feat: release 2.0.0

Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat
docs: update 2.0 README.md
2026-06-02 18:17:11 +08:00 · 2026-02-03 14:48:30 +08:00 · 2026-02-03 12:20:04 +08:00 · 2026-02-03 12:19:36 +08:00 · 2026-02-03 03:18:49 +08:00 · 2026-02-03 03:17:11 +08:00
197 changed files with 25225 additions and 1928 deletions
--- a/.flake8
+++ b/.flake8
@@ -1,13 +0,0 @@
-[flake8]
-max-line-length = 176
-select = E303,W293,W291,W292,E305,E231,E302
-exclude =
-    .tox,
-    __pycache__,
-    *.pyc,
-    .env
-    venv/*
-    .venv/*
-    reports/*
-    dist/*
-    lib/*
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,9 @@ tmp
 plugins.json
 itchat.pkl
 *.log
+logs/
+workspace
+config.yaml
 user_datas.pkl
 chatgpt_tool_hub/
 plugins/**/
@@ -30,4 +33,8 @@ plugins/banwords/lib/__pycache__
 !plugins/role
 !plugins/keyword
 !plugins/linkai
+!plugins/agent
 client_config.json
+ref/
+.cursor/
+local/
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,30 +0,0 @@
-repos:
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-      - id: fix-byte-order-marker
-      - id: check-case-conflict
-      - id: check-merge-conflict
-      - id: debug-statements
-      - id: pretty-format-json
-        types: [text]
-        files: \.json(.template)?$
-        args: [ --autofix , --no-ensure-ascii, --indent=2, --no-sort-keys]
-      - id: trailing-whitespace
-        exclude: '(\/|^)lib\/'
-        args: [ --markdown-linebreak-ext=md ]
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        exclude: '(\/|^)lib\/'
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-      - id: black
-        exclude: '(\/|^)lib\/'
-  - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
-    hooks:
-      - id: flake8
-        exclude: '(\/|^)lib\/'
--- a/README.md
+++ b/README.md
@@ -1,99 +1,104 @@
+<p align="center"><img src= "https://github.com/user-attachments/assets/eca9a9ec-8534-4615-9e0f-96c5ac1d10a3" alt="Chatgpt-on-Wechat" width="550" /></p>
+
+<p align="center">
+   <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/>
+</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。
+
+📖能力介绍：[CowAgent 2.0](/docs/agent.md)
+
 # 简介

-> chatgpt-on-wechat（简称CoW）项目是基于大模型的智能对话机器人，支持微信公众号、企业微信应用、飞书、钉钉接入，可选择GPT3.5/GPT4.0/Claude/Gemini/LinkAI/ChatGLM/KIMI/文心一言/讯飞星火/通义千问/LinkAI，能处理文本、语音和图片，通过插件访问操作系统和互联网等外部资源，支持基于自有知识库定制企业AI应用。
+> 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FTS5 not available, using LIKE-based keyword searc度扩展的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：

-最新版本支持的功能如下：
-
-  ✅   **多端部署：** 有多种部署方式可选择且功能完备，目前已支持微信公众号、企业微信应用、飞书、钉钉等部署方式
-  ✅   **基础对话：** 私聊及群聊的消息智能回复，支持多轮会话上下文记忆，支持 GPT-3.5, GPT-4, GPT-4o, Claude-3.5, Gemini, 文心一言, 讯飞星火, 通义千问，ChatGLM-4，Kimi(月之暗面), MiniMax
-  ✅   **语音能力：** 可识别语音消息，通过文字或语音回复，支持 azure, baidu, google, openai(whisper/tts) 等多种语音模型
-  ✅   **图像能力：** 支持图片生成、图片识别、图生图（如照片修复），可选择 Dall-E-3, stable diffusion, replicate, midjourney, CogView-3, vision模型
-  ✅   **丰富插件：** 支持个性化插件扩展，已实现多角色切换、文字冒险、敏感词过滤、聊天记录总结、文档总结和对话、联网搜索等插件
-  ✅   **知识库：** 通过上传知识库文件自定义专属机器人，可作为数字分身、智能客服、私域助手使用，基于 [LinkAI](https://link-ai.tech) 实现
+-  ✅  **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
+-  ✅  **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、通义千问, Kimi等国内外主流模型厂商
+-  ✅  **多端部署：** 支持运行在本地计算机或服务器，可集成到网页、飞书、钉钉、微信公众号、企业微信应用中使用
+-  ✅  **知识库：** 集成企业知识库能力，让Agent成为专属数字员工，基于[LinkAI](https://link-ai.tech)平台实现

 ## 声明

-1. 本项目遵循 [MIT开源协议](/LICENSE)，仅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
-2. 境内使用该项目时，请使用国内厂商的大模型服务，并进行必要的内容安全审核及过滤
-3. 本项目主要接入协同办公平台，推荐使用公众号、企微自建应用、钉钉、飞书等接入通道，其他通道为历史产物已不维护
-4. 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
+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

 ## 演示

-DEMO视频：https://cdn.link-ai.tech/doc/cow_demo.mp4
+使用说明(Agent模式)：[CowAgent介绍](/docs/agent.md)
+
+DEMO视频(对话模式)：https://cdn.link-ai.tech/doc/cow_demo.mp4

 ## 社区

 添加小助手微信加入开源项目交流群：

-<img width="160" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png">
+<img width="140" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/open-community.png">

-<br>
+<br/>

 # 企业服务

-<a href="https://link-ai.tech" target="_blank"><img width="800" src="https://cdn.link-ai.tech/image/link-ai-intro.jpg"></a>
+<a href="https://link-ai.tech" target="_blank"><img width="720" 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应用平台，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、私有化部署多种模式。
+> [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 目前 已在私域运营、智能客服、企业效率助手等场景积累了丰富的 AI 解决方案， 在电商、文教、健康、新消费、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。

-**企业服务和产品咨询** 可联系产品顾问：
+**产品咨询和企业服务** 可联系产品客服：

-<img width="160" src="https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/github-product-consult.png">
+<img width="150" src="https://cdn.link-ai.tech/portal/linkai-customer-service.png">

-<br>
+<br/>

 # 🏷 更新日志

->**2024.07.05：** [1.6.8版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.6.8) 和 [1.6.7版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.6.7)，Claude3.5, Gemini 1.5 Pro, MiniMax模型、工作流图片输入、模型列表完善
+>**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。

->**2024.06.04：** [1.6.6版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.6.6) 和 [1.6.5版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.6.5)，gpt-4o模型、钉钉流式卡片、讯飞语音识别/合成
+>**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

->**2024.04.26：** [1.6.0版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.6.0)，新增 Kimi 接入、gpt-4-turbo版本升级、文件总结和语音识别问题修复
+>**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

->**2024.03.26：** [1.5.8版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.8) 和 [1.5.7版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.7)，新增 GLM-4、Claude-3 模型，edge-tts 语音支持
+>**2024.12.13：** [1.7.4版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.4) 新增 Gemini 2.0 模型、新增web channel、解决内存泄漏问题、解决 `#reloadp` 命令重载不生效问题

->**2024.01.26：** [1.5.6版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.6) 和 [1.5.5版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.5)，钉钉接入，tool插件升级，4-turbo模型更新
+>**2024.10.31：** [1.7.3版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.7.3) 程序稳定性提升、数据库功能、Claude模型优化、linkai插件优化、离线通知

->**2023.11.11：** [1.5.3版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.3) 和 [1.5.4版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.4)，新增通义千问模型、Google Gemini
+更多更新历史请查看: [更新日志](/docs/release/history.md)

->**2023.11.10：** [1.5.2版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.2)，新增飞书通道、图像识别对话、黑名单配置
-
->**2023.11.10：** [1.5.0版本](https://github.com/zhayujie/chatgpt-on-wechat/releases/tag/1.5.0)，新增 `gpt-4-turbo`, `dall-e-3`, `tts` 模型接入，完善图像理解&生成、语音识别&生成的多模态能力
-
->**2023.10.16：** 支持通过意图识别使用LinkAI联网搜索、数学计算、网页访问等插件，参考[插件文档](https://docs.link-ai.tech/platform/plugins)
-
->**2023.09.26：** 插件增加 文件/文章链接 一键总结和对话的功能，使用参考：[插件说明](https://github.com/zhayujie/chatgpt-on-wechat/tree/master/plugins/linkai#3%E6%96%87%E6%A1%A3%E6%80%BB%E7%BB%93%E5%AF%B9%E8%AF%9D%E5%8A%9F%E8%83%BD)
-
->**2023.08.08：** 接入百度文心一言模型，通过 [插件](https://github.com/zhayujie/chatgpt-on-wechat/tree/master/plugins/linkai) 支持 Midjourney 绘图
-
->**2023.06.12：** 接入 [LinkAI](https://link-ai.tech/console) 平台，可在线创建领域知识库，打造专属客服机器人。使用参考 [接入文档](https://link-ai.tech/platform/link-app/wechat)。
-
-更早更新日志查看: [归档日志](/docs/version/old-version.md)
-
-<br>
+<br/>

 # 🚀 快速开始

-快速开始详细文档：[项目搭建文档](https://docs.link-ai.tech/cow/quick-start)
+项目提供了一键安装、配置、启动、管理程序的脚本，推荐使用脚本快速运行，也可以根据下文中的详细指引一步步安装运行。
+
+在终端执行以下命令：
+
+```bash
+bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)
+```
+
+脚本使用说明：[一键运行脚本](https://github.com/zhayujie/chatgpt-on-wechat/wiki/CowAgentQuickStart)
+

 ## 一、准备

-### 1. 账号注册
+### 1. 模型API

-项目默认使用OpenAI接口，需前往 [OpenAI注册页面](https://beta.openai.com/signup) 创建账号，创建完账号则前往 [API管理页面](https://beta.openai.com/account/api-keys) 创建一个 API Key 并保存下来，后面需要在项目中配置这个key。接口需要海外网络访问及绑定信用卡支付。
+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)。

-> 默认对话模型是 openai 的 gpt-3.5-turbo，计费方式是约每 1000tokens (约750个英文单词 或 500汉字，包含请求和回复) 消耗 $0.002，图片生成是Dell E模型，每张消耗 $0.016。
+> 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： Claude(claude-sonnet-4-5、claude-sonnet-4-0)、Gemini(gemini-3-flash-preview、gemini-3-pro-preview)、GLM(glm-4.7)、MiniMAx(MiniMax-M2.1)、Qwen(qwen3-max)

-项目同时也支持使用 LinkAI 接口，无需代理，可使用 Kimi、文心、讯飞、GPT-3.5、GPT-4o 等模型，支持 定制化知识库、联网搜索、MJ绘图、文档总结、工作流等能力。修改配置即可一键使用，参考 [接入文档](https://link-ai.tech/platform/link-app/wechat)。
+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)。

-### 2.运行环境
+### 2.环境安装

-支持 Linux、MacOS、Windows 系统（可在Linux服务器上长期运行)，同时需安装 `Python`。
-> 建议Python版本在 3.7.1~3.9.X 之间，推荐3.8版本，3.10及以上版本在 MacOS 可用，其他系统上不确定能否正常运行。
+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：Docker 或 Railway 部署无需安装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：**

@@ -102,10 +107,10 @@ git clone https://github.com/zhayujie/chatgpt-on-wechat
 cd chatgpt-on-wechat/
 ```

-注: 如遇到网络问题可选择国内镜像 https://gitee.com/zhayujie/chatgpt-on-wechat
+若遇到网络问题可使用国内仓库地址：https://gitee.com/zhayujie/chatgpt-on-wechat

 **(2) 安装核心依赖 (必选)：**
-> 能够使用`itchat`创建机器人，并具有文字交流功能所需的最小依赖集合。
+
 ```bash
 pip3 install -r requirements.txt
 ```
@@ -115,7 +120,7 @@ pip3 install -r requirements.txt
 ```bash
 pip3 install -r requirements-optional.txt
 ```
-> 如果某项依赖安装失败可注释掉对应的行再继续
+如果某项依赖安装失败可注释掉对应的行后重试。

 ## 二、配置

@@ -125,130 +130,115 @@ pip3 install -r requirements-optional.txt
  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格式的完整）：
+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
+# config.json 文件内容示例
 {
-  "model": "gpt-3.5-turbo",                                   # 模型名称, 支持 gpt-3.5-turbo, gpt-4, gpt-4-turbo, wenxin, xunfei, glm-4, claude-3-haiku, moonshot
-  "open_ai_api_key": "YOUR API KEY",                          # 如果使用openAI模型则填入上面创建的 OpenAI API KEY
-  "proxy": "",                                                # 代理客户端的ip和端口，国内环境开启代理的需要填写该项，如 "127.0.0.1:7890"
-  "single_chat_prefix": ["bot", "@bot"],                      # 私聊时文本需要包含该前缀才能触发机器人回复
-  "single_chat_reply_prefix": "[bot] ",                       # 私聊时自动回复的前缀，用于区分真人
-  "group_chat_prefix": ["@bot"],                              # 群聊时包含该前缀则会触发机器人回复
-  "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_chat_in_one_session": ["ChatGPT测试群"],              # 支持会话上下文共享的群名称  
-  "image_create_prefix": ["画", "看", "找"],                   # 开启图片回复的前缀
-  "conversation_max_tokens": 1000,                            # 支持上下文记忆的最多字符数
+  "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": "claude-sonnet-4-5",                               # 模型名称
+  "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
+  "open_ai_api_key": "",                                      # OpenAI API Key
+  "open_ai_api_base": "https://api.openai.com/v1",            # OpenAI API 地址
+  "gemini_api_key": "",                                       # Gemini API Key
+  "gemini_api_base": "https://generativelanguage.googleapis.com", # Gemini API地址
+  "zhipu_ai_api_key": "",                                     # 智谱GLM API Key
+  "minimax_api_key": "",                                      # MiniMax API Key
+  "dashscope_api_key": "",                                    # 百炼(通义千问)API Key
+  "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"
  "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
-  "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。",  # 人格描述
-  # 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。
-  "subscribe_msg": "感谢您的关注！\n这里是ChatGPT，可以自由对话。\n支持语音对话。\n支持图片输出，画字开头的消息将按要求创作图片。\n支持角色扮演和文字冒险等丰富插件。\n输入{trigger_prefix}#help 查看详细指令。",
-  "use_linkai": false,                                        # 是否使用LinkAI接口，默认关闭，开启后可国内访问，使用知识库和MJ
-  "linkai_api_key": "",                                       # LinkAI Api Key
-  "linkai_app_code": ""                                       # LinkAI 应用或工作流code
+  "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
 }
 ```
-**配置说明：**

-**1.个人聊天**
+**配置补充说明:** 

-+ 个人聊天中，需要以 "bot"或"@bot" 为开头的内容触发机器人，对应配置项 `single_chat_prefix` (如果不需要以前缀触发可以填写  `"single_chat_prefix": [""]`)
-+ 机器人回复的内容会以 "[bot] " 作为前缀， 以区分真人，对应的配置项为 `single_chat_reply_prefix` (如果不需要前缀可以填写 `"single_chat_reply_prefix": ""`)
-
-**2.群组聊天**
-
-+ 群组聊天中，群名称需配置在 `group_name_white_list ` 中才能开启群聊自动回复。如果想对所有群聊生效，可以直接填写 `"group_name_white_list": ["ALL_GROUP"]`
-+ 默认只要被人 @ 就会触发机器人自动回复；另外群聊天中只要检测到以 "@bot" 开头的内容，同样会自动回复（方便自己触发），这对应配置项 `group_chat_prefix`
-+ 可选配置: `group_name_keyword_white_list`配置项支持模糊匹配群名称，`group_chat_keyword`配置项则支持模糊匹配群消息内容，用法与上述两个配置项相同。（Contributed by [evolay](https://github.com/evolay))
-+ `group_chat_in_one_session`：使群聊共享一个会话上下文，配置 `["ALL_GROUP"]` 则作用于所有群聊
-
-**3.语音识别**
+<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 `"voice_reply_voice": true` 将开启语音回复语音（同时作用于私聊和群聊）
+</details>

-**4.其他配置**
+<details>
+<summary>2. 其他配置</summary>

-+ `model`: 模型名称，目前支持 `gpt-3.5-turbo`, `gpt-4o`, `gpt-4-turbo`, `gpt-4`, `wenxin` , `claude` , `gemini`, `glm-4`,  `xunfei`, `moonshot`等，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
-+ `temperature`,`frequency_penalty`,`presence_penalty`: Chat API接口参数，详情参考[OpenAI官方文档。](https://platform.openai.com/docs/api-reference/chat)
-+ `proxy`：由于目前 `openai` 接口国内无法访问，需配置代理客户端的地址，详情参考  [#351](https://github.com/zhayujie/chatgpt-on-wechat/issues/351)
-+ 对于图像生成，在满足个人或群组触发条件外，还需要额外的关键词前缀来触发，对应配置 `image_create_prefix `
-+ 关于OpenAI对话及图片接口的参数配置（内容自由度、回复字数限制、图片大小等），可以参考 [对话接口](https://beta.openai.com/docs/api-reference/completions) 和 [图像接口](https://beta.openai.com/docs/api-reference/completions)  文档，在[`config.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py)中检查哪些参数在本项目中是可配置的。
-+ `conversation_max_tokens`：表示能够记忆的上下文最大字数（一问一答为一组对话，如果累积的对话字数超出限制，就会优先移除最早的一组对话）
-+ `rate_limit_chatgpt`，`rate_limit_dalle`：每分钟最高问答速率、画图速率，超速后排队按序处理。
-+ `clear_memory_commands`: 对话内指令，主动清空前文记忆，字符串数组可自定义指令别名。
-+ `hot_reload`: 程序退出后，暂存等于状态，默认关闭。
-+ `character_desc` 配置中保存着你对机器人说的一段话，他会记住这段话并作为他的设定，你可以为他定制任何人格      (关于会话上下文的更多内容参考该 [issue](https://github.com/zhayujie/chatgpt-on-wechat/issues/43))
+ `model`: 模型名称，Agent模式下推荐使用 `claude-sonnet-4-5`、`claude-sonnet-4-0`、`gemini-3-flash-preview`、`gemini-3-pro-preview`、`glm-4.7`、`MiniMax-M2.1`、`qwen3-max`，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>

-**5.LinkAI配置 (可选)**
+<details>
+<summary>5. LinkAI配置</summary>

-+ `use_linkai`: 是否使用LinkAI接口，开启后可国内访问，使用知识库和 `Midjourney` 绘画, 参考 [文档](https://link-ai.tech/platform/link-app/wechat)
+ `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)
 + `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
+ `linkai_app_code`: LinkAI 应用或工作流的code，选填，普通对话模式中使用。
+</details>

-**本说明文档可能会未及时更新，当前所有可选的配置项均在该[`config.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/config.py)中列出。**
+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。

 ## 三、运行

 ### 1.本地运行

-如果是开发机 **本地运行**，直接在项目根目录下执行：
+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
 ```

-终端输出二维码后，进行扫码登录，当输出 "Start auto replying" 时表示自动回复程序已经成功运行了（注意：用于登录的账号需要在支付处已完成实名认证）。扫码登录后你的账号就成为机器人了，可以在手机端通过配置的关键词触发自动回复 (任意好友发送消息给你，或是自己发消息给好友)，参考[#142](https://github.com/zhayujie/chatgpt-on-wechat/issues/142)。
+运行后默认会启动web服务，可通过访问 `http://localhost:9899/chat` 在网页端对话。
+
+如果需要接入其他应用通道只需修改 `config.json` 配置文件中的 `channel_type` 参数，详情参考：[通道说明](#通道说明)。
+

 ### 2.服务器部署

-使用nohup命令在后台运行程序：
+在服务器中可使用 `nohup` 命令在后台运行程序：

 ```bash
-nohup python3 app.py & tail -f nohup.out          # 在后台运行程序并通过日志输出二维码
+nohup python3 app.py & tail -f nohup.out
 ```
-扫码登录后程序即可运行于服务器后台，此时可通过 `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，`scripts` 目录下有一键运行、关闭程序的脚本供使用。

-> **多账号支持：** 将项目复制多份，分别启动程序，用不同账号扫码登录即可实现同时运行。
+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`。 

-> **特殊指令：** 用户向机器人发送 **#reset** 即可清空该用户的上下文记忆。
+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。


 ### 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。
+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`，安装成功的表现是执行 `docker -v` 和 `docker-compose version` (或 docker compose version) 可以查看到版本号，可前往 [docker官网](https://docs.docker.com/engine/install/) 进行下载。
+> 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://open-1317903499.cos.ap-guangzhou.myqcloud.com/docker-compose.yml
+wget https://cdn.link-ai.tech/code/cow/docker-compose.yml
 ```

-下载完成后打开 `docker-compose.yml` 修改所需配置，如 `OPEN_AI_API_KEY` 和 `GROUP_NAME_WHITE_LIST` 等。
+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。

 **(2) 启动容器**

 在 `docker-compose.yml` 所在目录下执行以下命令启动容器：

 ```bash
-sudo docker compose up -d
+sudo docker compose up -d         # 若docker-compose为 1.X 版本，则执行 `sudo  docker-compose up -d`
 ```

-运行 `sudo docker ps` 能查看到 NAMES 为 chatgpt-on-wechat 的容器即表示运行成功。
-
-注意：
-
- - 如果 `docker-compose` 是 1.X 版本 则需要执行 `sudo  docker-compose up -d` 来启动容器
- - 该命令会自动去 [docker hub](https://hub.docker.com/r/zhayujie/chatgpt-on-wechat) 拉取 latest 版本的镜像，latest 镜像会在每次项目 release 新的版本时生成
-
-最后运行以下命令可查看容器运行日志，扫描日志中的二维码即可完成登录：
+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
@@ -263,34 +253,500 @@ sudo docker logs -f chatgpt-on-wechat
 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/)

-### 4. Railway部署

-> Railway 每月提供5刀和最多500小时的免费额度。 (07.11更新: 目前大部分账号已无法免费部署)
+## 模型说明

-1. 进入 [Railway](https://railway.app/template/qApznZ?referralCode=RC3znh)
-2. 点击 `Deploy Now` 按钮。
-3. 设置环境变量来重载程序运行的参数，例如`open_ai_api_key`, `character_desc`。
+以下对所有可支持的模型的配置和使用方法进行说明，模型接口实现在项目的 `models/` 目录下。
+
+<details>
+<summary>OpenAI</summary>
+
+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",
+    "open_ai_api_key": "YOUR_API_KEY",
+    "open_ai_api_base": "https://api.openai.com/v1",
+    "bot_type": "chatGPT"
+}
+```
+
+ - `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
+ - `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`
+</details>
+
+<details>
+<summary>LinkAI</summary>
+
+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
+{
+    "use_linkai": true,
+    "linkai_api_key": "YOUR API KEY",
+    "linkai_app_code": "YOUR APP CODE"
+}
+```
+
+ `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
+</details>
+
+<details>
+<summary>Claude</summary>
+
+1. API Key创建：在 [Claude控制台](https://console.anthropic.com/settings/keys) 创建API Key
+
+2. 填写配置
+
+```json
+{
+    "model": "claude-sonnet-4-5",
+    "claude_api_key": "YOUR_API_KEY"
+}
+```
+ - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
+</details>
+
+<details>
+<summary>Gemini</summary>
+
+API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建API Key ，配置如下
+```json
+{
+    "model": "gemini-3-flash-preview",
+    "gemini_api_key": ""
+}
+```
+ - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
+</details>
+
+<details>
+<summary>DeepSeek</summary>
+
+1. API Key创建：在 [DeepSeek平台](https://platform.deepseek.com/api_keys) 创建API Key 
+
+2. 填写配置
+
+```json
+{
+    "model": "deepseek-chat",
+    "open_ai_api_key": "sk-xxxxxxxxxxx",
+    "open_ai_api_base": "https://api.deepseek.com/v1", 
+    "bot_type": "chatGPT"
+
+}
+```
+
+ - `bot_type`: OpenAI兼容方式
+ - `model`: 可填 `deepseek-chat、deepseek-reasoner`，分别对应的是 DeepSeek-V3 和 DeepSeek-R1 模型
+ - `open_ai_api_key`: DeepSeek平台的 API Key
+ - `open_ai_api_base`: DeepSeek平台 BASE URL
+</details>
+
+<details>
+<summary>通义千问 (Qwen)</summary>
+
+方式一：官方SDK接入，配置如下(推荐)：
+
+```json
+{
+    "model": "qwen3-max",
+    "dashscope_api_key": "sk-qVxxxxG"
+}
+```
+ - `model`: 可填写 `qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
+ - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
+ 
+方式二：OpenAI兼容方式接入，配置如下：
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "qwen3-max",
+  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+  "open_ai_api_key": "sk-qVxxxxG"
+}
+```
+- `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_key`: 通义千问的 API-KEY
+</details>
+
+<details>
+<summary>MiniMax</summary>
+
+方式一：官方接入，配置如下(推荐)：
+
+```json
+{
+    "model": "MiniMax-M2.1",
+    "minimax_api_key": ""
+}
+```
+ - `model`: 可填写 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
+ - `minimax_api_key`：MiniMax平台的API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建
+ 
+方式二：OpenAI兼容方式接入，配置如下：
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "MiniMax-M2.1",
+  "open_ai_api_base": "https://api.minimaxi.com/v1",
+  "open_ai_api_key": ""
+}
+```
+- `bot_type`: OpenAI兼容方式
+- `model`: 可填 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
+- `open_ai_api_base`: MiniMax平台API的 BASE URL
+- `open_ai_api_key`: MiniMax平台的API-KEY
+</details>
+
+<details>
+<summary>智谱AI (GLM)</summary>
+
+方式一：官方接入，配置如下(推荐)：
+
+```json
+{
+  "model": "glm-4.7",
+  "zhipu_ai_api_key": ""
+}
+```
+ - `model`: 可填 `glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm-4系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
+ - `zhipu_ai_api_key`: 智谱AI平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建
+ 
+方式二：OpenAI兼容方式接入，配置如下：
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "glm-4.7",
+  "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
+  "open_ai_api_key": ""
+}
+```
+- `bot_type`: OpenAI兼容方式
+- `model`: 可填 `glm-4.7、glm-4.6、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
+- `open_ai_api_base`: 智谱AI平台的 BASE URL
+- `open_ai_api_key`: 智谱AI平台的 API KEY
+</details>
+
+<details>
+<summary>Kimi (Moonshot)</summary>
+
+方式一：官方接入，配置如下：
+
+```json
+{
+    "model": "moonshot-v1-128k",
+    "moonshot_api_key": ""
+}
+```
+ - `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+ - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
+ 
+方式二：OpenAI兼容方式接入，配置如下：
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "moonshot-v1-128k",
+  "open_ai_api_base": "https://api.moonshot.cn/v1",
+  "open_ai_api_key": ""
+}
+```
+- `bot_type`: OpenAI兼容方式
+- `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+- `open_ai_api_base`: Moonshot的 BASE URL
+- `open_ai_api_key`: Moonshot的 API-KEY
+</details>
+
+<details>
+<summary>Azure</summary>
+
+1. API Key创建：在 [Azure平台](https://oai.azure.com/) 创建API Key 
+
+2. 填写配置
+
+```json
+{
+  "model": "",
+  "use_azure_chatgpt": true,
+  "open_ai_api_key": "",
+  "open_ai_api_base": "",
+  "azure_deployment_id": "",
+  "azure_api_version": "2025-01-01-preview"
+}
+```
+
+ - `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
+</details>
+
+<details>
+<summary>百度文心</summary>
+方式一：官方SDK接入，配置如下：
+
+```json
+{
+    "model": "wenxin-4", 
+    "baidu_wenxin_api_key": "IajztZ0bDxgnP9bEykU7lBer",
+    "baidu_wenxin_secret_key": "EDPZn6L24uAS9d8RWFfotK47dPvkjD6G"
+}
+```
+ - `model`: 可填 `wenxin`和`wenxin-4`，对应模型为 文心-3.5 和 文心-4.0
+ - `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：
+```json
+{
+  "bot_type": "chatGPT",
+  "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
+- `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
+
+</details>
+
+<details>
+<summary>讯飞星火</summary>
+
+方式一：官方接入，配置如下：
+参考 [官方文档-快速指引](https://www.xfyun.cn/doc/platform/quickguide.html#%E7%AC%AC%E4%BA%8C%E6%AD%A5-%E5%88%9B%E5%BB%BA%E6%82%A8%E7%9A%84%E7%AC%AC%E4%B8%80%E4%B8%AA%E5%BA%94%E7%94%A8-%E5%BC%80%E5%A7%8B%E4%BD%BF%E7%94%A8%E6%9C%8D%E5%8A%A1) 获取 `APPID、 APISecret、 APIKey` 三个参数
+
+```json
+{
+  "model": "xunfei",
+  "xunfei_app_id": "",
+  "xunfei_api_key": "",
+  "xunfei_api_secret": "",
+  "xunfei_domain": "4.0Ultra",
+  "xunfei_spark_url": "wss://spark-api.xf-yun.com/v4.0/chat"
+}
+```
+ - `model`: 填 `xunfei`
+ - `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：
+```json
+{
+  "bot_type": "chatGPT",
+  "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
+- `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
+</details>
+
+<details>
+<summary>ModelScope</summary>
+
+```json
+{
+  "bot_type": "modelscope",
+  "model": "Qwen/QwQ-32B",
+  "modelscope_api_key": "your_api_key",
+  "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
+  "text_to_image": "MusePublic/489_ckpt_FLUX_1"
+}
+```
+
+- `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
+- `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>
+
+
+## 通道说明
+
+以下对可接入通道的配置方式进行说明，应用通道代码在项目的 `channel/` 目录下。
+
+<details>
+<summary>1. Web</summary>
+
+项目启动后默认运行Web通道，配置如下：
+
+```json
+{
+    "channel_type": "web",
+    "web_port": 9899
+}
+```
+
+- `web_port`: 默认为 9899，可按需更改，需要服务器防火墙和安全组放行该端口
+- 如本地运行，启动后请访问 `http://localhost:9899/chat` ；如服务器运行，请访问 `http://ip:9899/chat` 
+> 注：请将上述 url 中的 ip 或者 port 替换为实际的值
+</details>
+
+<details>
+<summary>2. Feishu - 飞书</summary>
+
+飞书支持两种事件接收模式：WebSocket 长连接（推荐）和 Webhook。
+
+**方式一：WebSocket 模式（推荐，无需公网 IP）**
+
+```json
+{
+    "channel_type": "feishu",
+    "feishu_app_id": "APP_ID",
+    "feishu_app_secret": "APP_SECRET",
+    "feishu_event_mode": "websocket"
+}
+```
+
+**方式二：Webhook 模式（需要公网 IP）**
+
+```json
+{
+    "channel_type": "feishu",
+    "feishu_app_id": "APP_ID",
+    "feishu_app_secret": "APP_SECRET",
+    "feishu_token": "VERIFICATION_TOKEN",
+    "feishu_event_mode": "webhook",
+    "feishu_port": 9891
+}
+```
+
+- `feishu_event_mode`: 事件接收模式，`websocket`（推荐）或 `webhook`
+- WebSocket 模式需安装依赖：`pip3 install lark-oapi`
+
+详细步骤和参数说明参考 [飞书接入](https://docs.link-ai.tech/cow/multi-platform/feishu)
+
+</details>
+
+<details>
+<summary>3. 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`：
+
+```json
+{
+    "channel_type": "dingtalk",
+    "dingtalk_client_id": "CLIENT_ID",
+    "dingtalk_client_secret": "CLIENT_SECRET"
+}
+```
+详细步骤和参数说明参考 [钉钉接入](https://docs.link-ai.tech/cow/multi-platform/dingtalk)
+</details>
+
+<details>
+<summary>4. 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：
+
+```json
+{
+    "channel_type": "wechatcom_app",
+    "wechatcom_corp_id": "CORPID",
+    "wechatcomapp_token": "TOKEN",
+    "wechatcomapp_port": 9898,
+    "wechatcomapp_secret": "SECRET",
+    "wechatcomapp_agent_id": "AGENTID",
+    "wechatcomapp_aes_key": "AESKEY"
+}
+```
+详细步骤和参数说明参考 [企微自建应用接入](https://docs.link-ai.tech/cow/multi-platform/wechat-com)
+
+</details>
+
+<details>
+<summary>5. 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。
+
+**个人订阅号（wechatmp）**
+
+```json
+{
+    "channel_type": "wechatmp",
+    "wechatmp_token": "TOKEN",
+    "wechatmp_port": 80,
+    "wechatmp_app_id": "APPID",
+    "wechatmp_app_secret": "APPSECRET",
+    "wechatmp_aes_key": ""
+}
+```
+
+**企业服务号（wechatmp_service）**
+
+```json
+{
+    "channel_type": "wechatmp_service",
+    "wechatmp_token": "TOKEN",
+    "wechatmp_port": 80,
+    "wechatmp_app_id": "APPID",
+    "wechatmp_app_secret": "APPSECRET",
+    "wechatmp_aes_key": ""
+}
+```
+
+详细步骤和参数说明参考 [微信公众号接入](https://docs.link-ai.tech/cow/multi-platform/wechat-mp)
+
+</details>
+
+<details>
+<summary>6. Terminal - 终端</summary>
+
+修改 `config.json` 中的 `channel_type` 字段：
+
+```json
+{
+    "channel_type": "terminal"
+}
+```
+
+运行后可在终端与机器人进行对话。
+
+</details>
+
+<br/>
+
+# 🔗 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。

-**一键部署:**
-  
-  [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/qApznZ?referralCode=RC3znh)

-<br>

 # 🔎 常见问题

 FAQs： <https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs>

-或直接在线咨询 [项目小助手](https://link-ai.tech/app/Kv2fXJcH)  (语料持续完善中，回复仅供参考)
+或直接在线咨询 [项目小助手](https://link-ai.tech/app/Kv2fXJcH)  (知识库持续完善中，回复供参考)

 # 🛠️ 开发

-欢迎接入更多应用，参考 [Terminal代码](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/channel/terminal/terminal_channel.py) 实现接收和发送消息逻辑即可接入。 同时欢迎增加新的插件，参考 [插件说明文档](https://github.com/zhayujie/chatgpt-on-wechat/tree/master/plugins)。
+欢迎接入更多应用通道，参考 [飞书通道](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/channel/feishu/feishu_channel.py) 新增自定义通道，实现接收和发送消息逻辑即可完成接入。 同时欢迎贡献新的Skills，参考 [Skill创造器说明](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/skills/skill-creator/SKILL.md)。

 # ✉ 联系

-欢迎提交PR、Issues，以及Star支持一下。程序运行遇到问题可以查看 [常见问题列表](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs) ，其次前往 [Issues](https://github.com/zhayujie/chatgpt-on-wechat/issues) 中搜索。个人开发者可加入开源交流群参与更多讨论，企业用户可联系[产品顾问](https://img-1317903499.cos.ap-guangzhou.myqcloud.com/docs/product-manager-qrcode.jpg)咨询。
+欢迎提交PR、Issues进行反馈，以及通过 🌟Star 支持并关注项目更新。项目运行遇到问题可以查看 [常见问题列表](https://github.com/zhayujie/chatgpt-on-wechat/wiki/FAQs) ，以及前往 [Issues](https://github.com/zhayujie/chatgpt-on-wechat/issues) 中搜索。个人开发者可加入开源交流群参与更多讨论，企业用户可联系[产品客服](https://cdn.link-ai.tech/portal/linkai-customer-service.png)咨询。

