
     time.sleep(1.5)
     if chat_client.client_id:
         logger.info("[CloudClient] Console: https://link-ai.tech/console/clients")
+        if channel_mgr:
+            channel_mgr.cloud_mode = True
+            threading.Thread(target=_report_existing_channels, args=(chat_client, channel_mgr), daemon=True).start()
+
+
+def _report_existing_channels(client: CloudClient, mgr):
+    """Report status for all channels that were started before cloud client connected."""
+    try:
+        for name, ch in list(mgr._channels.items()):
+            if name == "web":
+                continue
+            ch.cloud_mode = True
+            client._report_channel_startup(name)
+    except Exception as e:
+        logger.warning(f"[CloudClient] Failed to report existing channel status: {e}")
 
 
 def _build_config():
@@ -368,6 +752,12 @@ def _build_config():
     elif current_channel_type in ("wechatmp", "wechatmp_service"):
         config["app_id"] = local_conf.get("wechatmp_app_id")
         config["app_secret"] = local_conf.get("wechatmp_app_secret")
+    elif current_channel_type == "wecom_bot":
+        config["app_id"] = local_conf.get("wecom_bot_id")
+        config["app_secret"] = local_conf.get("wecom_bot_secret")
+    elif current_channel_type == "qq":
+        config["app_id"] = local_conf.get("qq_app_id")
+        config["app_secret"] = local_conf.get("qq_app_secret")
     elif current_channel_type == "wechatcom_app":
         config["app_id"] = local_conf.get("wechatcomapp_agent_id")
         config["app_secret"] = local_conf.get("wechatcomapp_secret")

common/const.py

@@ -1,6 +1,7 @@
 # 厂商类型
 OPEN_AI = "openAI"
-CHATGPT = "chatGPT"
+OPENAI = "openai"
+CHATGPT = "chatGPT"  # legacy alias for OPENAI, kept for backward compatibility
 BAIDU = "baidu"
 XUNFEI = "xunfei"
 CHATGPTONAZURE = "chatGPTOnAzure"
@@ -9,9 +10,10 @@ CLAUDEAPI= "claudeAPI"
 QWEN = "qwen"  # 旧版千问接入
 QWEN_DASHSCOPE = "dashscope"  # 新版千问接入(百炼)
 GEMINI = "gemini" 
-ZHIPU_AI = "glm-4"
+ZHIPU_AI = "zhipu"  
 MOONSHOT = "moonshot"
 MiniMax = "minimax"
+DEEPSEEK = "deepseek"
 MODELSCOPE = "modelscope"
 
 # 模型列表
@@ -41,6 +43,7 @@ GEMINI_25_PRO_PRE = "gemini-2.5-pro-preview-05-06"
 GEMINI_3_FLASH_PRE = "gemini-3-flash-preview"  # Gemini 3 Flash Preview - Agent推荐模型
 GEMINI_3_PRO_PRE = "gemini-3-pro-preview"  # Gemini 3 Pro Preview
 GEMINI_31_PRO_PRE = "gemini-3.1-pro-preview"  # Gemini 3.1 Pro Preview - Agent推荐模型
+GEMINI_31_FLASH_LITE_PRE = "gemini-3.1-flash-lite-preview"  # Gemini 3.1 Flash Lite Preview - Agent推荐模型
 
 # OpenAI
 GPT35 = "gpt-3.5-turbo"
@@ -65,6 +68,9 @@ GPT_41_NANO = "gpt-4.1-nano"
 GPT_5 = "gpt-5"
 GPT_5_MINI = "gpt-5-mini"
 GPT_5_NANO = "gpt-5-nano"
+GPT_54 = "gpt-5.4"  # GPT-5.4 - Agent recommended model
+GPT_54_MINI = "gpt-5.4-mini"
+GPT_54_NANO = "gpt-5.4-nano"
 O1 = "o1-preview"
 O1_MINI = "o1-mini"
 WHISPER_1 = "whisper-1"
@@ -86,14 +92,16 @@ QWEN35_PLUS = "qwen3.5-plus"  # Qwen3.5 Plus - Omni model (MultiModalConversatio
 QWQ_PLUS = "qwq-plus"
 
 # MiniMax
-MINIMAX_M2_5 = "MiniMax-M2.5"  # MiniMax M2.5 - Latest
-MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1 - Agent推荐模型
+MINIMAX_M2_7 = "MiniMax-M2.7"  # MiniMax M2.7 - Latest
+MINIMAX_M2_5 = "MiniMax-M2.5"  # MiniMax M2.5
+MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1
 MINIMAX_M2_1_LIGHTNING = "MiniMax-M2.1-lightning"  # MiniMax M2.1 极速版
 MINIMAX_M2 = "MiniMax-M2"  # MiniMax M2
 MINIMAX_ABAB6_5 = "abab6.5-chat"  # MiniMax abab6.5
 
 # GLM (智谱AI)
-GLM_5 = "glm-5"  # 智谱 GLM-5 - Latest
+GLM_5_TURBO = "glm-5-turbo"  # 智谱 GLM-5-Turbo - Latest
+GLM_5 = "glm-5"  # 智谱 GLM-5
 GLM_4 = "glm-4"
 GLM_4_PLUS = "glm-4-plus"
 GLM_4_flash = "glm-4-flash"
@@ -140,7 +148,7 @@ MODEL_LIST = [
               "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3.5-sonnet",
               
               # Gemini
-              GEMINI_31_PRO_PRE, GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE, 
+              GEMINI_31_FLASH_LITE_PRE, GEMINI_31_PRO_PRE, GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE,
               GEMINI_20_FLASH, GEMINI_20_flash_exp, GEMINI_15_PRO, GEMINI_15_flash, GEMINI_PRO, GEMINI,
               
               # OpenAI
@@ -150,6 +158,7 @@ MODEL_LIST = [
               GPT_4o, GPT_4O_0806, GPT_4o_MINI,
               GPT_41, GPT_41_MINI, GPT_41_NANO,
               GPT_5, GPT_5_MINI, GPT_5_NANO,
+              GPT_54, GPT_54_MINI, GPT_54_NANO,
               O1, O1_MINI,
               
               # DeepSeek
@@ -159,10 +168,10 @@ MODEL_LIST = [
               QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX, QWEN35_PLUS,
               
               # MiniMax
-              MiniMax, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
+              MiniMax, MINIMAX_M2_7, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
 
               # GLM
-              ZHIPU_AI, GLM_5, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS,
+              ZHIPU_AI, GLM_5_TURBO, GLM_5, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS,
               GLM_4_0520, GLM_4_AIR, GLM_4_AIRX, GLM_4_7,
 