 # 🌟 贡献者

--- a/agent/memory/init.py
+++ b/agent/memory/init.py
@@ -0,0 +1,11 @@
+"""
+Memory module for AgentMesh
+
+Provides long-term memory capabilities with hybrid search (vector + keyword)
+"""
+
+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
+
+__all__ = ['MemoryManager', 'MemoryConfig', 'get_default_memory_config', 'set_global_memory_config', 'create_embedding_provider']
--- a/agent/memory/chunker.py
+++ b/agent/memory/chunker.py
@@ -0,0 +1,140 @@
+"""
+Text chunking utilities for memory
+
+Splits text into chunks with token limits and overlap
+"""
+
+from __future__ import annotations
+from typing import List, Tuple
+from dataclasses import dataclass
+
+
+@dataclass
+class TextChunk:
+    """Represents a text chunk with line numbers"""
+    text: str
+    start_line: int
+    end_line: int
+
+
+class TextChunker:
+    """Chunks text by line count with token estimation"""
+    
+    def __init__(self, max_tokens: int = 500, overlap_tokens: int = 50):
+        """
+        Initialize chunker
+        
+        Args:
+            max_tokens: Maximum tokens per chunk
+            overlap_tokens: Overlap tokens between chunks
+        """
+        self.max_tokens = max_tokens
+        self.overlap_tokens = overlap_tokens
+        # Rough estimation: ~4 chars per token for English/Chinese mixed
+        self.chars_per_token = 4
+    
+    def chunk_text(self, text: str) -> List[TextChunk]:
+        """
+        Chunk text into overlapping segments
+        
+        Args:
+            text: Input text to chunk
+            
+        Returns:
+            List of TextChunk objects
+        """
+        if not text.strip():
+            return []
+        
+        lines = text.split('\n')
+        chunks = []
+        
+        max_chars = self.max_tokens * self.chars_per_token
+        overlap_chars = self.overlap_tokens * self.chars_per_token
+        
+        current_chunk = []
+        current_chars = 0
+        start_line = 1
+        
+        for i, line in enumerate(lines, start=1):
+            line_chars = len(line)
+            
+            # If single line exceeds max, split it
+            if line_chars > max_chars:
+                # Save current chunk if exists
+                if current_chunk:
+                    chunks.append(TextChunk(
+                        text='\n'.join(current_chunk),
+                        start_line=start_line,
+                        end_line=i - 1
+                    ))
+                    current_chunk = []
+                    current_chars = 0
+                
+                # Split long line into multiple chunks
+                for sub_chunk in self._split_long_line(line, max_chars):
+                    chunks.append(TextChunk(
+                        text=sub_chunk,
+                        start_line=i,
+                        end_line=i
+                    ))
+                
+                start_line = i + 1
+                continue
+            
+            # Check if adding this line would exceed limit
+            if current_chars + line_chars > max_chars and current_chunk:
+                # Save current chunk
+                chunks.append(TextChunk(
+                    text='\n'.join(current_chunk),
+                    start_line=start_line,
+                    end_line=i - 1
+                ))
+                
+                # Start new chunk with overlap
+                overlap_lines = self._get_overlap_lines(current_chunk, overlap_chars)
+                current_chunk = overlap_lines + [line]
+                current_chars = sum(len(l) for l in current_chunk)
+                start_line = i - len(overlap_lines)
+            else:
+                # Add line to current chunk
+                current_chunk.append(line)
+                current_chars += line_chars
+        
+        # Save last chunk
+        if current_chunk:
+            chunks.append(TextChunk(
+                text='\n'.join(current_chunk),
+                start_line=start_line,
+                end_line=len(lines)
+            ))
+        
+        return chunks
+    
+    def _split_long_line(self, line: str, max_chars: int) -> List[str]:
+        """Split a single long line into multiple chunks"""
+        chunks = []
+        for i in range(0, len(line), max_chars):
+            chunks.append(line[i:i + max_chars])
+        return chunks
+    
+    def _get_overlap_lines(self, lines: List[str], target_chars: int) -> List[str]:
+        """Get last few lines that fit within target_chars for overlap"""
+        overlap = []
+        chars = 0
+        
+        for line in reversed(lines):
+            line_chars = len(line)
+            if chars + line_chars > target_chars:
+                break
+            overlap.insert(0, line)
+            chars += line_chars
+        
+        return overlap
+    
+    def chunk_markdown(self, text: str) -> List[TextChunk]:
+        """
+        Chunk markdown text while respecting structure
+        (For future enhancement: respect markdown sections)
+        """
+        return self.chunk_text(text)
--- a/agent/memory/config.py
+++ b/agent/memory/config.py
@@ -0,0 +1,119 @@
+"""
+Memory configuration module
+
+Provides global memory configuration with simplified workspace structure
+"""
+
+from __future__ import annotations
+import os
+from dataclasses import dataclass, field
+from typing import Optional, List
+from pathlib import Path
+
+
+@dataclass
+class MemoryConfig:
+    """Configuration for memory storage and search"""
+    
+    # Storage paths (default: ~/cow)
+    workspace_root: str = field(default_factory=lambda: os.path.expanduser("~/cow"))
+    
+    # Embedding config
+    embedding_provider: str = "openai"  # "openai" | "local"
+    embedding_model: str = "text-embedding-3-small"
+    embedding_dim: int = 1536
+    
+    # Chunking config
+    chunk_max_tokens: int = 500
+    chunk_overlap_tokens: int = 50
+    
+    # Search config
+    max_results: int = 10
+    min_score: float = 0.1
+    
+    # Hybrid search weights
+    vector_weight: float = 0.7
+    keyword_weight: float = 0.3
+    
+    # Memory sources
+    sources: List[str] = field(default_factory=lambda: ["memory", "session"])
+    
+    # Sync config
+    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"""
+        return Path(self.workspace_root)
+    
+    def get_memory_dir(self) -> Path:
+        """Get memory files directory"""
+        return self.get_workspace() / "memory"
+    
+    def get_db_path(self) -> Path:
+        """Get SQLite database path for long-term memory index"""
+        index_dir = self.get_memory_dir() / "long-term"
+        index_dir.mkdir(parents=True, exist_ok=True)
+        return index_dir / "index.db"
+    
+    def get_skills_dir(self) -> Path:
+        """Get skills directory"""
+        return self.get_workspace() / "skills"
+    
+    def get_agent_workspace(self, agent_name: Optional[str] = None) -> Path:
+        """
+        Get workspace directory for an agent
+        
+        Args:
+            agent_name: Optional agent name (not used in current implementation)
+            
+        Returns:
+            Path to workspace directory
+        """
+        workspace = self.get_workspace()
+        # Ensure workspace directory exists
+        workspace.mkdir(parents=True, exist_ok=True)
+        return workspace
+
+
+# Global memory configuration
+_global_memory_config: Optional[MemoryConfig] = None
+
+
+def get_default_memory_config() -> MemoryConfig:
+    """
+    Get the global memory configuration.
+    If not set, returns a default configuration.
+    
+    Returns:
+        MemoryConfig instance
+    """
+    global _global_memory_config
+    if _global_memory_config is None:
+        _global_memory_config = MemoryConfig()
+    return _global_memory_config
+
+
+def set_global_memory_config(config: MemoryConfig):
+    """
+    Set the global memory configuration.
+    This should be called before creating any MemoryManager instances.
+    
+    Args:
+        config: MemoryConfig instance to use globally
+        
+    Example:
+        >>> from agent.memory import MemoryConfig, set_global_memory_config
+        >>> config = MemoryConfig(
+        ...     workspace_root="~/my_agents",
+        ...     embedding_provider="openai",
+        ...     vector_weight=0.8
+        ... )
+        >>> set_global_memory_config(config)
+    """
+    global _global_memory_config
+    _global_memory_config = config
--- a/agent/memory/embedding.py
+++ b/agent/memory/embedding.py
@@ -0,0 +1,161 @@
+"""
+Embedding providers for memory
+
+Supports OpenAI and local embedding models
+"""
+
+import hashlib
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+
+class EmbeddingProvider(ABC):
+    """Base class for embedding providers"""
+
+    @abstractmethod
+    def embed(self, text: str) -> List[float]:
+        """Generate embedding for text"""
+        pass
+
+    @abstractmethod
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for multiple texts"""
+        pass
+    
+    @property
+    @abstractmethod
+    def dimensions(self) -> int:
+        """Get embedding dimensions"""
+        pass
+
+
+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):
+        """
+        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
+        """
+        self.model = model
+        self.api_key = api_key
+        self.api_base = api_base or "https://api.openai.com/v1"
+
+        # Validate API key
+        if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            raise ValueError("OpenAI API key is not configured. Please set 'open_ai_api_key' in config.json")
+
+        # Set dimensions based on model
+        self._dimensions = 1536 if "small" in model else 3072
+
+    def _call_api(self, input_data):
+        """Call OpenAI embedding API using requests"""
+        import requests
+
+        url = f"{self.api_base}/embeddings"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}"
+        }
+        data = {
+            "input": input_data,
+            "model": self.model
+        }
+
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=5)
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.ConnectionError as e:
+            raise ConnectionError(f"Failed to connect to OpenAI API at {url}. Please check your network connection and api_base configuration. Error: {str(e)}")
+        except requests.exceptions.Timeout as e:
+            raise TimeoutError(f"OpenAI API request timed out after 10s. Please check your network connection. Error: {str(e)}")
+        except requests.exceptions.HTTPError as e:
+            if e.response.status_code == 401:
+                raise ValueError(f"Invalid OpenAI API key. Please check your 'open_ai_api_key' in config.json")
+            elif e.response.status_code == 429:
+                raise ValueError(f"OpenAI API rate limit exceeded. Please try again later.")
+            else:
+                raise ValueError(f"OpenAI API request failed: {e.response.status_code} - {e.response.text}")
+
+    def embed(self, text: str) -> List[float]:
+        """Generate embedding for text"""
+        result = self._call_api(text)
+        return result["data"][0]["embedding"]
+
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for multiple texts"""
+        if not texts:
+            return []
+
+        result = self._call_api(texts)
+        return [item["embedding"] for item in result["data"]]
+
+    @property
+    def dimensions(self) -> int:
+        return self._dimensions
+
+
+# LocalEmbeddingProvider removed - only use OpenAI embedding or keyword search
+
+
+class EmbeddingCache:
+    """Cache for embeddings to avoid recomputation"""
+
+    def __init__(self):
+        self.cache = {}
+
+    def get(self, text: str, provider: str, model: str) -> Optional[List[float]]:
+        """Get cached embedding"""
+        key = self._compute_key(text, provider, model)
+        return self.cache.get(key)
+    
+    def put(self, text: str, provider: str, model: str, embedding: List[float]):
+        """Cache embedding"""
+        key = self._compute_key(text, provider, model)
+        self.cache[key] = embedding
+    
+    @staticmethod
+    def _compute_key(text: str, provider: str, model: str) -> str:
+        """Compute cache key"""
+        content = f"{provider}:{model}:{text}"
+        return hashlib.md5(content.encode('utf-8')).hexdigest()
+    
+    def clear(self):
+        """Clear cache"""
+        self.cache.clear()
+
+
+def create_embedding_provider(
+    provider: str = "openai",
+    model: Optional[str] = None,
+    api_key: Optional[str] = None,
+    api_base: Optional[str] = None
+) -> EmbeddingProvider:
+    """
+    Factory function to create embedding provider
+    
+    Only supports OpenAI embedding via REST API.
+    If initialization fails, caller should fall back to keyword-only search.
+    
+    Args:
+        provider: Provider name (only "openai" is supported)
+        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)
+        
+    Returns:
+        EmbeddingProvider instance
+        
+    Raises:
+        ValueError: If provider is not "openai" or api_key is missing
+    """
+    if provider != "openai":
+        raise ValueError(f"Only 'openai' provider is supported, got: {provider}")
+
+    model = model or "text-embedding-3-small"
+    return OpenAIEmbeddingProvider(model=model, api_key=api_key, api_base=api_base)
--- a/agent/memory/manager.py
+++ b/agent/memory/manager.py
@@ -0,0 +1,622 @@
+"""
+Memory manager for AgentMesh
+
+Provides high-level interface for memory operations
+"""
+
+import os
+from typing import List, Optional, Dict, Any
+from pathlib import Path
+import hashlib
+from datetime import datetime, timedelta
+
+from agent.memory.config import MemoryConfig, get_default_memory_config
+from agent.memory.storage import MemoryStorage, MemoryChunk, SearchResult
+from agent.memory.chunker import TextChunker
+from agent.memory.embedding import create_embedding_provider, EmbeddingProvider
+from agent.memory.summarizer import MemoryFlushManager, create_memory_files_if_needed
+
+
+class MemoryManager:
+    """
+    Memory manager with hybrid search capabilities
+    
+    Provides long-term memory for agents with vector and keyword search
+    """
+    
+    def __init__(
+        self,
+        config: Optional[MemoryConfig] = None,
+        embedding_provider: Optional[EmbeddingProvider] = None,
+        llm_model: Optional[Any] = None
+    ):
+        """
+        Initialize memory manager
+        
+        Args:
+            config: Memory configuration (uses global config if not provided)
+            embedding_provider: Custom embedding provider (optional)
+            llm_model: LLM model for summarization (optional)
+        """
+        self.config = config or get_default_memory_config()
+        
+        # Initialize storage
+        db_path = self.config.get_db_path()
+        self.storage = MemoryStorage(db_path)
+        
+        # Initialize chunker
+        self.chunker = TextChunker(
+            max_tokens=self.config.chunk_max_tokens,
+            overlap_tokens=self.config.chunk_overlap_tokens
+        )
+        
+        # Initialize embedding provider (optional)
+        self.embedding_provider = None
+        if embedding_provider:
+            self.embedding_provider = embedding_provider
+        else:
+            # Try to create embedding provider, but allow failure
+            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
+                )
+            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.info(f"[MemoryManager] Memory will work with keyword search only (no vector search)")
+        
+        # Initialize memory flush manager
+        workspace_dir = self.config.get_workspace()
+        self.flush_manager = MemoryFlushManager(
+            workspace_dir=workspace_dir,
+            llm_model=llm_model
+        )
+        
+        # Ensure workspace directories exist
+        self._init_workspace()
+        
+        self._dirty = False
+    
+    def _init_workspace(self):
+        """Initialize workspace directories"""
+        memory_dir = self.config.get_memory_dir()
+        memory_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Create default memory files
+        workspace_dir = self.config.get_workspace()
+        create_memory_files_if_needed(workspace_dir)
+    
+    async def search(
+        self,
+        query: str,
+        user_id: Optional[str] = None,
+        max_results: Optional[int] = None,
+        min_score: Optional[float] = None,
+        include_shared: bool = True
+    ) -> List[SearchResult]:
+        """
+        Search memory with hybrid search (vector + keyword)
+        
+        Args:
+            query: Search query
+            user_id: User ID for scoped search
+            max_results: Maximum results to return
+            min_score: Minimum score threshold
+            include_shared: Include shared memories
+            
+        Returns:
+            List of search results sorted by relevance
+        """
+        max_results = max_results or self.config.max_results
+        min_score = min_score or self.config.min_score
+        
+        # Determine scopes
+        scopes = []
+        if include_shared:
+            scopes.append("shared")
+        if user_id:
+            scopes.append("user")
+        
+        if not scopes:
+            return []
+        
+        # Sync if needed
+        if self.config.sync_on_search and self._dirty:
+            await self.sync()
+        
+        # Perform vector search (if embedding provider available)
+        vector_results = []
+        if self.embedding_provider:
+            try:
+                from common.log import logger
+                query_embedding = self.embedding_provider.embed(query)
+                vector_results = self.storage.search_vector(
+                    query_embedding=query_embedding,
+                    user_id=user_id,
+                    scopes=scopes,
+                    limit=max_results * 2  # Get more candidates for merging
+                )
+                logger.info(f"[MemoryManager] Vector search found {len(vector_results)} results for query: {query}")
+            except Exception as e:
+                from common.log import logger
+                logger.warning(f"[MemoryManager] Vector search failed: {e}")
+        
+        # Perform keyword search
+        keyword_results = self.storage.search_keyword(
+            query=query,
+            user_id=user_id,
+            scopes=scopes,
+            limit=max_results * 2
+        )
+        from common.log import logger
+        logger.info(f"[MemoryManager] Keyword search found {len(keyword_results)} results for query: {query}")
+        
+        # Merge results
+        merged = self._merge_results(
+            vector_results,
+            keyword_results,
+            self.config.vector_weight,
+            self.config.keyword_weight
+        )
+        
+        # Filter by min score and limit
+        filtered = [r for r in merged if r.score >= min_score]
+        return filtered[:max_results]
+    
+    async def add_memory(
+        self,
+        content: str,
+        user_id: Optional[str] = None,
+        scope: str = "shared",
+        source: str = "memory",
+        path: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None
+    ):
+        """
+        Add new memory content
+        
+        Args:
+            content: Memory content
+            user_id: User ID for user-scoped memory
+            scope: Memory scope ("shared", "user", "session")
+            source: Memory source ("memory" or "session")
+            path: File path (auto-generated if not provided)
+            metadata: Additional metadata
+        """
+        if not content.strip():
+            return
+        
+        # Generate path if not provided
+        if not path:
+            content_hash = hashlib.md5(content.encode('utf-8')).hexdigest()[:8]
+            if user_id and scope == "user":
+                path = f"memory/users/{user_id}/memory_{content_hash}.md"
+            else:
+                path = f"memory/shared/memory_{content_hash}.md"
+        
+        # Chunk content
+        chunks = self.chunker.chunk_text(content)
+        
+        # Generate embeddings (if provider available)
+        texts = [chunk.text for chunk in chunks]
+        if self.embedding_provider:
+            embeddings = self.embedding_provider.embed_batch(texts)
+        else:
+            # No embeddings, just use None
+            embeddings = [None] * len(texts)
+        
+        # Create memory chunks
+        memory_chunks = []
+        for chunk, embedding in zip(chunks, embeddings):
+            chunk_id = self._generate_chunk_id(path, chunk.start_line, chunk.end_line)
+            chunk_hash = MemoryStorage.compute_hash(chunk.text)
+            
+            memory_chunks.append(MemoryChunk(
+                id=chunk_id,
+                user_id=user_id,
+                scope=scope,
+                source=source,
+                path=path,
+                start_line=chunk.start_line,
+                end_line=chunk.end_line,
+                text=chunk.text,
+                embedding=embedding,
+                hash=chunk_hash,
+                metadata=metadata
+            ))
+        
+        # Save to storage
+        self.storage.save_chunks_batch(memory_chunks)
+        
+        # Update file metadata
+        file_hash = MemoryStorage.compute_hash(content)
+        self.storage.update_file_metadata(
+            path=path,
+            source=source,
+            file_hash=file_hash,
+            mtime=int(os.path.getmtime(__file__)),  # Use current time
+            size=len(content)
+        )
+    
+    async def sync(self, force: bool = False):
+        """
+        Synchronize memory from files
+        
+        Args:
+            force: Force full reindex
+        """
+        memory_dir = self.config.get_memory_dir()
+        workspace_dir = self.config.get_workspace()
+        
+        # Scan MEMORY.md (workspace root)
+        memory_file = Path(workspace_dir) / "MEMORY.md"
+        if memory_file.exists():
+            await self._sync_file(memory_file, "memory", "shared", None)
+        
+        # Scan memory directory (including daily summaries)
+        if memory_dir.exists():
+            for file_path in memory_dir.rglob("*.md"):
+                # Determine scope and user_id from path
+                rel_path = file_path.relative_to(workspace_dir)
+                parts = rel_path.parts
+                
+                # Check if it's in daily summary directory
+                if "daily" in parts:
+                    # Daily summary files
+                    if "users" in parts or len(parts) > 3:
+                        # User-scoped daily summary: memory/daily/{user_id}/2024-01-29.md
+                        user_idx = parts.index("daily") + 1
+                        user_id = parts[user_idx] if user_idx < len(parts) else None
+                        scope = "user"
+                    else:
+                        # Shared daily summary: memory/daily/2024-01-29.md
+                        user_id = None
+                        scope = "shared"
+                elif "users" in parts:
+                    # User-scoped memory
+                    user_idx = parts.index("users") + 1
+                    user_id = parts[user_idx] if user_idx < len(parts) else None
+                    scope = "user"
+                else:
+                    # Shared memory
+                    user_id = None
+                    scope = "shared"
+                
+                await self._sync_file(file_path, "memory", scope, user_id)
+        
+        self._dirty = False
+    
+    async def _sync_file(
+        self,
+        file_path: Path,
+        source: str,
+        scope: str,
+        user_id: Optional[str]
+    ):
+        """Sync a single file"""
+        # Compute file hash
+        content = file_path.read_text()
+        file_hash = MemoryStorage.compute_hash(content)
+        
+        # Get relative path
+        workspace_dir = self.config.get_workspace()
+        rel_path = str(file_path.relative_to(workspace_dir))
+        
+        # Check if file changed
+        stored_hash = self.storage.get_file_hash(rel_path)
+        if stored_hash == file_hash:
+            return  # No changes
+        
+        # Delete old chunks
+        self.storage.delete_by_path(rel_path)
+        
+        # Chunk and embed
+        chunks = self.chunker.chunk_text(content)
+        if not chunks:
+            return
+        
+        texts = [chunk.text for chunk in chunks]
+        if self.embedding_provider:
+            embeddings = self.embedding_provider.embed_batch(texts)
+        else:
+            embeddings = [None] * len(texts)
+        
+        # Create memory chunks
+        memory_chunks = []
+        for chunk, embedding in zip(chunks, embeddings):
+            chunk_id = self._generate_chunk_id(rel_path, chunk.start_line, chunk.end_line)
+            chunk_hash = MemoryStorage.compute_hash(chunk.text)
+            
+            memory_chunks.append(MemoryChunk(
+                id=chunk_id,
+                user_id=user_id,
+                scope=scope,
+                source=source,
+                path=rel_path,
+                start_line=chunk.start_line,
+                end_line=chunk.end_line,
+                text=chunk.text,
+                embedding=embedding,
+                hash=chunk_hash,
+                metadata=None
+            ))
+        
+        # Save
+        self.storage.save_chunks_batch(memory_chunks)
+        
+        # Update file metadata
+        stat = file_path.stat()
+        self.storage.update_file_metadata(
+            path=rel_path,
+            source=source,
+            file_hash=file_hash,
+            mtime=int(stat.st_mtime),
+            size=stat.st_size
+        )
+    
+    def should_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,
+        user_id: Optional[str] = None,
+        **executor_kwargs
+    ) -> 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.
+        
+        Args:
+            agent_executor: Async function to execute agent with prompt
+            current_tokens: Current session token count
+            user_id: Optional user ID
+            **executor_kwargs: Additional kwargs for agent executor
+            
+        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
+            ...     )
+        """
+        success = await self.flush_manager.execute_flush(
+            agent_executor=agent_executor,
+            current_tokens=current_tokens,
+            user_id=user_id,
+            **executor_kwargs
+        )
+        
+        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()
+        return {
+            'chunks': stats['chunks'],
+            'files': stats['files'],
+            'workspace': str(self.config.get_workspace()),
+            'dirty': self._dirty,
+            'embedding_enabled': self.embedding_provider is not None,
+            'embedding_provider': self.config.embedding_provider if self.embedding_provider else 'disabled',
+            'embedding_model': self.config.embedding_model if self.embedding_provider else 'N/A',
+            'search_mode': 'hybrid (vector + keyword)' if self.embedding_provider else 'keyword only (FTS5)'
+        }
+    
+    def mark_dirty(self):
+        """Mark memory as dirty (needs sync)"""
+        self._dirty = True
+    
+    def close(self):
+        """Close memory manager and release resources"""
+        self.storage.close()
+    
+    # Helper methods
+    
+    def _generate_chunk_id(self, path: str, start_line: int, end_line: int) -> str:
+        """Generate unique chunk ID"""
+        content = f"{path}:{start_line}:{end_line}"
+        return hashlib.md5(content.encode('utf-8')).hexdigest()
+    
+    def _merge_results(
+        self,
+        vector_results: List[SearchResult],
+        keyword_results: List[SearchResult],
+        vector_weight: float,
+        keyword_weight: float
+    ) -> List[SearchResult]:
+        """Merge vector and keyword search results"""
+        # Create a map by (path, start_line, end_line)
+        merged_map = {}
+        
+        for result in vector_results:
+            key = (result.path, result.start_line, result.end_line)
+            merged_map[key] = {
+                'result': result,
+                'vector_score': result.score,
+                'keyword_score': 0.0
+            }
+        
+        for result in keyword_results:
+            key = (result.path, result.start_line, result.end_line)
+            if key in merged_map:
+                merged_map[key]['keyword_score'] = result.score
+            else:
+                merged_map[key] = {
+                    'result': result,
+                    'vector_score': 0.0,
+                    'keyword_score': result.score
+                }
+        
+        # Calculate combined scores
+        merged_results = []
+        for entry in merged_map.values():
+            combined_score = (
+                vector_weight * entry['vector_score'] +
+                keyword_weight * entry['keyword_score']
+            )
+            
+            result = entry['result']
+            merged_results.append(SearchResult(
+                path=result.path,
+                start_line=result.start_line,
+                end_line=result.end_line,
+                score=combined_score,
+                snippet=result.snippet,
+                source=result.source,
+                user_id=result.user_id
+            ))
+        
+        # Sort by score
+        merged_results.sort(key=lambda r: r.score, reverse=True)
+        return merged_results
--- a/agent/memory/storage.py
+++ b/agent/memory/storage.py
@@ -0,0 +1,589 @@
+"""
+Storage layer for memory using SQLite + FTS5
+
+Provides vector and keyword search capabilities
+"""
+
+from __future__ import annotations
+import sqlite3
+import json
+import hashlib
+from typing import List, Dict, Optional, Any
+from pathlib import Path
+from dataclasses import dataclass
+
+
+@dataclass
+class MemoryChunk:
+    """Represents a memory chunk with text and embedding"""
+    id: str
+    user_id: Optional[str]
+    scope: str  # "shared" | "user" | "session"
+    source: str  # "memory" | "session"
+    path: str
+    start_line: int
+    end_line: int
+    text: str
+    embedding: Optional[List[float]]
+    hash: str
+    metadata: Optional[Dict[str, Any]] = None
+
+
+@dataclass
+class SearchResult:
+    """Search result with score and snippet"""
+    path: str
+    start_line: int
+    end_line: int
+    score: float
+    snippet: str
+    source: str
+    user_id: Optional[str] = None
+
+
+class MemoryStorage:
+    """SQLite-based storage with FTS5 for keyword search"""
+    
+    def __init__(self, db_path: Path):
+        self.db_path = db_path
+        self.conn: Optional[sqlite3.Connection] = None
+        self.fts5_available = False  # Track FTS5 availability
+        self._init_db()
+    
+    def _check_fts5_support(self) -> bool:
+        """Check if SQLite has FTS5 support"""
+        try:
+            self.conn.execute("CREATE VIRTUAL TABLE IF NOT EXISTS fts5_test USING fts5(test)")
+            self.conn.execute("DROP TABLE IF EXISTS fts5_test")
+            return True
+        except sqlite3.OperationalError as e:
+            if "no such module: fts5" in str(e):
+                return False
+            raise
+    
+    def _init_db(self):
+        """Initialize database with schema"""
+        try:
+            self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
+            self.conn.row_factory = sqlite3.Row
+            
+            # Check FTS5 support
+            self.fts5_available = self._check_fts5_support()
+            if not self.fts5_available:
+                from common.log import logger
+                logger.debug("[MemoryStorage] FTS5 not available, using LIKE-based keyword search")
+            
+            # Check database integrity
+            try:
+                result = self.conn.execute("PRAGMA integrity_check").fetchone()
+                if result[0] != 'ok':
+                    print(f"⚠️  Database integrity check failed: {result[0]}")
+                    print(f"   Recreating database...")
+                    self.conn.close()
+                    self.conn = None
+                    # Remove corrupted database
+                    self.db_path.unlink(missing_ok=True)
+                    # Remove WAL files
+                    Path(str(self.db_path) + '-wal').unlink(missing_ok=True)
+                    Path(str(self.db_path) + '-shm').unlink(missing_ok=True)
+                    # Reconnect to create new database
+                    self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
+                    self.conn.row_factory = sqlite3.Row
+            except sqlite3.DatabaseError:
+                # Database is corrupted, recreate it
+                print(f"⚠️  Database is corrupted, recreating...")
+                if self.conn:
+                    self.conn.close()
+                    self.conn = None
+                self.db_path.unlink(missing_ok=True)
+                Path(str(self.db_path) + '-wal').unlink(missing_ok=True)
+                Path(str(self.db_path) + '-shm').unlink(missing_ok=True)
+                self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
+                self.conn.row_factory = sqlite3.Row
+            
+            # Enable WAL mode for better concurrency
+            self.conn.execute("PRAGMA journal_mode=WAL")
+            # Set busy timeout to avoid "database is locked" errors
+            self.conn.execute("PRAGMA busy_timeout=5000")
+        except Exception as e:
+            print(f"⚠️  Unexpected error during database initialization: {e}")
+            raise
+        
+        # Create chunks table with embeddings
+        self.conn.execute("""
+            CREATE TABLE IF NOT EXISTS chunks (
+                id TEXT PRIMARY KEY,
+                user_id TEXT,
+                scope TEXT NOT NULL DEFAULT 'shared',
+                source TEXT NOT NULL DEFAULT 'memory',
+                path TEXT NOT NULL,
+                start_line INTEGER NOT NULL,
+                end_line INTEGER NOT NULL,
+                text TEXT NOT NULL,
+                embedding TEXT,
+                hash TEXT NOT NULL,
+                metadata TEXT,
+                created_at INTEGER DEFAULT (strftime('%s', 'now')),
+                updated_at INTEGER DEFAULT (strftime('%s', 'now'))
+            )
+        """)
+        
+        # Create indexes
+        self.conn.execute("""
+            CREATE INDEX IF NOT EXISTS idx_chunks_user 
+            ON chunks(user_id)
+        """)
+        
+        self.conn.execute("""
+            CREATE INDEX IF NOT EXISTS idx_chunks_scope 
+            ON chunks(scope)
+        """)
+        
+        self.conn.execute("""
+            CREATE INDEX IF NOT EXISTS idx_chunks_hash 
+            ON chunks(path, hash)
+        """)
+        
+        # Create FTS5 virtual table for keyword search (only if supported)
+        if self.fts5_available:
+            # Use default unicode61 tokenizer (stable and compatible)
+            # For CJK support, we'll use LIKE queries as fallback
+            self.conn.execute("""
+                CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(
+                    text,
+                    id UNINDEXED,
+                    user_id UNINDEXED,
+                    path UNINDEXED,
+                    source UNINDEXED,
+                    scope UNINDEXED,
+                    content='chunks',
+                    content_rowid='rowid'
+                )
+            """)
+            
+            # Create triggers to keep FTS in sync
+            self.conn.execute("""
+                CREATE TRIGGER IF NOT EXISTS chunks_ai AFTER INSERT ON chunks BEGIN
+                    INSERT INTO chunks_fts(rowid, text, id, user_id, path, source, scope)
+                    VALUES (new.rowid, new.text, new.id, new.user_id, new.path, new.source, new.scope);
+                END
+            """)
+            
+            self.conn.execute("""
+                CREATE TRIGGER IF NOT EXISTS chunks_ad AFTER DELETE ON chunks BEGIN
+                    DELETE FROM chunks_fts WHERE rowid = old.rowid;
+                END
+            """)
+            
+            self.conn.execute("""
+                CREATE TRIGGER IF NOT EXISTS chunks_au AFTER UPDATE ON chunks BEGIN
+                    UPDATE chunks_fts SET text = new.text, id = new.id,
+                                         user_id = new.user_id, path = new.path, source = new.source, scope = new.scope
+                    WHERE rowid = new.rowid;
+                END
+            """)
+        
+        # Create files metadata table
+        self.conn.execute("""
+            CREATE TABLE IF NOT EXISTS files (
+                path TEXT PRIMARY KEY,
+                source TEXT NOT NULL DEFAULT 'memory',
+                hash TEXT NOT NULL,
+                mtime INTEGER NOT NULL,
+                size INTEGER NOT NULL,
+                updated_at INTEGER DEFAULT (strftime('%s', 'now'))
+            )
+        """)
+        
+        self.conn.commit()
+    
+    def save_chunk(self, chunk: MemoryChunk):
+        """Save a memory chunk"""
+        self.conn.execute("""
+            INSERT OR REPLACE INTO chunks 
+            (id, user_id, scope, source, path, start_line, end_line, text, embedding, hash, metadata, updated_at)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, strftime('%s', 'now'))
+        """, (
+            chunk.id,
+            chunk.user_id,
+            chunk.scope,
+            chunk.source,
+            chunk.path,
+            chunk.start_line,
+            chunk.end_line,
+            chunk.text,
+            json.dumps(chunk.embedding) if chunk.embedding else None,
+            chunk.hash,
+            json.dumps(chunk.metadata) if chunk.metadata else None
+        ))
+        self.conn.commit()
+    
+    def save_chunks_batch(self, chunks: List[MemoryChunk]):
+        """Save multiple chunks in a batch"""
+        self.conn.executemany("""
+            INSERT OR REPLACE INTO chunks 
+            (id, user_id, scope, source, path, start_line, end_line, text, embedding, hash, metadata, updated_at)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, strftime('%s', 'now'))
+        """, [
+            (
+                c.id, c.user_id, c.scope, c.source, c.path,
+                c.start_line, c.end_line, c.text,
+                json.dumps(c.embedding) if c.embedding else None,
+                c.hash,
+                json.dumps(c.metadata) if c.metadata else None
+            )
+            for c in chunks
+        ])
+        self.conn.commit()
+    
+    def get_chunk(self, chunk_id: str) -> Optional[MemoryChunk]:
+        """Get a chunk by ID"""
+        row = self.conn.execute("""
+            SELECT * FROM chunks WHERE id = ?
+        """, (chunk_id,)).fetchone()
+        
+        if not row:
+            return None
+        
+        return self._row_to_chunk(row)
+    
+    def search_vector(
+        self,
+        query_embedding: List[float],
+        user_id: Optional[str] = None,
+        scopes: List[str] = None,
+        limit: int = 10
+    ) -> List[SearchResult]:
+        """
+        Vector similarity search using in-memory cosine similarity
+        (sqlite-vec can be added later for better performance)
+        """
+        if scopes is None:
+            scopes = ["shared"]
+            if user_id:
+                scopes.append("user")
+        
+        # Build query
+        scope_placeholders = ','.join('?' * len(scopes))
+        params = scopes
+        
+        if user_id:
+            query = f"""
+                SELECT * FROM chunks 
+                WHERE scope IN ({scope_placeholders})
+                AND (scope = 'shared' OR user_id = ?)
+                AND embedding IS NOT NULL
+            """
+            params.append(user_id)
+        else:
+            query = f"""
+                SELECT * FROM chunks 
+                WHERE scope IN ({scope_placeholders})
+                AND embedding IS NOT NULL
+            """
+        
+        rows = self.conn.execute(query, params).fetchall()
+        
+        # Calculate cosine similarity
+        results = []
+        for row in rows:
+            embedding = json.loads(row['embedding'])
+            similarity = self._cosine_similarity(query_embedding, embedding)
+            
+            if similarity > 0:
+                results.append((similarity, row))
+        
+        # Sort by similarity and limit
+        results.sort(key=lambda x: x[0], reverse=True)
+        results = results[:limit]
+        
+        return [
+            SearchResult(
+                path=row['path'],
+                start_line=row['start_line'],
+                end_line=row['end_line'],
+                score=score,
+                snippet=self._truncate_text(row['text'], 500),
+                source=row['source'],
+                user_id=row['user_id']
+            )
+            for score, row in results
+        ]
+    
+    def search_keyword(
+        self,
+        query: str,
+        user_id: Optional[str] = None,
+        scopes: List[str] = None,
+        limit: int = 10
+    ) -> List[SearchResult]:
+        """
+        Keyword search using FTS5 + LIKE fallback
+        
+        Strategy:
+        1. If FTS5 available: Try FTS5 search first (good for English and word-based languages)
+        2. If no FTS5 or no results and query contains CJK: Use LIKE search
+        """
+        if scopes is None:
+            scopes = ["shared"]
+            if user_id:
+                scopes.append("user")
+        
+        # Try FTS5 search first (if available)
+        if self.fts5_available:
+            fts_results = self._search_fts5(query, user_id, scopes, limit)
+            if fts_results:
+                return fts_results
+        
+        # Fallback to LIKE search (always for CJK, or if FTS5 not available)
+        if not self.fts5_available or MemoryStorage._contains_cjk(query):
+            return self._search_like(query, user_id, scopes, limit)
+        
+        return []
+    
+    def _search_fts5(
+        self,
+        query: str,
+        user_id: Optional[str],
+        scopes: List[str],
+        limit: int
+    ) -> List[SearchResult]:
+        """FTS5 full-text search"""
+        fts_query = self._build_fts_query(query)
+        if not fts_query:
+            return []
+        
+        scope_placeholders = ','.join('?' * len(scopes))
+        params = [fts_query] + scopes
+        
+        if user_id:
+            sql_query = f"""
+                SELECT chunks.*, bm25(chunks_fts) as rank
+                FROM chunks_fts
+                JOIN chunks ON chunks.id = chunks_fts.id
+                WHERE chunks_fts MATCH ? 
+                AND chunks.scope IN ({scope_placeholders})
+                AND (chunks.scope = 'shared' OR chunks.user_id = ?)
+                ORDER BY rank
+                LIMIT ?
+            """
+            params.extend([user_id, limit])
+        else:
+            sql_query = f"""
+                SELECT chunks.*, bm25(chunks_fts) as rank
+                FROM chunks_fts
+                JOIN chunks ON chunks.id = chunks_fts.id
+                WHERE chunks_fts MATCH ? 
+                AND chunks.scope IN ({scope_placeholders})
+                ORDER BY rank
+                LIMIT ?
+            """
+            params.append(limit)
+        
+        try:
+            rows = self.conn.execute(sql_query, params).fetchall()
+            return [
+                SearchResult(
+                    path=row['path'],
+                    start_line=row['start_line'],
+                    end_line=row['end_line'],
+                    score=self._bm25_rank_to_score(row['rank']),
+                    snippet=self._truncate_text(row['text'], 500),
+                    source=row['source'],
+                    user_id=row['user_id']
+                )
+                for row in rows
+            ]
+        except Exception:
+            return []
+    
+    def _search_like(
+        self,
+        query: str,
+        user_id: Optional[str],
+        scopes: List[str],
+        limit: int
+    ) -> List[SearchResult]:
+        """LIKE-based search for CJK characters"""
+        import re
+        # Extract CJK words (2+ characters)
+        cjk_words = re.findall(r'[\u4e00-\u9fff]{2,}', query)
+        if not cjk_words:
+            return []
+        
+        scope_placeholders = ','.join('?' * len(scopes))
+        
+        # Build LIKE conditions for each word
+        like_conditions = []
+        params = []
+        for word in cjk_words:
+            like_conditions.append("text LIKE ?")
+            params.append(f'%{word}%')
+        
+        where_clause = ' OR '.join(like_conditions)
+        params.extend(scopes)
+        
+        if user_id:
+            sql_query = f"""
+                SELECT * FROM chunks
+                WHERE ({where_clause})
+                AND scope IN ({scope_placeholders})
+                AND (scope = 'shared' OR user_id = ?)
+                LIMIT ?
+            """
+            params.extend([user_id, limit])
+        else:
+            sql_query = f"""
+                SELECT * FROM chunks
+                WHERE ({where_clause})
+                AND scope IN ({scope_placeholders})
+                LIMIT ?
+            """
+            params.append(limit)
+        
+        try:
+            rows = self.conn.execute(sql_query, params).fetchall()
+            return [
+                SearchResult(
+                    path=row['path'],
+                    start_line=row['start_line'],
+                    end_line=row['end_line'],
+                    score=0.5,  # Fixed score for LIKE search
+                    snippet=self._truncate_text(row['text'], 500),
+                    source=row['source'],
+                    user_id=row['user_id']
+                )
+                for row in rows
+            ]
+        except Exception:
+            return []
+    
+    def delete_by_path(self, path: str):
+        """Delete all chunks from a file"""
+        self.conn.execute("""
+            DELETE FROM chunks WHERE path = ?
+        """, (path,))
+        self.conn.commit()
+    
+    def get_file_hash(self, path: str) -> Optional[str]:
+        """Get stored file hash"""
+        row = self.conn.execute("""
+            SELECT hash FROM files WHERE path = ?
+        """, (path,)).fetchone()
+        return row['hash'] if row else None
+    
+    def update_file_metadata(self, path: str, source: str, file_hash: str, mtime: int, size: int):
+        """Update file metadata"""
+        self.conn.execute("""
+            INSERT OR REPLACE INTO files (path, source, hash, mtime, size, updated_at)
+            VALUES (?, ?, ?, ?, ?, strftime('%s', 'now'))
+        """, (path, source, file_hash, mtime, size))
+        self.conn.commit()
+    
+    def get_stats(self) -> Dict[str, int]:
+        """Get storage statistics"""
+        chunks_count = self.conn.execute("""
+            SELECT COUNT(*) as cnt FROM chunks
+        """).fetchone()['cnt']
+        
+        files_count = self.conn.execute("""
+            SELECT COUNT(*) as cnt FROM files
+        """).fetchone()['cnt']
+        
+        return {
+            'chunks': chunks_count,
+            'files': files_count
+        }
+    
+    def close(self):
+        """Close database connection"""
+        if self.conn:
+            try:
+                self.conn.commit()  # Ensure all changes are committed
+                self.conn.close()
+                self.conn = None  # Mark as closed
+            except Exception as e:
+                print(f"⚠️  Error closing database connection: {e}")
+    
+    def __del__(self):
+        """Destructor to ensure connection is closed"""
+        try:
+            self.close()
+        except:
+            pass  # Ignore errors during cleanup
+    
+    # Helper methods
+    
+    def _row_to_chunk(self, row) -> MemoryChunk:
+        """Convert database row to MemoryChunk"""
+        return MemoryChunk(
+            id=row['id'],
+            user_id=row['user_id'],
+            scope=row['scope'],
+            source=row['source'],
+            path=row['path'],
+            start_line=row['start_line'],
+            end_line=row['end_line'],
+            text=row['text'],
+            embedding=json.loads(row['embedding']) if row['embedding'] else None,
+            hash=row['hash'],
+            metadata=json.loads(row['metadata']) if row['metadata'] else None
+        )
+    
+    @staticmethod
+    def _cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
+        """Calculate cosine similarity between two vectors"""
+        if len(vec1) != len(vec2):
+            return 0.0
+        
+        dot_product = sum(a * b for a, b in zip(vec1, vec2))
+        norm1 = sum(a * a for a in vec1) ** 0.5
+        norm2 = sum(b * b for b in vec2) ** 0.5
+        
+        if norm1 == 0 or norm2 == 0:
+            return 0.0
+        
+        return dot_product / (norm1 * norm2)
+    
+    @staticmethod
+    def _contains_cjk(text: str) -> bool:
+        """Check if text contains CJK (Chinese/Japanese/Korean) characters"""
+        import re
+        return bool(re.search(r'[\u4e00-\u9fff]', text))
+    
+    @staticmethod
+    def _build_fts_query(raw_query: str) -> Optional[str]:
+        """
+        Build FTS5 query from raw text
+        
+        Works best for English and word-based languages.
+        For CJK characters, LIKE search will be used as fallback.
+        """
+        import re
+        # Extract words (primarily English words and numbers)
+        tokens = re.findall(r'[A-Za-z0-9_]+', raw_query)
+        if not tokens:
+            return None
+        
+        # Quote tokens for exact matching
+        quoted = [f'"{t}"' for t in tokens]
+        # Use OR for more flexible matching
+        return ' OR '.join(quoted)
+    
+    @staticmethod
+    def _bm25_rank_to_score(rank: float) -> float:
+        """Convert BM25 rank to 0-1 score"""
+        normalized = max(0, rank) if rank is not None else 999
+        return 1 / (1 + normalized)
+    
+    @staticmethod
+    def _truncate_text(text: str, max_chars: int) -> str:
+        """Truncate text to max characters"""
+        if len(text) <= max_chars:
+            return text
+        return text[:max_chars] + "..."
+    
+    @staticmethod
+    def compute_hash(content: str) -> str:
+        """Compute SHA256 hash of content"""
+        return hashlib.sha256(content.encode('utf-8')).hexdigest()
--- a/agent/memory/summarizer.py
+++ b/agent/memory/summarizer.py
@@ -0,0 +1,256 @@
+"""
+Memory flush manager
+
+Triggers memory flush before context compaction (similar to clawdbot)
+"""
+
+from typing import Optional, Callable, Any
+from pathlib import Path
+from datetime import datetime
+
+
+class MemoryFlushManager:
+    """
+    Manages memory flush operations before context compaction
+    
+    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
+    """
+    
+    def __init__(
+        self,
+        workspace_dir: Path,
+        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
+    
+    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
+        """
+        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"
+        else:
+            return self.memory_dir / f"{today}.md"
+    
+    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
+        """
+        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())
+        }
+
+
+def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] = None):
+    """
+    Create default memory files if they don't exist
+    
+    Args:
+        workspace_dir: Workspace directory
+        user_id: Optional user ID for user-specific files
+    """
+    memory_dir = workspace_dir / "memory"
+    memory_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Create main MEMORY.md in workspace root
+    if user_id:
+        user_dir = memory_dir / "users" / user_id
+        user_dir.mkdir(parents=True, exist_ok=True)
+        main_memory = user_dir / "MEMORY.md"
+    else:
+        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("")
+    
+    # Create today's memory file
+    today = datetime.now().strftime("%Y-%m-%d")
+    if user_id:
+        user_dir = memory_dir / "users" / user_id
+        today_memory = user_dir / f"{today}.md"
+    else:
+        today_memory = memory_dir / f"{today}.md"
+    
+    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"
+        )
--- a/agent/prompt/init.py
+++ b/agent/prompt/init.py
@@ -0,0 +1,13 @@
+"""
+Agent Prompt Module - 系统提示词构建模块
+"""
+
+from .builder import PromptBuilder, build_agent_system_prompt
+from .workspace import ensure_workspace, load_context_files
+
+__all__ = [
+    'PromptBuilder',
+    'build_agent_system_prompt',
+    'ensure_workspace',
+    'load_context_files',
+]
--- a/agent/prompt/builder.py
+++ b/agent/prompt/builder.py
@@ -0,0 +1,502 @@
+"""
+System Prompt Builder - 系统提示词构建器
+
+实现模块化的系统提示词构建，支持工具、技能、记忆等多个子系统
+"""
+
+from __future__ import annotations
+import os
+from typing import List, Dict, Optional, Any
+from dataclasses import dataclass
+
+from common.log import logger
+
+
+@dataclass
+class ContextFile:
+    """上下文文件"""
+    path: str
+    content: str
+
+
+class PromptBuilder:
+    """提示词构建器"""
+    
+    def __init__(self, workspace_dir: str, language: str = "zh"):
+        """
+        初始化提示词构建器
+        
+        Args:
+            workspace_dir: 工作空间目录
+            language: 语言 ("zh" 或 "en")
+        """
+        self.workspace_dir = workspace_dir
+        self.language = language
+    
+    def build(
+        self,
+        base_persona: Optional[str] = None,
+        user_identity: Optional[Dict[str, str]] = None,
+        tools: Optional[List[Any]] = None,
+        context_files: Optional[List[ContextFile]] = None,
+        skill_manager: Any = None,
+        memory_manager: Any = None,
+        runtime_info: Optional[Dict[str, Any]] = None,
+        is_first_conversation: bool = False,
+        **kwargs
+    ) -> str:
+        """
+        构建完整的系统提示词
+        
+        Args:
+            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）
+            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:
+            完整的系统提示词
+        """
+        return build_agent_system_prompt(
+            workspace_dir=self.workspace_dir,
+            language=self.language,
+            base_persona=base_persona,
+            user_identity=user_identity,
+            tools=tools,
+            context_files=context_files,
+            skill_manager=skill_manager,
+            memory_manager=memory_manager,
+            runtime_info=runtime_info,
+            is_first_conversation=is_first_conversation,
+            **kwargs
+        )
+
+
+def build_agent_system_prompt(
+    workspace_dir: str,
+    language: str = "zh",
+    base_persona: Optional[str] = None,
+    user_identity: Optional[Dict[str, str]] = None,
+    tools: Optional[List[Any]] = None,
+    context_files: Optional[List[ContextFile]] = None,
+    skill_manager: Any = None,
+    memory_manager: Any = None,
+    runtime_info: Optional[Dict[str, Any]] = None,
+    is_first_conversation: bool = False,
+    **kwargs
+) -> str:
+    """
+    构建Agent系统提示词
+    
+    顺序说明（按重要性和逻辑关系排列）:
+    1. 工具系统 - 核心能力，最先介绍
+    2. 技能系统 - 紧跟工具，因为技能需要用 read 工具读取
+    3. 记忆系统 - 独立的记忆能力
+    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）
+    7. 运行时信息 - 元信息（时间、模型等）
+    
+    Args:
+        workspace_dir: 工作空间目录
+        language: 语言 ("zh" 或 "en")
+        base_persona: 基础人格描述（已废弃，由AGENT.md定义）
+        user_identity: 用户身份信息
+        tools: 工具列表
+        context_files: 上下文文件列表
+        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:
+        完整的系统提示词
+    """
+    sections = []
+    
+    # 1. 工具系统（最重要，放在最前面）
+    if tools:
+        sections.extend(_build_tooling_section(tools, language))
+    
+    # 2. 技能系统（紧跟工具，因为需要用 read 工具）
+    if skill_manager:
+        sections.extend(_build_skills_section(skill_manager, tools, language))
+    
+    # 3. 记忆系统（独立的记忆能力）
+    if memory_manager:
+        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))
+    
+    # 5. 用户身份（如果有）
+    if user_identity:
+        sections.extend(_build_user_identity_section(user_identity, language))
+    
+    # 6. 项目上下文文件（AGENT.md, USER.md, RULE.md - 定义人格）
+    if context_files:
+        sections.extend(_build_context_files_section(context_files, language))
+    
+    # 7. 运行时信息（元信息，放在最后）
+    if runtime_info:
+        sections.extend(_build_runtime_section(runtime_info, language))
+    
+    return "\n".join(sections)
+
+
+def _build_identity_section(base_persona: Optional[str], language: str) -> List[str]:
+    """构建基础身份section - 不再需要，身份由AGENT.md定义"""
+    # 不再生成基础身份section，完全由AGENT.md定义
+    return []
+
+
+def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
+    """构建工具说明section"""
+    lines = [
+        "## 工具系统",
+        "",
+        "你可以使用以下工具来完成任务。工具名称是大小写敏感的，请严格按照列表中的名称调用。",
+        "",
+        "### 可用工具",
+        "",
+    ]
+    
+    # 工具分类和排序
+    tool_categories = {
+        "文件操作": ["read", "write", "edit", "ls", "grep", "find"],
+        "命令执行": ["bash", "terminal"],
+        "网络搜索": ["web_search", "web_fetch", "browser"],
+        "记忆系统": ["memory_search", "memory_get"],
+        "其他": []
+    }
+    
+    # 构建工具映射
+    tool_map = {}
+    tool_descriptions = {
+        "read": "读取文件内容",
+        "write": "创建新文件或完全覆盖现有文件（会删除原内容！追加内容请用 edit）。注意：单次 write 内容不要超过 10KB，超大文件请分步创建",
+        "edit": "精确编辑文件（追加、修改、删除部分内容）",
+        "ls": "列出目录内容",
+        "grep": "在文件中搜索内容",
+        "find": "按照模式查找文件",
+        "bash": "执行shell命令",
+        "terminal": "管理后台进程",
+        "web_search": "网络搜索（使用搜索引擎）",
+        "web_fetch": "获取URL内容",
+        "browser": "控制浏览器",
+        "memory_search": "搜索记忆文件",
+        "memory_get": "获取记忆文件内容",
+        "calculator": "计算器",
+        "current_time": "获取当前时间",
+    }
+    
+    for tool in tools:
+        tool_name = tool.name if hasattr(tool, 'name') else str(tool)
+        tool_desc = tool.description if hasattr(tool, 'description') else tool_descriptions.get(tool_name, "")
+        tool_map[tool_name] = tool_desc
+    
+    # 按分类添加工具
+    for category, tool_names in tool_categories.items():
+        category_tools = [(name, tool_map.get(name, "")) for name in tool_names if name in tool_map]
+        if category_tools:
+            lines.append(f"**{category}**:")
+            for name, desc in category_tools:
+                if desc:
+                    lines.append(f"- `{name}`: {desc}")
+                else:
+                    lines.append(f"- `{name}`")
+                del tool_map[name]  # 移除已添加的工具
+            lines.append("")
+    
+    # 添加其他未分类的工具
+    if tool_map:
+        lines.append("**其他工具**:")
+        for name, desc in sorted(tool_map.items()):
+            if desc:
+                lines.append(f"- `{name}`: {desc}")
+            else:
+                lines.append(f"- `{name}`")
+        lines.append("")
+    
+    # 工具使用指南
+    lines.extend([
+        "### 工具调用风格",
+        "",
+        "默认规则: 对于常规、低风险的工具调用，直接调用即可，无需叙述。",
+        "",
+        "需要叙述的情况:",
+        "- 多步骤、复杂的任务",
+        "- 敏感操作（如删除文件）",
+        "- 用户明确要求解释过程",
+        "",
+        "叙述要求: 保持简洁、信息密度高，避免重复显而易见的步骤。",
+        "",
+        "完成标准:",
+        "- 确保用户的需求得到实际解决，而不仅仅是制定计划。",
+        "- 当任务需要多次工具调用时，持续推进直到完成, 解决完后向用户报告结果或回复用户的问题",
+        "- 每次工具调用后，评估是否已获得足够信息来推进或完成任务",
+        "- 避免重复调用相同的工具和相同参数获取相同的信息，除非用户明确要求",
+        "",
+        "**安全提醒**: 回复中涉及密钥、令牌、密码等敏感信息时，必须脱敏处理，禁止直接显示完整内容。",
+        "",
+    ])
+    
+    return lines
+
+
+def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
+    """构建技能系统section"""
+    if not skill_manager:
+        return []
+    
+    # 获取read工具名称
+    read_tool_name = "read"
+    if tools:
+        for tool in tools:
+            tool_name = tool.name if hasattr(tool, 'name') else str(tool)
+            if tool_name.lower() == "read":
+                read_tool_name = tool_name
+                break
+    
+    lines = [
+        "## 技能系统",
+        "",
+        "在回复之前：扫描下方 <available_skills> 中的 <description> 条目。",
+        "",
+        f"- 如果恰好有一个技能明确适用：使用 `{read_tool_name}` 工具读取其 <location> 路径下的 SKILL.md 文件，然后遵循它",
+        "- 如果多个技能都适用：选择最具体的一个，然后读取并遵循",
+        "- 如果没有明确适用的：不要读取任何 SKILL.md",
+        "",
+        "**约束**: 永远不要一次性读取多个技能；只在选择后再读取。",
+        "",
+    ]
+    
+    # 添加技能列表（通过skill_manager获取）
+    try:
+        skills_prompt = skill_manager.build_skills_prompt()
+        logger.debug(f"[PromptBuilder] Skills prompt length: {len(skills_prompt) if skills_prompt else 0}")
+        if skills_prompt:
+            lines.append(skills_prompt.strip())
+            lines.append("")
+        else:
+            logger.warning("[PromptBuilder] No skills prompt generated - skills_prompt is empty")
+    except Exception as e:
+        logger.warning(f"Failed to build skills prompt: {e}")
+        import traceback
+        logger.debug(f"Skills prompt error traceback: {traceback.format_exc()}")
+    
+    return lines
+
+
+def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
+    """构建记忆系统section"""
+    if not memory_manager:
+        return []
+    
+    # 检查是否有memory工具
+    has_memory_tools = False
+    if tools:
+        tool_names = [tool.name if hasattr(tool, 'name') else str(tool) for tool in tools]
+        has_memory_tools = any(name in ['memory_search', 'memory_get'] for name in tool_names)
+    
+    if not has_memory_tools:
+        return []
+    
+    lines = [
+        "## 记忆系统",
+        "",
+        "在回答关于以前的工作、决定、日期、人物、偏好或待办事项的任何问题之前：",
+        "",
+        "1. 不确定记忆文件位置 → 先用 `memory_search` 通过关键词和语义检索相关内容",
+        "2. 已知文件位置 → 直接用 `memory_get` 读取相应的行 (例如：MEMORY.md, memory/YYYY-MM-DD.md)",
+        "3. search 无结果 → 尝试用 `memory_get` 读取MEMORY.md及最近两天记忆文件",
+        "",
+        "**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",
+        "",
+        "**写入记忆**:",
+        "- 追加内容 → `edit` 工具，oldText 留空",
+        "- 修改内容 → `edit` 工具，oldText 填写要替换的文本",
+        "- 新建文件 → `write` 工具",
+        "- **禁止写入敏感信息**：API密钥、令牌等敏感信息严禁写入记忆文件",
+        "",
+        "**使用原则**: 自然使用记忆，就像你本来就知道；不用刻意提起，除非用户问起。",
+        "",
+    ]
+    
+    return lines
+
+
+def _build_user_identity_section(user_identity: Dict[str, str], language: str) -> List[str]:
+    """构建用户身份section"""
+    if not user_identity:
+        return []
+    
+    lines = [
+        "## 用户身份",
+        "",
+    ]
+    
+    if user_identity.get("name"):
+        lines.append(f"**用户姓名**: {user_identity['name']}")
+    if user_identity.get("nickname"):
+        lines.append(f"**称呼**: {user_identity['nickname']}")
+    if user_identity.get("timezone"):
+        lines.append(f"**时区**: {user_identity['timezone']}")
+    if user_identity.get("notes"):
+        lines.append(f"**备注**: {user_identity['notes']}")
+    
+    lines.append("")
+    
+    return lines
+
+
+def _build_docs_section(workspace_dir: str, language: str) -> List[str]:
+    """构建文档路径section - 已移除，不再需要"""
+    # 不再生成文档section
+    return []
+
+
+def _build_workspace_section(workspace_dir: str, language: str, is_first_conversation: bool = False) -> List[str]:
+    """构建工作空间section"""
+    lines = [
+        "## 工作空间",
+        "",
+        f"你的工作目录是: `{workspace_dir}`",
+        "",
+        "**路径使用规则** (非常重要):",
+        "",
+        f"1. **相对路径的基准目录**: 所有相对路径都是相对于 `{workspace_dir}` 而言的",
+        f"   - ✅ 正确: 访问工作空间内的文件用相对路径，如 `AGENT.md`",
+        f"   - ❌ 错误: 用相对路径访问其他目录的文件 (如果它不在 `{workspace_dir}` 内)",
+        "",
+        "2. **访问其他目录**: 如果要访问工作空间之外的目录（如项目代码、系统文件），**必须使用绝对路径**",
+        f"   - ✅ 正确: 例如 `~/chatgpt-on-wechat`、`/usr/local/`",
+        f"   - ❌ 错误: 假设相对路径会指向其他目录",
+        "",
+        "3. **路径解析示例**:",
+        f"   - 相对路径 `memory/` → 实际路径 `{workspace_dir}/memory/`",
+        f"   - 绝对路径 `~/chatgpt-on-wechat/docs/` → 实际路径 `~/chatgpt-on-wechat/docs/`",
+        "",
+        "4. **不确定时**: 先用 `bash pwd` 确认当前目录，或用 `ls .` 查看当前位置",
+        "",
+        "**重要说明 - 文件已自动加载**:",
+        "",
+        "以下文件在会话启动时**已经自动加载**到系统提示词的「项目上下文」section 中，你**无需再用 read 工具读取它们**：",
+        "",
+        "- ✅ `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",
+        "",
+        "**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」",
+        "",
+    ]
+    
+    # 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）",
+            "",
+        ])
+    
+    return lines
+
+
+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:
+        return []
+    
+    # 检查是否有AGENT.md
+    has_agent = any(
+        f.path.lower().endswith('agent.md') or 'agent.md' in f.path.lower()
+        for f in context_files
+    )
+    
+    lines = [
+        "# 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("")
+    
+    # 添加每个文件的内容
+    for file in context_files:
+        lines.append(f"## {file.path}")
+        lines.append("")
+        lines.append(file.content)
+        lines.append("")
+    
+    return lines
+
+
+def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[str]:
+    """构建运行时信息section"""
+    if not runtime_info:
+        return []
+    
+    lines = [
+        "## 运行时信息",
+        "",
+    ]
+    
+    # Add current time if available
+    if runtime_info.get("current_time"):
+        time_str = runtime_info["current_time"]
+        weekday = runtime_info.get("weekday", "")
+        timezone = runtime_info.get("timezone", "")
+        
+        time_line = f"当前时间: {time_str}"
+        if weekday:
+            time_line += f" {weekday}"
+        if timezone:
+            time_line += f" ({timezone})"
+        
+        lines.append(time_line)
+        lines.append("")
+    
+    # Add other runtime info
+    runtime_parts = []
+    if runtime_info.get("model"):
+        runtime_parts.append(f"模型={runtime_info['model']}")
+    if runtime_info.get("workspace"):
+        runtime_parts.append(f"工作空间={runtime_info['workspace']}")
+    # Only add channel if it's not the default "web"
+    if runtime_info.get("channel") and runtime_info.get("channel") != "web":
+        runtime_parts.append(f"渠道={runtime_info['channel']}")
+    
+    if runtime_parts:
+        lines.append("运行时: " + " | ".join(runtime_parts))
+        lines.append("")
+    
+    return lines
--- a/agent/prompt/workspace.py
+++ b/agent/prompt/workspace.py
@@ -0,0 +1,357 @@
+"""
+Workspace Management - 工作空间管理模块
+
+负责初始化工作空间、创建模板文件、加载上下文文件
+"""
+
+from __future__ import annotations
+import os
+import json
+from typing import List, Optional, Dict
+from dataclasses import dataclass
+
+from common.log import logger
+from .builder import ContextFile
+
+
+# 默认文件名常量
+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"
+
+
+@dataclass
+class WorkspaceFiles:
+    """工作空间文件路径"""
+    agent_path: str
+    user_path: str
+    rule_path: str
+    memory_path: str
+    memory_dir: str
+    state_path: str
+
+
+def ensure_workspace(workspace_dir: str, create_templates: bool = True) -> WorkspaceFiles:
+    """
+    确保工作空间存在，并创建必要的模板文件
+    
+    Args:
+        workspace_dir: 工作空间目录路径
+        create_templates: 是否创建模板文件（首次运行时）
+        
+    Returns:
+        WorkspaceFiles对象，包含所有文件路径
+    """
+    # 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)
+    
+    # 如果需要，创建模板文件
+    if create_templates:
+        _create_template_if_missing(agent_path, _get_agent_template())
+        _create_template_if_missing(user_path, _get_user_template())
+        _create_template_if_missing(rule_path, _get_rule_template())
+        _create_template_if_missing(memory_path, _get_memory_template())
+        
+        logger.debug(f"[Workspace] Initialized workspace at: {workspace_dir}")
+    
+    return WorkspaceFiles(
+        agent_path=agent_path,
+        user_path=user_path,
+        rule_path=rule_path,
+        memory_path=memory_path,
+        memory_dir=memory_dir,
+        state_path=state_path
+    )
+
+
+def load_context_files(workspace_dir: str, files_to_load: Optional[List[str]] = None) -> List[ContextFile]:
+    """
+    加载工作空间的上下文文件
+    
+    Args:
+        workspace_dir: 工作空间目录
+        files_to_load: 要加载的文件列表（相对路径），如果为None则加载所有标准文件
+        
+    Returns:
+        ContextFile对象列表
+    """
+    if files_to_load is None:
+        # 默认加载的文件（按优先级排序）
+        files_to_load = [
+            DEFAULT_AGENT_FILENAME,
+            DEFAULT_USER_FILENAME,
+            DEFAULT_RULE_FILENAME,
+        ]
+    
+    context_files = []
+    
+    for filename in files_to_load:
+        filepath = os.path.join(workspace_dir, filename)
+        
+        if not os.path.exists(filepath):
+            continue
+        
+        try:
+            with open(filepath, 'r', encoding='utf-8') as f:
+                content = f.read().strip()
+            
+            # 跳过空文件或只包含模板占位符的文件
+            if not content or _is_template_placeholder(content):
+                continue
+            
+            context_files.append(ContextFile(
+                path=filename,
+                content=content
+            ))
+            
+            logger.debug(f"[Workspace] Loaded context file: {filename}")
+            
+        except Exception as e:
+            logger.warning(f"[Workspace] Failed to load {filename}: {e}")
+    
+    return context_files
+
+
+def _create_template_if_missing(filepath: str, template_content: str):
+    """如果文件不存在，创建模板文件"""
+    if not os.path.exists(filepath):
+        try:
+            with open(filepath, 'w', encoding='utf-8') as f:
+                f.write(template_content)
+            logger.debug(f"[Workspace] Created template: {os.path.basename(filepath)}")
+        except Exception as e:
+            logger.error(f"[Workspace] Failed to create template {filepath}: {e}")
+
+
+def _is_template_placeholder(content: str) -> bool:
+    """检查内容是否为模板占位符"""
+    # 常见的占位符模式
+    placeholders = [
+        "*(填写",
+        "*(在首次对话时填写",
+        "*(可选)",
+        "*(根据需要添加",
+    ]
+    
+    lines = content.split('\n')
+    non_empty_lines = [line.strip() for line in lines if line.strip() and not line.strip().startswith('#')]
+    
+    # 如果没有实际内容（只有标题和占位符）
+    if len(non_empty_lines) <= 3:
+        for placeholder in placeholders:
+            if any(placeholder in line for line in non_empty_lines):
+                return True
+    
+    return False
+
+
+# ============= 模板内容 =============
+
+def _get_agent_template() -> str:
+    """Agent人格设定模板"""
+    return """# AGENT.md - 我是谁？
+
+*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
+
+- **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
+
+*(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
+
+*(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
+
+## 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
+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
+
+---
+
+**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。
+"""
+
+
+def _get_user_template() -> str:
+    """用户身份信息模板"""
+    return """# USER.md - 用户基本信息
+
+*这个文件只存放不会变的基本身份信息。爱好、偏好、计划等动态信息请写入 MEMORY.md。*
+
+## 基本信息
+
+- **姓名**: *(在首次对话时询问)*
+- **称呼**: *(用户希望被如何称呼)*
+- **职业**: *(可选)*
+- **时区**: *(例如: Asia/Shanghai)*
+
+## 联系方式
+
+- **微信**: 
+- **邮箱**: 
+- **其他**: 
+
+## 重要日期
+
+- **生日**: 
+- **纪念日**: 
+
+---
+
+**注意**: 这个文件存放静态的身份信息
+"""
+
+
+def _get_rule_template() -> str:
+    """工作空间规则模板"""
+    return """# RULE.md - 工作空间规则
+
+这个文件夹是你的家。好好对待它。
+
+## 记忆系统
+
+你每次会话都是全新的，记忆文件让你保持连续性：
+
+### 📝 每日记忆：`memory/YYYY-MM-DD.md`
+- 原始的对话日志
+- 记录当天发生的事情
+- 如果 `memory/` 目录不存在，创建它
+
+### 🧠 长期记忆：`MEMORY.md`
+- 你精选的记忆，就像人类的长期记忆
+- **仅在主会话中加载**（与用户的直接聊天）
+- **不要在共享上下文中加载**（群聊、与其他人的会话）
+- 这是为了**安全** - 包含不应泄露给陌生人的个人上下文
+- 记录重要事件、想法、决定、观点、经验教训
+- 这是你精选的记忆 - 精华，而不是原始日志
+- 用 `edit` 工具追加新的记忆内容
+
+### 📝 写下来 - 不要"记在心里"！
+- **记忆是有限的** - 如果你想记住某事，写入文件
+- "记在心里"不会在会话重启后保留，文件才会
+- 当有人说"记住这个" → 更新 `MEMORY.md` 或 `memory/YYYY-MM-DD.md`
+- 当你学到教训 → 更新 RULE.md 或相关技能
+- 当你犯错 → 记录下来，这样未来的你不会重复，**文字 > 大脑** 📝
+
+### 存储规则
+
+当用户分享信息时，根据类型选择存储位置：
+
+1. **静态身份 → USER.md**（仅限：姓名、职业、时区、联系方式、生日）
+2. **动态记忆 → MEMORY.md**（爱好、偏好、决策、目标、项目、教训、待办事项）
+3. **当天对话 → memory/YYYY-MM-DD.md**（今天聊的内容）
+
+## 安全
+
+- 永远不要泄露秘钥等私人数据
+- 不要在未经询问的情况下运行破坏性命令
+- 当有疑问时，先问
+
+## 工作空间演化
+
+这个工作空间会随着你的使用而不断成长。当你学到新东西、发现更好的方式，或者犯错后改正时，记录下来。你可以随时更新这个规则文件。
+"""
+
+
+def _get_memory_template() -> str:
+    """长期记忆模板 - 创建一个空文件，由 Agent 自己填充"""
+    return """# MEMORY.md - 长期记忆
+
+*这是你的长期记忆文件。记录重要的事件、决策、偏好、学到的教训。*
+
+---
+
+"""
+
+
+# ============= 状态管理 =============
+
+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
+
+
+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}")
+
--- a/agent/protocol/init.py
+++ b/agent/protocol/init.py
@@ -0,0 +1,20 @@
+from .agent import Agent
+from .agent_stream import AgentStreamExecutor
+from .task import Task, TaskType, TaskStatus
+from .result import AgentResult, AgentAction, AgentActionType, ToolResult
+from .models import LLMModel, LLMRequest, ModelFactory
+
+__all__ = [
+    'Agent', 
+    'AgentStreamExecutor',
+    'Task', 
+    'TaskType', 
+    'TaskStatus',
+    'AgentResult',
+    'AgentAction',
+    'AgentActionType', 
+    'ToolResult',
+    'LLMModel',
+    'LLMRequest', 
+    'ModelFactory'
+]
--- a/agent/protocol/agent.py
+++ b/agent/protocol/agent.py
@@ -0,0 +1,392 @@
+import json
+import time
+import threading
+
+from common.log import logger
+from agent.protocol.models import LLMRequest, LLMModel
+from agent.protocol.agent_stream import AgentStreamExecutor
+from agent.protocol.result import AgentAction, AgentActionType, ToolResult, AgentResult
+from agent.tools.base_tool import BaseTool, ToolStage
+
+
+class Agent:
+    def __init__(self, system_prompt: str, description: str = "AI Agent", model: LLMModel = None,
+                 tools=None, output_mode="print", max_steps=100, max_context_tokens=None, 
+                 context_reserve_tokens=None, memory_manager=None, name: str = None,
+                 workspace_dir: str = None, skill_manager=None, enable_skills: bool = True):
+        """
+        Initialize the Agent with system prompt, model, description.
+
+        :param system_prompt: The system prompt for the agent.
+        :param description: A description of the agent.
+        :param model: An instance of LLMModel to be used by the agent.
+        :param tools: Optional list of tools for the agent to use.
+        :param output_mode: Control how execution progress is displayed: 
+                           "print" for console output or "logger" for using logger
+        :param max_steps: Maximum number of steps the agent can take (default: 100)
+        :param max_context_tokens: Maximum tokens to keep in context (default: None, auto-calculated based on model)
+        :param context_reserve_tokens: Reserve tokens for new requests (default: None, auto-calculated)
+        :param memory_manager: Optional MemoryManager instance for memory operations
+        :param name: [Deprecated] The name of the agent (no longer used in single-agent system)
+        :param workspace_dir: Optional workspace directory for workspace-specific skills
+        :param skill_manager: Optional SkillManager instance (will be created if None and enable_skills=True)
+        :param enable_skills: Whether to enable skills support (default: True)
+        """
+        self.name = name or "Agent"
+        self.system_prompt = system_prompt
+        self.model: LLMModel = model  # Instance of LLMModel
+        self.description = description
+        self.tools: list = []
+        self.max_steps = max_steps  # max tool-call steps, default 100
+        self.max_context_tokens = max_context_tokens  # max tokens in context
+        self.context_reserve_tokens = context_reserve_tokens  # reserve tokens for new requests
+        self.captured_actions = []  # Initialize captured actions list
+        self.output_mode = output_mode
+        self.last_usage = None  # Store last API response usage info
+        self.messages = []  # Unified message history for stream mode
+        self.messages_lock = threading.Lock()  # Lock for thread-safe message operations
+        self.memory_manager = memory_manager  # Memory manager for auto memory flush
+        self.workspace_dir = workspace_dir  # Workspace directory
+        self.enable_skills = enable_skills  # Skills enabled flag
+        
+        # Initialize skill manager
+        self.skill_manager = None
+        if enable_skills:
+            if skill_manager:
+                self.skill_manager = skill_manager
+            else:
+                # Auto-create skill manager
+                try:
+                    from agent.skills import SkillManager
+                    self.skill_manager = SkillManager(workspace_dir=workspace_dir)
+                    logger.debug(f"Initialized SkillManager with {len(self.skill_manager.skills)} skills")
+                except Exception as e:
+                    logger.warning(f"Failed to initialize SkillManager: {e}")
+        
+        if tools:
+            for tool in tools:
+                self.add_tool(tool)
+
+    def add_tool(self, tool: BaseTool):
+        """
+        Add a tool to the agent.
+
+        :param tool: The tool to add (either a tool instance or a tool name)
+        """
+        # If tool is already an instance, use it directly
+        tool.model = self.model
+        self.tools.append(tool)
+
+    def get_skills_prompt(self, skill_filter=None) -> str:
+        """
+        Get the skills prompt to append to system prompt.
+        
+        :param skill_filter: Optional list of skill names to include
+        :return: Formatted skills prompt or empty string
+        """
+        if not self.skill_manager:
+            return ""
+        
+        try:
+            return self.skill_manager.build_skills_prompt(skill_filter=skill_filter)
+        except Exception as e:
+            logger.warning(f"Failed to build skills prompt: {e}")
+            return ""
+    
+    def get_full_system_prompt(self, skill_filter=None) -> str:
+        """
+        Get the full system prompt including skills.
+        
+        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
+        """
+        # Skills are now included in system_prompt by PromptBuilder
+        # No need to append them here
+        return self.system_prompt
+    
+    def refresh_skills(self):
+        """Refresh the loaded skills."""
+        if self.skill_manager:
+            self.skill_manager.refresh_skills()
+            logger.info(f"Refreshed skills: {len(self.skill_manager.skills)} skills loaded")
+    
+    def list_skills(self):
+        """
+        List all loaded skills.
+        
+        :return: List of skill entries or empty list
+        """
+        if not self.skill_manager:
+            return []
+        return self.skill_manager.list_skills()
+
+    def _get_model_context_window(self) -> int:
+        """
+        Get the model's context window size in tokens.
+        Auto-detect based on model name.
+        
+        Model context windows:
+        - Claude 3.5/3.7 Sonnet: 200K tokens
+        - Claude 3 Opus: 200K tokens
+        - GPT-4 Turbo/128K: 128K tokens
+        - GPT-4: 8K-32K tokens
+        - GPT-3.5: 16K tokens
+        - DeepSeek: 64K tokens
+        
+        :return: Context window size in tokens
+        """
+        if self.model and hasattr(self.model, 'model'):
+            model_name = self.model.model.lower()
+
+            # Claude models - 200K context
+            if 'claude-3' in model_name or 'claude-sonnet' in model_name:
+                return 200000
+
+            # GPT-4 models
+            elif 'gpt-4' in model_name:
+                if 'turbo' in model_name or '128k' in model_name:
+                    return 128000
+                elif '32k' in model_name:
+                    return 32000
+                else:
+                    return 8000
+
+            # GPT-3.5
+            elif 'gpt-3.5' in model_name:
+                if '16k' in model_name:
+                    return 16000
+                else:
+                    return 4000
+
+            # DeepSeek
+            elif 'deepseek' in model_name:
+                return 64000
+            
+            # Gemini models
+            elif 'gemini' in model_name:
+                if '2.0' in model_name or 'exp' in model_name:
+                    return 2000000  # Gemini 2.0: 2M tokens
+                else:
+                    return 1000000  # Gemini 1.5: 1M tokens
+
+        # Default conservative value
+        return 128000
+
+    def _get_context_reserve_tokens(self) -> int:
+        """
+        Get the number of tokens to reserve for new requests.
+        This prevents context overflow by keeping a buffer.
+        
+        :return: Number of tokens to reserve
+        """
+        if self.context_reserve_tokens is not None:
+            return self.context_reserve_tokens
+
+        # Reserve ~10% of context window, with min 10K and max 200K
+        context_window = self._get_model_context_window()
+        reserve = int(context_window * 0.1)
+        return max(10000, min(200000, reserve))
+
+    def _estimate_message_tokens(self, message: dict) -> int:
+        """
+        Estimate token count for a message using chars/4 heuristic.
+        This is a conservative estimate (tends to overestimate).
+
+        :param message: Message dict with 'role' and 'content'
+        :return: Estimated token count
+        """
+        content = message.get('content', '')
+        if isinstance(content, str):
+            return max(1, len(content) // 4)
+        elif isinstance(content, list):
+            # Handle multi-part content (text + images)
+            total_chars = 0
+            for part in content:
+                if isinstance(part, dict) and part.get('type') == 'text':
+                    total_chars += len(part.get('text', ''))
+                elif isinstance(part, dict) and part.get('type') == 'image':
+                    # Estimate images as ~1200 tokens
+                    total_chars += 4800
+            return max(1, total_chars // 4)
+        return 1
+
+    def _find_tool(self, tool_name: str):
+        """Find and return a tool with the specified name"""
+        for tool in self.tools:
+            if tool.name == tool_name:
+                # Only pre-process stage tools can be actively called
+                if tool.stage == ToolStage.PRE_PROCESS:
+                    tool.model = self.model
+                    tool.context = self  # Set tool context
+                    return tool
+                else:
+                    # If it's a post-process tool, return None to prevent direct calling
+                    logger.warning(f"Tool {tool_name} is a post-process tool and cannot be called directly.")
+                    return None
+        return None
+
+    # output function based on mode
+    def output(self, message="", end="\n"):
+        if self.output_mode == "print":
+            print(message, end=end)
+        elif message:
+            logger.info(message)
+
+    def _execute_post_process_tools(self):
+        """Execute all post-process stage tools"""
+        # Get all post-process stage tools
+        post_process_tools = [tool for tool in self.tools if tool.stage == ToolStage.POST_PROCESS]
+
+        # Execute each tool
+        for tool in post_process_tools:
+            # Set tool context
+            tool.context = self
+
+            # Record start time for execution timing
+            start_time = time.time()
+
+            # Execute tool (with empty parameters, tool will extract needed info from context)
+            result = tool.execute({})
+
+            # Calculate execution time
+            execution_time = time.time() - start_time
+
+            # Capture tool use for tracking
+            self.capture_tool_use(
+                tool_name=tool.name,
+                input_params={},  # Post-process tools typically don't take parameters
+                output=result.result,
+                status=result.status,
+                error_message=str(result.result) if result.status == "error" else None,
+                execution_time=execution_time
+            )
+
+            # Log result
+            if result.status == "success":
+                # Print tool execution result in the desired format
+                self.output(f"\n🛠️ {tool.name}: {json.dumps(result.result)}")
+            else:
+                # Print failure in print mode
+                self.output(f"\n🛠️ {tool.name}: {json.dumps({'status': 'error', 'message': str(result.result)})}")
+
+    def capture_tool_use(self, tool_name, input_params, output, status, thought=None, error_message=None,
+                         execution_time=0.0):
+        """
+        Capture a tool use action.
+        
+        :param thought: thought content
+        :param tool_name: Name of the tool used
+        :param input_params: Parameters passed to the tool
+        :param output: Output from the tool
+        :param status: Status of the tool execution
+        :param error_message: Error message if the tool execution failed
+        :param execution_time: Time taken to execute the tool
+        """
+        tool_result = ToolResult(
+            tool_name=tool_name,
+            input_params=input_params,
+            output=output,
+            status=status,
+            error_message=error_message,
+            execution_time=execution_time
+        )
+
+        action = AgentAction(
+            agent_id=self.id if hasattr(self, 'id') else str(id(self)),
+            agent_name=self.name,
+            action_type=AgentActionType.TOOL_USE,
+            tool_result=tool_result,
+            thought=thought
+        )
+
+        self.captured_actions.append(action)
+
+        return action
+
+    def run_stream(self, user_message: str, on_event=None, clear_history: bool = False, skill_filter=None) -> str:
+        """
+        Execute single agent task with streaming (based on tool-call)
+
+        This method supports:
+        - Streaming output
+        - Multi-turn reasoning based on tool-call
+        - Event callbacks
+        - Persistent conversation history across calls
+
+        Args:
+            user_message: User message
+            on_event: Event callback function callback(event: dict)
+                     event = {"type": str, "timestamp": float, "data": dict}
+            clear_history: If True, clear conversation history before this call (default: False)
+            skill_filter: Optional list of skill names to include in this run
+
+        Returns:
+            Final response text
+
+        Example:
+            # Multi-turn conversation with memory
+            response1 = agent.run_stream("My name is Alice")
+            response2 = agent.run_stream("What's my name?")  # Will remember Alice
+
+            # Single-turn without memory
+            response = agent.run_stream("Hello", clear_history=True)
+        """
+        # Clear history if requested
+        if clear_history:
+            with self.messages_lock:
+                self.messages = []
+
+        # Get model to use
+        if not self.model:
+            raise ValueError("No model available for agent")
+
+        # Get full system prompt with skills
+        full_system_prompt = self.get_full_system_prompt(skill_filter=skill_filter)
+
+        # Create a copy of messages for this execution to avoid concurrent modification
+        # Record the original length to track which messages are new
+        with self.messages_lock:
+            messages_copy = self.messages.copy()
+            original_length = len(self.messages)
+
+        # Get max_context_turns from config
+        from config import conf
+        max_context_turns = conf().get("agent_max_context_turns", 30)
+        
+        # Create stream executor with copied message history
+        executor = AgentStreamExecutor(
+            agent=self,
+            model=self.model,
+            system_prompt=full_system_prompt,
+            tools=self.tools,
+            max_turns=self.max_steps,
+            on_event=on_event,
+            messages=messages_copy,  # Pass copied message history
+            max_context_turns=max_context_turns
+        )
+
+        # Execute
+        response = executor.run_stream(user_message)
+
+        # Append only the NEW messages from this execution (thread-safe)
+        # This allows concurrent requests to both contribute to history
+        with self.messages_lock:
+            new_messages = executor.messages[original_length:]
+            self.messages.extend(new_messages)
+        
+        # Store executor reference for agent_bridge to access files_to_send
+        self.stream_executor = executor
+
+        # Execute all post-process tools
+        self._execute_post_process_tools()
+
+        return response
+
+    def clear_history(self):
+        """Clear conversation history and captured actions"""
+        self.messages = []
+        self.captured_actions = []
--- a/agent/protocol/agent_stream.py
+++ b/agent/protocol/agent_stream.py
--- a/agent/protocol/context.py
+++ b/agent/protocol/context.py
@@ -0,0 +1,27 @@
+class TeamContext:
+    def __init__(self, name: str, description: str, rule: str, agents: list, max_steps: int = 100):
+        """
+        Initialize the TeamContext with a name, description, rules, a list of agents, and a user question.
+        :param name: The name of the group context.
+        :param description: A description of the group context.
+        :param rule: The rules governing the group context.
+        :param agents: A list of agents in the context.
+        """
+        self.name = name
+        self.description = description
+        self.rule = rule
+        self.agents = agents
+        self.user_task = ""  # For backward compatibility
+        self.task = None  # Will be a Task instance
+        self.model = None  # Will be an instance of LLMModel
+        self.task_short_name = None  # Store the task directory name
+        # List of agents that have been executed
+        self.agent_outputs: list = []
+        self.current_steps = 0
+        self.max_steps = max_steps
+
+
+class AgentOutput:
+    def __init__(self, agent_name: str, output: str):
+        self.agent_name = agent_name
+        self.output = output
--- a/agent/protocol/models.py
+++ b/agent/protocol/models.py
@@ -0,0 +1,57 @@
+"""
+Models module for agent system.
+Provides basic model classes needed by tools and bridge integration.
+"""
+
+from typing import Any, Dict, List, Optional
+
+
+class LLMRequest:
+    """Request model for LLM operations"""
+    
+    def __init__(self, messages: List[Dict[str, str]] = None, model: Optional[str] = None,
+                 temperature: float = 0.7, max_tokens: Optional[int] = None, 
+                 stream: bool = False, tools: Optional[List] = None, **kwargs):
+        self.messages = messages or []
+        self.model = model
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.stream = stream
+        self.tools = tools
+        # Allow extra attributes
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+
+class LLMModel:
+    """Base class for LLM models"""
+    
+    def __init__(self, model: str = None, **kwargs):
+        self.model = model
+        self.config = kwargs
+    
+    def call(self, request: LLMRequest):
+        """
+        Call the model with a request.
+        This is a placeholder implementation.
+        """
+        raise NotImplementedError("LLMModel.call not implemented in this context")
+    
+    def call_stream(self, request: LLMRequest):
+        """
+        Call the model with streaming.
+        This is a placeholder implementation.
+        """
+        raise NotImplementedError("LLMModel.call_stream not implemented in this context")
+
+
+class ModelFactory:
+    """Factory for creating model instances"""
+
+    @staticmethod
+    def create_model(model_type: str, **kwargs):
+        """
+        Create a model instance based on type.
+        This is a placeholder implementation.
+        """
+        raise NotImplementedError("ModelFactory.create_model not implemented in this context")
--- a/agent/protocol/result.py
+++ b/agent/protocol/result.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+import time
+import uuid
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import List, Dict, Any, Optional
+
+from agent.protocol.task import Task, TaskStatus
+
+
+class AgentActionType(Enum):
+    """Enum representing different types of agent actions."""
+    TOOL_USE = "tool_use"
+    THINKING = "thinking"
+    FINAL_ANSWER = "final_answer"
+
+
+@dataclass
+class ToolResult:
+    """
+    Represents the result of a tool use.
+    
+    Attributes:
+        tool_name: Name of the tool used
+        input_params: Parameters passed to the tool
+        output: Output from the tool
+        status: Status of the tool execution (success/error)
+        error_message: Error message if the tool execution failed
+        execution_time: Time taken to execute the tool
+    """
+    tool_name: str
+    input_params: Dict[str, Any]
+    output: Any
+    status: str
+    error_message: Optional[str] = None
+    execution_time: float = 0.0
+
+
+@dataclass
+class AgentAction:
+    """
+    Represents an action taken by an agent.
+    
+    Attributes:
+        id: Unique identifier for the action
+        agent_id: ID of the agent that performed the action
+        agent_name: Name of the agent that performed the action
+        action_type: Type of action (tool use, thinking, final answer)
+        content: Content of the action (thought content, final answer content)
+        tool_result: Tool use details if action_type is TOOL_USE
+        timestamp: When the action was performed
+    """
+    agent_id: str
+    agent_name: str
+    action_type: AgentActionType
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    content: str = ""
+    tool_result: Optional[ToolResult] = None
+    thought: Optional[str] = None
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class AgentResult:
+    """
+    Represents the result of an agent's execution.
+
+    Attributes:
+        final_answer: The final answer provided by the agent
+        step_count: Number of steps taken by the agent
+        status: Status of the execution (success/error)
+        error_message: Error message if execution failed
+    """
+    final_answer: str
+    step_count: int
+    status: str = "success"
+    error_message: Optional[str] = None
+
+    @classmethod
+    def success(cls, final_answer: str, step_count: int) -> "AgentResult":
+        """Create a successful result"""
+        return cls(final_answer=final_answer, step_count=step_count)
+
+    @classmethod
+    def error(cls, error_message: str, step_count: int = 0) -> "AgentResult":
+        """Create an error result"""
+        return cls(
+            final_answer=f"Error: {error_message}",
+            step_count=step_count,
+            status="error",
+            error_message=error_message
+        )
+
+    @property
+    def is_error(self) -> bool:
+        """Check if the result represents an error"""
+        return self.status == "error"
--- a/agent/protocol/task.py
+++ b/agent/protocol/task.py
@@ -0,0 +1,96 @@
+from __future__ import annotations
+import time
+import uuid
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, Any, List
+
+
+class TaskType(Enum):
+    """Enum representing different types of tasks."""
+    TEXT = "text"
+    IMAGE = "image"
+    VIDEO = "video"
+    AUDIO = "audio"
+    FILE = "file"
+    MIXED = "mixed"
+
+
+class TaskStatus(Enum):
+    """Enum representing the status of a task."""