               # Kimi
@@ -182,3 +191,6 @@ MODEL_LIST = MODEL_LIST + GITEE_AI_MODEL_LIST + MODELSCOPE_MODEL_LIST
 # channel
 FEISHU = "feishu"
 DINGTALK = "dingtalk"
+WECOM_BOT = "wecom_bot"
+QQ = "qq"
+WEIXIN = "weixin"

common/utils.py

@@ -115,3 +115,22 @@ def expand_path(path: str) -> str:
                 expanded = os.path.join(home, path[2:])
     
     return expanded
+
+
+def get_cloud_headers(api_key: str) -> dict:
+    """
+    Build standard headers for LinkAI API requests,
+    including client_id when available.
+    """
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}",
+    }
+    try:
+        from linkai import LinkAIClient
+        client_id = LinkAIClient.fetch_client_id()
+        if client_id:
+            headers["X-Client-Id"] = client_id
+    except Exception:
+        pass
+    return headers

common/ws_client_compat.py

@@ -0,0 +1,17 @@
+import inspect
+from typing import Any
+
+
+def websocket_app_run_forever(ws: Any, **kwargs: Any) -> None:
+    """
+    Call WebSocketApp.run_forever; strip reconnect= if the installed
+    websocket-client is too old (reconnect was added in a later 1.x release).
+    """
+    if "reconnect" in kwargs:
+        try:
+            params = inspect.signature(ws.run_forever).parameters
+        except (TypeError, ValueError):
+            params = {}
+        if "reconnect" not in params:
+            kwargs = {k: v for k, v in kwargs.items() if k != "reconnect"}
+    ws.run_forever(**kwargs)

config-template.json

@@ -1,6 +1,6 @@
 {
-  "channel_type": "web",
-  "model": "MiniMax-M2.5",
+  "channel_type": "weixin",
+  "model": "MiniMax-M2.7",
   "minimax_api_key": "",
   "zhipu_ai_api_key": "",
   "ark_api_key": "",
@@ -20,11 +20,12 @@
   "use_linkai": false,
   "linkai_api_key": "",
   "linkai_app_code": "",
-  "feishu_bot_name": "",
   "feishu_app_id": "",
   "feishu_app_secret": "",
   "dingtalk_client_id": "",
   "dingtalk_client_secret":"",
+  "wecom_bot_id": "",
+  "wecom_bot_secret": "",
   "agent": true,
   "agent_max_context_tokens": 40000,
   "agent_max_context_turns": 20,

config.py

@@ -20,7 +20,7 @@ available_setting = {
     "proxy": "",  # openai使用的代理
     # chatgpt模型， 当use_azure_chatgpt为true时，其名称为Azure上model deployment名称
     "model": "gpt-3.5-turbo",  # 可选择: gpt-4o, pt-4o-mini, gpt-4-turbo, claude-3-sonnet, wenxin, moonshot, qwen-turbo, xunfei, glm-4, minimax, gemini等模型，全部可选模型详见common/const.py文件
-    "bot_type": "",  # 可选配置，使用兼容openai格式的三方服务时候，需填"chatGPT"。bot具体名称详见common/const.py文件列出的bot_type，如不填根据model名称判断，
+    "bot_type": "",  # 可选配置，使用兼容openai格式的三方服务时候，需填"openai"（历史值"chatGPT"仍兼容）。bot具体名称详见common/const.py文件，如不填根据model名称判断
     "use_azure_chatgpt": False,  # 是否使用azure的chatgpt
     "azure_deployment_id": "",  # azure 模型部署名称
     "azure_api_version": "",  # azure api版本
@@ -37,7 +37,7 @@ available_setting = {
     "group_name_white_list": ["ChatGPT测试群", "ChatGPT测试群2"],  # 开启自动回复的群名称列表
     "group_name_keyword_white_list": [],  # 开启自动回复的群名称关键词列表
     "group_chat_in_one_session": ["ChatGPT测试群"],  # 支持会话上下文共享的群名称
-    "group_shared_session": True,  # 群聊是否共享会话上下文（所有成员共享），默认为True。False时每个用户在群内有独立会话
+    "group_shared_session": False,  # 群聊是否共享会话上下文（所有成员共享）。False时每个用户在群内有独立会话
     "nick_name_black_list": [],  # 用户昵称黑名单
     "group_welcome_msg": "",  # 配置新人进群固定欢迎语，不配置则使用随机风格欢迎
     "trigger_by_self": False,  # 是否允许机器人触发
@@ -95,8 +95,6 @@ available_setting = {
     "dashscope_api_key": "",
     # Google Gemini Api Key
     "gemini_api_key": "",
-    # wework的通用配置
-    "wework_smart": True,  # 配置wework是否使用已登录的企业微信，False为多开
     # 语音设置
     "speech_recognition": True,  # 是否开启语音识别
     "group_speech_recognition": False,  # 是否开启群组语音识别
@@ -118,7 +116,7 @@ available_setting = {
     # elevenlabs 语音api配置
     "xi_api_key": "",  # 获取ap的方法可以参考https://docs.elevenlabs.io/api-reference/quick-start/authentication
     "xi_voice_id": "",  # ElevenLabs提供了9种英式、美式等英语发音id，分别是“Adam/Antoni/Arnold/Bella/Domi/Elli/Josh/Rachel/Sam”
-    # 服务时间限制，目前支持itchat
+    # 服务时间限制
     "chat_time_module": False,  # 是否开启服务时间限制
     "chat_start_time": "00:00",  # 服务开始时间
     "chat_stop_time": "24:00",  # 服务结束时间
@@ -127,10 +125,6 @@ available_setting = {
     # baidu翻译api的配置
     "baidu_translate_app_id": "",  # 百度翻译api的appid
     "baidu_translate_app_key": "",  # 百度翻译api的秘钥
-    # itchat的配置
-    "hot_reload": False,  # 是否开启热重载
-    # wechaty的配置
-    "wechaty_puppet_service_token": "",  # wechaty的token
     # wechatmp的配置
     "wechatmp_token": "",  # 微信公众平台的Token
     "wechatmp_port": 8080,  # 微信公众平台的端口,需要端口转发到80或443
@@ -156,11 +150,18 @@ available_setting = {
     "dingtalk_client_id": "",  # 钉钉机器人Client ID 
     "dingtalk_client_secret": "",  # 钉钉机器人Client Secret
     "dingtalk_card_enabled": False,
-    
+    # 企微智能机器人配置(长连接模式)
+    "wecom_bot_id": "",  # 企微智能机器人BotID
+    "wecom_bot_secret": "",  # 企微智能机器人长连接Secret
+    # 微信配置
+    "weixin_token": "",  # 微信登录后获取的bot_token，留空则启动时自动扫码登录
+    "weixin_base_url": "https://ilinkai.weixin.qq.com",  # Weixin ilink API base URL
+    "weixin_cdn_base_url": "https://novac2c.cdn.weixin.qq.com/c2c",  # CDN base URL
+    "weixin_credentials_path": "~/.weixin_cow_credentials.json",  # credentials file path
     # chatgpt指令自定义触发词
     "clear_memory_commands": ["#清除记忆"],  # 重置会话指令，必须以#开头
     # channel配置
-    "channel_type": "",  # 通道类型，支持多渠道同时运行。单个: "feishu"，多个: "feishu, dingtalk" 或 ["feishu", "dingtalk"]。可选值: web,feishu,dingtalk,wechatmp,wechatmp_service,wechatcom_app
+    "channel_type": "",  # 通道类型，支持多渠道同时运行。单个: "feishu"，多个: "feishu, dingtalk" 或 ["feishu", "dingtalk"]。可选值: web,feishu,dingtalk,wecom_bot,weixin,wechatmp,wechatmp_service,wechatcom_app
     "web_console": True,  # 是否自动启动Web控制台（默认启动）。设为False可禁用
     "subscribe_msg": "",  # 订阅消息, 支持: wechatmp, wechatmp_service, wechatcom_app
     "debug": False,  # 是否开启debug模式，开启后会打印更多日志
@@ -188,6 +189,7 @@ available_setting = {
     "linkai_app_code": "",
     "linkai_api_base": "https://api.link-ai.tech",  # linkAI服务地址
     "cloud_host": "client.link-ai.tech",
+    "cloud_deployment_id": "",
     "minimax_api_key": "",
     "Minimax_group_id": "",
     "Minimax_base_url": "",
@@ -355,6 +357,49 @@ def load_config():
     logger.info("[INIT] Debug: {}".format(config.get("debug", False)))
     logger.info("[INIT] ========================================")
 
+    # Sync selected config values to environment variables so that
+    # subprocesses (e.g. shell skill scripts) can access them directly.
+    # Existing env vars are NOT overwritten (env takes precedence).
+    _CONFIG_TO_ENV = {
+        "open_ai_api_key": "OPENAI_API_KEY",
+        "open_ai_api_base": "OPENAI_API_BASE",
+        "linkai_api_key": "LINKAI_API_KEY",
+        "linkai_api_base": "LINKAI_API_BASE",
+        "claude_api_key": "CLAUDE_API_KEY",
+        "claude_api_base": "CLAUDE_API_BASE",
+        "gemini_api_key": "GEMINI_API_KEY",
+        "gemini_api_base": "GEMINI_API_BASE",
+        "minimax_api_key": "MINIMAX_API_KEY",
+        "minimax_api_base": "MINIMAX_API_BASE",
+        "zhipu_ai_api_key": "ZHIPU_AI_API_KEY",
+        "zhipu_ai_api_base": "ZHIPU_AI_API_BASE",
+        "moonshot_api_key": "MOONSHOT_API_KEY",
+        "moonshot_api_base": "MOONSHOT_API_BASE",
+        "ark_api_key": "ARK_API_KEY",
+        "ark_api_base": "ARK_API_BASE",
+        # Channel credentials (used by skills that check env vars)
+        "feishu_app_id": "FEISHU_APP_ID",
+        "feishu_app_secret": "FEISHU_APP_SECRET",
+        "dingtalk_client_id": "DINGTALK_CLIENT_ID",
+        "dingtalk_client_secret": "DINGTALK_CLIENT_SECRET",
+        "wechatmp_app_id": "WECHATMP_APP_ID",
+        "wechatmp_app_secret": "WECHATMP_APP_SECRET",
+        "wechatcomapp_agent_id": "WECHATCOMAPP_AGENT_ID",
+        "wechatcomapp_secret": "WECHATCOMAPP_SECRET",
+        "qq_app_id": "QQ_APP_ID",
+        "qq_app_secret": "QQ_APP_SECRET",
+        "weixin_token": "WEIXIN_TOKEN",
+    }
+    injected = 0
+    for conf_key, env_key in _CONFIG_TO_ENV.items():
+        if env_key not in os.environ:
+            val = config.get(conf_key, "")
+            if val:
+                os.environ[env_key] = str(val)
+                injected += 1
+    if injected:
+        logger.info("[INIT] Synced {} config values to environment variables".format(injected))
+
     config.load_user_datas()
 
 

docker/Dockerfile.latest

@@ -17,19 +17,16 @@ RUN apt-get update \
     && cp config-template.json config.json \
     && /usr/local/bin/python -m pip install --no-cache --upgrade pip \
     && pip install --no-cache -r requirements.txt \
-    && pip install --no-cache -r requirements-optional.txt \
-    && pip install azure-cognitiveservices-speech
+    && pip install --no-cache -r requirements-optional.txt
 
 WORKDIR ${BUILD_PREFIX}
 
 ADD docker/entrypoint.sh /entrypoint.sh
 
 RUN chmod +x /entrypoint.sh \
-    && mkdir -p /home/noroot \
-    && groupadd -r noroot \
-    && useradd -r -g noroot -s /bin/bash -d /home/noroot noroot \
-    && chown -R noroot:noroot /home/noroot ${BUILD_PREFIX} /usr/local/lib
-
-USER noroot
+    && mkdir -p /home/agent/cow \
+    && groupadd -r agent \
+    && useradd -r -g agent -s /bin/bash -d /home/agent agent \
+    && chown -R agent:agent /home/agent ${BUILD_PREFIX} /usr/local/lib
 
 ENTRYPOINT ["/entrypoint.sh"]

docker/docker-compose.yml

@@ -5,22 +5,39 @@ services:
     container_name: chatgpt-on-wechat
     security_opt:
       - seccomp:unconfined
+    ports:
+      - "9899:9899"
     environment:
-      CHANNEL_TYPE: 'web'
-      OPEN_AI_API_KEY: 'YOUR API KEY'
-      MODEL: ''
-      PROXY: ''
-      SINGLE_CHAT_PREFIX: '["bot", "@bot"]'
-      SINGLE_CHAT_REPLY_PREFIX: '"[bot] "'
-      GROUP_CHAT_PREFIX: '["@bot"]'
-      GROUP_NAME_WHITE_LIST: '["ChatGPT测试群", "ChatGPT测试群2"]'
-      IMAGE_CREATE_PREFIX: '["画", "看", "找"]'
-      CONVERSATION_MAX_TOKENS: 1000
-      SPEECH_RECOGNITION: 'False'
-      CHARACTER_DESC: '你是基于大语言模型的AI智能助手，旨在回答并解决人们的任何问题，并且可以使用多种语言与人交流。'
-      EXPIRES_IN_SECONDS: 3600
-      USE_GLOBAL_PLUGIN_CONFIG: 'True'
+      CHANNEL_TYPE: 'weixin'
+      MODEL: 'MiniMax-M2.7'
+      MINIMAX_API_KEY: ''
+      ZHIPU_AI_API_KEY: ''
+      ARK_API_KEY: ''
+      MOONSHOT_API_KEY: ''
+      DASHSCOPE_API_KEY: ''
+      CLAUDE_API_KEY: ''
+      CLAUDE_API_BASE: 'https://api.anthropic.com/v1'
+      OPEN_AI_API_KEY: ''
+      OPEN_AI_API_BASE: 'https://api.openai.com/v1'
+      GEMINI_API_KEY: ''
+      GEMINI_API_BASE: 'https://generativelanguage.googleapis.com'
+      VOICE_TO_TEXT: 'openai'
+      TEXT_TO_VOICE: 'openai'
+      VOICE_REPLY_VOICE: 'False'
+      SPEECH_RECOGNITION: 'True'
+      GROUP_SPEECH_RECOGNITION: 'False'
       USE_LINKAI: 'False'
-      AGENT: 'True'
       LINKAI_API_KEY: ''
       LINKAI_APP_CODE: ''
+      FEISHU_APP_ID: ''
+      FEISHU_APP_SECRET: ''
+      DINGTALK_CLIENT_ID: ''
+      DINGTALK_CLIENT_SECRET: ''
+      WECOM_BOT_ID: ''
+      WECOM_BOT_SECRET: ''
+      AGENT: 'True'
+      AGENT_MAX_CONTEXT_TOKENS: 40000
+      AGENT_MAX_CONTEXT_TURNS: 20
+      AGENT_MAX_STEPS: 15
+    volumes:
+      - ./cow:/home/agent/cow

docker/entrypoint.sh

@@ -43,9 +43,15 @@ fi
 # fi
 
 
-# go to prefix dir
+# fix ownership of mounted volumes then drop to non-root user
+if [ "$(id -u)" = "0" ]; then
+    mkdir -p /home/agent/cow
+    chown agent:agent /home/agent/cow
+    exec su agent -s /bin/bash -c "cd $CHATGPT_ON_WECHAT_PREFIX && $CHATGPT_ON_WECHAT_EXEC"
+fi
+
+# fallback: already running as agent
 cd $CHATGPT_ON_WECHAT_PREFIX
-# excute
 $CHATGPT_ON_WECHAT_EXEC
 