+    INIT = "init"  # Initial state
+    PROCESSING = "processing"  # In progress
+    COMPLETED = "completed"  # Completed
+    FAILED = "failed"  # Failed
+
+
+@dataclass
+class Task:
+    """
+    Represents a task to be processed by an agent.
+    
+    Attributes:
+        id: Unique identifier for the task
+        content: The primary text content of the task
+        type: Type of the task
+        status: Current status of the task
+        created_at: Timestamp when the task was created
+        updated_at: Timestamp when the task was last updated
+        metadata: Additional metadata for the task
+        images: List of image URLs or base64 encoded images
+        videos: List of video URLs
+        audios: List of audio URLs or base64 encoded audios
+        files: List of file URLs or paths
+    """
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    content: str = ""
+    type: TaskType = TaskType.TEXT
+    status: TaskStatus = TaskStatus.INIT
+    created_at: float = field(default_factory=time.time)
+    updated_at: float = field(default_factory=time.time)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    # Media content
+    images: List[str] = field(default_factory=list)
+    videos: List[str] = field(default_factory=list)
+    audios: List[str] = field(default_factory=list)
+    files: List[str] = field(default_factory=list)
+
+    def __init__(self, content: str = "", **kwargs):
+        """
+        Initialize a Task with content and optional keyword arguments.
+        
+        Args:
+            content: The text content of the task
+            **kwargs: Additional attributes to set
+        """
+        self.id = kwargs.get('id', str(uuid.uuid4()))
+        self.content = content
+        self.type = kwargs.get('type', TaskType.TEXT)
+        self.status = kwargs.get('status', TaskStatus.INIT)
+        self.created_at = kwargs.get('created_at', time.time())
+        self.updated_at = kwargs.get('updated_at', time.time())
+        self.metadata = kwargs.get('metadata', {})
+        self.images = kwargs.get('images', [])
+        self.videos = kwargs.get('videos', [])
+        self.audios = kwargs.get('audios', [])
+        self.files = kwargs.get('files', [])
+
+    def get_text(self) -> str:
+        """
+        Get the text content of the task.
+        
+        Returns:
+            The text content
+        """
+        return self.content
+
+    def update_status(self, status: TaskStatus) -> None:
+        """
+        Update the status of the task.
+        
+        Args:
+            status: The new status
+        """
+        self.status = status
+        self.updated_at = time.time()
--- a/agent/skills/init.py
+++ b/agent/skills/init.py
@@ -0,0 +1,29 @@
+"""
+Skills module for agent system.
+
+This module provides the framework for loading, managing, and executing skills.
+Skills are markdown files with frontmatter that provide specialized instructions
+for specific tasks.
+"""
+
+from agent.skills.types import (
+    Skill,
+    SkillEntry,
+    SkillMetadata,
+    SkillInstallSpec,
+    LoadSkillsResult,
+)
+from agent.skills.loader import SkillLoader
+from agent.skills.manager import SkillManager
+from agent.skills.formatter import format_skills_for_prompt
+
+__all__ = [
+    "Skill",
+    "SkillEntry",
+    "SkillMetadata",
+    "SkillInstallSpec",
+    "LoadSkillsResult",
+    "SkillLoader",
+    "SkillManager",
+    "format_skills_for_prompt",
+]
--- a/agent/skills/config.py
+++ b/agent/skills/config.py
@@ -0,0 +1,184 @@
+"""
+Configuration support for skills.
+"""
+
+import os
+import platform
+from typing import Dict, Optional, List
+from agent.skills.types import SkillEntry
+
+
+def resolve_runtime_platform() -> str:
+    """Get the current runtime platform."""
+    return platform.system().lower()
+
+
+def has_binary(bin_name: str) -> bool:
+    """
+    Check if a binary is available in PATH.
+    
+    :param bin_name: Binary name to check
+    :return: True if binary is available
+    """
+    import shutil
+    return shutil.which(bin_name) is not None
+
+
+def has_any_binary(bin_names: List[str]) -> bool:
+    """
+    Check if any of the given binaries is available.
+    
+    :param bin_names: List of binary names to check
+    :return: True if at least one binary is available
+    """
+    return any(has_binary(bin_name) for bin_name in bin_names)
+
+
+def has_env_var(env_name: str) -> bool:
+    """
+    Check if an environment variable is set.
+    
+    :param env_name: Environment variable name
+    :return: True if environment variable is set
+    """
+    return env_name in os.environ and bool(os.environ[env_name].strip())
+
+
+def get_skill_config(config: Optional[Dict], skill_name: str) -> Optional[Dict]:
+    """
+    Get skill-specific configuration.
+    
+    :param config: Global configuration dictionary
+    :param skill_name: Name of the skill
+    :return: Skill configuration or None
+    """
+    if not config:
+        return None
+    
+    skills_config = config.get('skills', {})
+    if not isinstance(skills_config, dict):
+        return None
+    
+    entries = skills_config.get('entries', {})
+    if not isinstance(entries, dict):
+        return None
+    
+    return entries.get(skill_name)
+
+
+def should_include_skill(
+    entry: SkillEntry,
+    config: Optional[Dict] = None,
+    current_platform: Optional[str] = None,
+) -> bool:
+    """
+    Determine if a skill should be included based on requirements.
+    
+    Simple rule: Skills are auto-enabled if their requirements are met.
+    - Has required API keys → enabled
+    - Missing API keys → disabled
+    - Wrong keys → enabled but will fail at runtime (LLM will handle error)
+    
+    :param entry: SkillEntry to check
+    :param config: Configuration dictionary (currently unused, reserved for future)
+    :param current_platform: Current platform (default: auto-detect)
+    :return: True if skill should be included
+    """
+    metadata = entry.metadata
+    
+    # No metadata = always include (no requirements)
+    if not metadata:
+        return True
+    
+    # Check platform requirements (can't work on wrong platform)
+    if metadata.os:
+        platform_name = current_platform or resolve_runtime_platform()
+        # Map common platform names
+        platform_map = {
+            'darwin': 'darwin',
+            'linux': 'linux',
+            'windows': 'win32',
+        }
+        normalized_platform = platform_map.get(platform_name, platform_name)
+        
+        if normalized_platform not in metadata.os:
+            return False
+    
+    # If skill has 'always: true', include it regardless of other requirements
+    if metadata.always:
+        return True
+    
+    # Check requirements
+    if metadata.requires:
+        # Check required binaries (all must be present)
+        required_bins = metadata.requires.get('bins', [])
+        if required_bins:
+            if not all(has_binary(bin_name) for bin_name in required_bins):
+                return False
+        
+        # Check anyBins (at least one must be present)
+        any_bins = metadata.requires.get('anyBins', [])
+        if any_bins:
+            if not has_any_binary(any_bins):
+                return False
+        
+        # Check environment variables (API keys)
+        # Simple rule: 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
+    
+    return True
+
+
+def is_config_path_truthy(config: Dict, path: str) -> bool:
+    """
+    Check if a config path resolves to a truthy value.
+    
+    :param config: Configuration dictionary
+    :param path: Dot-separated path (e.g., 'skills.enabled')
+    :return: True if path resolves to truthy value
+    """
+    parts = path.split('.')
+    current = config
+    
+    for part in parts:
+        if not isinstance(current, dict):
+            return False
+        current = current.get(part)
+        if current is None:
+            return False
+    
+    # Check if value is truthy
+    if isinstance(current, bool):
+        return current
+    if isinstance(current, (int, float)):
+        return current != 0
+    if isinstance(current, str):
+        return bool(current.strip())
+    
+    return bool(current)
+
+
+def resolve_config_path(config: Dict, path: str):
+    """
+    Resolve a dot-separated config path to its value.
+    
+    :param config: Configuration dictionary
+    :param path: Dot-separated path
+    :return: Value at path or None
+    """
+    parts = path.split('.')
+    current = config
+    
+    for part in parts:
+        if not isinstance(current, dict):
+            return None
+        current = current.get(part)
+        if current is None:
+            return None
+    
+    return current
--- a/agent/skills/formatter.py
+++ b/agent/skills/formatter.py
@@ -0,0 +1,63 @@
+"""
+Skill formatter for generating prompts from skills.
+"""
+
+from typing import List
+from agent.skills.types import Skill, SkillEntry
+
+
+def format_skills_for_prompt(skills: List[Skill]) -> str:
+    """
+    Format skills for inclusion in a system prompt.
+    
+    Uses XML format per Agent Skills standard.
+    Skills with disable_model_invocation=True are excluded.
+    
+    :param skills: List of skills to format
+    :return: Formatted prompt text
+    """
+    # Filter out skills that should not be invoked by the model
+    visible_skills = [s for s in skills if not s.disable_model_invocation]
+    
+    if not visible_skills:
+        return ""
+    
+    lines = [
+        "\n\nThe following skills provide specialized instructions for specific tasks.",
+        "Use the read tool to load a skill's file when the task matches its description.",
+        "",
+        "<available_skills>",
+    ]
+    
+    for skill in visible_skills:
+        lines.append("  <skill>")
+        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>")
+    
+    return "\n".join(lines)
+
+
+def format_skill_entries_for_prompt(entries: List[SkillEntry]) -> str:
+    """
+    Format skill entries for inclusion in a system prompt.
+    
+    :param entries: List of skill entries to format
+    :return: Formatted prompt text
+    """
+    skills = [entry.skill for entry in entries]
+    return format_skills_for_prompt(skills)
+
+
+def _escape_xml(text: str) -> str:
+    """Escape XML special characters."""
+    return (text
+            .replace('&', '&amp;')
+            .replace('<', '&lt;')
+            .replace('>', '&gt;')
+            .replace('"', '&quot;')
+            .replace("'", '&apos;'))
--- a/agent/skills/frontmatter.py
+++ b/agent/skills/frontmatter.py
@@ -0,0 +1,172 @@
+"""
+Frontmatter parsing for skills.
+"""
+
+import re
+import json
+from typing import Dict, Any, Optional, List
+from agent.skills.types import SkillMetadata, SkillInstallSpec
+
+
+def parse_frontmatter(content: str) -> Dict[str, Any]:
+    """
+    Parse YAML-style frontmatter from markdown content.
+    
+    Returns a dictionary of frontmatter fields.
+    """
+    frontmatter = {}
+    
+    # Match frontmatter block between --- markers
+    match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL)
+    if not match:
+        return frontmatter
+    
+    frontmatter_text = match.group(1)
+    
+    # Try to use PyYAML for proper YAML parsing
+    try:
+        import yaml
+        frontmatter = yaml.safe_load(frontmatter_text)
+        if not isinstance(frontmatter, dict):
+            frontmatter = {}
+        return frontmatter
+    except ImportError:
+        # Fallback to simple parsing if PyYAML not available
+        pass
+    except Exception:
+        # If YAML parsing fails, fall back to simple parsing
+        pass
+    
+    # Simple YAML-like parsing (supports key: value format only)
+    # This is a fallback for when PyYAML is not available
+    for line in frontmatter_text.split('\n'):
+        line = line.strip()
+        if not line or line.startswith('#'):
+            continue
+        
+        if ':' in line:
+            key, value = line.split(':', 1)
+            key = key.strip()
+            value = value.strip()
+            
+            # Try to parse as JSON if it looks like JSON
+            if value.startswith('{') or value.startswith('['):
+                try:
+                    value = json.loads(value)
+                except json.JSONDecodeError:
+                    pass
+            # Parse boolean values
+            elif value.lower() in ('true', 'false'):
+                value = value.lower() == 'true'
+            # Parse numbers
+            elif value.isdigit():
+                value = int(value)
+            
+            frontmatter[key] = value
+    
+    return frontmatter
+
+
+def parse_metadata(frontmatter: Dict[str, Any]) -> Optional[SkillMetadata]:
+    """
+    Parse skill metadata from frontmatter.
+    
+    Looks for 'metadata' field containing JSON with skill configuration.
+    """
+    metadata_raw = frontmatter.get('metadata')
+    if not metadata_raw:
+        return None
+    
+    # If it's a string, try to parse as JSON
+    if isinstance(metadata_raw, str):
+        try:
+            metadata_raw = json.loads(metadata_raw)
+        except json.JSONDecodeError:
+            return None
+    
+    if not isinstance(metadata_raw, dict):
+        return None
+    
+    # Use metadata_raw directly (COW format)
+    meta_obj = metadata_raw
+    
+    # Parse install specs
+    install_specs = []
+    install_raw = meta_obj.get('install', [])
+    if isinstance(install_raw, list):
+        for spec_raw in install_raw:
+            if not isinstance(spec_raw, dict):
+                continue
+            
+            kind = spec_raw.get('kind', spec_raw.get('type', '')).lower()
+            if not kind:
+                continue
+            
+            spec = SkillInstallSpec(
+                kind=kind,
+                id=spec_raw.get('id'),
+                label=spec_raw.get('label'),
+                bins=_normalize_string_list(spec_raw.get('bins')),
+                os=_normalize_string_list(spec_raw.get('os')),
+                formula=spec_raw.get('formula'),
+                package=spec_raw.get('package'),
+                module=spec_raw.get('module'),
+                url=spec_raw.get('url'),
+                archive=spec_raw.get('archive'),
+                extract=spec_raw.get('extract', False),
+                strip_components=spec_raw.get('stripComponents'),
+                target_dir=spec_raw.get('targetDir'),
+            )
+            install_specs.append(spec)
+    
+    # Parse requires
+    requires = {}
+    requires_raw = meta_obj.get('requires', {})
+    if isinstance(requires_raw, dict):
+        for key, value in requires_raw.items():
+            requires[key] = _normalize_string_list(value)
+    
+    return SkillMetadata(
+        always=meta_obj.get('always', False),
+        skill_key=meta_obj.get('skillKey'),
+        primary_env=meta_obj.get('primaryEnv'),
+        emoji=meta_obj.get('emoji'),
+        homepage=meta_obj.get('homepage'),
+        os=_normalize_string_list(meta_obj.get('os')),
+        requires=requires,
+        install=install_specs,
+    )
+
+
+def _normalize_string_list(value: Any) -> List[str]:
+    """Normalize a value to a list of strings."""
+    if not value:
+        return []
+    
+    if isinstance(value, list):
+        return [str(v).strip() for v in value if v]
+    
+    if isinstance(value, str):
+        return [v.strip() for v in value.split(',') if v.strip()]
+    
+    return []
+
+
+def parse_boolean_value(value: Optional[str], default: bool = False) -> bool:
+    """Parse a boolean value from frontmatter."""
+    if value is None:
+        return default
+    
+    if isinstance(value, bool):
+        return value
+    
+    if isinstance(value, str):
+        return value.lower() in ('true', '1', 'yes', 'on')
+    
+    return default
+
+
+def get_frontmatter_value(frontmatter: Dict[str, Any], key: str) -> Optional[str]:
+    """Get a frontmatter value as a string."""
+    value = frontmatter.get(key)
+    return str(value) if value is not None else None
--- a/agent/skills/loader.py
+++ b/agent/skills/loader.py
@@ -0,0 +1,297 @@
+"""
+Skill loader for discovering and loading skills from directories.
+"""
+
+import os
+from pathlib import Path
+from typing import List, Optional, Dict
+from common.log import logger
+from agent.skills.types import Skill, SkillEntry, LoadSkillsResult, SkillMetadata
+from agent.skills.frontmatter import parse_frontmatter, parse_metadata, parse_boolean_value, get_frontmatter_value
+
+
+class SkillLoader:
+    """Loads skills from various directories."""
+    
+    def __init__(self, workspace_dir: Optional[str] = None):
+        """
+        Initialize the skill loader.
+        
+        :param workspace_dir: Agent workspace directory (for workspace-specific skills)
+        """
+        self.workspace_dir = workspace_dir
+    
+    def load_skills_from_dir(self, dir_path: str, source: str) -> LoadSkillsResult:
+        """
+        Load skills from a directory.
+        
+        Discovery rules:
+        - Direct .md files in the root directory
+        - Recursive SKILL.md files under subdirectories
+        
+        :param dir_path: Directory path to scan
+        :param source: Source identifier (e.g., 'managed', 'workspace', 'bundled')
+        :return: LoadSkillsResult with skills and diagnostics
+        """
+        skills = []
+        diagnostics = []
+        
+        if not os.path.exists(dir_path):
+            diagnostics.append(f"Directory does not exist: {dir_path}")
+            return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+        
+        if not os.path.isdir(dir_path):
+            diagnostics.append(f"Path is not a directory: {dir_path}")
+            return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+        
+        # Load skills from root-level .md files and subdirectories
+        result = self._load_skills_recursive(dir_path, source, include_root_files=True)
+        
+        return result
+    
+    def _load_skills_recursive(
+        self, 
+        dir_path: str, 
+        source: str, 
+        include_root_files: bool = False
+    ) -> LoadSkillsResult:
+        """
+        Recursively load skills from a directory.
+        
+        :param dir_path: Directory to scan
+        :param source: Source identifier
+        :param include_root_files: Whether to include root-level .md files
+        :return: LoadSkillsResult
+        """
+        skills = []
+        diagnostics = []
+        
+        try:
+            entries = os.listdir(dir_path)
+        except Exception as e:
+            diagnostics.append(f"Failed to list directory {dir_path}: {e}")
+            return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+        
+        for entry in entries:
+            # Skip hidden files and directories
+            if entry.startswith('.'):
+                continue
+            
+            # Skip common non-skill directories
+            if entry in ('node_modules', '__pycache__', 'venv', '.git'):
+                continue
+            
+            full_path = os.path.join(dir_path, entry)
+            
+            # Handle directories
+            if os.path.isdir(full_path):
+                # Recursively scan subdirectories
+                sub_result = self._load_skills_recursive(full_path, source, include_root_files=False)
+                skills.extend(sub_result.skills)
+                diagnostics.extend(sub_result.diagnostics)
+                continue
+            
+            # Handle files
+            if not os.path.isfile(full_path):
+                continue
+            
+            # Check if this is a skill file
+            is_root_md = include_root_files and entry.endswith('.md')
+            is_skill_md = not include_root_files and entry == 'SKILL.md'
+            
+            if not (is_root_md or is_skill_md):
+                continue
+            
+            # Load the skill
+            skill_result = self._load_skill_from_file(full_path, source)
+            if skill_result.skills:
+                skills.extend(skill_result.skills)
+            diagnostics.extend(skill_result.diagnostics)
+        
+        return LoadSkillsResult(skills=skills, diagnostics=diagnostics)
+    
+    def _load_skill_from_file(self, file_path: str, source: str) -> LoadSkillsResult:
+        """
+        Load a single skill from a markdown file.
+        
+        :param file_path: Path to the skill markdown file
+        :param source: Source identifier
+        :return: LoadSkillsResult
+        """
+        diagnostics = []
+        
+        try:
+            with open(file_path, 'r', encoding='utf-8') as f:
+                content = f.read()
+        except Exception as e:
+            diagnostics.append(f"Failed to read skill file {file_path}: {e}")
+            return LoadSkillsResult(skills=[], diagnostics=diagnostics)
+        
+        # Parse frontmatter
+        frontmatter = parse_frontmatter(content)
+        
+        # Get skill name and description
+        skill_dir = os.path.dirname(file_path)
+        parent_dir_name = os.path.basename(skill_dir)
+        
+        name = frontmatter.get('name', parent_dir_name)
+        description = frontmatter.get('description', '')
+        
+        # Normalize name (handle both string and list)
+        if isinstance(name, list):
+            name = name[0] if name else parent_dir_name
+        elif not isinstance(name, str):
+            name = str(name) if name else parent_dir_name
+        
+        # Normalize description (handle both string and list)
+        if isinstance(description, list):
+            description = ' '.join(str(d) for d in description if d)
+        elif not isinstance(description, str):
+            description = str(description) if description else ''
+        
+        # Special handling for linkai-agent: dynamically load apps from config.json
+        if name == 'linkai-agent':
+            description = self._load_linkai_agent_description(skill_dir, description)
+        
+        if not description or not description.strip():
+            diagnostics.append(f"Skill {name} has no description: {file_path}")
+            return LoadSkillsResult(skills=[], diagnostics=diagnostics)
+        
+        # Parse disable-model-invocation flag
+        disable_model_invocation = parse_boolean_value(
+            get_frontmatter_value(frontmatter, 'disable-model-invocation'),
+            default=False
+        )
+        
+        # Create skill object
+        skill = Skill(
+            name=name,
+            description=description,
+            file_path=file_path,
+            base_dir=skill_dir,
+            source=source,
+            content=content,
+            disable_model_invocation=disable_model_invocation,
+            frontmatter=frontmatter,
+        )
+        
+        return LoadSkillsResult(skills=[skill], diagnostics=diagnostics)
+    
+    def _load_linkai_agent_description(self, skill_dir: str, default_description: str) -> str:
+        """
+        Dynamically load LinkAI agent description from config.json
+        
+        :param skill_dir: Skill directory
+        :param default_description: Default description from SKILL.md
+        :return: Dynamic description with app list
+        """
+        import json
+        
+        config_path = os.path.join(skill_dir, "config.json")
+        template_path = os.path.join(skill_dir, "config.json.template")
+        
+        # Try to load config.json or fallback to template
+        config_file = config_path if os.path.exists(config_path) else template_path
+        
+        if not os.path.exists(config_file):
+            return default_description
+        
+        try:
+            with open(config_file, 'r', encoding='utf-8') as f:
+                config = json.load(f)
+            
+            apps = config.get("apps", [])
+            if not apps:
+                return default_description
+            
+            # Build dynamic description with app details
+            app_descriptions = "; ".join([
+                f"{app['app_name']}({app['app_code']}: {app['app_description']})"
+                for app in apps
+            ])
+            
+            return f"Call LinkAI apps/workflows. {app_descriptions}"
+        
+        except Exception as e:
+            logger.warning(f"[SkillLoader] Failed to load linkai-agent config: {e}")
+            return default_description
+    
+    def load_all_skills(
+        self,
+        managed_dir: Optional[str] = None,
+        workspace_skills_dir: Optional[str] = None,
+        extra_dirs: Optional[List[str]] = None,
+    ) -> Dict[str, SkillEntry]:
+        """
+        Load skills from all configured locations with precedence.
+        
+        Precedence (lowest to highest):
+        1. Extra directories
+        2. Managed skills directory
+        3. Workspace skills directory
+        
+        :param managed_dir: Managed skills directory (e.g., ~/.cow/skills)
+        :param workspace_skills_dir: Workspace skills directory (e.g., workspace/skills)
+        :param extra_dirs: Additional directories to load skills from
+        :return: Dictionary mapping skill name to SkillEntry
+        """
+        skill_map: Dict[str, SkillEntry] = {}
+        all_diagnostics = []
+        
+        # Load from extra directories (lowest precedence)
+        if extra_dirs:
+            for extra_dir in extra_dirs:
+                if not os.path.exists(extra_dir):
+                    continue
+                result = self.load_skills_from_dir(extra_dir, source='extra')
+                all_diagnostics.extend(result.diagnostics)
+                for skill in result.skills:
+                    entry = self._create_skill_entry(skill)
+                    skill_map[skill.name] = entry
+        
+        # Load from managed directory
+        if managed_dir and os.path.exists(managed_dir):
+            result = self.load_skills_from_dir(managed_dir, source='managed')
+            all_diagnostics.extend(result.diagnostics)
+            for skill in result.skills:
+                entry = self._create_skill_entry(skill)
+                skill_map[skill.name] = entry
+        
+        # Load from workspace directory (highest precedence)
+        if workspace_skills_dir and os.path.exists(workspace_skills_dir):
+            result = self.load_skills_from_dir(workspace_skills_dir, source='workspace')
+            all_diagnostics.extend(result.diagnostics)
+            for skill in result.skills:
+                entry = self._create_skill_entry(skill)
+                skill_map[skill.name] = entry
+        
+        # Log diagnostics
+        if all_diagnostics:
+            logger.debug(f"Skill loading diagnostics: {len(all_diagnostics)} issues")
+            for diag in all_diagnostics[:5]:  # Log first 5
+                logger.debug(f"  - {diag}")
+        
+        logger.debug(f"Loaded {len(skill_map)} skills from all sources")
+        
+        return skill_map
+    
+    def _create_skill_entry(self, skill: Skill) -> SkillEntry:
+        """
+        Create a SkillEntry from a Skill with parsed metadata.
+        
+        :param skill: The skill to create an entry for
+        :return: SkillEntry with metadata
+        """
+        metadata = parse_metadata(skill.frontmatter)
+        
+        # Parse user-invocable flag
+        user_invocable = parse_boolean_value(
+            get_frontmatter_value(skill.frontmatter, 'user-invocable'),
+            default=True
+        )
+        
+        return SkillEntry(
+            skill=skill,
+            metadata=metadata,
+            user_invocable=user_invocable,
+        )
--- a/agent/skills/manager.py
+++ b/agent/skills/manager.py
@@ -0,0 +1,227 @@
+"""
+Skill manager for managing skill lifecycle and operations.
+"""
+
+import os
+from typing import Dict, List, Optional
+from pathlib import Path
+from common.log import logger
+from agent.skills.types import Skill, SkillEntry, SkillSnapshot
+from agent.skills.loader import SkillLoader
+from agent.skills.formatter import format_skill_entries_for_prompt
+
+
+class SkillManager:
+    """Manages skills for an agent."""
+    
+    def __init__(
+        self,
+        workspace_dir: Optional[str] = None,
+        managed_skills_dir: Optional[str] = None,
+        extra_dirs: Optional[List[str]] = None,
+        config: Optional[Dict] = None,
+    ):
+        """
+        Initialize the skill manager.
+        
+        :param workspace_dir: Agent workspace directory
+        :param managed_skills_dir: Managed skills directory (e.g., ~/.cow/skills)
+        :param extra_dirs: Additional skill directories
+        :param config: Configuration dictionary
+        """
+        self.workspace_dir = workspace_dir
+        self.managed_skills_dir = managed_skills_dir or self._get_default_managed_dir()
+        self.extra_dirs = extra_dirs or []
+        self.config = config or {}
+        
+        self.loader = SkillLoader(workspace_dir=workspace_dir)
+        self.skills: Dict[str, SkillEntry] = {}
+        
+        # Load skills on initialization
+        self.refresh_skills()
+    
+    def _get_default_managed_dir(self) -> str:
+        """Get the default managed skills directory."""
+        # Use project root skills directory as default
+        import os
+        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        return os.path.join(project_root, 'skills')
+    
+    def refresh_skills(self):
+        """Reload all skills from configured directories."""
+        workspace_skills_dir = None
+        if self.workspace_dir:
+            workspace_skills_dir = os.path.join(self.workspace_dir, 'skills')
+        
+        self.skills = self.loader.load_all_skills(
+            managed_dir=self.managed_skills_dir,
+            workspace_skills_dir=workspace_skills_dir,
+            extra_dirs=self.extra_dirs,
+        )
+        
+        logger.debug(f"SkillManager: Loaded {len(self.skills)} skills")
+    
+    def get_skill(self, name: str) -> Optional[SkillEntry]:
+        """
+        Get a skill by name.
+        
+        :param name: Skill name
+        :return: SkillEntry or None if not found
+        """
+        return self.skills.get(name)
+    
+    def list_skills(self) -> List[SkillEntry]:
+        """
+        Get all loaded skills.
+        
+        :return: List of all skill entries
+        """
+        return list(self.skills.values())
+    
+    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
+        
+        :param skill_filter: List of skill names to include (None = all)
+        :param include_disabled: Whether to include skills with disable_model_invocation=True
+        :return: Filtered list of skill entries
+        """
+        from agent.skills.config import should_include_skill
+        
+        entries = list(self.skills.values())
+        
+        # Check requirements (platform, binaries, env vars)
+        entries = [e for e in entries if should_include_skill(e, self.config)]
+        
+        # Apply skill filter
+        if skill_filter is not None:
+            # Flatten and normalize skill names (handle both strings and nested lists)
+            normalized = []
+            for item in skill_filter:
+                if isinstance(item, str):
+                    name = item.strip()
+                    if name:
+                        normalized.append(name)
+                elif isinstance(item, list):
+                    # Handle nested lists
+                    for subitem in item:
+                        if isinstance(subitem, str):
+                            name = subitem.strip()
+                            if name:
+                                normalized.append(name)
+            
+            if normalized:
+                entries = [e for e in entries if e.skill.name in normalized]
+        
+        # Filter out disabled skills unless explicitly requested
+        if not include_disabled:
+            entries = [e for e in entries if not e.skill.disable_model_invocation]
+        
+        return entries
+    
+    def build_skills_prompt(
+        self,
+        skill_filter: Optional[List[str]] = None,
+    ) -> str:
+        """
+        Build a formatted prompt containing available skills.
+        
+        :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)
+        logger.debug(f"[SkillManager] Generated prompt length: {len(result)}")
+        return result
+    
+    def build_skill_snapshot(
+        self,
+        skill_filter: Optional[List[str]] = None,
+        version: Optional[int] = None,
+    ) -> SkillSnapshot:
+        """
+        Build a snapshot of skills for a specific run.
+        
+        :param skill_filter: Optional list of skill names to include
+        :param version: Optional version number for the snapshot
+        :return: SkillSnapshot
+        """
+        entries = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
+        prompt = format_skill_entries_for_prompt(entries)
+        
+        skills_info = []
+        resolved_skills = []
+        
+        for entry in entries:
+            skills_info.append({
+                'name': entry.skill.name,
+                'primary_env': entry.metadata.primary_env if entry.metadata else None,
+            })
+            resolved_skills.append(entry.skill)
+        
+        return SkillSnapshot(
+            prompt=prompt,
+            skills=skills_info,
+            resolved_skills=resolved_skills,
+            version=version,
+        )
+    
+    def sync_skills_to_workspace(self, target_workspace_dir: str):
+        """
+        Sync all loaded skills to a target workspace directory.
+        
+        This is useful for sandbox environments where skills need to be copied.
+        
+        :param target_workspace_dir: Target workspace directory
+        """
+        import shutil
+        
+        target_skills_dir = os.path.join(target_workspace_dir, 'skills')
+        
+        # Remove existing skills directory
+        if os.path.exists(target_skills_dir):
+            shutil.rmtree(target_skills_dir)
+        
+        # Create new skills directory
+        os.makedirs(target_skills_dir, exist_ok=True)
+        
+        # Copy each skill
+        for entry in self.skills.values():
+            skill_name = entry.skill.name
+            source_dir = entry.skill.base_dir
+            target_dir = os.path.join(target_skills_dir, skill_name)
+            
+            try:
+                shutil.copytree(source_dir, target_dir)
+                logger.debug(f"Synced skill '{skill_name}' to {target_dir}")
+            except Exception as e:
+                logger.warning(f"Failed to sync skill '{skill_name}': {e}")
+        
+        logger.info(f"Synced {len(self.skills)} skills to {target_skills_dir}")
+    
+    def get_skill_by_key(self, skill_key: str) -> Optional[SkillEntry]:
+        """
+        Get a skill by its skill key (which may differ from name).
+        
+        :param skill_key: Skill key to look up
+        :return: SkillEntry or None
+        """
+        for entry in self.skills.values():
+            if entry.metadata and entry.metadata.skill_key == skill_key:
+                return entry
+            if entry.skill.name == skill_key:
+                return entry
+        return None
--- a/agent/skills/types.py
+++ b/agent/skills/types.py
@@ -0,0 +1,75 @@
+"""
+Type definitions for skills system.
+"""
+
+from __future__ import annotations
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, field
+
+
+@dataclass
+class SkillInstallSpec:
+    """Specification for installing skill dependencies."""
+    kind: str  # brew, pip, npm, download, etc.
+    id: Optional[str] = None
+    label: Optional[str] = None
+    bins: List[str] = field(default_factory=list)
+    os: List[str] = field(default_factory=list)
+    formula: Optional[str] = None  # for brew
+    package: Optional[str] = None  # for pip/npm
+    module: Optional[str] = None
+    url: Optional[str] = None  # for download
+    archive: Optional[str] = None
+    extract: bool = False
+    strip_components: Optional[int] = None
+    target_dir: Optional[str] = None
+
+
+@dataclass
+class SkillMetadata:
+    """Metadata for a skill from frontmatter."""
+    always: bool = False  # Always include this skill
+    skill_key: Optional[str] = None  # Override skill key
+    primary_env: Optional[str] = None  # Primary environment variable
+    emoji: Optional[str] = None
+    homepage: Optional[str] = None
+    os: List[str] = field(default_factory=list)  # Supported OS platforms
+    requires: Dict[str, List[str]] = field(default_factory=dict)  # Requirements
+    install: List[SkillInstallSpec] = field(default_factory=list)
+
+
+@dataclass
+class Skill:
+    """Represents a skill loaded from a markdown file."""
+    name: str
+    description: str
+    file_path: str
+    base_dir: str
+    source: str  # managed, workspace, bundled, etc.
+    content: str  # Full markdown content
+    disable_model_invocation: bool = False
+    frontmatter: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class SkillEntry:
+    """A skill with parsed metadata."""
+    skill: Skill
+    metadata: Optional[SkillMetadata] = None
+    user_invocable: bool = True  # Can users invoke this skill directly
+
+
+@dataclass
+class LoadSkillsResult:
+    """Result of loading skills from a directory."""
+    skills: List[Skill]
+    diagnostics: List[str] = field(default_factory=list)
+
+
+@dataclass
+class SkillSnapshot:
+    """Snapshot of skills for a specific run."""
+    prompt: str  # Formatted prompt text
+    skills: List[Dict[str, str]]  # List of skill info (name, primary_env)
+    resolved_skills: List[Skill] = field(default_factory=list)
+    version: Optional[int] = None
--- a/agent/tools/init.py
+++ b/agent/tools/init.py
@@ -0,0 +1,101 @@
+# Import base tool
+from agent.tools.base_tool import BaseTool
+from agent.tools.tool_manager import ToolManager
+
+# Import file operation tools
+from agent.tools.read.read import Read
+from agent.tools.write.write import Write
+from agent.tools.edit.edit import Edit
+from agent.tools.bash.bash import Bash
+from agent.tools.ls.ls import Ls
+from agent.tools.send.send import Send
+
+# Import memory tools
+from agent.tools.memory.memory_search import MemorySearchTool
+from agent.tools.memory.memory_get import MemoryGetTool
+
+# Import tools with optional dependencies
+def _import_optional_tools():
+    """Import tools that have optional dependencies"""
+    from common.log import logger
+    tools = {}
+    
+    # EnvConfig Tool (requires python-dotenv)
+    try:
+        from agent.tools.env_config.env_config import EnvConfig
+        tools['EnvConfig'] = EnvConfig
+    except ImportError as e:
+        logger.error(
+            f"[Tools] EnvConfig tool not loaded - missing dependency: {e}\n"
+            f"  To enable environment variable management, run:\n"
+            f"    pip install python-dotenv>=1.0.0"
+        )
+    except Exception as e:
+        logger.error(f"[Tools] EnvConfig tool failed to load: {e}")
+    
+    # Scheduler Tool (requires croniter)
+    try:
+        from agent.tools.scheduler.scheduler_tool import SchedulerTool
+        tools['SchedulerTool'] = SchedulerTool
+    except ImportError as e:
+        logger.error(
+            f"[Tools] Scheduler tool not loaded - missing dependency: {e}\n"
+            f"  To enable scheduled tasks, run:\n"
+            f"    pip install croniter>=2.0.0"
+        )
+    except Exception as e:
+        logger.error(f"[Tools] Scheduler tool failed to load: {e}")
+    
+    
+    return tools
+
+# Load optional tools
+_optional_tools = _import_optional_tools()
+EnvConfig = _optional_tools.get('EnvConfig')
+SchedulerTool = _optional_tools.get('SchedulerTool')
+GoogleSearch = _optional_tools.get('GoogleSearch')
+FileSave = _optional_tools.get('FileSave') 
+Terminal = _optional_tools.get('Terminal')
+
+
+# Delayed import for BrowserTool
+def _import_browser_tool():
+    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'."
+                )
+
+        return BrowserToolPlaceholder
+
+
+# Dynamically set BrowserTool
+# BrowserTool = _import_browser_tool()
+
+# Export all tools (including optional ones that might be None)
+__all__ = [
+    'BaseTool',
+    'ToolManager',
+    'Read',
+    'Write',
+    'Edit',
+    'Bash',
+    'Ls',
+    'Send',
+    'MemorySearchTool',
+    'MemoryGetTool',
+    'EnvConfig',
+    'SchedulerTool',
+    # Optional tools (may be None if dependencies not available)
+    # 'BrowserTool'
+]
+
+"""
+Tools module for Agent.
+"""
--- a/agent/tools/base_tool.py
+++ b/agent/tools/base_tool.py
@@ -0,0 +1,99 @@
+from enum import Enum
+from typing import Any, Optional
+from common.log import logger
+import copy
+
+
+class ToolStage(Enum):
+    """Enum representing tool decision stages"""
+    PRE_PROCESS = "pre_process"  # Tools that need to be actively selected by the agent
+    POST_PROCESS = "post_process"  # Tools that automatically execute after final_answer
+
+
+class ToolResult:
+    """Tool execution result"""
+    
+    def __init__(self, status: str = None, result: Any = None, ext_data: Any = None):
+        self.status = status
+        self.result = result
+        self.ext_data = ext_data
+
+    @staticmethod
+    def success(result, ext_data: Any = None):
+        return ToolResult(status="success", result=result, ext_data=ext_data)
+
+    @staticmethod
+    def fail(result, ext_data: Any = None):
+        return ToolResult(status="error", result=result, ext_data=ext_data)
+
+
+class BaseTool:
+    """Base class for all tools."""
+
+    # Default decision stage is pre-process
+    stage = ToolStage.PRE_PROCESS
+
+    # Class attributes must be inherited
+    name: str = "base_tool"
+    description: str = "Base tool"
+    params: dict = {}  # Store JSON Schema
+    model: Optional[Any] = None  # LLM model instance, type depends on bot implementation
+
+    @classmethod
+    def get_json_schema(cls) -> dict:
+        """Get the standard description of the tool"""
+        return {
+            "name": cls.name,
+            "description": cls.description,
+            "parameters": cls.params
+        }
+
+    def execute_tool(self, params: dict) -> ToolResult:
+        try:
+            return self.execute(params)
+        except Exception as e:
+            logger.error(e)
+
+    def execute(self, params: dict) -> ToolResult:
+        """Specific logic to be implemented by subclasses"""
+        raise NotImplementedError
+
+    @classmethod
+    def _parse_schema(cls) -> dict:
+        """Convert JSON Schema to Pydantic fields"""
+        fields = {}
+        for name, prop in cls.params["properties"].items():
+            # Convert JSON Schema types to Python types
+            type_map = {
+                "string": str,
+                "number": float,
+                "integer": int,
+                "boolean": bool,
+                "array": list,
+                "object": dict
+            }
+            fields[name] = (
+                type_map[prop["type"]],
+                prop.get("default", ...)
+            )
+        return fields
+
+    def should_auto_execute(self, context) -> bool:
+        """
+        Determine if this tool should be automatically executed based on context.
+
+        :param context: The agent context
+        :return: True if the tool should be executed, False otherwise
+        """
+        # Only tools in post-process stage will be automatically executed
+        return self.stage == ToolStage.POST_PROCESS
+
+    def close(self):
+        """
+        Close any resources used by the tool.
+        This method should be overridden by tools that need to clean up resources
+        such as browser connections, file handles, etc.
+
+        By default, this method does nothing.
+        """
+        pass
--- a/agent/tools/bash/init.py
+++ b/agent/tools/bash/init.py
@@ -0,0 +1,3 @@
+from .bash import Bash
+
+__all__ = ['Bash']
--- a/agent/tools/bash/bash.py
+++ b/agent/tools/bash/bash.py
@@ -0,0 +1,260 @@
+"""
+Bash tool - Execute bash commands
+"""
+
+import os
+import sys
+import subprocess
+import tempfile
+from typing import Dict, Any
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.utils.truncate import truncate_tail, format_size, DEFAULT_MAX_LINES, DEFAULT_MAX_BYTES
+from common.log import logger
+
+
+class Bash(BaseTool):
+    """Tool for executing bash commands"""
+
+    name: str = "bash"
+    description: str = f"""Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last {DEFAULT_MAX_LINES} lines or {DEFAULT_MAX_BYTES // 1024}KB (whichever is hit first). If truncated, full output is saved to a temp file.
+
+IMPORTANT SAFETY GUIDELINES:
+- You can freely create, modify, and delete files within the current workspace
+- For operations outside the workspace or potentially destructive commands (rm -rf, system commands, etc.), always explain what you're about to do and ask for user confirmation first
+- When in doubt, describe the command's purpose and ask for permission before executing"""
+
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "command": {
+                "type": "string",
+                "description": "Bash command to execute"
+            },
+            "timeout": {
+                "type": "integer",
+                "description": "Timeout in seconds (optional, default: 30)"
+            }
+        },
+        "required": ["command"]
+    }
+
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        # Ensure working directory exists
+        if not os.path.exists(self.cwd):
+            os.makedirs(self.cwd, exist_ok=True)
+        self.default_timeout = self.config.get("timeout", 30)
+        # Enable safety mode by default (can be disabled in config)
+        self.safety_mode = self.config.get("safety_mode", True)
+
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        """
+        Execute a bash command
+        
+        :param args: Dictionary containing the command and optional timeout
+        :return: Command output or error
+        """
+        command = args.get("command", "").strip()
+        timeout = args.get("timeout", self.default_timeout)
+
+        if not command:
+            return ToolResult.fail("Error: command parameter is required")
+
+        # Security check: Prevent accessing sensitive config files
+        if "~/.cow/.env" in command or "~/.cow" in command:
+            return ToolResult.fail(
+                "Error: Access denied. API keys and credentials must be accessed through the env_config tool only."
+            )
+
+        # Optional safety check - only warn about extremely dangerous commands
+        if self.safety_mode:
+            warning = self._get_safety_warning(command)
+            if warning:
+                return ToolResult.fail(
+                    f"Safety Warning: {warning}\n\nIf you believe this command is safe and necessary, please ask the user for confirmation first, explaining what the command does and why it's needed.")
+
+        try:
+            # Prepare environment with .env file variables
+            env = os.environ.copy()
+            
+            # Load environment variables from ~/.cow/.env if it exists
+            env_file = os.path.expanduser("~/.cow/.env")
+            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}")
+                except ImportError:
+                    logger.debug("[Bash] python-dotenv not installed, skipping .env loading")
+                except Exception as e:
+                    logger.debug(f"[Bash] Failed to load .env: {e}")
+            
+            # Debug logging
+            logger.debug(f"[Bash] CWD: {self.cwd}")
+            logger.debug(f"[Bash] Command: {command[:500]}")
+            logger.debug(f"[Bash] OPENAI_API_KEY in env: {'OPENAI_API_KEY' in env}")