 

docs/agent.md

@@ -127,7 +127,7 @@ Agent可根据智能体的名称和描述进行决策，并通过 app_code 调
 在命令行中执行：
 
 ```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)
 ```
 
 详细说明及后续程序管理参考：[项目启动脚本](https://github.com/zhayujie/chatgpt-on-wechat/wiki/CowAgentQuickStart)
@@ -137,13 +137,14 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
 
 Agent模式推荐使用以下模型，可根据效果及成本综合选择：
 
-- **MiniMax**: `MiniMax-M2.5`
-- **GLM**: `glm-5`
+- **MiniMax**: `MiniMax-M2.7`
+- **GLM**: `glm-5-turbo`
 - **Kimi**: `kimi-k2.5`
 - **Doubao**: `doubao-seed-2-0-code-preview-260215`
 - **Qwen**: `qwen3.5-plus`
 - **Claude**: `claude-sonnet-4-6`
-- **Gemini**: `gemini-3.1-pro-preview`
+- **Gemini**: `gemini-3.1-flash-lite-preview`
+- **OpenAI**: `gpt-5.4`
 
 详细模型配置方式参考 [README.md 模型说明](../README.md#模型说明)
 
@@ -178,5 +179,7 @@ Agent支持在多种渠道中使用，只需修改 `config.json` 中的 `channel
 - **飞书接入**：[飞书接入文档](https://docs.link-ai.tech/cow/multi-platform/feishu)
 - **钉钉接入**：[钉钉接入文档](https://docs.link-ai.tech/cow/multi-platform/dingtalk)
 - **企业微信应用接入**：[企微应用文档](https://docs.link-ai.tech/cow/multi-platform/wechat-com)
+- **企微智能机器人**：[企微智能机器人文档](https://docs.link-ai.tech/cow/multi-platform/wecom-bot)
+- **QQ机器人**：[QQ机器人文档](https://docs.link-ai.tech/cow/multi-platform/qq)
 
 更多渠道配置参考：[通道说明](../README.md#通道说明)

docs/channels/dingtalk.mdx

@@ -7,15 +7,29 @@ description: 将 CowAgent 接入钉钉应用
 
 ## 一、创建应用
 
-1. 进入 [钉钉开发者后台](https://open-dev.dingtalk.com/fe/app#/corp/app)，点击 **创建应用**，填写应用信息
-2. 点击添加应用能力，选择 **机器人** 能力并添加
-3. 配置机器人信息后点击 **发布**
+1. 进入 [钉钉开发者后台](https://open-dev.dingtalk.com/fe/app#/corp/app)，登录后点击 **创建应用**，填写应用相关信息：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-create-app.png" width="800"/>
+
+2. 点击添加应用能力，选择 **机器人** 能力，点击 **添加**：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-add-bot.png" width="800"/>
+
+3. 配置机器人信息后点击 **发布**。发布后，点击 "**点击调试**"，会自动创建测试群聊，可在客户端查看：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-config-bot.png" width="600"/>
+
+4. 点击 **版本管理与发布**，创建新版本发布：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-publish-bot.png" width="700"/>
 
 ## 二、项目配置
 
-1. 在 **凭证与基础信息** 中获取 `Client ID` 和 `Client Secret`
+1. 点击 **凭证与基础信息**，获取 `Client ID` 和 `Client Secret`：
 
-2. 填入 `config.json`：
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-get-secret.png" width="700"/>
+
+2. 将以下配置加入项目根目录的 `config.json` 文件：
 
 ```json
 {
@@ -31,8 +45,12 @@ description: 将 CowAgent 接入钉钉应用
 pip3 install dingtalk_stream
 ```
 
-4. 启动项目后，在钉钉开发者后台点击 **事件订阅**，点击 **已完成接入，验证连接通道**，显示"连接接入成功"即表示配置完成
+4. 启动项目后，在钉钉开发者后台点击 **事件订阅**，点击 **已完成接入，验证连接通道**，显示 **连接接入成功** 即表示配置完成：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-event-sub.png" width="700"/>
 
 ## 三、使用
 
-与机器人私聊或将机器人拉入企业群中均可开启对话。
+与机器人私聊或将机器人拉入企业群中均可开启对话：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/dingtalk-hosting-demo.png" width="650"/>

docs/channels/feishu.mdx

@@ -3,65 +3,67 @@ title: 飞书
 description: 将 CowAgent 接入飞书应用
 ---
 
-通过自建应用将 CowAgent 接入飞书，支持 WebSocket 长连接（推荐）和 Webhook 两种事件接收模式。
+通过自建应用将 CowAgent 接入飞书，需要是飞书企业用户且具有企业管理权限。
 
 ## 一、创建企业自建应用
 
 ### 1. 创建应用
 
-进入 [飞书开发平台](https://open.feishu.cn/app/)，点击 **创建企业自建应用**，填写必要信息后创建。
+进入 [飞书开发平台](https://open.feishu.cn/app/)，点击 **创建企业自建应用**，填写必要信息后点击 **创建**：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-create-app.jpg" width="500"/>
 
 ### 2. 添加机器人能力
 
-在 **添加应用能力** 菜单中，为应用添加 **机器人** 能力。
+在 **添加应用能力** 菜单中，为应用添加 **机器人** 能力：
+
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-add-bot.jpg" width="800"/>
 
 ### 3. 配置应用权限
 
-点击 **权限管理**，粘贴以下权限配置，全选并批量开通：
+点击 **权限管理**，复制以下权限配置，粘贴到 **权限配置** 下方的输入框内，全选筛选出来的权限，点击 **批量开通** 并确认：
 
 ```
 im:message,im:message.group_at_msg,im:message.group_at_msg:readonly,im:message.p2p_msg,im:message.p2p_msg:readonly,im:message:send_as_bot,im:resource
 ```
 
+<img src="https://cdn.link-ai.tech/doc/feishu-hosting-add-auth2.png" width="800"/>
+
 ## 二、项目配置
 
-在 **凭证与基础信息** 中获取 `App ID` 和 `App Secret`，填入 `config.json`：
+1. 在 **凭证与基础信息** 中获取 `App ID` 和 `App Secret`：
 
-<Tabs>
-  <Tab title="WebSocket 模式（推荐）">
-    无需公网 IP，配置如下：
+<img src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/feishu-hosting-appid-secret.jpg" width="800"/>
 
-    ```json
-    {
-      "channel_type": "feishu",
-      "feishu_app_id": "YOUR_APP_ID",
-      "feishu_app_secret": "YOUR_APP_SECRET",
-      "feishu_event_mode": "websocket"
-    }
-    ```
+2. 将以下配置加入项目根目录的 `config.json` 文件：
 
-    需安装依赖：`pip3 install lark-oapi`
-  </Tab>
-  <Tab title="Webhook 模式">
-    需要公网 IP，配置如下：
+```json
+{
+  "channel_type": "feishu",
+  "feishu_app_id": "YOUR_APP_ID",
+  "feishu_app_secret": "YOUR_APP_SECRET",
+  "feishu_bot_name": "YOUR_BOT_NAME"
+}
+```
 
-    ```json
-    {
-      "channel_type": "feishu",
-      "feishu_app_id": "YOUR_APP_ID",
-      "feishu_app_secret": "YOUR_APP_SECRET",
-      "feishu_token": "VERIFICATION_TOKEN",
-      "feishu_event_mode": "webhook",
-      "feishu_port": 9891
-    }
-    ```
-  </Tab>
-</Tabs>
+| 参数 | 说明 |
+| --- | --- |
+| `feishu_app_id` | 飞书机器人应用 App ID |
+| `feishu_app_secret` | 飞书机器人 App Secret |
+| `feishu_bot_name` | 飞书机器人名称（创建应用时设置），群聊中使用依赖此配置 |
+
+配置完成后启动项目。
 
 ## 三、配置事件订阅
 
-1. 启动项目后，在飞书开放平台点击 **事件与回调**，选择 **长连接** 方式并保存
-2. 点击 **添加事件**，搜索 "接收消息"，选择 "接收消息v2.0"，确认添加
-3. 点击 **版本管理与发布**，创建版本并申请线上发布，审核通过后即可使用
+1. 成功运行项目后，在飞书开放平台点击 **事件与回调**，选择 **长连接** 方式，点击保存：
+
+<img src="https://cdn.link-ai.tech/doc/202601311731183.png" width="600"/>
+
+2. 点击下方的 **添加事件**，搜索 "接收消息"，选择 "**接收消息v2.0**"，确认添加。
+
+3. 点击 **版本管理与发布**，创建版本并申请 **线上发布**，在飞书客户端查看审批消息并审核通过：
+
+<img src="https://cdn.link-ai.tech/doc/202601311807356.png" width="600"/>
 
 完成后在飞书中搜索机器人名称，即可开始对话。

docs/channels/qq.mdx

@@ -0,0 +1,88 @@
+---
+title: QQ 机器人
+description: 将 CowAgent 接入 QQ 机器人（WebSocket 长连接模式）
+---
+
+> 通过 QQ 开放平台的机器人接口接入 CowAgent，支持 QQ 单聊、QQ 群聊（@机器人）、频道消息和频道私信，无需公网 IP，使用 WebSocket 长连接模式。
+
+<Note>
+  QQ 机器人通过 QQ 开放平台创建，使用 WebSocket 长连接接收消息，通过 OpenAPI 发送消息，无需公网 IP 和域名。
+</Note>
+
+## 一、创建 QQ 机器人
+
+> 进入[QQ 开放平台](https://q.qq.com)，QQ扫码登录，如果未注册开放平台账号，请先完成[账号注册](https://q.qq.com/#/register)。
+
+1.在 [QQ开放平台-机器人列表页](https://q.qq.com/#/apps)，点击创建机器人:
+
+<img src="https://cdn.link-ai.tech/doc/20260317162900.png" width="800"/>
+
+2.填写机器人名称、头像等基本信息，完成创建：
+
+<img src="https://cdn.link-ai.tech/doc/20260317163005.png" width="800"/>
+
+3.点击进入机器人配置页面，选择**开发管理**菜单，完成以下步骤：
+
+  - 复制并记录 **AppID**（机器人ID）
+  - 生成并记录 **AppSecret**（机器人秘钥）
+
+<img src="https://cdn.link-ai.tech/doc/20260317164955.png" width="800"/>
+
+## 二、配置和运行
+
+### 方式一：Web 控制台接入
+
+启动 Cow项目后打开 Web 控制台 (本地链接为: http://127.0.0.1:9899/ )，选择 **通道** 菜单，点击 **接入通道**，选择 **QQ 机器人**，填写上一步保存的 AppID 和 AppSecret，点击接入即可。
+
+<img src="https://cdn.link-ai.tech/doc/20260317165425.png" width="800"/>
+
+### 方式二：配置文件接入
+
+在 `config.json` 中添加以下配置：
+
+```json
+{
+  "channel_type": "qq",
+  "qq_app_id": "YOUR_APP_ID",
+  "qq_app_secret": "YOUR_APP_SECRET"
+}
+```
+
+| 参数 | 说明 |
+| --- | --- |
+| `qq_app_id` | QQ 机器人的 AppID，在开放平台开发管理中获取 |
+| `qq_app_secret` | QQ 机器人的 AppSecret，在开放平台开发管理中获取 |
+
+配置完成后启动程序，日志显示 `[QQ] ✅ Connected successfully` 即表示连接成功。
+
+
+## 三、使用
+
+在 QQ开放平台 - 管理 - **使用范围和人员** 菜单中，使用QQ客户端扫描 "添加到群和消息列表" 的二维码，即可开始与QQ机器人的聊天：
+
+<img src="https://cdn.link-ai.tech/doc/20260317165947.png" width="800"/>
+
+对话效果：
+<img src="https://cdn.link-ai.tech/doc/20260317171508.png" width="800"/>
+
+## 四、功能说明
+
+> 注意：若需在群聊及频道中使用QQ机器人，需完成发布上架审核并在使用范围配置权限使用范围。
+
+| 功能 | 支持情况 |
+| --- | --- |
+| QQ 单聊 | ✅ |
+| QQ 群聊（@机器人） | ✅ |
+| 频道消息（@机器人） | ✅ |
+| 频道私信 | ✅ |
+| 文本消息 | ✅ 收发 |
+| 图片消息 | ✅ 收发（群聊和单聊） |
+| 文件消息 | ✅ 发送（群聊和单聊） |
+| 定时任务 | ✅ 主动推送（每月每用户限 4 条） |
+
+
+## 五、注意事项
+
+- **被动消息限制**：QQ 单聊被动消息有效期为 60 分钟，每条消息最多回复 5 次；QQ 群聊被动消息有效期为 5 分钟。
+- **主动消息限制**：单聊和群聊每月主动消息上限为 4 条，在使用定时任务功能时需要注意这个限制
+- **事件权限**：默认订阅 `GROUP_AND_C2C_EVENT`（QQ群/单聊）和 `PUBLIC_GUILD_MESSAGES`（频道公域消息），如需其他事件类型请在开放平台申请权限。

docs/channels/web.mdx

@@ -1,9 +1,9 @@
 ---
-title: Web 网页
-description: 通过 Web 网页端使用 CowAgent
+title: Web 控制台
+description: 通过 Web 控制台使用 CowAgent
 ---
 
-Web 是 CowAgent 的默认通道，启动后会自动运行 Web 控制台，通过浏览器即可与 Agent 对话。
+Web 控制台是 CowAgent 的默认通道，启动后会自动运行，通过浏览器即可与 Agent 对话，并支持在线管理模型、技能、记忆、通道等配置。
 