+            logger.debug(f"[Bash] SHELL: {env.get('SHELL', 'not set')}")
+            logger.debug(f"[Bash] Python executable: {sys.executable}")
+            logger.debug(f"[Bash] Process UID: {os.getuid()}")
+            
+            # Execute command with inherited environment variables
+            result = subprocess.run(
+                command,
+                shell=True,
+                cwd=self.cwd,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True,
+                timeout=timeout,
+                env=env
+            )
+            
+            logger.debug(f"[Bash] Exit code: {result.returncode}")
+            logger.debug(f"[Bash] Stdout length: {len(result.stdout)}")
+            logger.debug(f"[Bash] Stderr length: {len(result.stderr)}")
+            
+            # Workaround for exit code 126 with no output
+            if result.returncode == 126 and not result.stdout and not result.stderr:
+                logger.warning(f"[Bash] Exit 126 with no output - trying alternative execution method")
+                # Try using argument list instead of shell=True
+                import shlex
+                try:
+                    parts = shlex.split(command)
+                    if len(parts) > 0:
+                        logger.info(f"[Bash] Retrying with argument list: {parts[:3]}...")
+                        retry_result = subprocess.run(
+                            parts,
+                            cwd=self.cwd,
+                            stdout=subprocess.PIPE,
+                            stderr=subprocess.PIPE,
+                            text=True,
+                            timeout=timeout,
+                            env=env
+                        )
+                        logger.debug(f"[Bash] Retry exit code: {retry_result.returncode}, stdout: {len(retry_result.stdout)}, stderr: {len(retry_result.stderr)}")
+                        
+                        # If retry succeeded, use retry result
+                        if retry_result.returncode == 0 or retry_result.stdout or retry_result.stderr:
+                            result = retry_result
+                        else:
+                            # Both attempts failed - check if this is openai-image-vision skill
+                            if 'openai-image-vision' in command or 'vision.sh' in command:
+                                # Create a mock result with helpful error message
+                                from types import SimpleNamespace
+                                result = SimpleNamespace(
+                                    returncode=1,
+                                    stdout='{"error": "图片无法解析", "reason": "该图片格式可能不受支持，或图片文件存在问题", "suggestion": "请尝试其他图片"}',
+                                    stderr=''
+                                )
+                                logger.info(f"[Bash] Converted exit 126 to user-friendly image error message for vision skill")
+                except Exception as retry_err:
+                    logger.warning(f"[Bash] Retry failed: {retry_err}")
+
+            # Combine stdout and stderr
+            output = result.stdout
+            if result.stderr:
+                output += "\n" + result.stderr
+
+            # Check if we need to save full output to temp file
+            temp_file_path = None
+            total_bytes = len(output.encode('utf-8'))
+
+            if total_bytes > DEFAULT_MAX_BYTES:
+                # Save full output to temp file
+                with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.log', prefix='bash-') as f:
+                    f.write(output)
+                    temp_file_path = f.name
+
+            # Apply tail truncation
+            truncation = truncate_tail(output)
+            output_text = truncation.content or "(no output)"
+
+            # Build result
+            details = {}
+
+            if truncation.truncated:
+                details["truncation"] = truncation.to_dict()
+                if temp_file_path:
+                    details["full_output_path"] = temp_file_path
+
+                # Build notice
+                start_line = truncation.total_lines - truncation.output_lines + 1
+                end_line = truncation.total_lines
+
+                if truncation.last_line_partial:
+                    # Edge case: last line alone > 30KB
+                    last_line = output.split('\n')[-1] if output else ""
+                    last_line_size = format_size(len(last_line.encode('utf-8')))
+                    output_text += f"\n\n[Showing last {format_size(truncation.output_bytes)} of line {end_line} (line is {last_line_size}). Full output: {temp_file_path}]"
+                elif truncation.truncated_by == "lines":
+                    output_text += f"\n\n[Showing lines {start_line}-{end_line} of {truncation.total_lines}. Full output: {temp_file_path}]"
+                else:
+                    output_text += f"\n\n[Showing lines {start_line}-{end_line} of {truncation.total_lines} ({format_size(DEFAULT_MAX_BYTES)} limit). Full output: {temp_file_path}]"
+
+            # Check exit code
+            if result.returncode != 0:
+                output_text += f"\n\nCommand exited with code {result.returncode}"
+                return ToolResult.fail({
+                    "output": output_text,
+                    "exit_code": result.returncode,
+                    "details": details if details else None
+                })
+
+            return ToolResult.success({
+                "output": output_text,
+                "exit_code": result.returncode,
+                "details": details if details else None
+            })
+
+        except subprocess.TimeoutExpired:
+            return ToolResult.fail(f"Error: Command timed out after {timeout} seconds")
+        except Exception as e:
+            return ToolResult.fail(f"Error executing command: {str(e)}")
+
+    def _get_safety_warning(self, command: str) -> str:
+        """
+        Get safety warning for potentially dangerous commands
+        Only warns about extremely dangerous system-level operations
+        
+        :param command: Command to check
+        :return: Warning message if dangerous, empty string if safe
+        """
+        cmd_lower = command.lower().strip()
+
+        # Only block extremely dangerous system operations
+        dangerous_patterns = [
+            # System shutdown/reboot
+            ("shutdown", "This command will shut down the system"),
+            ("reboot", "This command will reboot the system"),
+            ("halt", "This command will halt the system"),
+            ("poweroff", "This command will power off the system"),
+
+            # Critical system modifications
+            ("rm -rf /", "This command will delete the entire filesystem"),
+            ("rm -rf /*", "This command will delete the entire filesystem"),
+            ("dd if=/dev/zero", "This command can destroy disk data"),
+            ("mkfs", "This command will format a filesystem, destroying all data"),
+            ("fdisk", "This command modifies disk partitions"),
+
+            # User/system management (only if targeting system users)
+            ("userdel root", "This command will delete the root user"),
+            ("passwd root", "This command will change the root password"),
+        ]
+
+        for pattern, warning in dangerous_patterns:
+            if pattern in cmd_lower:
+                return warning
+
+        # Check for recursive deletion outside workspace
+        if "rm" in cmd_lower and "-rf" in cmd_lower:
+            # Allow deletion within current workspace
+            if not any(path in cmd_lower for path in ["./", self.cwd.lower()]):
+                # Check if targeting system directories
+                system_dirs = ["/bin", "/usr", "/etc", "/var", "/home", "/root", "/sys", "/proc"]
+                if any(sysdir in cmd_lower for sysdir in system_dirs):
+                    return "This command will recursively delete system directories"
+
+        return ""  # No warning needed
--- a/agent/tools/browser_tool.py
+++ b/agent/tools/browser_tool.py
@@ -0,0 +1,18 @@
+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 
--- a/agent/tools/edit/init.py
+++ b/agent/tools/edit/init.py
@@ -0,0 +1,3 @@
+from .edit import Edit
+
+__all__ = ['Edit']
--- a/agent/tools/edit/edit.py
+++ b/agent/tools/edit/edit.py
@@ -0,0 +1,184 @@
+"""
+Edit tool - Precise file editing
+Edit files through exact text replacement
+"""
+
+import os
+from typing import Dict, Any
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.utils.diff import (
+    strip_bom,
+    detect_line_ending,
+    normalize_to_lf,
+    restore_line_endings,
+    normalize_for_fuzzy_match,
+    fuzzy_find_text,
+    generate_diff_string
+)
+
+
+class Edit(BaseTool):
+    """Tool for precise file editing"""
+    
+    name: str = "edit"
+    description: str = "Edit a file by replacing exact text, or append to end if oldText is empty. For append: use empty oldText. For replace: oldText must match exactly (including whitespace)."
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Path to the file to edit (relative or absolute)"
+            },
+            "oldText": {
+                "type": "string",
+                "description": "Text to find and replace. Use empty string to append to end of file. For replacement: must match exactly including whitespace."
+            },
+            "newText": {
+                "type": "string",
+                "description": "New text to replace the old text with"
+            }
+        },
+        "required": ["path", "oldText", "newText"]
+    }
+    
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        self.memory_manager = self.config.get("memory_manager", None)
+    
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        """
+        Execute file edit operation
+        
+        :param args: Contains file path, old text and new text
+        :return: Operation result
+        """
+        path = args.get("path", "").strip()
+        old_text = args.get("oldText", "")
+        new_text = args.get("newText", "")
+        
+        if not path:
+            return ToolResult.fail("Error: path parameter is required")
+        
+        # Resolve path
+        absolute_path = self._resolve_path(path)
+        
+        # Check if file exists
+        if not os.path.exists(absolute_path):
+            return ToolResult.fail(f"Error: File not found: {path}")
+        
+        # Check if readable/writable
+        if not os.access(absolute_path, os.R_OK | os.W_OK):
+            return ToolResult.fail(f"Error: File is not readable/writable: {path}")
+        
+        try:
+            # Read file
+            with open(absolute_path, 'r', encoding='utf-8') as f:
+                raw_content = f.read()
+            
+            # Remove BOM (LLM won't include invisible BOM in oldText)
+            bom, content = strip_bom(raw_content)
+            
+            # Detect original line ending
+            original_ending = detect_line_ending(content)
+            
+            # Normalize to LF
+            normalized_content = normalize_to_lf(content)
+            normalized_old_text = normalize_to_lf(old_text)
+            normalized_new_text = normalize_to_lf(new_text)
+            
+            # Special case: empty oldText means append to end of file
+            if not old_text or not old_text.strip():
+                # Append mode: add newText to the end
+                # Add newline before newText if file doesn't end with one
+                if normalized_content and not normalized_content.endswith('\n'):
+                    new_content = normalized_content + '\n' + normalized_new_text
+                else:
+                    new_content = normalized_content + normalized_new_text
+                base_content = normalized_content  # For verification
+            else:
+                # Normal edit mode: find and replace
+                # Use fuzzy matching to find old text (try exact match first, then fuzzy match)
+                match_result = fuzzy_find_text(normalized_content, normalized_old_text)
+                
+                if not match_result.found:
+                    return ToolResult.fail(
+                        f"Error: Could not find the exact text in {path}. "
+                        "The old text must match exactly including all whitespace and newlines."
+                    )
+                
+                # Calculate occurrence count (use fuzzy normalized content for consistency)
+                fuzzy_content = normalize_for_fuzzy_match(normalized_content)
+                fuzzy_old_text = normalize_for_fuzzy_match(normalized_old_text)
+                occurrences = fuzzy_content.count(fuzzy_old_text)
+                
+                if occurrences > 1:
+                    return ToolResult.fail(
+                        f"Error: Found {occurrences} occurrences of the text in {path}. "
+                        "The text must be unique. Please provide more context to make it unique."
+                    )
+                
+                # Execute replacement (use matched text position)
+                base_content = match_result.content_for_replacement
+                new_content = (
+                    base_content[:match_result.index] +
+                    normalized_new_text +
+                    base_content[match_result.index + match_result.match_length:]
+                )
+            
+            # Verify replacement actually changed content
+            if base_content == new_content:
+                return ToolResult.fail(
+                    f"Error: No changes made to {path}. "
+                    "The replacement produced identical content. "
+                    "This might indicate an issue with special characters or the text not existing as expected."
+                )
+            
+            # Restore original line endings
+            final_content = bom + restore_line_endings(new_content, original_ending)
+            
+            # Write file
+            with open(absolute_path, 'w', encoding='utf-8') as f:
+                f.write(final_content)
+            
+            # Generate diff
+            diff_result = generate_diff_string(base_content, new_content)
+            
+            result = {
+                "message": f"Successfully replaced text in {path}",
+                "path": path,
+                "diff": diff_result['diff'],
+                "first_changed_line": diff_result['first_changed_line']
+            }
+            
+            # Notify memory manager if file is in memory directory
+            if self.memory_manager and "memory/" in path:
+                try:
+                    self.memory_manager.mark_dirty()
+                except Exception as e:
+                    # Don't fail the edit if memory notification fails
+                    pass
+            
+            return ToolResult.success(result)
+            
+        except UnicodeDecodeError:
+            return ToolResult.fail(f"Error: File is not a valid text file (encoding error): {path}")
+        except PermissionError:
+            return ToolResult.fail(f"Error: Permission denied accessing {path}")
+        except Exception as e:
+            return ToolResult.fail(f"Error editing file: {str(e)}")
+    
+    def _resolve_path(self, path: str) -> str:
+        """
+        Resolve path to absolute path
+        
+        :param path: Relative or absolute path
+        :return: Absolute path
+        """
+        # Expand ~ to user home directory
+        path = os.path.expanduser(path)
+        if os.path.isabs(path):
+            return path
+        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/env_config/init.py
+++ b/agent/tools/env_config/init.py
@@ -0,0 +1,3 @@
+from agent.tools.env_config.env_config import EnvConfig
+
+__all__ = ['EnvConfig']
--- a/agent/tools/env_config/env_config.py
+++ b/agent/tools/env_config/env_config.py
@@ -0,0 +1,284 @@
+"""
+Environment Configuration Tool - Manage API keys and environment variables
+"""
+
+import os
+import re
+from typing import Dict, Any
+from pathlib import Path
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from common.log import logger
+
+
+# API Key 知识库：常见的环境变量及其描述
+API_KEY_REGISTRY = {
+    # AI 模型服务
+    "OPENAI_API_KEY": "OpenAI API 密钥 (用于GPT模型、Embedding模型)",
+    "GEMINI_API_KEY": "Google Gemini API 密钥",
+    "CLAUDE_API_KEY": "Claude API 密钥 (用于Claude模型)",
+    "LINKAI_API_KEY": "LinkAI智能体平台 API 密钥，支持多种模型切换",
+    # 搜索服务
+    "BOCHA_API_KEY": "博查 AI 搜索 API 密钥 ",
+}
+
+class EnvConfig(BaseTool):
+    """Tool for managing environment variables (API keys, etc.)"""
+    
+    name: str = "env_config"
+    description: str = (
+        "Manage API keys and skill configurations securely. "
+        "Use this tool when user wants to configure API keys (like BOCHA_API_KEY, OPENAI_API_KEY), "
+        "view configured keys, or manage skill settings. "
+        "Actions: 'set' (add/update key), 'get' (view specific key), 'list' (show all configured keys), 'delete' (remove key). "
+        "Values are automatically masked for security. Changes take effect immediately via hot reload."
+    )
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "description": "Action to perform: 'set', 'get', 'list', 'delete'",
+                "enum": ["set", "get", "list", "delete"]
+            },
+            "key": {
+                "type": "string",
+                "description": (
+                    "Environment variable key name. Common keys:\n"
+                    "- OPENAI_API_KEY: OpenAI API (GPT models)\n"
+                    "- OPENAI_API_BASE: OpenAI API base URL\n"
+                    "- CLAUDE_API_KEY: Anthropic Claude API\n"
+                    "- GEMINI_API_KEY: Google Gemini API\n"
+                    "- LINKAI_API_KEY: LinkAI platform\n"
+                    "- BOCHA_API_KEY: Bocha AI search (博查搜索)\n"
+                    "Use exact key names (case-sensitive, all uppercase with underscores)"
+                )
+            },
+            "value": {
+                "type": "string",
+                "description": "Value to set for the environment variable (for 'set' action)"
+            }
+        },
+        "required": ["action"]
+    }
+    
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        # Store env config in ~/.cow directory (outside workspace for security)
+        self.env_dir = os.path.expanduser("~/.cow")
+        self.env_path = os.path.join(self.env_dir, '.env')
+        self.agent_bridge = self.config.get("agent_bridge")  # Reference to AgentBridge for hot reload
+        # Don't create .env file in __init__ to avoid issues during tool discovery
+        # It will be created on first use in execute()
+    
+    def _ensure_env_file(self):
+        """Ensure the .env file exists"""
+        # Create ~/.cow directory if it doesn't exist
+        os.makedirs(self.env_dir, exist_ok=True)
+        
+        if not os.path.exists(self.env_path):
+            Path(self.env_path).touch()
+            logger.info(f"[EnvConfig] Created .env file at {self.env_path}")
+    
+    def _mask_value(self, value: str) -> str:
+        """Mask sensitive parts of a value for logging"""
+        if not value or len(value) <= 10:
+            return "***"
+        return f"{value[:6]}***{value[-4:]}"
+    
+    def _read_env_file(self) -> Dict[str, str]:
+        """Read all key-value pairs from .env file"""
+        env_vars = {}
+        if os.path.exists(self.env_path):
+            with open(self.env_path, 'r', encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    # Skip empty lines and comments
+                    if not line or line.startswith('#'):
+                        continue
+                    # Parse KEY=VALUE
+                    match = re.match(r'^([^=]+)=(.*)$', line)
+                    if match:
+                        key, value = match.groups()
+                        env_vars[key.strip()] = value.strip()
+        return env_vars
+    
+    def _write_env_file(self, env_vars: Dict[str, str]):
+        """Write all key-value pairs to .env file"""
+        with open(self.env_path, 'w', encoding='utf-8') as f:
+            f.write("# Environment variables for agent skills\n")
+            f.write("# Auto-managed by env_config tool\n\n")
+            for key, value in sorted(env_vars.items()):
+                f.write(f"{key}={value}\n")
+    
+    def _reload_env(self):
+        """Reload environment variables from .env file"""
+        env_vars = self._read_env_file()
+        for key, value in env_vars.items():
+            os.environ[key] = value
+        logger.debug(f"[EnvConfig] Reloaded {len(env_vars)} environment variables")
+    
+    def _refresh_skills(self):
+        """Refresh skills after environment variable changes"""
+        if self.agent_bridge:
+            try:
+                # Reload .env file
+                self._reload_env()
+                
+                # Refresh skills in all agent instances
+                refreshed = self.agent_bridge.refresh_all_skills()
+                logger.info(f"[EnvConfig] Refreshed skills in {refreshed} agent instance(s)")
+                return True
+            except Exception as e:
+                logger.warning(f"[EnvConfig] Failed to refresh skills: {e}")
+                return False
+        return False
+    
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        """
+        Execute environment configuration operation
+        
+        :param args: Contains action, key, and value parameters
+        :return: Result of the operation
+        """
+        # Ensure .env file exists on first use
+        self._ensure_env_file()
+        
+        action = args.get("action")
+        key = args.get("key")
+        value = args.get("value")
+        
+        try:
+            if action == "set":
+                if not key or not value:
+                    return ToolResult.fail("Error: 'key' and 'value' are required for 'set' action.")
+                
+                # Read current env vars
+                env_vars = self._read_env_file()
+                
+                # Update the key
+                env_vars[key] = value
+                
+                # Write back to file
+                self._write_env_file(env_vars)
+                
+                # Update current process env
+                os.environ[key] = value
+                
+                logger.info(f"[EnvConfig] Set {key}={self._mask_value(value)}")
+                
+                # Try to refresh skills immediately
+                refreshed = self._refresh_skills()
+                
+                result = {
+                    "message": f"Successfully set {key}",
+                    "key": key,
+                    "value": self._mask_value(value),
+                }
+                
+                if refreshed:
+                    result["note"] = "✅ Skills refreshed automatically - changes are now active"
+                else:
+                    result["note"] = "⚠️ Skills not refreshed - restart agent to load new skills"
+                
+                return ToolResult.success(result)
+            
+            elif action == "get":
+                if not key:
+                    return ToolResult.fail("Error: 'key' is required for 'get' action.")
+                
+                # Check in file first, then in current env
+                env_vars = self._read_env_file()
+                value = env_vars.get(key) or os.getenv(key)
+                
+                # Get description from registry
+                description = API_KEY_REGISTRY.get(key, "未知用途的环境变量")
+                
+                if value is not None:
+                    logger.info(f"[EnvConfig] Got {key}={self._mask_value(value)}")
+                    return ToolResult.success({
+                        "key": key,
+                        "value": self._mask_value(value),
+                        "description": description,
+                        "exists": True
+                    })
+                else:
+                    return ToolResult.success({
+                        "key": key,
+                        "description": description,
+                        "exists": False,
+                        "message": f"Environment variable '{key}' is not set"
+                    })
+            
+            elif action == "list":
+                env_vars = self._read_env_file()
+                
+                # Build detailed variable list with descriptions
+                variables_with_info = {}
+                for key, value in env_vars.items():
+                    variables_with_info[key] = {
+                        "value": self._mask_value(value),
+                        "description": API_KEY_REGISTRY.get(key, "未知用途的环境变量")
+                    }
+                
+                logger.info(f"[EnvConfig] Listed {len(env_vars)} environment variables")
+                
+                if not env_vars:
+                    return ToolResult.success({
+                        "message": "No environment variables configured",
+                        "variables": {},
+                        "note": "常用的 API 密钥可以通过 env_config(action='set', key='KEY_NAME', value='your-key') 来配置"
+                    })
+                
+                return ToolResult.success({
+                    "message": f"Found {len(env_vars)} environment variable(s)",
+                    "variables": variables_with_info
+                })
+            
+            elif action == "delete":
+                if not key:
+                    return ToolResult.fail("Error: 'key' is required for 'delete' action.")
+                
+                # Read current env vars
+                env_vars = self._read_env_file()
+                
+                if key not in env_vars:
+                    return ToolResult.success({
+                        "message": f"Environment variable '{key}' was not set",
+                        "key": key
+                    })
+                
+                # Remove the key
+                del env_vars[key]
+                
+                # Write back to file
+                self._write_env_file(env_vars)
+                
+                # Remove from current process env
+                if key in os.environ:
+                    del os.environ[key]
+                
+                logger.info(f"[EnvConfig] Deleted {key}")
+                
+                # Try to refresh skills immediately
+                refreshed = self._refresh_skills()
+                
+                result = {
+                    "message": f"Successfully deleted {key}",
+                    "key": key,
+                }
+                
+                if refreshed:
+                    result["note"] = "✅ Skills refreshed automatically - changes are now active"
+                else:
+                    result["note"] = "⚠️ Skills not refreshed - restart agent to apply changes"
+                
+                return ToolResult.success(result)
+            
+            else:
+                return ToolResult.fail(f"Error: Unknown action '{action}'. Use 'set', 'get', 'list', or 'delete'.")
+        
+        except Exception as e:
+            logger.error(f"[EnvConfig] Error: {e}", exc_info=True)
+            return ToolResult.fail(f"EnvConfig tool error: {str(e)}")
--- a/agent/tools/ls/init.py
+++ b/agent/tools/ls/init.py
@@ -0,0 +1,3 @@
+from .ls import Ls
+
+__all__ = ['Ls']
--- a/agent/tools/ls/ls.py
+++ b/agent/tools/ls/ls.py
@@ -0,0 +1,139 @@
+"""
+Ls tool - List directory contents
+"""
+
+import os
+from typing import Dict, Any
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.utils.truncate import truncate_head, format_size, DEFAULT_MAX_BYTES
+
+
+DEFAULT_LIMIT = 500
+
+
+class Ls(BaseTool):
+    """Tool for listing directory contents"""
+    
+    name: str = "ls"
+    description: str = f"List directory contents. Returns entries sorted alphabetically, with '/' suffix for directories. Includes dotfiles. Output is truncated to {DEFAULT_LIMIT} entries or {DEFAULT_MAX_BYTES // 1024}KB (whichever is hit first)."
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Directory to list. IMPORTANT: Relative paths are based on workspace directory. To access directories outside workspace, use absolute paths starting with ~ or /."
+            },
+            "limit": {
+                "type": "integer",
+                "description": f"Maximum number of entries to return (default: {DEFAULT_LIMIT})"
+            }
+        },
+        "required": []
+    }
+    
+    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:
+        """
+        Execute directory listing
+        
+        :param args: Listing parameters
+        :return: Directory contents or error
+        """
+        path = args.get("path", ".").strip()
+        limit = args.get("limit", DEFAULT_LIMIT)
+        
+        # Resolve path
+        absolute_path = self._resolve_path(path)
+        
+        # Security check: Prevent accessing sensitive config directory
+        env_config_dir = os.path.expanduser("~/.cow")
+        if os.path.abspath(absolute_path) == os.path.abspath(env_config_dir):
+            return ToolResult.fail(
+                "Error: Access denied. API keys and credentials must be accessed through the env_config tool only."
+            )
+        
+        if not os.path.exists(absolute_path):
+            # Provide helpful hint if using relative path
+            if not os.path.isabs(path) and not path.startswith('~'):
+                return ToolResult.fail(
+                    f"Error: Path not found: {path}\n"
+                    f"Resolved to: {absolute_path}\n"
+                    f"Hint: Relative paths are based on workspace ({self.cwd}). For files outside workspace, use absolute paths."
+                )
+            return ToolResult.fail(f"Error: Path not found: {path}")
+        
+        if not os.path.isdir(absolute_path):
+            return ToolResult.fail(f"Error: Not a directory: {path}")
+        
+        try:
+            # Read directory entries
+            entries = os.listdir(absolute_path)
+            
+            # Sort alphabetically (case-insensitive)
+            entries.sort(key=lambda x: x.lower())
+            
+            # Format entries with directory indicators
+            results = []
+            entry_limit_reached = False
+            
+            for entry in entries:
+                if len(results) >= limit:
+                    entry_limit_reached = True
+                    break
+                
+                full_path = os.path.join(absolute_path, entry)
+                
+                try:
+                    if os.path.isdir(full_path):
+                        results.append(entry + '/')
+                    else:
+                        results.append(entry)
+                except:
+                    # Skip entries we can't stat
+                    continue
+            
+            if not results:
+                return ToolResult.success({"message": "(empty directory)", "entries": []})
+            
+            # Format output
+            raw_output = '\n'.join(results)
+            truncation = truncate_head(raw_output, max_lines=999999)  # Only limit by bytes
+            
+            output = truncation.content
+            details = {}
+            notices = []
+            
+            if entry_limit_reached:
+                notices.append(f"{limit} entries limit reached. Use limit={limit * 2} for more")
+                details["entry_limit_reached"] = limit
+            
+            if truncation.truncated:
+                notices.append(f"{format_size(DEFAULT_MAX_BYTES)} limit reached")
+                details["truncation"] = truncation.to_dict()
+            
+            if notices:
+                output += f"\n\n[{'. '.join(notices)}]"
+            
+            return ToolResult.success({
+                "output": output,
+                "entry_count": len(results),
+                "details": details if details else None
+            })
+            
+        except PermissionError:
+            return ToolResult.fail(f"Error: Permission denied reading directory: {path}")
+        except Exception as e:
+            return ToolResult.fail(f"Error listing directory: {str(e)}")
+    
+    def _resolve_path(self, path: str) -> str:
+        """Resolve path to absolute path"""
+        # Expand ~ to user home directory
+        path = os.path.expanduser(path)
+        if os.path.isabs(path):
+            return path
+        return os.path.abspath(os.path.join(self.cwd, path))
--- a/agent/tools/memory/init.py
+++ b/agent/tools/memory/init.py
@@ -0,0 +1,10 @@
+"""
+Memory tools for Agent
+
+Provides memory_search and memory_get tools
+"""
+
+from agent.tools.memory.memory_search import MemorySearchTool
+from agent.tools.memory.memory_get import MemoryGetTool
+
+__all__ = ['MemorySearchTool', 'MemoryGetTool']
--- a/agent/tools/memory/memory_get.py
+++ b/agent/tools/memory/memory_get.py
@@ -0,0 +1,111 @@
+"""
+Memory get tool
+
+Allows agents to read specific sections from memory files
+"""
+
+from agent.tools.base_tool import BaseTool
+
+
+class MemoryGetTool(BaseTool):
+    """Tool for reading memory file contents"""
+    
+    name: str = "memory_get"
+    description: str = (
+        "Read specific content from memory files. "
+        "Use this to get full context from a memory file or specific line range."
+    )
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Relative path to the memory file (e.g. 'MEMORY.md', 'memory/2026-01-01.md')"
+            },
+            "start_line": {
+                "type": "integer",
+                "description": "Starting line number (optional, default: 1)",
+                "default": 1
+            },
+            "num_lines": {
+                "type": "integer",
+                "description": "Number of lines to read (optional, reads all if not specified)"
+            }
+        },
+        "required": ["path"]
+    }
+    
+    def __init__(self, memory_manager):
+        """
+        Initialize memory get tool
+        
+        Args:
+            memory_manager: MemoryManager instance
+        """
+        super().__init__()
+        self.memory_manager = memory_manager
+    
+    def execute(self, args: dict):
+        """
+        Execute memory file read
+        
+        Args:
+            args: Dictionary with path, start_line, num_lines
+            
+        Returns:
+            ToolResult with file content
+        """
+        from agent.tools.base_tool import ToolResult
+        
+        path = args.get("path")
+        start_line = args.get("start_line", 1)
+        num_lines = args.get("num_lines")
+        
+        if not path:
+            return ToolResult.fail("Error: path parameter is required")
+        
+        try:
+            workspace_dir = self.memory_manager.config.get_workspace()
+            
+            # Auto-prepend memory/ if not present and not absolute path
+            # Exception: MEMORY.md is in the root directory
+            if not path.startswith('memory/') and not path.startswith('/') and path != 'MEMORY.md':
+                path = f'memory/{path}'
+            
+            file_path = workspace_dir / path
+            
+            if not file_path.exists():
+                return ToolResult.fail(f"Error: File not found: {path}")
+            
+            content = file_path.read_text()
+            lines = content.split('\n')
+            
+            # Handle line range
+            if start_line < 1:
+                start_line = 1
+            
+            start_idx = start_line - 1
+            
+            if num_lines:
+                end_idx = start_idx + num_lines
+                selected_lines = lines[start_idx:end_idx]
+            else:
+                selected_lines = lines[start_idx:]
+            
+            result = '\n'.join(selected_lines)
+            
+            # Add metadata
+            total_lines = len(lines)
+            shown_lines = len(selected_lines)
+            
+            output = [
+                f"File: {path}",
+                f"Lines: {start_line}-{start_line + shown_lines - 1} (total: {total_lines})",
+                "",
+                result
+            ]
+            
+            return ToolResult.success('\n'.join(output))
+            
+        except Exception as e:
+            return ToolResult.fail(f"Error reading memory file: {str(e)}")
--- a/agent/tools/memory/memory_search.py
+++ b/agent/tools/memory/memory_search.py
@@ -0,0 +1,102 @@
+"""
+Memory search tool
+
+Allows agents to search their memory using semantic and keyword search
+"""
+
+from typing import Dict, Any, Optional
+from agent.tools.base_tool import BaseTool
+
+
+class MemorySearchTool(BaseTool):
+    """Tool for searching agent memory"""
+    
+    name: str = "memory_search"
+    description: str = (
+        "Search agent's long-term memory using semantic and keyword search. "
+        "Use this to recall past conversations, preferences, and knowledge."
+    )
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "Search query (can be natural language question or keywords)"
+            },
+            "max_results": {
+                "type": "integer",
+                "description": "Maximum number of results to return (default: 10)",
+                "default": 10
+            },
+            "min_score": {
+                "type": "number",
+                "description": "Minimum relevance score (0-1, default: 0.1)",
+                "default": 0.1
+            }
+        },
+        "required": ["query"]
+    }
+    
+    def __init__(self, memory_manager, user_id: Optional[str] = None):
+        """
+        Initialize memory search tool
+        
+        Args:
+            memory_manager: MemoryManager instance
+            user_id: Optional user ID for scoped search
+        """
+        super().__init__()
+        self.memory_manager = memory_manager
+        self.user_id = user_id
+    
+    def execute(self, args: dict):
+        """
+        Execute memory search
+        
+        Args:
+            args: Dictionary with query, max_results, min_score
+            
+        Returns:
+            ToolResult with formatted search results
+        """
+        from agent.tools.base_tool import ToolResult
+        import asyncio
+        
+        query = args.get("query")
+        max_results = args.get("max_results", 10)
+        min_score = args.get("min_score", 0.1)
+        
+        if not query:
+            return ToolResult.fail("Error: query parameter is required")
+        
+        try:
+            # Run async search in sync context
+            results = asyncio.run(self.memory_manager.search(
+                query=query,
+                user_id=self.user_id,
+                max_results=max_results,
+                min_score=min_score,
+                include_shared=True
+            ))
+            
+            if not results:
+                # Return clear message that no memories exist yet
+                # This prevents infinite retry loops
+                return ToolResult.success(
+                    f"No memories found for '{query}'. "
+                    f"This is normal if no memories have been stored yet. "
+                    f"You can store new memories by writing to MEMORY.md or memory/YYYY-MM-DD.md files."
+                )
+            
+            # Format results
+            output = [f"Found {len(results)} relevant memories:\n"]
+            
+            for i, result in enumerate(results, 1):
+                output.append(f"\n{i}. {result.path} (lines {result.start_line}-{result.end_line})")
+                output.append(f"   Score: {result.score:.3f}")
+                output.append(f"   Snippet: {result.snippet}")
+            
+            return ToolResult.success("\n".join(output))
+            
+        except Exception as e:
+            return ToolResult.fail(f"Error searching memory: {str(e)}")
--- a/agent/tools/read/init.py
+++ b/agent/tools/read/init.py
@@ -0,0 +1,3 @@
+from .read import Read
+
+__all__ = ['Read']
--- a/agent/tools/read/read.py
+++ b/agent/tools/read/read.py
@@ -0,0 +1,440 @@
+"""
+Read tool - Read file contents
+Supports text files, images (jpg, png, gif, webp), and PDF files
+"""
+
+import os
+from typing import Dict, Any
+from pathlib import Path
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from agent.tools.utils.truncate import truncate_head, format_size, DEFAULT_MAX_LINES, DEFAULT_MAX_BYTES
+
+
+class Read(BaseTool):
+    """Tool for reading file contents"""
+    
+    name: str = "read"
+    description: str = f"Read or inspect file contents. For text/PDF files, returns content (truncated to {DEFAULT_MAX_LINES} lines or {DEFAULT_MAX_BYTES // 1024}KB). For images/videos/audio, returns metadata only (file info, size, type). Use offset/limit for large text files."
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Path to the file to read. IMPORTANT: Relative paths are based on workspace directory. To access files outside workspace, use absolute paths starting with ~ or /."
+            },
+            "offset": {
+                "type": "integer",
+                "description": "Line number to start reading from (1-indexed, optional). Use negative values to read from end (e.g. -20 for last 20 lines)"
+            },
+            "limit": {
+                "type": "integer",
+                "description": "Maximum number of lines to read (optional)"
+            }
+        },
+        "required": ["path"]
+    }
+    
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        
+        # File type categories
+        self.image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp', '.svg', '.ico'}
+        self.video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.webm', '.m4v'}
+        self.audio_extensions = {'.mp3', '.wav', '.ogg', '.m4a', '.flac', '.aac', '.wma'}
+        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'}
+        
+        # Readable text formats (will be read with truncation)
+        self.text_extensions = {
+            '.txt', '.md', '.markdown', '.rst', '.log', '.csv', '.tsv', '.json', '.xml', '.yaml', '.yml',
+            '.py', '.js', '.ts', '.java', '.c', '.cpp', '.h', '.hpp', '.go', '.rs', '.rb', '.php',
+            '.html', '.css', '.scss', '.sass', '.less', '.vue', '.jsx', '.tsx',
+            '.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:
+        """
+        Execute file read operation
+        
+        :param args: Contains file path and optional offset/limit parameters
+        :return: File content or error message
+        """
+        path = args.get("path", "").strip()
+        offset = args.get("offset")
+        limit = args.get("limit")
+        
+        if not path:
+            return ToolResult.fail("Error: path parameter is required")
+        
+        # Resolve path
+        absolute_path = self._resolve_path(path)
+        
+        # Security check: Prevent reading sensitive config files
+        env_config_path = os.path.expanduser("~/.cow/.env")
+        if os.path.abspath(absolute_path) == os.path.abspath(env_config_path):
+            return ToolResult.fail(
+                "Error: Access denied. API keys and credentials must be accessed through the env_config tool only."
+            )
+        
+        # Check if file exists
+        if not os.path.exists(absolute_path):
+            # Provide helpful hint if using relative path
+            if not os.path.isabs(path) and not path.startswith('~'):
+                return ToolResult.fail(
+                    f"Error: File not found: {path}\n"
+                    f"Resolved to: {absolute_path}\n"
+                    f"Hint: Relative paths are based on workspace ({self.cwd}). For files outside workspace, use absolute paths."
+                )
+            return ToolResult.fail(f"Error: File not found: {path}")
+        
+        # Check if readable
+        if not os.access(absolute_path, os.R_OK):
+            return ToolResult.fail(f"Error: File is not readable: {path}")
+        
+        # Check file type
+        file_ext = Path(absolute_path).suffix.lower()
+        file_size = os.path.getsize(absolute_path)
+        
+        # Check if image - return metadata for sending
+        if file_ext in self.image_extensions:
+            return self._read_image(absolute_path, file_ext)
+        
+        # Check if video/audio/binary/archive - return metadata only
+        if file_ext in self.video_extensions:
+            return self._return_file_metadata(absolute_path, "video", file_size)
+        if file_ext in self.audio_extensions:
+            return self._return_file_metadata(absolute_path, "audio", file_size)
+        if file_ext in self.binary_extensions or file_ext in self.archive_extensions:
+            return self._return_file_metadata(absolute_path, "binary", file_size)
+        
+        # Check if PDF
+        if file_ext in self.pdf_extensions:
+            return self._read_pdf(absolute_path, path, offset, limit)
+        
+        # Read text file (with truncation for large files)
+        return self._read_text(absolute_path, path, offset, limit)
+    
+    def _resolve_path(self, path: str) -> str:
+        """
+        Resolve path to absolute path
+        
+        :param path: Relative or absolute path
+        :return: Absolute path
+        """
+        # Expand ~ to user home directory
+        path = os.path.expanduser(path)
+        if os.path.isabs(path):
+            return path
+        return os.path.abspath(os.path.join(self.cwd, path))
+    
+    def _return_file_metadata(self, absolute_path: str, file_type: str, file_size: int) -> ToolResult:
+        """
+        Return file metadata for non-readable files (video, audio, binary, etc.)
+        
+        :param absolute_path: Absolute path to the file
+        :param file_type: Type of file (video, audio, binary, etc.)
+        :param file_size: File size in bytes
+        :return: File metadata
+        """
+        file_name = Path(absolute_path).name
+        file_ext = Path(absolute_path).suffix.lower()
+        
+        # Determine MIME type
+        mime_types = {
+            # Video
+            '.mp4': 'video/mp4', '.avi': 'video/x-msvideo', '.mov': 'video/quicktime',
+            '.mkv': 'video/x-matroska', '.webm': 'video/webm',
+            # Audio
+            '.mp3': 'audio/mpeg', '.wav': 'audio/wav', '.ogg': 'audio/ogg',
+            '.m4a': 'audio/mp4', '.flac': 'audio/flac',
+            # Binary
+            '.zip': 'application/zip', '.tar': 'application/x-tar',
+            '.gz': 'application/gzip', '.rar': 'application/x-rar-compressed',
+        }
+        mime_type = mime_types.get(file_ext, 'application/octet-stream')
+        
+        result = {
+            "type": f"{file_type}_metadata",
+            "file_type": file_type,
+            "path": absolute_path,
+            "file_name": file_name,
+            "mime_type": mime_type,
+            "size": file_size,
+            "size_formatted": format_size(file_size),
+            "message": f"{file_type.capitalize()} 文件: {file_name} ({format_size(file_size)})\n提示: 如果需要发送此文件，请使用 send 工具。"
+        }
+        
+        return ToolResult.success(result)
+    
+    def _read_image(self, absolute_path: str, file_ext: str) -> ToolResult:
+        """
+        Read image file - always return metadata only (images should be sent, not read into context)
+        
+        :param absolute_path: Absolute path to the image file
+        :param file_ext: File extension
+        :return: Result containing image metadata for sending
+        """
+        try:
+            # Get file size
+            file_size = os.path.getsize(absolute_path)
+            
+            # Determine MIME type
+            mime_type_map = {
+                '.jpg': 'image/jpeg',
+                '.jpeg': 'image/jpeg',
+                '.png': 'image/png',
+                '.gif': 'image/gif',
+                '.webp': 'image/webp'
+            }
+            mime_type = mime_type_map.get(file_ext, 'image/jpeg')
+            
+            # Return metadata for images (NOT file_to_send - use send tool to actually send)
+            result = {
+                "type": "image_metadata",
+                "file_type": "image",
+                "path": absolute_path,
+                "mime_type": mime_type,
+                "size": file_size,
+                "size_formatted": format_size(file_size),
+                "message": f"图片文件: {Path(absolute_path).name} ({format_size(file_size)})\n提示: 如果需要发送此图片，请使用 send 工具。"
+            }
+            
+            return ToolResult.success(result)
+            
+        except Exception as e:
+            return ToolResult.fail(f"Error reading image file: {str(e)}")
+    
+    def _read_text(self, absolute_path: str, display_path: str, offset: int = None, limit: int = None) -> ToolResult:
+        """
+        Read text file
+        
+        :param absolute_path: Absolute path to the file
+        :param display_path: Path to display
+        :param offset: Starting line number (1-indexed)
+        :param limit: Maximum number of lines to read
+        :return: File content or error message
+        """
+        try:
+            # Check file size first
+            file_size = os.path.getsize(absolute_path)
+            MAX_FILE_SIZE = 50 * 1024 * 1024  # 50MB
+            
+            if file_size > MAX_FILE_SIZE:
+                # File too large, return metadata only
+                return ToolResult.success({
+                    "type": "file_to_send",
+                    "file_type": "document",
+                    "path": absolute_path,
+                    "size": file_size,
+                    "size_formatted": format_size(file_size),
+                    "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:
+                content = f.read()
+            
+            # Truncate content if too long (20K characters max for model context)
+            MAX_CONTENT_CHARS = 20 * 1024  # 20K characters
+            content_truncated = False
+            if len(content) > MAX_CONTENT_CHARS:
+                content = content[:MAX_CONTENT_CHARS]
+                content_truncated = True
+            
+            all_lines = content.split('\n')
+            total_file_lines = len(all_lines)
+            
+            # Apply offset (if specified)
+            start_line = 0
+            if offset is not None:
+                if offset < 0:
+                    # Negative offset: read from end
+                    # -20 means "last 20 lines" → start from (total - 20)
+                    start_line = max(0, total_file_lines + offset)
+                else:
+                    # Positive offset: read from start (1-indexed)
+                    start_line = max(0, offset - 1)  # Convert to 0-indexed
+                    if start_line >= total_file_lines:
+                        return ToolResult.fail(
+                            f"Error: Offset {offset} is beyond end of file ({total_file_lines} lines total)"
+                        )
+            
+            start_line_display = start_line + 1  # For display (1-indexed)
+            
+            # If user specified limit, use it
+            selected_content = content
+            user_limited_lines = None
+            if limit is not None:
+                end_line = min(start_line + limit, total_file_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:])
+            
+            # Apply truncation (considering line count and byte limits)
+            truncation = truncate_head(selected_content)
+            
+            output_text = ""
+            details = {}
+            
+            # Add truncation warning if content was truncated
+            if content_truncated:
+                output_text = f"[文件内容已截断到前 {format_size(MAX_CONTENT_CHARS)}，完整文件大小: {format_size(file_size)}]\n\n"
+            
+            if truncation.first_line_exceeds_limit:
+                # First line exceeds 30KB limit
+                first_line_size = format_size(len(all_lines[start_line].encode('utf-8')))
+                output_text = f"[Line {start_line_display} is {first_line_size}, exceeds {format_size(DEFAULT_MAX_BYTES)} limit. Use bash tool to read: head -c {DEFAULT_MAX_BYTES} {display_path} | tail -n +{start_line_display}]"
+                details["truncation"] = truncation.to_dict()
+            elif truncation.truncated:
+                # Truncation occurred
+                end_line_display = start_line_display + truncation.output_lines - 1
+                next_offset = end_line_display + 1
+                
+                output_text = truncation.content
+                
+                if truncation.truncated_by == "lines":
+                    output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_file_lines}. Use offset={next_offset} to continue.]"
+                else:
+                    output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_file_lines} ({format_size(DEFAULT_MAX_BYTES)} limit). Use offset={next_offset} to continue.]"
+                
+                details["truncation"] = truncation.to_dict()
+            elif user_limited_lines is not None and start_line + user_limited_lines < total_file_lines:
+                # User specified limit, more content available, but no truncation
+                remaining = total_file_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:
+                # No truncation, no exceeding user limit
+                output_text = truncation.content
+            
+            result = {
+                "content": output_text,
+                "total_lines": total_file_lines,
+                "start_line": start_line_display,
+                "output_lines": truncation.output_lines
+            }
+            
+            if details:
+                result["details"] = details
+            
+            return ToolResult.success(result)
+            
+        except UnicodeDecodeError:
+            return ToolResult.fail(f"Error: File is not a valid text file (encoding error): {display_path}")
+        except Exception as e:
+            return ToolResult.fail(f"Error reading file: {str(e)}")
+    
+    def _read_pdf(self, absolute_path: str, display_path: str, offset: int = None, limit: int = None) -> ToolResult:
+        """
+        Read PDF file content
+        
+        :param absolute_path: Absolute path to the file
+        :param display_path: Path to display
+        :param offset: Starting line number (1-indexed)
+        :param limit: Maximum number of lines to read
+        :return: PDF text content or error message
+        """
+        try:
+            # Try to import pypdf
+            try:
+                from pypdf import PdfReader
+            except ImportError:
+                return ToolResult.fail(
+                    "Error: pypdf library not installed. Install with: pip install pypdf"
+                )
+            
+            # Read PDF
+            reader = PdfReader(absolute_path)
+            total_pages = len(reader.pages)
+            
+            # Extract text from all pages
+            text_parts = []
+            for page_num, page in enumerate(reader.pages, 1):
+                page_text = page.extract_text()
+                if page_text.strip():
+                    text_parts.append(f"--- Page {page_num} ---\n{page_text}")
+            
+            if not text_parts:
+                return ToolResult.success({
+                    "content": f"[PDF file with {total_pages} pages, but no text content could be extracted]",
+                    "total_pages": total_pages,
+                    "message": "PDF may contain only images or be encrypted"
+                })
+            
+            # Merge all text
+            full_content = "\n\n".join(text_parts)
+            all_lines = full_content.split('\n')
+            total_lines = len(all_lines)
+            
+            # Apply offset and limit (same logic as text files)
+            start_line = 0
+            if offset is not None:
+                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)"
+                    )
+            
+            start_line_display = start_line + 1
+            
+            selected_content = full_content
+            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:])
+            
+            # Apply truncation
+            truncation = truncate_head(selected_content)
+            
+            output_text = ""
+            details = {}
+            
+            if truncation.truncated:
+                end_line_display = start_line_display + truncation.output_lines - 1
+                next_offset = end_line_display + 1
+                
+                output_text = truncation.content
+                
+                if truncation.truncated_by == "lines":
+                    output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_lines}. Use offset={next_offset} to continue.]"
+                else:
+                    output_text += f"\n\n[Showing lines {start_line_display}-{end_line_display} of {total_lines} ({format_size(DEFAULT_MAX_BYTES)} limit). Use offset={next_offset} to continue.]"
+                
+                details["truncation"] = truncation.to_dict()
+            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
+            
+            result = {
+                "content": output_text,
+                "total_pages": total_pages,
+                "total_lines": total_lines,
+                "start_line": start_line_display,
+                "output_lines": truncation.output_lines
+            }
+            
+            if details:
+                result["details"] = details
+            
+            return ToolResult.success(result)
+            
+        except Exception as e:
+            return ToolResult.fail(f"Error reading PDF file: {str(e)}")
--- a/agent/tools/scheduler/README.md
+++ b/agent/tools/scheduler/README.md
@@ -0,0 +1,287 @@
+# 定时任务工具 (Scheduler Tool)
+
+## 功能简介
+
+定时任务工具允许 Agent 创建、管理和执行定时任务，支持：
+
+- ⏰ **定时提醒**: 在指定时间发送消息
+- 🔄 **周期性任务**: 按固定间隔或 cron 表达式重复执行
+- 🔧 **动态工具调用**: 定时执行其他工具并发送结果（如搜索新闻、查询天气等）
+- 📋 **任务管理**: 查询、启用、禁用、删除任务
+
+## 安装依赖
+
+```bash
+pip install croniter>=2.0.0
+```
+
+## 使用方法
+
+### 1. 创建定时任务
+
+Agent 可以通过自然语言创建定时任务，支持两种类型：
+
+#### 1.1 静态消息任务
+
+发送预定义的消息：
+
+**示例对话：**
+```
+用户: 每天早上9点提醒我开会
+Agent: [调用 scheduler 工具]
+      action: create
+      name: 每日开会提醒
+      message: 该开会了！
+      schedule_type: cron
+      schedule_value: 0 9 * * *
+```
+
+#### 1.2 动态工具调用任务
+
+定时执行工具并发送结果：
+
+**示例对话：**
+```
+用户: 每天早上8点帮我读取一下今日日程
+Agent: [调用 scheduler 工具]
+      action: create
+      name: 每日日程
+      tool_call:
+        tool_name: read
+        tool_params:
+          file_path: ~/cow/schedule.txt
+        result_prefix: 📅 今日日程
+      schedule_type: cron
+      schedule_value: 0 8 * * *
+```
+
+**工具调用参数说明：**
+- `tool_name`: 要调用的工具名称（如 `bash`、`read`、`write` 等内置工具）
+- `tool_params`: 工具的参数（字典格式）
+- `result_prefix`: 可选，在结果前添加的前缀文本
+
+**注意：** 如果要使用 skills（如 bocha-search），需要通过 `bash` 工具调用 skill 脚本
+
+### 2. 支持的调度类型
+
+#### Cron 表达式 (`cron`)
+使用标准 cron 表达式：
+
+```
+0 9 * * *      # 每天 9:00
+0 */2 * * *    # 每 2 小时
+30 8 * * 1-5   # 工作日 8:30
+0 0 1 * *      # 每月 1 号
+```
+
+#### 固定间隔 (`interval`)
+以秒为单位的间隔：
+
+```
+3600           # 每小时
+86400          # 每天
+1800           # 每 30 分钟
+```
+
+#### 一次性任务 (`once`)
+指定具体时间（ISO 格式）：
+
+```
+2024-12-25T09:00:00
+2024-12-31T23:59:59
+```
+
+### 3. 查询任务列表
+
+```
+用户: 查看我的定时任务
+Agent: [调用 scheduler 工具]
+      action: list
+```
+
+### 4. 查看任务详情
+
+```
+用户: 查看任务 abc123 的详情
+Agent: [调用 scheduler 工具]
+      action: get
+      task_id: abc123
+```
+
+### 5. 删除任务
+
+```
+用户: 删除任务 abc123
+Agent: [调用 scheduler 工具]
+      action: delete
+      task_id: abc123
+```
+
+### 6. 启用/禁用任务
+
+```
+用户: 暂停任务 abc123
+Agent: [调用 scheduler 工具]
+      action: disable
+      task_id: abc123
+
+用户: 恢复任务 abc123
+Agent: [调用 scheduler 工具]
+      action: enable
+      task_id: abc123
+```
+
+## 任务存储
+
+任务保存在 JSON 文件中：
+```
+~/cow/scheduler/tasks.json
+```
+
+任务数据结构：
+
+**静态消息任务：**
+```json
+{
+  "id": "abc123",
+  "name": "每日提醒",
+  "enabled": true,
+  "created_at": "2024-01-01T10:00:00",
+  "updated_at": "2024-01-01T10:00:00",
+  "schedule": {
+    "type": "cron",
+    "expression": "0 9 * * *"
+  },
+  "action": {
+    "type": "send_message",
+    "content": "该开会了！",
+    "receiver": "wxid_xxx",
+    "receiver_name": "张三",
+    "is_group": false,
+    "channel_type": "wechat"
+  },
+  "next_run_at": "2024-01-02T09:00:00",
+  "last_run_at": "2024-01-01T09:00:00"
+}
+```
+
+**动态工具调用任务：**
+```json
+{
+  "id": "def456",
+  "name": "每日日程",
+  "enabled": true,
+  "created_at": "2024-01-01T10:00:00",
+  "updated_at": "2024-01-01T10:00:00",
+  "schedule": {
+    "type": "cron",
+    "expression": "0 8 * * *"
+  },
+  "action": {
+    "type": "tool_call",
+    "tool_name": "read",
+    "tool_params": {
+      "file_path": "~/cow/schedule.txt"
+    },
+    "result_prefix": "📅 今日日程",
+    "receiver": "wxid_xxx",
+    "receiver_name": "张三",
+    "is_group": false,
+    "channel_type": "wechat"
+  },
+  "next_run_at": "2024-01-02T08:00:00"
+}
+```
+
+## 后台服务
+
+定时任务由后台服务 `SchedulerService` 管理：
+
+- 每 30 秒检查一次到期任务
+- 自动执行到期任务
+- 计算下次执行时间
+- 记录执行历史和错误
+
+服务在 Agent 初始化时自动启动，无需手动配置。
+
+## 接收者确定
+
+定时任务会发送给**创建任务时的对话对象**：
+
+- 如果在私聊中创建，发送给该用户
+- 如果在群聊中创建，发送到该群
+- 接收者信息在创建时自动保存
+
+## 常见用例
+
+### 1. 每日提醒（静态消息）
+```
+用户: 每天早上8点提醒我吃药
+Agent: ✅ 定时任务创建成功
+       任务ID: a1b2c3d4
+       调度: 每天 8:00
+       消息: 该吃药了！
+```
+
+### 2. 工作日提醒（静态消息）
+```
+用户: 工作日下午6点提醒我下班
+Agent: [创建 cron: 0 18 * * 1-5]
+       消息: 该下班了！
+```
+
+### 3. 倒计时提醒（静态消息）
+```
+用户: 1小时后提醒我
+Agent: [创建 interval: 3600]
+```
+
+### 4. 每日日程推送（动态工具调用）
+```
+用户: 每天早上8点帮我读取今日日程
+Agent: ✅ 定时任务创建成功
+       任务ID: schedule001
+       调度: 每天 8:00
+       工具: read(file_path='~/cow/schedule.txt')
+       前缀: 📅 今日日程
+```
+
+### 5. 定时文件备份（动态工具调用）
+```
+用户: 每天晚上11点备份工作文件
+Agent: [创建 cron: 0 23 * * *]
+       工具: bash(command='cp ~/cow/work.txt ~/cow/backup/work_$(date +%Y%m%d).txt')
+       前缀: ✅ 文件已备份
+```
+
+### 6. 周报提醒（静态消息）
+```
+用户: 每周五下午5点提醒我写周报
+Agent: [创建 cron: 0 17 * * 5]
+       消息: 📊 该写周报了！
+```
+
+### 4. 特定日期提醒
+```
+用户: 12月25日早上9点提醒我圣诞快乐
+Agent: [创建 once: 2024-12-25T09:00:00]
+```
+
+## 注意事项
+
+1. **时区**: 使用系统本地时区
+2. **精度**: 检查间隔为 30 秒，实际执行可能有 ±30 秒误差
+3. **持久化**: 任务保存在文件中，重启后自动恢复
+4. **一次性任务**: 执行后自动禁用，不会删除（可手动删除）
+5. **错误处理**: 执行失败会记录错误，不影响其他任务
+
+## 技术实现
+
+- **TaskStore**: 任务持久化存储
+- **SchedulerService**: 后台调度服务
+- **SchedulerTool**: Agent 工具接口
+- **Integration**: 与 AgentBridge 集成
+
+## 依赖
+
+- `croniter`: Cron 表达式解析（轻量级，仅 ~50KB）
--- a/agent/tools/scheduler/init.py
+++ b/agent/tools/scheduler/init.py
@@ -0,0 +1,7 @@
+"""
+Scheduler tool for managing scheduled tasks
+"""
+
+from .scheduler_tool import SchedulerTool
+
+__all__ = ["SchedulerTool"]
--- a/agent/tools/scheduler/integration.py
+++ b/agent/tools/scheduler/integration.py
@@ -0,0 +1,447 @@
+"""
+Integration module for scheduler with AgentBridge
+"""
+
+import os
+from typing import Optional
+from config import conf
+from common.log import logger
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+
+# Global scheduler service instance
+_scheduler_service = None
+_task_store = None
+
+
+def init_scheduler(agent_bridge) -> bool:
+    """
+    Initialize scheduler service
+    
+    Args:
+        agent_bridge: AgentBridge instance
+        
+    Returns:
+        True if initialized successfully
+    """
+    global _scheduler_service, _task_store
+    
+    try:
+        from agent.tools.scheduler.task_store import TaskStore
+        from agent.tools.scheduler.scheduler_service import SchedulerService
+        
+        # Get workspace from config
+        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
+        
+        # Create task store
+        _task_store = TaskStore(store_path)
+        logger.debug(f"[Scheduler] Task store initialized: {store_path}")
+        
+        # Create execute callback
+        def execute_task_callback(task: dict):
+            """Callback to execute a scheduled task"""
+            try:
+                action = task.get("action", {})
+                action_type = action.get("type")
+                
+                if action_type == "agent_task":
+                    _execute_agent_task(task, agent_bridge)
+                elif action_type == "send_message":
+                    # Legacy support for old tasks
+                    _execute_send_message(task, agent_bridge)
+                elif action_type == "tool_call":
+                    # Legacy support for old tasks
+                    _execute_tool_call(task, agent_bridge)
+                elif action_type == "skill_call":
+                    # Legacy support for old tasks
+                    _execute_skill_call(task, agent_bridge)
+                else:
+                    logger.warning(f"[Scheduler] Unknown action type: {action_type}")
+            except Exception as e:
+                logger.error(f"[Scheduler] Error executing task {task.get('id')}: {e}")
+        
+        # Create scheduler service
+        _scheduler_service = SchedulerService(_task_store, execute_task_callback)
+        _scheduler_service.start()
+        
+        logger.debug("[Scheduler] Scheduler service initialized and started")
+        return True
+        
+    except Exception as e:
+        logger.error(f"[Scheduler] Failed to initialize scheduler: {e}")
+        return False
+
+
+def get_task_store():
+    """Get the global task store instance"""
+    return _task_store
+
+
+def get_scheduler_service():
+    """Get the global scheduler service instance"""
+    return _scheduler_service
+
+
+def _execute_agent_task(task: dict, agent_bridge):
+    """
+    Execute an agent_task action - let Agent handle the task
+    
+    Args:
+        task: Task dictionary
+        agent_bridge: AgentBridge instance
+    """
+    try:
+        action = task.get("action", {})
+        task_description = action.get("task_description")
+        receiver = action.get("receiver")
+        is_group = action.get("is_group", False)
+        channel_type = action.get("channel_type", "unknown")
+        
+        if not task_description:
+            logger.error(f"[Scheduler] Task {task['id']}: No task_description specified")
+            return
+        
+        if not receiver:
+            logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
+            return
+        
+        # Check for unsupported channels
+        if channel_type == "dingtalk":
+            logger.warning(f"[Scheduler] Task {task['id']}: DingTalk channel does not support scheduled messages (Stream mode limitation). Task will execute but message cannot be sent.")
+        
+        logger.info(f"[Scheduler] Task {task['id']}: Executing agent task '{task_description}'")
+        
+        # Create context for Agent
+        context = Context(ContextType.TEXT, task_description)
+        context["receiver"] = receiver
+        context["isgroup"] = is_group
+        context["session_id"] = receiver
+        
+        # Channel-specific setup
+        if channel_type == "web":
+            import uuid
+            request_id = f"scheduler_{task['id']}_{uuid.uuid4().hex[:8]}"
+            context["request_id"] = request_id
+        elif channel_type == "feishu":
+            context["receive_id_type"] = "chat_id" if is_group else "open_id"
+            context["msg"] = None
+        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
+        
+        # Use Agent to execute the task
+        # Mark this as a scheduled task execution to prevent recursive task creation
+        context["is_scheduled_task"] = True
+        
+        try:
+            reply = agent_bridge.agent_reply(task_description, context=context, on_event=None, clear_history=True)
+            
+            if reply and reply.content:
+                # Send the reply via channel
+                from channel.channel_factory import create_channel
+                
+                try:
+                    channel = create_channel(channel_type)
+                    if channel:
+                        # For web channel, register request_id
+                        if channel_type == "web" and hasattr(channel, 'request_to_session'):
+                            request_id = context.get("request_id")
+                            if request_id:
+                                channel.request_to_session[request_id] = receiver
+                                logger.debug(f"[Scheduler] Registered request_id {request_id} -> session {receiver}")
+                        
+                        # Send the reply
+                        channel.send(reply, context)
+                        logger.info(f"[Scheduler] Task {task['id']} executed successfully, result sent to {receiver}")
+                    else:
+                        logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+                except Exception as e:
+                    logger.error(f"[Scheduler] Failed to send result: {e}")
+            else:
+                logger.error(f"[Scheduler] Task {task['id']}: No result from agent execution")
+                
+        except Exception as e:
+            logger.error(f"[Scheduler] Failed to execute task via Agent: {e}")
+            import traceback
+            logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+            
+    except Exception as e:
+        logger.error(f"[Scheduler] Error in _execute_agent_task: {e}")
+        import traceback
+        logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+
+
+def _execute_send_message(task: dict, agent_bridge):
+    """
+    Execute a send_message action
+    
+    Args:
+        task: Task dictionary
+        agent_bridge: AgentBridge instance
+    """
+    try:
+        action = task.get("action", {})
+        content = action.get("content", "")
+        receiver = action.get("receiver")
+        is_group = action.get("is_group", False)
+        channel_type = action.get("channel_type", "unknown")
+        
+        if not receiver:
+            logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
+            return
+        
+        # Create context for sending message
+        context = Context(ContextType.TEXT, content)
+        context["receiver"] = receiver
+        context["isgroup"] = is_group
+        context["session_id"] = receiver
+        
+        # Channel-specific context setup
+        if channel_type == "web":
+            # Web channel needs request_id
+            import uuid
+            request_id = f"scheduler_{task['id']}_{uuid.uuid4().hex[:8]}"
+            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)
+            # Use chat_id for groups, open_id for private chats
+            context["receive_id_type"] = "chat_id" if is_group else "open_id"
+            # Keep isgroup as is, but set msg to None (no original message to reply to)
+            # Feishu channel will detect this and send as new message instead of reply
+            context["msg"] = None
+            logger.debug(f"[Scheduler] Feishu: receive_id_type={context['receive_id_type']}, is_group={is_group}, receiver={receiver}")
+        elif channel_type == "dingtalk":
+            # DingTalk channel setup
+            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
+                    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")
+        
+        # Create reply
+        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 message to {receiver}")
+            else:
+                logger.error(f"[Scheduler] Failed to create channel: {channel_type}")
+        except Exception as e:
+            logger.error(f"[Scheduler] Failed to send message: {e}")
+            import traceback
+            logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+            
+    except Exception as e:
+        logger.error(f"[Scheduler] Error in _execute_send_message: {e}")
+        import traceback
+        logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+
+
+def _execute_tool_call(task: dict, agent_bridge):
+    """
+    Execute a tool_call action
+    
+    Args:
+        task: Task dictionary
+        agent_bridge: AgentBridge instance
+    """
+    try:
+        action = task.get("action", {})
+        # Support both old and new field names
+        tool_name = action.get("call_name") or action.get("tool_name")
+        tool_params = action.get("call_params") or action.get("tool_params", {})
+        result_prefix = action.get("result_prefix", "")
+        receiver = action.get("receiver")
+        is_group = action.get("is_group", False)
+        channel_type = action.get("channel_type", "unknown")
+        
+        if not tool_name:
+            logger.error(f"[Scheduler] Task {task['id']}: No tool_name specified")
+            return
+        
+        if not receiver:
+            logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
+            return
+        
+        # Get tool manager and create tool instance
+        from agent.tools.tool_manager import ToolManager
+        tool_manager = ToolManager()
+        tool = tool_manager.create_tool(tool_name)
+        
+        if not tool:
+            logger.error(f"[Scheduler] Task {task['id']}: Tool '{tool_name}' not found")
+            return
+        
+        # Execute tool
+        logger.info(f"[Scheduler] Task {task['id']}: Executing tool '{tool_name}' with params {tool_params}")
+        result = tool.execute(tool_params)
+        
+        # Get result content
+        if hasattr(result, 'result'):
+            content = result.result
+        else:
+            content = str(result)
+        
+        # Add prefix if specified
+        if result_prefix:
+            content = f"{result_prefix}\n\n{content}"
+        
+        # Send result as message
+        context = Context(ContextType.TEXT, content)
+        context["receiver"] = receiver
+        context["isgroup"] = is_group
+        context["session_id"] = receiver
+        
+        # Channel-specific context setup
+        if channel_type == "web":
+            # Web channel needs request_id
+            import uuid
+            request_id = f"scheduler_{task['id']}_{uuid.uuid4().hex[:8]}"
+            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}")
+        
+        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}")
+
+
+def _execute_skill_call(task: dict, agent_bridge):
+    """
+    Execute a skill_call action by asking Agent to run the skill
+    
+    Args:
+        task: Task dictionary
+        agent_bridge: AgentBridge instance
+    """
+    try:
+        action = task.get("action", {})
+        # Support both old and new field names
+        skill_name = action.get("call_name") or action.get("skill_name")
+        skill_params = action.get("call_params") or action.get("skill_params", {})
+        result_prefix = action.get("result_prefix", "")
+        receiver = action.get("receiver")
+        is_group = action.get("isgroup", False)
+        channel_type = action.get("channel_type", "unknown")
+        
+        if not skill_name:
+            logger.error(f"[Scheduler] Task {task['id']}: No skill_name specified")
+            return
+        
+        if not receiver:
+            logger.error(f"[Scheduler] Task {task['id']}: No receiver specified")
+            return
+        
+        logger.info(f"[Scheduler] Task {task['id']}: Executing skill '{skill_name}' with params {skill_params}")
+        
+        # Build a natural language query for the Agent to execute the skill
+        # Format: "Use skill-name to do something with params"
+        param_str = ", ".join([f"{k}={v}" for k, v in skill_params.items()])
+        query = f"Use {skill_name} skill"
+        if param_str:
+            query += f" with {param_str}"
+        
+        # Create context for Agent
+        context = Context(ContextType.TEXT, query)
+        context["receiver"] = receiver
+        context["isgroup"] = is_group
+        context["session_id"] = receiver
+        
+        # Channel-specific setup
+        if channel_type == "web":
+            import uuid
+            request_id = f"scheduler_{task['id']}_{uuid.uuid4().hex[:8]}"
+            context["request_id"] = request_id
+        elif channel_type == "feishu":
+            context["receive_id_type"] = "chat_id" if is_group else "open_id"
+            context["msg"] = None
+        
+        # Use Agent to execute the skill
+        try:
+            reply = agent_bridge.agent_reply(query, context=context, on_event=None, clear_history=True)
+            
+            if reply and reply.content:
+                content = reply.content
+                
+                # Add prefix if specified
+                if result_prefix:
+                    content = f"{result_prefix}\n\n{content}"
+                
+                logger.info(f"[Scheduler] Task {task['id']} executed: skill result sent to {receiver}")
+            else:
+                logger.error(f"[Scheduler] Task {task['id']}: No result from skill execution")
+                
+        except Exception as e:
+            logger.error(f"[Scheduler] Failed to execute skill via Agent: {e}")
+            import traceback
+            logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+            
+    except Exception as e:
+        logger.error(f"[Scheduler] Error in _execute_skill_call: {e}")
+        import traceback
+        logger.error(f"[Scheduler] Traceback: {traceback.format_exc()}")
+
+
+def attach_scheduler_to_tool(tool, context: Context = None):
+    """
+    Attach scheduler components to a SchedulerTool instance
+    
+    Args:
+        tool: SchedulerTool instance
+        context: Current context (optional)
+    """
+    if _task_store:
+        tool.task_store = _task_store
+    
+    if context:
+        tool.current_context = context
+        
+        # Also set channel_type from config
+        channel_type = conf().get("channel_type", "unknown")
+        if not tool.config:
+            tool.config = {}
+        tool.config["channel_type"] = channel_type
--- a/agent/tools/scheduler/scheduler_service.py
+++ b/agent/tools/scheduler/scheduler_service.py
@@ -0,0 +1,220 @@
+"""
+Background scheduler service for executing scheduled tasks
+"""
+
+import time
+import threading
+from datetime import datetime, timedelta
+from typing import Callable, Optional
+from croniter import croniter
+from common.log import logger
+
+
+class SchedulerService:
+    """
+    Background service that executes scheduled tasks
+    """
+    
+    def __init__(self, task_store, execute_callback: Callable):
+        """
+        Initialize scheduler service
+        
+        Args:
+            task_store: TaskStore instance
+            execute_callback: Function to call when executing a task
+        """
+        self.task_store = task_store
+        self.execute_callback = execute_callback
+        self.running = False
+        self.thread = None
+        self._lock = threading.Lock()
+    
+    def start(self):
+        """Start the scheduler service"""
+        with self._lock:
+            if self.running:
+                logger.warning("[Scheduler] Service already running")
+                return
+            
+            self.running = True
+            self.thread = threading.Thread(target=self._run_loop, daemon=True)
+            self.thread.start()
+            logger.debug("[Scheduler] Service started")
+    
+    def stop(self):
+        """Stop the scheduler service"""
+        with self._lock:
+            if not self.running:
+                return
+            
+            self.running = False
+            if self.thread:
+                self.thread.join(timeout=5)
+            logger.info("[Scheduler] Service stopped")
+    
+    def _run_loop(self):
+        """Main scheduler loop"""
+        logger.debug("[Scheduler] Scheduler loop started")
+        
+        while self.running:
+            try:
+                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):
+        """Check for due tasks and execute them"""
+        now = datetime.now()
+        tasks = self.task_store.list_tasks(enabled_only=True)
+        
+        for task in tasks:
+            try:
+                # Check if task is due
+                if self._is_task_due(task, now):
+                    logger.info(f"[Scheduler] Executing task: {task['id']} - {task['name']}")
+                    self._execute_task(task)
+                    
+                    # Update next run time
+                    next_run = self._calculate_next_run(task, now)
+                    if next_run:
+                        self.task_store.update_task(task['id'], {
+                            "next_run_at": next_run.isoformat(),
+                            "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']}")
+            except Exception as e:
+                logger.error(f"[Scheduler] Error processing task {task.get('id')}: {e}")
+    
+    def _is_task_due(self, task: dict, now: datetime) -> bool:
+        """
+        Check if a task is due to run
+        
+        Args:
+            task: Task dictionary
+            now: Current datetime
+            
+        Returns:
+            True if task should run now
+        """
+        next_run_str = task.get("next_run_at")
+        if not next_run_str:
+            # Calculate initial next_run_at
+            next_run = self._calculate_next_run(task, now)
+            if next_run:
+                self.task_store.update_task(task['id'], {
+                    "next_run_at": next_run.isoformat()
+                })
+                return False
+            return False
+        
+        try:
+            next_run = datetime.fromisoformat(next_run_str)
+            
+            # Check if task is overdue (e.g., service restart)
+            if next_run < now:
+                time_diff = (now - next_run).total_seconds()
+                
+                # If overdue by more than 5 minutes, skip this run and schedule next
+                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
+                    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")
+                        return False
+                    
+                    # For recurring tasks, calculate next run from now
+                    next_next_run = self._calculate_next_run(task, now)
+                    if next_next_run:
+                        self.task_store.update_task(task['id'], {
+                            "next_run_at": next_next_run.isoformat()
+                        })
+                        logger.info(f"[Scheduler] Rescheduled task {task['id']} to {next_next_run}")
+                    return False
+            
+            return now >= next_run
+        except:
+            return False
+    
+    def _calculate_next_run(self, task: dict, from_time: datetime) -> Optional[datetime]:
+        """
+        Calculate next run time for a task
+        
+        Args:
+            task: Task dictionary
+            from_time: Calculate from this time
+            
+        Returns:
+            Next run datetime or None for one-time tasks
+        """
+        schedule = task.get("schedule", {})
+        schedule_type = schedule.get("type")
+        
+        if schedule_type == "cron":
+            # Cron expression
+            expression = schedule.get("expression")
+            if not expression:
+                return None
+            
+            try:
+                cron = croniter(expression, from_time)
+                return cron.get_next(datetime)
+            except Exception as e:
+                logger.error(f"[Scheduler] Invalid cron expression '{expression}': {e}")
+                return None
+        
+        elif schedule_type == "interval":
+            # Interval in seconds
+            seconds = schedule.get("seconds", 0)
+            if seconds <= 0:
+                return None
+            return from_time + timedelta(seconds=seconds)
+        
+        elif schedule_type == "once":
+            # One-time task at specific time
+            run_at_str = schedule.get("run_at")
+            if not run_at_str:
+                return None
+            
+            try:
+                run_at = datetime.fromisoformat(run_at_str)
+                # Only return if in the future
+                if run_at > from_time:
+                    return run_at
+            except:
+                pass
+            return None
+        
+        return None
+    
+    def _execute_task(self, task: dict):
+        """
+        Execute a task
+        
+        Args:
+            task: Task dictionary
+        """
+        try:
+            # Call the execute callback
+            self.execute_callback(task)
+        except Exception as e:
+            logger.error(f"[Scheduler] Error executing task {task['id']}: {e}")
+            # Update task with error
+            self.task_store.update_task(task['id'], {
+                "last_error": str(e),
+                "last_error_at": datetime.now().isoformat()
+            })
--- a/agent/tools/scheduler/scheduler_tool.py
+++ b/agent/tools/scheduler/scheduler_tool.py
@@ -0,0 +1,442 @@
+"""
+Scheduler tool for creating and managing scheduled tasks
+"""
+
+import uuid
+from datetime import datetime
+from typing import Any, Dict, Optional
+from croniter import croniter
+
+from agent.tools.base_tool import BaseTool, ToolResult
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+
+
+class SchedulerTool(BaseTool):
+    """
+    Tool for managing scheduled tasks (reminders, notifications, etc.)
+    """
+    
+    name: str = "scheduler"
+    description: str = (
+        "创建、查询和管理定时任务。支持固定消息和AI任务两种类型。\n\n"
+        "使用方法：\n"
+        "- 创建：action='create', name='任务名', message/ai_task='内容', schedule_type='once/interval/cron', schedule_value='...'\n"
+        "- 查询：action='list' / action='get', task_id='任务ID'\n"
+        "- 管理：action='delete/enable/disable', task_id='任务ID'\n\n"
+        "调度类型：\n"
+        "- once: 一次性任务，支持相对时间(+5s,+10m,+1h,+1d)或ISO时间\n"
+        "- interval: 固定间隔(秒)，如3600表示每小时\n"
+        "- cron: cron表达式，如'0 8 * * *'表示每天8点\n\n"
+        "注意：'X秒后'用once+相对时间，'每X秒'用interval"
+    )
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "enum": ["create", "list", "get", "delete", "enable", "disable"],
+                "description": "操作类型: create(创建), list(列表), get(查询), delete(删除), enable(启用), disable(禁用)"
+            },
+            "task_id": {
+                "type": "string",
+                "description": "任务ID (用于 get/delete/enable/disable 操作)"
+            },
+            "name": {
+                "type": "string",
+                "description": "任务名称 (用于 create 操作)"
+            },
+            "message": {
+                "type": "string",
+                "description": "固定消息内容 (与ai_task二选一)"
+            },
+            "ai_task": {
+                "type": "string",
+                "description": "AI任务描述 (与message二选一)，如'搜索今日新闻'、'查询天气'"
+            },
+            "schedule_type": {
+                "type": "string",
+                "enum": ["cron", "interval", "once"],
+                "description": "调度类型 (用于 create 操作): cron(cron表达式), interval(固定间隔秒数), once(一次性)"
+            },
+            "schedule_value": {
+                "type": "string",
+                "description": "调度值: cron表达式/间隔秒数/时间(+5s,+10m,+1h或ISO格式)"
+            }
+        },
+        "required": ["action"]
+    }
+    
+    def __init__(self, config: dict = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Will be set by agent bridge
+        self.task_store = None
+        self.current_context = None
+    
+    def execute(self, params: dict) -> ToolResult:
+        """
+        Execute scheduler operations
+        
+        Args:
+            params: Dictionary containing:
+                - action: Operation type (create/list/get/delete/enable/disable)
+                - Other parameters depending on action
+            
+        Returns:
+            ToolResult object
+        """
+        # Extract parameters
+        action = params.get("action")
+        kwargs = params
+        
+        if not self.task_store:
+            return ToolResult.fail("错误: 定时任务系统未初始化")
+        
+        try:
+            if action == "create":
+                result = self._create_task(**kwargs)
+                return ToolResult.success(result)
+            elif action == "list":
+                result = self._list_tasks(**kwargs)
+                return ToolResult.success(result)
+            elif action == "get":
+                result = self._get_task(**kwargs)
+                return ToolResult.success(result)
+            elif action == "delete":
+                result = self._delete_task(**kwargs)
+                return ToolResult.success(result)
+            elif action == "enable":
+                result = self._enable_task(**kwargs)
+                return ToolResult.success(result)
+            elif action == "disable":
+                result = self._disable_task(**kwargs)
+                return ToolResult.success(result)
+            else:
+                return ToolResult.fail(f"未知操作: {action}")
+        except Exception as e:
+            logger.error(f"[SchedulerTool] Error: {e}")
+            return ToolResult.fail(f"操作失败: {str(e)}")
+    
+    def _create_task(self, **kwargs) -> str:
+        """Create a new scheduled task"""
+        name = kwargs.get("name")
+        message = kwargs.get("message")
+        ai_task = kwargs.get("ai_task")
+        schedule_type = kwargs.get("schedule_type")
+        schedule_value = kwargs.get("schedule_value")
+        
+        # Validate required fields
+        if not name:
+            return "错误: 缺少任务名称 (name)"
+        
+        # Check that exactly one of message/ai_task is provided
+        if not message and not ai_task:
+            return "错误: 必须提供 message（固定消息）或 ai_task（AI任务）之一"
+        if message and ai_task:
+            return "错误: message 和 ai_task 只能提供其中一个"
+        
+        if not schedule_type:
+            return "错误: 缺少调度类型 (schedule_type)"
+        if not schedule_value:
+            return "错误: 缺少调度值 (schedule_value)"
+        
+        # Validate schedule
+        schedule = self._parse_schedule(schedule_type, schedule_value)
+        if not schedule:
+            return f"错误: 无效的调度配置 - type: {schedule_type}, value: {schedule_value}"
+        
+        # Get context info for receiver
+        if not self.current_context:
+            return "错误: 无法获取当前对话上下文"
+        
+        context = self.current_context
+        
+        # Create task
+        task_id = str(uuid.uuid4())[:8]
+        
+        # Build action based on message or ai_task
+        if message:
+            action = {
+                "type": "send_message",
+                "content": message,
+                "receiver": context.get("receiver"),
+                "receiver_name": self._get_receiver_name(context),
+                "is_group": context.get("isgroup", False),
+                "channel_type": self.config.get("channel_type", "unknown")
+            }
+        else:  # ai_task
+            action = {
+                "type": "agent_task",
+                "task_description": ai_task,
+                "receiver": context.get("receiver"),
+                "receiver_name": self._get_receiver_name(context),
+                "is_group": context.get("isgroup", False),
+                "channel_type": self.config.get("channel_type", "unknown")
+            }
+        
+        # 针对钉钉单聊，额外存储 sender_staff_id
+        msg = context.kwargs.get("msg")
+        if msg and hasattr(msg, 'sender_staff_id') and not context.get("isgroup", False):
+            action["dingtalk_sender_staff_id"] = msg.sender_staff_id
+        
+        task_data = {
+            "id": task_id,
+            "name": name,
+            "enabled": True,
+            "created_at": datetime.now().isoformat(),
+            "updated_at": datetime.now().isoformat(),
+            "schedule": schedule,
+            "action": action
+        }
+        
+        # Calculate initial next_run_at
+        next_run = self._calculate_next_run(task_data)
+        if next_run:
+            task_data["next_run_at"] = next_run.isoformat()
+        
+        # Save task
+        self.task_store.add_task(task_data)
+        
+        # Format response
+        schedule_desc = self._format_schedule_description(schedule)
+        receiver_desc = task_data["action"]["receiver_name"] or task_data["action"]["receiver"]
+        
+        if message:
+            content_desc = f"💬 固定消息: {message}"
+        else:
+            content_desc = f"🤖 AI任务: {ai_task}"
+        
+        return (
+            f"✅ 定时任务创建成功\n\n"
+            f"📋 任务ID: {task_id}\n"
+            f"📝 名称: {name}\n"
+            f"⏰ 调度: {schedule_desc}\n"
+            f"👤 接收者: {receiver_desc}\n"
+            f"{content_desc}\n"
+            f"🕐 下次执行: {next_run.strftime('%Y-%m-%d %H:%M:%S') if next_run else '未知'}"
+        )
+    
+    def _list_tasks(self, **kwargs) -> str:
+        """List all tasks"""
+        tasks = self.task_store.list_tasks()
+        
+        if not tasks:
+            return "📋 暂无定时任务"
+        
+        lines = [f"📋 定时任务列表 (共 {len(tasks)} 个)\n"]
+        
+        for task in tasks:
+            status = "✅" if task.get("enabled", True) else "❌"
+            schedule_desc = self._format_schedule_description(task.get("schedule", {}))
+            next_run = task.get("next_run_at")
+            next_run_str = datetime.fromisoformat(next_run).strftime('%m-%d %H:%M') if next_run else "未知"
+            
+            lines.append(
+                f"{status} [{task['id']}] {task['name']}\n"
+                f"   ⏰ {schedule_desc} | 下次: {next_run_str}"
+            )
+        
+        return "\n".join(lines)
+    
+    def _get_task(self, **kwargs) -> str:
+        """Get task details"""
+        task_id = kwargs.get("task_id")
+        if not task_id:
+            return "错误: 缺少任务ID (task_id)"
+        
+        task = self.task_store.get_task(task_id)
+        if not task:
+            return f"错误: 任务 '{task_id}' 不存在"
+        
+        status = "启用" if task.get("enabled", True) else "禁用"
+        schedule_desc = self._format_schedule_description(task.get("schedule", {}))
+        action = task.get("action", {})
+        next_run = task.get("next_run_at")
+        next_run_str = datetime.fromisoformat(next_run).strftime('%Y-%m-%d %H:%M:%S') if next_run else "未知"
+        last_run = task.get("last_run_at")
+        last_run_str = datetime.fromisoformat(last_run).strftime('%Y-%m-%d %H:%M:%S') if last_run else "从未执行"
+        
+        return (
+            f"📋 任务详情\n\n"
+            f"ID: {task['id']}\n"
+            f"名称: {task['name']}\n"
+            f"状态: {status}\n"
+            f"调度: {schedule_desc}\n"
+            f"接收者: {action.get('receiver_name', action.get('receiver'))}\n"
+            f"消息: {action.get('content')}\n"
+            f"下次执行: {next_run_str}\n"
+            f"上次执行: {last_run_str}\n"
+            f"创建时间: {datetime.fromisoformat(task['created_at']).strftime('%Y-%m-%d %H:%M:%S')}"
+        )
+    
+    def _delete_task(self, **kwargs) -> str:
+        """Delete a task"""
+        task_id = kwargs.get("task_id")
+        if not task_id:
+            return "错误: 缺少任务ID (task_id)"
+        
+        task = self.task_store.get_task(task_id)
+        if not task:
+            return f"错误: 任务 '{task_id}' 不存在"
+        
+        self.task_store.delete_task(task_id)