 ## 配置
 
@@ -19,13 +19,57 @@ Web 是 CowAgent 的默认通道，启动后会自动运行 Web 控制台，通
 | `channel_type` | 设为 `web` | `web` |
 | `web_port` | Web 服务监听端口 | `9899` |
 
-## 使用
+## 访问地址
 
 启动项目后访问：
 
-- 本地运行：`http://localhost:9899/chat`
-- 服务器运行：`http://<server-ip>:9899/chat`
+- 本地运行：`http://localhost:9899`
+- 服务器运行：`http://<server-ip>:9899`
 
 <Note>
   请确保服务器防火墙和安全组已放行对应端口。
 </Note>
+
+## 功能介绍
+
+### 对话界面
+
+支持流式输出，可实时展示 Agent 的思考过程（Reasoning）和工具调用过程（Tool Calls），更直观地观察 Agent 的决策过程：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227180120.png" />
+
+### 模型管理
+
+支持在线管理模型配置，无需手动编辑配置文件：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173811.png" />
+
+### 技能管理
+
+支持在线查看和管理 Agent 技能（Skills）：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173403.png" />
+
+### 记忆管理
+
+支持在线查看和管理 Agent 记忆：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173349.png" />
+
+### 通道管理
+
+支持在线管理接入通道，支持实时连接/断开操作：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173331.png" />
+
+### 定时任务
+
+支持在线查看和管理定时任务，包括一次性任务、固定间隔、Cron 表达式等多种调度方式的可视化管理：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173704.png" />
+
+### 日志
+
+支持在线实时查看 Agent 运行日志，便于监控运行状态和排查问题：
+
+<img width="850" src="https://cdn.link-ai.tech/doc/20260227173514.png" />

docs/channels/wechatmp.mdx

@@ -7,21 +7,22 @@ CowAgent 支持接入个人订阅号和企业服务号两种公众号类型。
 
 | 类型 | 要求 | 特点 |
 | --- | --- | --- |
-| **个人订阅号** | 个人可申请 | 回复生成后需用户主动发消息获取 |
+| **个人订阅号** | 个人可申请 | 收到消息时会回复一条提示，回复生成后需用户主动发消息获取 |
 | **企业服务号** | 企业申请，需通过微信认证开通客服接口 | 回复生成后可主动推送给用户 |
 
 <Note>
-  公众号仅支持服务器和 Docker 部署，需额外安装扩展依赖：`pip3 install -r requirements-optional.txt`
+  公众号仅支持服务器和 Docker 部署，不支持本地运行。需额外安装扩展依赖：`pip3 install -r requirements-optional.txt`
 </Note>
 
 ## 一、个人订阅号
 
-在 `config.json` 中配置：
+在 `config.json` 中添加以下配置：
 
 ```json
 {
   "channel_type": "wechatmp",
-  "wechatmp_app_id": "YOUR_APP_ID",
+  "single_chat_prefix": [""],
+  "wechatmp_app_id": "wx73f9******d1e48",
   "wechatmp_app_secret": "YOUR_APP_SECRET",
   "wechatmp_aes_key": "",
   "wechatmp_token": "YOUR_TOKEN",
@@ -31,22 +32,37 @@ CowAgent 支持接入个人订阅号和企业服务号两种公众号类型。
 
 ### 配置步骤
 
-1. 在 [微信公众平台](https://mp.weixin.qq.com/) 的 **设置与开发 → 基本配置 → 服务器配置** 中获取参数
-2. 启用开发者密码，将服务器 IP 加入白名单
-3. 启动程序（监听 80 端口）
-4. 在公众号后台 **启用服务器配置**，URL 格式为 `http://{HOST}/wx`
+这些配置需要和 [微信公众号后台](https://mp.weixin.qq.com/advanced/advanced?action=dev&t=advanced/dev) 中的保持一致，进入页面后，在左侧菜单选择 **设置与开发 → 基本配置 → 服务器配置**，按下图进行配置：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103506.png" width="480"/>
+
+1. 在公众平台启用开发者密码（对应配置 `wechatmp_app_secret`），并将服务器 IP 填入白名单
+2. 按上图填写 `config.json` 中与公众号相关的配置，要与公众号后台的配置一致
+3. 启动程序，启动后会监听 80 端口（若无权限监听，则在启动命令前加上 `sudo`；若 80 端口已被占用，则关闭该占用进程）
+4. 在公众号后台 **启用服务器配置** 并提交，保存成功则表示已成功配置。注意 **"服务器地址(URL)"** 需要配置为 `http://{HOST}/wx` 的格式，其中 `{HOST}` 可以是服务器的 IP 或域名
+
+随后关注公众号并发送消息即可看到以下效果：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103522.png" width="720"/>
+
+由于受订阅号限制，回复内容较短的情况下（15s 内），可以立即完成回复，但耗时较长的回复则会先回复一句 "正在思考中"，后续需要用户输入任意文字主动获取答案，而服务号则可以通过客服接口解决这一问题。
+
+<Tip>
+  **语音识别**：可利用微信自带的语音识别功能，需要在公众号管理页面的 "设置与开发 → 接口权限" 页面开启 "接收语音识别结果"。
+</Tip>
 
 ## 二、企业服务号
 
-与个人订阅号流程基本相同，差异如下：
+企业服务号与上述个人订阅号的接入过程基本相同，差异如下：
 
-1. 在公众平台申请企业服务号并完成微信认证，确认已获得 **客服接口** 权限
-2. 在 `config.json` 中设置 `"channel_type": "wechatmp_service"`
-3. 即使是较长耗时的回复，也可以主动推送给用户
+1. 在公众平台申请企业服务号并完成微信认证，在接口权限中确认已获得 **客服接口** 的权限
+2. 在 `config.json` 中设置 `"channel_type": "wechatmp_service"`，其他配置与上述订阅号相同
+3. 交互效果上，即使是较长耗时的回复，也可以主动推送给用户，无需用户手动获取
 
 ```json
 {
   "channel_type": "wechatmp_service",
+  "single_chat_prefix": [""],
   "wechatmp_app_id": "YOUR_APP_ID",
   "wechatmp_app_secret": "YOUR_APP_SECRET",
   "wechatmp_aes_key": "",

docs/channels/wecom-bot.mdx

@@ -0,0 +1,73 @@
+---
+title: 企微智能机器人
+description: 将 CowAgent 接入企业微信智能机器人（长连接模式）
+---
+
+> 通过企业微信智能机器人接入CowAgent，支持企业内部单聊和内部群聊，无需公网 IP，使用 WebSocket 长连接模式，支持Markdown渲染和流式输出。
+
+<Note>
+  智能机器人与企业微信自建应用是两种不同的接入方式。智能机器人使用 WebSocket 长连接，无需服务器公网 IP 和域名，配置更简单。
+</Note>
+
+## 一、创建智能机器人
+
+1. 打开企业微信客户端，进入工作台，点击**智能机器人**：
+
+<img src="https://cdn.link-ai.tech/doc/20260316180959.png" width="800"/>
+
+2. 点击创建机器人 - 手动创建：
+
+<img src="https://cdn.link-ai.tech/doc/20260316181118.png" width="800"/>
+
+3. 右侧窗口拖到最下方，选择**API模式创建**：
+
+<img src="https://cdn.link-ai.tech/doc/20260316181215.png" width="800"/>
+
+4. 设置机器人名称、头像、可见范围，并选择**长连接模式**，记录下 **Bot ID** 和 **Secret** 信息后点击保存。
+
+## 二、配置和运行
+
+### 方式一：Web 控制台接入
+
+启动Cow项目后打开 Web 控制台 (本地链接为: http://127.0.0.1:9899/ )，选择 **通道** 菜单，点击 **接入通道**，选择 **企微智能机器人**，填写上一步保存的 Bot ID 和 Secret，点击接入即可。
+
+<img src="https://cdn.link-ai.tech/doc/20260316181711.png" width="800"/>
+
+### 方式二：配置文件接入
+
+在 `config.json` 中添加以下配置：
+
+```json
+{
+  "channel_type": "wecom_bot",
+  "wecom_bot_id": "YOUR_BOT_ID",
+  "wecom_bot_secret": "YOUR_SECRET"
+}
+```
+
+| 参数 | 说明 |
+| --- | --- |
+| `wecom_bot_id` | 智能机器人的 BotID |
+| `wecom_bot_secret` | 智能机器人的 Secret |
+
+配置完成后启动程序，日志显示 `[WecomBot] Subscribe success` 即表示连接成功。
+
+## 三、功能说明
+
+| 功能 | 支持情况 |
+| --- | --- |
+| 单聊 | ✅ |
+| 群聊（@机器人） | ✅ |
+| 文本消息 | ✅ 收发 |
+| 图片消息 | ✅ 收发 |
+| 文件消息 | ✅ 收发 |
+| 流式回复 | ✅ |
+| 定时任务主动推送 | ✅ |
+
+## 四、使用
+
+在企业微信中搜索创建的机器人名称，即可开始单聊对话。
+
+如需在企微内部群聊中使用，将机器人添加到群中，@机器人发送消息即可。
+
+<img src="https://cdn.link-ai.tech/doc/20260316182902.png" width="800"/>

docs/channels/wecom.mdx

@@ -1,5 +1,5 @@
 ---
-title: 企业微信
+title: 企微自建应用
 description: 将 CowAgent 接入企业微信自建应用
 ---
 
@@ -19,17 +19,35 @@ description: 将 CowAgent 接入企业微信自建应用
 
 ## 二、创建企业微信应用
 
-1. 在 [企业微信管理后台](https://work.weixin.qq.com/wework_admin/frame#profile) **我的企业** 中获取 **企业ID**
-2. 切换到 **应用管理**，点击创建应用，记录 `AgentId` 和 `Secret`
-3. 点击 **设置API接收**，配置应用接口：
-   - URL 格式为 `http://ip:port/wxcomapp`（认证企业需使用备案域名）
-   - 随机获取 `Token` 和 `EncodingAESKey` 并保存
+1. 在 [企业微信管理后台](https://work.weixin.qq.com/wework_admin/frame#profile) 点击 **我的企业**，在最下方获取 **企业ID**（后续填写到 `wechatcom_corp_id` 字段中）。
+
+2. 切换到 **应用管理**，点击创建应用：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103156.png" width="480"/>
+
+3. 进入应用创建页面，记录 `AgentId` 和 `Secret`：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103218.png" width="580"/>
+
+4. 点击 **设置API接收**，配置应用接口：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103211.png" width="520"/>
+
+- URL 格式为 `http://ip:port/wxcomapp`（认证企业需使用备案域名）
+- 随机获取 `Token` 和 `EncodingAESKey` 并保存
+
+<Note>
+  此时保存 API 接收配置会失败，因为程序还未启动，等项目运行后再回来保存。
+</Note>
 