+        return f"✅ 任务 '{task['name']}' ({task_id}) 已删除"
+    
+    def _enable_task(self, **kwargs) -> str:
+        """Enable a task"""
+        task_id = kwargs.get("task_id")
+        if not task_id:
+            return "错误: 缺少任务ID (task_id)"
+        
+        task = self.task_store.get_task(task_id)
+        if not task:
+            return f"错误: 任务 '{task_id}' 不存在"
+        
+        self.task_store.enable_task(task_id, True)
+        return f"✅ 任务 '{task['name']}' ({task_id}) 已启用"
+    
+    def _disable_task(self, **kwargs) -> str:
+        """Disable a task"""
+        task_id = kwargs.get("task_id")
+        if not task_id:
+            return "错误: 缺少任务ID (task_id)"
+        
+        task = self.task_store.get_task(task_id)
+        if not task:
+            return f"错误: 任务 '{task_id}' 不存在"
+        
+        self.task_store.enable_task(task_id, False)
+        return f"✅ 任务 '{task['name']}' ({task_id}) 已禁用"
+    
+    def _parse_schedule(self, schedule_type: str, schedule_value: str) -> Optional[dict]:
+        """Parse and validate schedule configuration"""
+        try:
+            if schedule_type == "cron":
+                # Validate cron expression
+                croniter(schedule_value)
+                return {"type": "cron", "expression": schedule_value}
+            
+            elif schedule_type == "interval":
+                # Parse interval in seconds
+                seconds = int(schedule_value)
+                if seconds <= 0:
+                    return None
+                return {"type": "interval", "seconds": seconds}
+            
+            elif schedule_type == "once":
+                # Parse datetime - support both relative and absolute time
+                
+                # Check if it's relative time (e.g., "+5s", "+10m", "+1h", "+1d")
+                if schedule_value.startswith("+"):
+                    import re
+                    match = re.match(r'\+(\d+)([smhd])', schedule_value)
+                    if match:
+                        amount = int(match.group(1))
+                        unit = match.group(2)
+                        
+                        from datetime import timedelta
+                        now = datetime.now()
+                        
+                        if unit == 's':  # seconds
+                            target_time = now + timedelta(seconds=amount)
+                        elif unit == 'm':  # minutes
+                            target_time = now + timedelta(minutes=amount)
+                        elif unit == 'h':  # hours
+                            target_time = now + timedelta(hours=amount)
+                        elif unit == 'd':  # days
+                            target_time = now + timedelta(days=amount)
+                        else:
+                            return None
+                        
+                        return {"type": "once", "run_at": target_time.isoformat()}
+                    else:
+                        logger.error(f"[SchedulerTool] Invalid relative time format: {schedule_value}")
+                        return None
+                else:
+                    # Absolute time in ISO format
+                    datetime.fromisoformat(schedule_value)
+                    return {"type": "once", "run_at": schedule_value}
+            
+        except Exception as e:
+            logger.error(f"[SchedulerTool] Invalid schedule: {e}")
+            return None
+        
+        return None
+    
+    def _calculate_next_run(self, task: dict) -> Optional[datetime]:
+        """Calculate next run time for a task"""
+        schedule = task.get("schedule", {})
+        schedule_type = schedule.get("type")
+        now = datetime.now()
+        
+        if schedule_type == "cron":
+            expression = schedule.get("expression")
+            cron = croniter(expression, now)
+            return cron.get_next(datetime)
+        
+        elif schedule_type == "interval":
+            seconds = schedule.get("seconds", 0)
+            from datetime import timedelta
+            return now + timedelta(seconds=seconds)
+        
+        elif schedule_type == "once":
+            run_at_str = schedule.get("run_at")
+            return datetime.fromisoformat(run_at_str)
+        
+        return None
+    
+    def _format_schedule_description(self, schedule: dict) -> str:
+        """Format schedule as human-readable description"""
+        schedule_type = schedule.get("type")
+        
+        if schedule_type == "cron":
+            expr = schedule.get("expression", "")
+            # Try to provide friendly description
+            if expr == "0 9 * * *":
+                return "每天 9:00"
+            elif expr == "0 */1 * * *":
+                return "每小时"
+            elif expr == "*/30 * * * *":
+                return "每30分钟"
+            else:
+                return f"Cron: {expr}"
+        
+        elif schedule_type == "interval":
+            seconds = schedule.get("seconds", 0)
+            if seconds >= 86400:
+                days = seconds // 86400
+                return f"每 {days} 天"
+            elif seconds >= 3600:
+                hours = seconds // 3600
+                return f"每 {hours} 小时"
+            elif seconds >= 60:
+                minutes = seconds // 60
+                return f"每 {minutes} 分钟"
+            else:
+                return f"每 {seconds} 秒"
+        
+        elif schedule_type == "once":
+            run_at = schedule.get("run_at", "")
+            try:
+                dt = datetime.fromisoformat(run_at)
+                return f"一次性 ({dt.strftime('%Y-%m-%d %H:%M')})"
+            except:
+                return "一次性"
+        
+        return "未知"
+    
+    def _get_receiver_name(self, context: Context) -> str:
+        """Get receiver name from context"""
+        try:
+            msg = context.get("msg")
+            if msg:
+                if context.get("isgroup"):
+                    return msg.other_user_nickname or "群聊"
+                else:
+                    return msg.from_user_nickname or "用户"
+        except:
+            pass
+        return "未知"
--- a/agent/tools/scheduler/task_store.py
+++ b/agent/tools/scheduler/task_store.py
@@ -0,0 +1,200 @@
+"""
+Task storage management for scheduler
+"""
+
+import json
+import os
+import threading
+from datetime import datetime
+from typing import Dict, List, Optional
+from pathlib import Path
+
+
+class TaskStore:
+    """
+    Manages persistent storage of scheduled tasks
+    """
+    
+    def __init__(self, store_path: str = None):
+        """
+        Initialize task store
+        
+        Args:
+            store_path: Path to tasks.json file. Defaults to ~/cow/scheduler/tasks.json
+        """
+        if store_path is None:
+            # Default to ~/cow/scheduler/tasks.json
+            home = os.path.expanduser("~")
+            store_path = os.path.join(home, "cow", "scheduler", "tasks.json")
+        
+        self.store_path = store_path
+        self.lock = threading.Lock()
+        self._ensure_store_dir()
+    
+    def _ensure_store_dir(self):
+        """Ensure the storage directory exists"""
+        store_dir = os.path.dirname(self.store_path)
+        os.makedirs(store_dir, exist_ok=True)
+    
+    def load_tasks(self) -> Dict[str, dict]:
+        """
+        Load all tasks from storage
+        
+        Returns:
+            Dictionary of task_id -> task_data
+        """
+        with self.lock:
+            if not os.path.exists(self.store_path):
+                return {}
+            
+            try:
+                with open(self.store_path, 'r', encoding='utf-8') as f:
+                    data = json.load(f)
+                    return data.get("tasks", {})
+            except Exception as e:
+                print(f"Error loading tasks: {e}")
+                return {}
+    
+    def save_tasks(self, tasks: Dict[str, dict]):
+        """
+        Save all tasks to storage
+        
+        Args:
+            tasks: Dictionary of task_id -> task_data
+        """
+        with self.lock:
+            try:
+                # Create backup
+                if os.path.exists(self.store_path):
+                    backup_path = f"{self.store_path}.bak"
+                    try:
+                        with open(self.store_path, 'r') as src:
+                            with open(backup_path, 'w') as dst:
+                                dst.write(src.read())
+                    except:
+                        pass
+                
+                # Save tasks
+                data = {
+                    "version": 1,
+                    "updated_at": datetime.now().isoformat(),
+                    "tasks": tasks
+                }
+                
+                with open(self.store_path, 'w', encoding='utf-8') as f:
+                    json.dump(data, f, ensure_ascii=False, indent=2)
+            except Exception as e:
+                print(f"Error saving tasks: {e}")
+                raise
+    
+    def add_task(self, task: dict) -> bool:
+        """
+        Add a new task
+        
+        Args:
+            task: Task data dictionary
+            
+        Returns:
+            True if successful
+        """
+        tasks = self.load_tasks()
+        task_id = task.get("id")
+        
+        if not task_id:
+            raise ValueError("Task must have an 'id' field")
+        
+        if task_id in tasks:
+            raise ValueError(f"Task with id '{task_id}' already exists")
+        
+        tasks[task_id] = task
+        self.save_tasks(tasks)
+        return True
+    
+    def update_task(self, task_id: str, updates: dict) -> bool:
+        """
+        Update an existing task
+        
+        Args:
+            task_id: Task ID
+            updates: Dictionary of fields to update
+            
+        Returns:
+            True if successful
+        """
+        tasks = self.load_tasks()
+        
+        if task_id not in tasks:
+            raise ValueError(f"Task '{task_id}' not found")
+        
+        # Update fields
+        tasks[task_id].update(updates)
+        tasks[task_id]["updated_at"] = datetime.now().isoformat()
+        
+        self.save_tasks(tasks)
+        return True
+    
+    def delete_task(self, task_id: str) -> bool:
+        """
+        Delete a task
+        
+        Args:
+            task_id: Task ID
+            
+        Returns:
+            True if successful
+        """
+        tasks = self.load_tasks()
+        
+        if task_id not in tasks:
+            raise ValueError(f"Task '{task_id}' not found")
+        
+        del tasks[task_id]
+        self.save_tasks(tasks)
+        return True
+    
+    def get_task(self, task_id: str) -> Optional[dict]:
+        """
+        Get a specific task
+        
+        Args:
+            task_id: Task ID
+            
+        Returns:
+            Task data or None if not found
+        """
+        tasks = self.load_tasks()
+        return tasks.get(task_id)
+    
+    def list_tasks(self, enabled_only: bool = False) -> List[dict]:
+        """
+        List all tasks
+        
+        Args:
+            enabled_only: If True, only return enabled tasks
+            
+        Returns:
+            List of task dictionaries
+        """
+        tasks = self.load_tasks()
+        task_list = list(tasks.values())
+        
+        if enabled_only:
+            task_list = [t for t in task_list if t.get("enabled", True)]
+        
+        # Sort by next_run_at
+        task_list.sort(key=lambda t: t.get("next_run_at", float('inf')))
+        
+        return task_list
+    
+    def enable_task(self, task_id: str, enabled: bool = True) -> bool:
+        """
+        Enable or disable a task
+        
+        Args:
+            task_id: Task ID
+            enabled: True to enable, False to disable
+            
+        Returns:
+            True if successful
+        """
+        return self.update_task(task_id, {"enabled": enabled})
--- a/agent/tools/send/init.py
+++ b/agent/tools/send/init.py
@@ -0,0 +1,3 @@
+from .send import Send
+
+__all__ = ['Send']
--- a/agent/tools/send/send.py
+++ b/agent/tools/send/send.py
@@ -0,0 +1,159 @@
+"""
+Send tool - Send files to the user
+"""
+
+import os
+from typing import Dict, Any
+from pathlib import Path
+
+from agent.tools.base_tool import BaseTool, ToolResult
+
+
+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."
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Path to the file to send. Can be absolute path or relative to workspace."
+            },
+            "message": {
+                "type": "string",
+                "description": "Optional message to accompany the file"
+            }
+        },
+        "required": ["path"]
+    }
+    
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        
+        # Supported file types
+        self.image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp', '.svg', '.ico'}
+        self.video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.webm', '.m4v'}
+        self.audio_extensions = {'.mp3', '.wav', '.ogg', '.m4a', '.flac', '.aac', '.wma'}
+        self.document_extensions = {'.pdf', '.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx', '.txt', '.md'}
+    
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        """
+        Execute file send operation
+        
+        :param args: Contains file path and optional message
+        :return: File metadata for channel to send
+        """
+        path = args.get("path", "").strip()
+        message = args.get("message", "")
+        
+        if not path:
+            return ToolResult.fail("Error: path parameter is required")
+        
+        # Resolve path
+        absolute_path = self._resolve_path(path)
+        
+        # Check if file exists
+        if not os.path.exists(absolute_path):
+            return ToolResult.fail(f"Error: File not found: {path}")
+        
+        # Check if readable
+        if not os.access(absolute_path, os.R_OK):
+            return ToolResult.fail(f"Error: File is not readable: {path}")
+        
+        # Get file info
+        file_ext = Path(absolute_path).suffix.lower()
+        file_size = os.path.getsize(absolute_path)
+        file_name = Path(absolute_path).name
+        
+        # Determine file type
+        if file_ext in self.image_extensions:
+            file_type = "image"
+            mime_type = self._get_image_mime_type(file_ext)
+        elif file_ext in self.video_extensions:
+            file_type = "video"
+            mime_type = self._get_video_mime_type(file_ext)
+        elif file_ext in self.audio_extensions:
+            file_type = "audio"
+            mime_type = self._get_audio_mime_type(file_ext)
+        elif file_ext in self.document_extensions:
+            file_type = "document"
+            mime_type = self._get_document_mime_type(file_ext)
+        else:
+            file_type = "file"
+            mime_type = "application/octet-stream"
+        
+        # Return file_to_send metadata
+        result = {
+            "type": "file_to_send",
+            "file_type": file_type,
+            "path": absolute_path,
+            "file_name": file_name,
+            "mime_type": mime_type,
+            "size": file_size,
+            "size_formatted": self._format_size(file_size),
+            "message": message or f"正在发送 {file_name}"
+        }
+        
+        return ToolResult.success(result)
+    
+    def _resolve_path(self, path: str) -> str:
+        """Resolve path to absolute path"""
+        path = os.path.expanduser(path)
+        if os.path.isabs(path):
+            return path
+        return os.path.abspath(os.path.join(self.cwd, path))
+    
+    def _get_image_mime_type(self, ext: str) -> str:
+        """Get MIME type for image"""
+        mime_map = {
+            '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg',
+            '.png': 'image/png', '.gif': 'image/gif',
+            '.webp': 'image/webp', '.bmp': 'image/bmp',
+            '.svg': 'image/svg+xml', '.ico': 'image/x-icon'
+        }
+        return mime_map.get(ext, 'image/jpeg')
+    
+    def _get_video_mime_type(self, ext: str) -> str:
+        """Get MIME type for video"""
+        mime_map = {
+            '.mp4': 'video/mp4', '.avi': 'video/x-msvideo',
+            '.mov': 'video/quicktime', '.mkv': 'video/x-matroska',
+            '.webm': 'video/webm', '.flv': 'video/x-flv'
+        }
+        return mime_map.get(ext, 'video/mp4')
+    
+    def _get_audio_mime_type(self, ext: str) -> str:
+        """Get MIME type for audio"""
+        mime_map = {
+            '.mp3': 'audio/mpeg', '.wav': 'audio/wav',
+            '.ogg': 'audio/ogg', '.m4a': 'audio/mp4',
+            '.flac': 'audio/flac', '.aac': 'audio/aac'
+        }
+        return mime_map.get(ext, 'audio/mpeg')
+    
+    def _get_document_mime_type(self, ext: str) -> str:
+        """Get MIME type for document"""
+        mime_map = {
+            '.pdf': 'application/pdf',
+            '.doc': 'application/msword',
+            '.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+            '.xls': 'application/vnd.ms-excel',
+            '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+            '.ppt': 'application/vnd.ms-powerpoint',
+            '.pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+            '.txt': 'text/plain',
+            '.md': 'text/markdown'
+        }
+        return mime_map.get(ext, 'application/octet-stream')
+    
+    def _format_size(self, size_bytes: int) -> str:
+        """Format file size in human-readable format"""
+        for unit in ['B', 'KB', 'MB', 'GB']:
+            if size_bytes < 1024.0:
+                return f"{size_bytes:.1f}{unit}"
+            size_bytes /= 1024.0
+        return f"{size_bytes:.1f}TB"
--- a/agent/tools/tool_manager.py
+++ b/agent/tools/tool_manager.py
@@ -0,0 +1,248 @@
+import importlib
+import importlib.util
+from pathlib import Path
+from typing import Dict, Any, Type
+from agent.tools.base_tool import BaseTool
+from common.log import logger
+from config import conf
+
+
+class ToolManager:
+    """
+    Tool manager for managing tools.
+    """
+    _instance = None
+
+    def __new__(cls):
+        """Singleton pattern to ensure only one instance of ToolManager exists."""
+        if cls._instance is None:
+            cls._instance = super(ToolManager, cls).__new__(cls)
+            cls._instance.tool_classes = {}  # Store tool classes instead of instances
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        # Initialize only once
+        if not hasattr(self, 'tool_classes'):
+            self.tool_classes = {}  # Dictionary to store tool classes
+
+    def load_tools(self, tools_dir: str = "", config_dict=None):
+        """
+        Load tools from both directory and configuration.
+
+        :param tools_dir: Directory to scan for tool modules
+        """
+        if tools_dir:
+            self._load_tools_from_directory(tools_dir)
+            self._configure_tools_from_config()
+        else:
+            self._load_tools_from_init()
+            self._configure_tools_from_config(config_dict)
+
+    def _load_tools_from_init(self) -> bool:
+        """
+        Load tool classes from tools.__init__.__all__
+
+        :return: True if tools were loaded, False otherwise
+        """
+        try:
+            # Try to import the tools package
+            tools_package = importlib.import_module("agent.tools")
+
+            # Check if __all__ is defined
+            if hasattr(tools_package, "__all__"):
+                tool_classes = tools_package.__all__
+
+                # Import each tool class directly from the tools package
+                for class_name in tool_classes:
+                    try:
+                        # Skip base classes
+                        if class_name in ["BaseTool", "ToolManager"]:
+                            continue
+
+                        # Get the class directly from the tools package
+                        if hasattr(tools_package, class_name):
+                            cls = getattr(tools_package, class_name)
+
+                            if (
+                                    isinstance(cls, type)
+                                    and issubclass(cls, BaseTool)
+                                    and cls != BaseTool
+                            ):
+                                try:
+                                    # Skip memory tools (they need special initialization with memory_manager)
+                                    if class_name in ["MemorySearchTool", "MemoryGetTool"]:
+                                        logger.debug(f"Skipped tool {class_name} (requires memory_manager)")
+                                        continue
+                                    
+                                    # Create a temporary instance to get the name
+                                    temp_instance = cls()
+                                    tool_name = temp_instance.name
+                                    # Store the class, not the instance
+                                    self.tool_classes[tool_name] = cls
+                                    logger.debug(f"Loaded tool: {tool_name} from class {class_name}")
+                                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:
+                                        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"    playwright install chromium"
+                                        )
+                                    elif "markdownify" in error_msg:
+                                        logger.warning(
+                                            f"[ToolManager] {cls.__name__} not loaded - missing markdownify.\n"
+                                            f"  Install with: pip install markdownify"
+                                        )
+                                    else:
+                                        logger.warning(f"[ToolManager] {cls.__name__} not loaded due to missing dependency: {error_msg}")
+                                except Exception as e:
+                                    logger.error(f"Error initializing tool class {cls.__name__}: {e}")
+                    except Exception as e:
+                        logger.error(f"Error importing class {class_name}: {e}")
+
+                return len(self.tool_classes) > 0
+            return False
+        except ImportError:
+            logger.warning("Could not import agent.tools package")
+            return False
+        except Exception as e:
+            logger.error(f"Error loading tools from __init__.__all__: {e}")
+            return False
+
+    def _load_tools_from_directory(self, tools_dir: str):
+        """Dynamically load tool classes from directory"""
+        tools_path = Path(tools_dir)
+
+        # Traverse all .py files
+        for py_file in tools_path.rglob("*.py"):
+            # Skip initialization files and base tool files
+            if py_file.name in ["__init__.py", "base_tool.py", "tool_manager.py"]:
+                continue
+
+            # Get module name
+            module_name = py_file.stem
+
+            try:
+                # Load module directly from file
+                spec = importlib.util.spec_from_file_location(module_name, py_file)
+                if spec and spec.loader:
+                    module = importlib.util.module_from_spec(spec)
+                    spec.loader.exec_module(module)
+
+                    # Find tool classes in the module
+                    for attr_name in dir(module):
+                        cls = getattr(module, attr_name)
+                        if (
+                                isinstance(cls, type)
+                                and issubclass(cls, BaseTool)
+                                and cls != BaseTool
+                        ):
+                            try:
+                                # Skip memory tools (they need special initialization with memory_manager)
+                                if attr_name in ["MemorySearchTool", "MemoryGetTool"]:
+                                    logger.debug(f"Skipped tool {attr_name} (requires memory_manager)")
+                                    continue
+                                
+                                # Create a temporary instance to get the name
+                                temp_instance = cls()
+                                tool_name = temp_instance.name
+                                # Store the class, not the instance
+                                self.tool_classes[tool_name] = cls
+                            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:
+                                    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"    playwright install chromium"
+                                    )
+                                elif "markdownify" in error_msg:
+                                    logger.warning(
+                                        f"[ToolManager] {cls.__name__} not loaded - missing markdownify.\n"
+                                        f"  Install with: pip install markdownify"
+                                    )
+                                else:
+                                    logger.warning(f"[ToolManager] {cls.__name__} not loaded due to missing dependency: {error_msg}")
+                            except Exception as e:
+                                logger.error(f"Error initializing tool class {cls.__name__}: {e}")
+            except Exception as e:
+                print(f"Error importing module {py_file}: {e}")
+
+    def _configure_tools_from_config(self, config_dict=None):
+        """Configure tool classes based on configuration file"""
+        try:
+            # Get tools configuration
+            tools_config = config_dict or conf().get("tools", {})
+
+            # Record tools that are configured but not loaded
+            missing_tools = []
+
+            # Store configurations for later use when instantiating
+            self.tool_configs = tools_config
+
+            # Check which configured tools are missing
+            for tool_name in tools_config:
+                if tool_name not in self.tool_classes:
+                    missing_tools.append(tool_name)
+
+            # If there are missing tools, record warnings
+            if missing_tools:
+                for tool_name in missing_tools:
+                    if tool_name == "browser":
+                        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"    playwright install chromium"
+                        )
+                    elif tool_name == "google_search":
+                        logger.warning(
+                            f"[ToolManager] Google Search tool is configured but may need API key.\n"
+                            f"  Get API key from: https://serper.dev\n"
+                            f"  Configure in config.json: tools.google_search.api_key"
+                        )
+                    else:
+                        logger.warning(f"[ToolManager] Tool '{tool_name}' is configured but could not be loaded.")
+
+        except Exception as e:
+            logger.error(f"Error configuring tools from config: {e}")
+
+    def create_tool(self, name: str) -> BaseTool:
+        """
+        Get a new instance of a tool by name.
+
+        :param name: The name of the tool to get.
+        :return: A new instance of the tool or None if not found.
+        """
+        tool_class = self.tool_classes.get(name)
+        if tool_class:
+            # Create a new instance
+            tool_instance = tool_class()
+
+            # Apply configuration if available
+            if hasattr(self, 'tool_configs') and name in self.tool_configs:
+                tool_instance.config = self.tool_configs[name]
+
+            return tool_instance
+        return None
+
+    def list_tools(self) -> dict:
+        """
+        Get information about all loaded tools.
+
+        :return: A dictionary with tool information.
+        """
+        result = {}
+        for name, tool_class in self.tool_classes.items():
+            # Create a temporary instance to get schema
+            temp_instance = tool_class()
+            result[name] = {
+                "description": temp_instance.description,
+                "parameters": temp_instance.get_json_schema()
+            }
+        return result
--- a/agent/tools/utils/init.py
+++ b/agent/tools/utils/init.py
@@ -0,0 +1,40 @@
+from .truncate import (
+    truncate_head,
+    truncate_tail,
+    truncate_line,
+    format_size,
+    TruncationResult,
+    DEFAULT_MAX_LINES,
+    DEFAULT_MAX_BYTES,
+    GREP_MAX_LINE_LENGTH
+)
+
+from .diff import (
+    strip_bom,
+    detect_line_ending,
+    normalize_to_lf,
+    restore_line_endings,
+    normalize_for_fuzzy_match,
+    fuzzy_find_text,
+    generate_diff_string,
+    FuzzyMatchResult
+)
+
+__all__ = [
+    'truncate_head',
+    'truncate_tail',
+    'truncate_line',
+    'format_size',
+    'TruncationResult',
+    'DEFAULT_MAX_LINES',
+    'DEFAULT_MAX_BYTES',
+    'GREP_MAX_LINE_LENGTH',
+    'strip_bom',
+    'detect_line_ending',
+    'normalize_to_lf',
+    'restore_line_endings',
+    'normalize_for_fuzzy_match',
+    'fuzzy_find_text',
+    'generate_diff_string',
+    'FuzzyMatchResult'
+]
--- a/agent/tools/utils/diff.py
+++ b/agent/tools/utils/diff.py
@@ -0,0 +1,167 @@
+"""
+Diff tools for file editing
+Provides fuzzy matching and diff generation functionality
+"""
+
+import difflib
+import re
+from typing import Optional, Tuple
+
+
+def strip_bom(text: str) -> Tuple[str, str]:
+    """
+    Remove BOM (Byte Order Mark)
+    
+    :param text: Original text
+    :return: (BOM, text after removing BOM)
+    """
+    if text.startswith('\ufeff'):
+        return '\ufeff', text[1:]
+    return '', text
+
+
+def detect_line_ending(text: str) -> str:
+    """
+    Detect line ending type
+    
+    :param text: Text content
+    :return: Line ending type ('\r\n' or '\n')
+    """
+    if '\r\n' in text:
+        return '\r\n'
+    return '\n'
+
+
+def normalize_to_lf(text: str) -> str:
+    """
+    Normalize all line endings to LF (\n)
+    
+    :param text: Original text
+    :return: Normalized text
+    """
+    return text.replace('\r\n', '\n').replace('\r', '\n')
+
+
+def restore_line_endings(text: str, original_ending: str) -> str:
+    """
+    Restore original line endings
+    
+    :param text: LF normalized text
+    :param original_ending: Original line ending
+    :return: Text with restored line endings
+    """
+    if original_ending == '\r\n':
+        return text.replace('\n', '\r\n')
+    return text
+
+
+def normalize_for_fuzzy_match(text: str) -> str:
+    """
+    Normalize text for fuzzy matching
+    Remove excess whitespace but preserve basic structure
+    
+    :param text: Original text
+    :return: Normalized text
+    """
+    # Compress multiple spaces to one
+    text = re.sub(r'[ \t]+', ' ', text)
+    # Remove trailing spaces
+    text = re.sub(r' +\n', '\n', text)
+    # Remove leading spaces (but preserve indentation structure, only remove excess)
+    lines = text.split('\n')
+    normalized_lines = []
+    for line in lines:
+        # Preserve indentation but normalize to multiples of single spaces
+        stripped = line.lstrip()
+        if stripped:
+            indent_count = len(line) - len(stripped)
+            # Normalize indentation (convert tabs to spaces)
+            normalized_indent = ' ' * indent_count
+            normalized_lines.append(normalized_indent + stripped)
+        else:
+            normalized_lines.append('')
+    return '\n'.join(normalized_lines)
+
+
+class FuzzyMatchResult:
+    """Fuzzy match result"""
+    
+    def __init__(self, found: bool, index: int = -1, match_length: int = 0, content_for_replacement: str = ""):
+        self.found = found
+        self.index = index
+        self.match_length = match_length
+        self.content_for_replacement = content_for_replacement
+
+
+def fuzzy_find_text(content: str, old_text: str) -> FuzzyMatchResult:
+    """
+    Find text in content, try exact match first, then fuzzy match
+    
+    :param content: Content to search in
+    :param old_text: Text to find
+    :return: Match result
+    """
+    # First try exact match
+    index = content.find(old_text)
+    if index != -1:
+        return FuzzyMatchResult(
+            found=True,
+            index=index,
+            match_length=len(old_text),
+            content_for_replacement=content
+        )
+    
+    # Try fuzzy match
+    fuzzy_content = normalize_for_fuzzy_match(content)
+    fuzzy_old_text = normalize_for_fuzzy_match(old_text)
+    
+    index = fuzzy_content.find(fuzzy_old_text)
+    if index != -1:
+        # Fuzzy match successful, use normalized content for replacement
+        return FuzzyMatchResult(
+            found=True,
+            index=index,
+            match_length=len(fuzzy_old_text),
+            content_for_replacement=fuzzy_content
+        )
+    
+    # Not found
+    return FuzzyMatchResult(found=False)
+
+
+def generate_diff_string(old_content: str, new_content: str) -> dict:
+    """
+    Generate unified diff string
+    
+    :param old_content: Old content
+    :param new_content: New content
+    :return: Dictionary containing diff and first changed line number
+    """
+    old_lines = old_content.split('\n')
+    new_lines = new_content.split('\n')
+    
+    # Generate unified diff
+    diff_lines = list(difflib.unified_diff(
+        old_lines,
+        new_lines,
+        lineterm='',
+        fromfile='original',
+        tofile='modified'
+    ))
+    
+    # Find first changed line number
+    first_changed_line = None
+    for line in diff_lines:
+        if line.startswith('@@'):
+            # Parse @@ -1,3 +1,3 @@ format
+            match = re.search(r'@@ -\d+,?\d* \+(\d+)', line)
+            if match:
+                first_changed_line = int(match.group(1))
+                break
+    
+    diff_string = '\n'.join(diff_lines)
+    
+    return {
+        'diff': diff_string,
+        'first_changed_line': first_changed_line
+    }
--- a/agent/tools/utils/truncate.py
+++ b/agent/tools/utils/truncate.py
@@ -0,0 +1,292 @@
+"""
+Shared truncation utilities for tool outputs.
+
+Truncation is based on two independent limits - whichever is hit first wins:
+- Line limit (default: 2000 lines)
+- Byte limit (default: 50KB)
+
+Never returns partial lines (except bash tail truncation edge case).
+"""
+
+from typing import Dict, Any, Optional, Literal, Tuple
+
+
+DEFAULT_MAX_LINES = 2000
+DEFAULT_MAX_BYTES = 50 * 1024  # 50KB
+GREP_MAX_LINE_LENGTH = 500  # Max chars per grep match line
+
+
+class TruncationResult:
+    """Truncation result"""
+    
+    def __init__(
+        self,
+        content: str,
+        truncated: bool,
+        truncated_by: Optional[Literal["lines", "bytes"]],
+        total_lines: int,
+        total_bytes: int,
+        output_lines: int,
+        output_bytes: int,
+        last_line_partial: bool = False,
+        first_line_exceeds_limit: bool = False,
+        max_lines: int = DEFAULT_MAX_LINES,
+        max_bytes: int = DEFAULT_MAX_BYTES
+    ):
+        self.content = content
+        self.truncated = truncated
+        self.truncated_by = truncated_by
+        self.total_lines = total_lines
+        self.total_bytes = total_bytes
+        self.output_lines = output_lines
+        self.output_bytes = output_bytes
+        self.last_line_partial = last_line_partial
+        self.first_line_exceeds_limit = first_line_exceeds_limit
+        self.max_lines = max_lines
+        self.max_bytes = max_bytes
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary"""
+        return {
+            "content": self.content,
+            "truncated": self.truncated,
+            "truncated_by": self.truncated_by,
+            "total_lines": self.total_lines,
+            "total_bytes": self.total_bytes,
+            "output_lines": self.output_lines,
+            "output_bytes": self.output_bytes,
+            "last_line_partial": self.last_line_partial,
+            "first_line_exceeds_limit": self.first_line_exceeds_limit,
+            "max_lines": self.max_lines,
+            "max_bytes": self.max_bytes
+        }
+
+
+def format_size(bytes_count: int) -> str:
+    """Format bytes as human-readable size"""
+    if bytes_count < 1024:
+        return f"{bytes_count}B"
+    elif bytes_count < 1024 * 1024:
+        return f"{bytes_count / 1024:.1f}KB"
+    else:
+        return f"{bytes_count / (1024 * 1024):.1f}MB"
+
+
+def truncate_head(content: str, max_lines: Optional[int] = None, max_bytes: Optional[int] = None) -> TruncationResult:
+    """
+    Truncate content from the head (keep first N lines/bytes).
+    Suitable for file reads where you want to see the beginning.
+    
+    Never returns partial lines. If first line exceeds byte limit,
+    returns empty content with first_line_exceeds_limit=True.
+    
+    :param content: Content to truncate
+    :param max_lines: Maximum number of lines (default: 2000)
+    :param max_bytes: Maximum number of bytes (default: 50KB)
+    :return: Truncation result
+    """
+    if max_lines is None:
+        max_lines = DEFAULT_MAX_LINES
+    if max_bytes is None:
+        max_bytes = DEFAULT_MAX_BYTES
+    
+    total_bytes = len(content.encode('utf-8'))
+    lines = content.split('\n')
+    total_lines = len(lines)
+    
+    # Check if no truncation is needed
+    if total_lines <= max_lines and total_bytes <= max_bytes:
+        return TruncationResult(
+            content=content,
+            truncated=False,
+            truncated_by=None,
+            total_lines=total_lines,
+            total_bytes=total_bytes,
+            output_lines=total_lines,
+            output_bytes=total_bytes,
+            last_line_partial=False,
+            first_line_exceeds_limit=False,
+            max_lines=max_lines,
+            max_bytes=max_bytes
+        )
+    
+    # Check if first line alone exceeds byte limit
+    first_line_bytes = len(lines[0].encode('utf-8'))
+    if first_line_bytes > max_bytes:
+        return TruncationResult(
+            content="",
+            truncated=True,
+            truncated_by="bytes",
+            total_lines=total_lines,
+            total_bytes=total_bytes,
+            output_lines=0,
+            output_bytes=0,
+            last_line_partial=False,
+            first_line_exceeds_limit=True,
+            max_lines=max_lines,
+            max_bytes=max_bytes
+        )
+    
+    # Collect complete lines that fit
+    output_lines_arr = []
+    output_bytes_count = 0
+    truncated_by = "lines"
+    
+    for i, line in enumerate(lines):
+        if i >= max_lines:
+            break
+        
+        # Calculate line bytes (add 1 for newline if not first line)
+        line_bytes = len(line.encode('utf-8')) + (1 if i > 0 else 0)
+        
+        if output_bytes_count + line_bytes > max_bytes:
+            truncated_by = "bytes"
+            break
+        
+        output_lines_arr.append(line)
+        output_bytes_count += line_bytes
+    
+    # If exited due to line limit
+    if len(output_lines_arr) >= max_lines and output_bytes_count <= max_bytes:
+        truncated_by = "lines"
+    
+    output_content = '\n'.join(output_lines_arr)
+    final_output_bytes = len(output_content.encode('utf-8'))
+    
+    return TruncationResult(
+        content=output_content,
+        truncated=True,
+        truncated_by=truncated_by,
+        total_lines=total_lines,
+        total_bytes=total_bytes,
+        output_lines=len(output_lines_arr),
+        output_bytes=final_output_bytes,
+        last_line_partial=False,
+        first_line_exceeds_limit=False,
+        max_lines=max_lines,
+        max_bytes=max_bytes
+    )
+
+
+def truncate_tail(content: str, max_lines: Optional[int] = None, max_bytes: Optional[int] = None) -> TruncationResult:
+    """
+    Truncate content from tail (keep last N lines/bytes).
+    Suitable for bash output where you want to see the ending content (errors, final results).
+    
+    If the last line of original content exceeds byte limit, may return partial first line.
+    
+    :param content: Content to truncate
+    :param max_lines: Maximum lines (default: 2000)
+    :param max_bytes: Maximum bytes (default: 50KB)
+    :return: Truncation result
+    """
+    if max_lines is None:
+        max_lines = DEFAULT_MAX_LINES
+    if max_bytes is None:
+        max_bytes = DEFAULT_MAX_BYTES
+    
+    total_bytes = len(content.encode('utf-8'))
+    lines = content.split('\n')
+    total_lines = len(lines)
+    
+    # Check if no truncation is needed
+    if total_lines <= max_lines and total_bytes <= max_bytes:
+        return TruncationResult(
+            content=content,
+            truncated=False,
+            truncated_by=None,
+            total_lines=total_lines,
+            total_bytes=total_bytes,
+            output_lines=total_lines,
+            output_bytes=total_bytes,
+            last_line_partial=False,
+            first_line_exceeds_limit=False,
+            max_lines=max_lines,
+            max_bytes=max_bytes
+        )
+    
+    # Work backwards from the end
+    output_lines_arr = []
+    output_bytes_count = 0
+    truncated_by = "lines"
+    last_line_partial = False
+    
+    for i in range(len(lines) - 1, -1, -1):
+        if len(output_lines_arr) >= max_lines:
+            break
+        
+        line = lines[i]
+        # Calculate line bytes (add newline if not the first added line)
+        line_bytes = len(line.encode('utf-8')) + (1 if len(output_lines_arr) > 0 else 0)
+        
+        if output_bytes_count + line_bytes > max_bytes:
+            truncated_by = "bytes"
+            # Edge case: if we haven't added any lines yet and this line exceeds maxBytes,
+            # take the end portion of this line
+            if len(output_lines_arr) == 0:
+                truncated_line = _truncate_string_to_bytes_from_end(line, max_bytes)
+                output_lines_arr.insert(0, truncated_line)
+                output_bytes_count = len(truncated_line.encode('utf-8'))
+                last_line_partial = True
+            break
+        
+        output_lines_arr.insert(0, line)
+        output_bytes_count += line_bytes
+    
+    # If exited due to line limit
+    if len(output_lines_arr) >= max_lines and output_bytes_count <= max_bytes:
+        truncated_by = "lines"
+    
+    output_content = '\n'.join(output_lines_arr)
+    final_output_bytes = len(output_content.encode('utf-8'))
+    
+    return TruncationResult(
+        content=output_content,
+        truncated=True,
+        truncated_by=truncated_by,
+        total_lines=total_lines,
+        total_bytes=total_bytes,
+        output_lines=len(output_lines_arr),
+        output_bytes=final_output_bytes,
+        last_line_partial=last_line_partial,
+        first_line_exceeds_limit=False,
+        max_lines=max_lines,
+        max_bytes=max_bytes
+    )
+
+
+def _truncate_string_to_bytes_from_end(text: str, max_bytes: int) -> str:
+    """
+    Truncate string to fit byte limit (from end).
+    Properly handles multi-byte UTF-8 characters.
+    
+    :param text: String to truncate
+    :param max_bytes: Maximum bytes
+    :return: Truncated string
+    """
+    encoded = text.encode('utf-8')
+    if len(encoded) <= max_bytes:
+        return text
+    
+    # Start from end, skip back maxBytes
+    start = len(encoded) - max_bytes
+    
+    # Find valid UTF-8 boundary (character start)
+    while start < len(encoded) and (encoded[start] & 0xC0) == 0x80:
+        start += 1
+    
+    return encoded[start:].decode('utf-8', errors='ignore')
+
+
+def truncate_line(line: str, max_chars: int = GREP_MAX_LINE_LENGTH) -> Tuple[str, bool]:
+    """
+    Truncate single line to max characters, add [truncated] suffix.
+    Used for grep match lines.
+    
+    :param line: Line to truncate
+    :param max_chars: Maximum characters
+    :return: (truncated text, whether truncated)
+    """
+    if len(line) <= max_chars:
+        return line, False
+    return f"{line[:max_chars]}... [truncated]", True
--- a/agent/tools/write/init.py
+++ b/agent/tools/write/init.py
@@ -0,0 +1,3 @@
+from .write import Write
+
+__all__ = ['Write']
--- a/agent/tools/write/write.py
+++ b/agent/tools/write/write.py
@@ -0,0 +1,96 @@
+"""
+Write tool - Write file content
+Creates or overwrites files, automatically creates parent directories
+"""
+
+import os
+from typing import Dict, Any
+from pathlib import Path
+
+from agent.tools.base_tool import BaseTool, ToolResult
+
+
+class Write(BaseTool):
+    """Tool for writing file content"""
+    
+    name: str = "write"
+    description: str = "Write content to a file. Creates the file if it doesn't exist, overwrites if it does. Automatically creates parent directories. IMPORTANT: Single write should not exceed 10KB. For large files, create a skeleton first, then use edit to add content in chunks."
+    
+    params: dict = {
+        "type": "object",
+        "properties": {
+            "path": {
+                "type": "string",
+                "description": "Path to the file to write (relative or absolute)"
+            },
+            "content": {
+                "type": "string",
+                "description": "Content to write to the file"
+            }
+        },
+        "required": ["path", "content"]
+    }
+    
+    def __init__(self, config: dict = None):
+        self.config = config or {}
+        self.cwd = self.config.get("cwd", os.getcwd())
+        self.memory_manager = self.config.get("memory_manager", None)
+    
+    def execute(self, args: Dict[str, Any]) -> ToolResult:
+        """
+        Execute file write operation
+        
+        :param args: Contains file path and content
+        :return: Operation result
+        """
+        path = args.get("path", "").strip()
+        content = args.get("content", "")
+        
+        if not path:
+            return ToolResult.fail("Error: path parameter is required")
+        
+        # Resolve path
+        absolute_path = self._resolve_path(path)
+        
+        try:
+            # Create parent directory (if needed)
+            parent_dir = os.path.dirname(absolute_path)
+            if parent_dir:
+                os.makedirs(parent_dir, exist_ok=True)
+            
+            # Write file
+            with open(absolute_path, 'w', encoding='utf-8') as f:
+                f.write(content)
+            
+            # Get bytes written
+            bytes_written = len(content.encode('utf-8'))
+            
+            # Auto-sync to memory database if this is a memory file
+            if self.memory_manager and 'memory/' in path:
+                self.memory_manager.mark_dirty()
+            
+            result = {
+                "message": f"Successfully wrote {bytes_written} bytes to {path}",
+                "path": path,
+                "bytes_written": bytes_written
+            }
+            
+            return ToolResult.success(result)
+            
+        except PermissionError:
+            return ToolResult.fail(f"Error: Permission denied writing to {path}")
+        except Exception as e:
+            return ToolResult.fail(f"Error writing file: {str(e)}")
+    
+    def _resolve_path(self, path: str) -> str:
+        """
+        Resolve path to absolute path
+        
+        :param path: Relative or absolute path
+        :return: Absolute path
+        """
+        # Expand ~ to user home directory
+        path = os.path.expanduser(path)
+        if os.path.isabs(path):
+            return path
+        return os.path.abspath(os.path.join(self.cwd, path))
--- a/app.py
+++ b/app.py
@@ -27,7 +27,7 @@ def sigterm_handler_wrap(_signo):