 ## 三、配置和运行
 
+在 `config.json` 中添加以下配置（各参数与企业微信后台的对应关系见上方截图）：
+
 ```json
 {
   "channel_type": "wechatcom_app",
+  "single_chat_prefix": [""],
   "wechatcom_corp_id": "YOUR_CORP_ID",
   "wechatcomapp_token": "YOUR_TOKEN",
   "wechatcomapp_secret": "YOUR_SECRET",
@@ -48,12 +66,33 @@ description: 将 CowAgent 接入企业微信自建应用
 | `wechatcomapp_aes_key` | API 接收配置中的 EncodingAESKey |
 | `wechatcomapp_port` | 监听端口，默认 9898 |
 
-启动程序后，回到企业微信后台保存 **消息服务器配置**，并将服务器 IP 添加到 **企业可信IP** 中。
+配置完成后启动程序。当后台日志显示 `http://0.0.0.0:9898/` 时说明程序运行成功，需要将该端口对外开放（如在云服务器安全组中放行）。
+
+程序启动后，回到企业微信后台保存 **消息服务器配置**，保存成功后还需将服务器 IP 添加到 **企业可信IP** 中，否则无法收发消息：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103224.png" width="520"/>
 
 <Warning>
-  如遇到配置失败：1. 确保防火墙和安全组已放行端口；2. 检查各参数配置是否一致；3. 认证企业需配置备案域名。
+  如遇到 URL 配置回调不通过或配置失败：
+  1. 确保服务器防火墙关闭且安全组放行监听端口
+  2. 仔细检查 Token、Secret Key 等参数配置是否一致，URL 格式是否正确
+  3. 认证企业微信需要配置与主体一致的备案域名
 </Warning>
 
 ## 四、使用
 
-在企业微信中搜索应用名称即可直接对话。如需让外部微信用户使用，可在 **我的企业 → 微信插件** 中分享邀请关注二维码。
+在企业微信中搜索刚创建的应用名称，即可直接对话：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103228.png" width="720"/>
+
+如需让外部个人微信用户使用，可在 **我的企业 → 微信插件** 中分享邀请关注二维码，个人微信扫码关注后即可与应用对话：
+
+<img src="https://cdn.link-ai.tech/doc/20260228103232.png" width="520"/>
+
+## 常见问题
+
+需要确保已安装以下依赖：
+
+```bash
+pip install websocket-client pycryptodome
+```

docs/channels/weixin.mdx

@@ -0,0 +1,74 @@
+---
+title: 微信
+description: 将 CowAgent 接入个人微信（基于官方接口）
+---
+
+> 接入个人微信，扫码登录即可使用，支持文本、图片、语音、文件、视频等消息的私聊收发。通过微信官方API进行接入，无安全风险，接入后会在会话中新增一个机器人助手，不影响当前账号的使用。
+
+## 一、配置和运行
+
+### 方式一：Web 控制台接入
+
+启动 Cow 项目后打开 Web 控制台 (本地链接为: http://127.0.0.1:9899/ )，选择 **通道** 菜单，点击 **接入通道**，选择 **微信**，点击接入后按照提示扫码登录。
+
+<img src="https://cdn.link-ai.tech/doc/20260322195114.png" width="800" />
+
+### 方式二：配置文件接入
+
+在 `config.json` 中设置 `channel_type` 为 `weixin`：
+
+```json
+{
+  "channel_type": "weixin"
+}
+```
+
+启动程序后，终端会显示二维码，使用微信扫码授权即可完成登录。
+
+<img src="https://cdn.link-ai.tech/doc/20260322195509.png" width="800" />
+
+
+<Note>
+  1. 兼容历史配置：`channel_type` 设为 `wx` 同样可以启用微信通道。
+  2. 注意微信客户端需要更新至 8.0.69 版本或以上
+</Note>
+
+## 二、使用说明
+
+微信扫码并进行授权确认后，即可完成接入并开始对话。接入微信后会在对话中创建出一个机器人助理，不会对已有账号的正常使用有任何影响。
+
+> 你可以通过搜索"微信ClawBot"随时找到这个机器人，还可以修改这个机器人的头像、备注等信息，将机器人置顶在消息列表等。
+
+<img src="https://cdn.link-ai.tech/doc/83ae8251d896219fde4803f4205205be.jpg" width="250" />
+
+
+
+## 三、登录说明
+
+### 扫码登录
+
+首次启动时，终端会显示一个二维码（有效期约 2 分钟）。使用微信扫描二维码并在手机上确认后即可完成登录。
+
+- 二维码过期后会自动刷新并重新显示
+- `requirements.txt` 中已默认包含 `qrcode` 依赖，安装后可在终端直接渲染二维码图案
+
+### 凭证保存
+
+登录成功后，凭证会自动保存至 `~/.weixin_cow_credentials.json`，下次启动时无需重新扫码。
+
+如需重新登录，删除该凭证文件后重启程序即可。
+
+### Session 过期
+
+当微信 session 过期时（errcode -14），程序会自动清除旧凭证并重新发起扫码登录，无需手动干预。
+
+## 四、功能说明
+
+| 功能 | 支持情况 |
+| --- | --- |
+| 单聊 | ✅ |
+| 文本消息 | ✅ 收发 |
+| 图片消息 | ✅ 收发 |
+| 文件消息 | ✅ 收发 |
+| 视频消息 | ✅ 收发 |
+| 语音消息 | ✅ 接收 (自带语音识别) |

docs/docs.json

@@ -59,7 +59,8 @@
                 "group": "安装部署",
                 "pages": [
                   "guide/quick-start",
-                  "guide/manual-install"
+                  "guide/manual-install",
+                  "guide/upgrade"
                 ]
               }
             ]
@@ -80,7 +81,8 @@
                   "models/gemini",
                   "models/openai",
                   "models/deepseek",
-                  "models/linkai"
+                  "models/linkai",
+                  "models/coding-plan"
                 ]
               }
             ]
@@ -153,9 +155,12 @@
               {
                 "group": "接入渠道",
                 "pages": [
+                  "channels/weixin",
                   "channels/web",
                   "channels/feishu",
                   "channels/dingtalk",
+                  "channels/wecom-bot",
+                  "channels/qq",
                   "channels/wecom",
                   "channels/wechatmp"
                 ]
@@ -169,6 +174,8 @@
                 "group": "发布记录",
                 "pages": [
                   "releases/overview",
+                  "releases/v2.0.4",
+                  "releases/v2.0.3",
                   "releases/v2.0.2",
                   "releases/v2.0.1",
                   "releases/v2.0.0"
@@ -222,7 +229,8 @@
                   "en/models/gemini",
                   "en/models/openai",
                   "en/models/deepseek",
-                  "en/models/linkai"
+                  "en/models/linkai",
+                  "en/models/coding-plan"
                 ]
               }
             ]
@@ -295,9 +303,12 @@
               {
                 "group": "Platforms",
                 "pages": [
+                  "en/channels/weixin",
                   "en/channels/web",
                   "en/channels/feishu",
                   "en/channels/dingtalk",
+                  "en/channels/wecom-bot",
+                  "en/channels/qq",
                   "en/channels/wecom",
                   "en/channels/wechatmp"
                 ]
@@ -311,6 +322,8 @@
                 "group": "Release Notes",
                 "pages": [
                   "en/releases/overview",
+                  "en/releases/v2.0.4",
+                  "en/releases/v2.0.2",
                   "en/releases/v2.0.1",
                   "en/releases/v2.0.0"
                 ]
@@ -318,6 +331,156 @@
             ]
           }
         ]
+      },
+      {
+        "language": "ja",
+        "tabs": [
+          {
+            "tab": "紹介",
+            "groups": [
+              {
+                "group": "概要",
+                "pages": [
+                  "ja/intro/index",
+                  "ja/intro/architecture",
+                  "ja/intro/features"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "クイックスタート",
+            "groups": [
+              {
+                "group": "インストール",
+                "pages": [
+                  "ja/guide/quick-start",
+                  "ja/guide/manual-install",
+                  "ja/guide/upgrade"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "モデル",
+            "groups": [
+              {
+                "group": "モデル設定",
+                "pages": [
+                  "ja/models/index",
+                  "ja/models/minimax",
+                  "ja/models/glm",
+                  "ja/models/qwen",
+                  "ja/models/kimi",
+                  "ja/models/doubao",
+                  "ja/models/claude",
+                  "ja/models/gemini",
+                  "ja/models/openai",
+                  "ja/models/deepseek",
+                  "ja/models/linkai",
+                  "ja/models/coding-plan"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "ツール",
+            "groups": [
+              {
+                "group": "ツールシステム",
+                "pages": [
+                  "ja/tools/index"
+                ]
+              },
+              {
+                "group": "内蔵ツール",
+                "pages": [
+                  "ja/tools/read",
+                  "ja/tools/write",
+                  "ja/tools/edit",
+                  "ja/tools/ls",
+                  "ja/tools/bash",
+                  "ja/tools/send",
+                  "ja/tools/memory",
+                  "ja/tools/env-config",
+                  "ja/tools/browser"
+                ]
+              },
+              {
+                "group": "オプションツール",
+                "pages": [
+                  "ja/tools/web-search",
+                  "ja/tools/scheduler"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "スキル",
+            "groups": [
+              {
+                "group": "スキルシステム",
+                "pages": [
+                  "ja/skills/index",
+                  "ja/skills/skill-creator"
+                ]
+              },
+              {
+                "group": "内蔵スキル",
+                "pages": [
+                  "ja/skills/image-vision",
+                  "ja/skills/linkai-agent",
+                  "ja/skills/web-fetch"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "メモリ",
+            "groups": [
+              {
+                "group": "メモリシステム",
+                "pages": [
+                  "ja/memory"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "チャネル",
+            "groups": [
+              {
+                "group": "プラットフォーム",
+                "pages": [
+                  "ja/channels/weixin",
+                  "ja/channels/web",
+                  "ja/channels/feishu",
+                  "ja/channels/dingtalk",
+                  "ja/channels/wecom-bot",
+                  "ja/channels/qq",
+                  "ja/channels/wecom",
+                  "ja/channels/wechatmp"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "リリース",
+            "groups": [
+              {
+                "group": "リリースノート",
+                "pages": [
+                  "ja/releases/overview",
+                  "ja/releases/v2.0.4",
+                  "ja/releases/v2.0.3",
+                  "ja/releases/v2.0.2",
+                  "ja/releases/v2.0.1",
+                  "ja/releases/v2.0.0"
+                ]
+              }
+            ]
+          }
+        ]
       }
     ]
   }