 def start_channel(channel_name: str):
    channel = channel_factory.create_channel(channel_name)
-    if channel_name in ["wx", "wxy", "terminal", "wechatmp", "wechatmp_service", "wechatcom_app", "wework",
+    if channel_name in ["wx", "wxy", "terminal", "wechatmp", "web", "wechatmp_service", "wechatcom_app", "wework",
                        const.FEISHU, const.DINGTALK]:
        PluginManager().load_plugins()

--- a/bot/claude/claude_ai_bot.py
+++ b/bot/claude/claude_ai_bot.py
@@ -1,222 +0,0 @@
-import re
-import time
-import json
-import uuid
-from curl_cffi import requests
-from bot.bot import Bot
-from bot.claude.claude_ai_session import ClaudeAiSession
-from bot.openai.open_ai_image import OpenAIImage
-from bot.session_manager import SessionManager
-from bridge.context import Context, ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf
-
-
-class ClaudeAIBot(Bot, OpenAIImage):
-    def __init__(self):
-        super().__init__()
-        self.sessions = SessionManager(ClaudeAiSession, model=conf().get("model") or "gpt-3.5-turbo")
-        self.claude_api_cookie = conf().get("claude_api_cookie")
-        self.proxy = conf().get("proxy")
-        self.con_uuid_dic = {}
-        if self.proxy:
-            self.proxies = {
-            "http": self.proxy,
-            "https": self.proxy
-        }
-        else:
-            self.proxies = None
-        self.error = ""
-        self.org_uuid = self.get_organization_id()
-
-    def generate_uuid(self):
-        random_uuid = uuid.uuid4()
-        random_uuid_str = str(random_uuid)
-        formatted_uuid = f"{random_uuid_str[0:8]}-{random_uuid_str[9:13]}-{random_uuid_str[14:18]}-{random_uuid_str[19:23]}-{random_uuid_str[24:]}"
-        return formatted_uuid
-        
-    def reply(self, query, context: Context = None) -> Reply:
-        if context.type == ContextType.TEXT:
-            return self._chat(query, context)
-        elif context.type == ContextType.IMAGE_CREATE:
-            ok, res = self.create_img(query, 0)
-            if ok:
-                reply = Reply(ReplyType.IMAGE_URL, res)
-            else:
-                reply = Reply(ReplyType.ERROR, res)
-            return reply
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def get_organization_id(self):
-        url = "https://claude.ai/api/organizations"
-        headers = {
-            'User-Agent':
-                'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0',
-            'Accept-Language': 'en-US,en;q=0.5',
-            'Referer': 'https://claude.ai/chats',
-            'Content-Type': 'application/json',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-            'Connection': 'keep-alive',
-            'Cookie': f'{self.claude_api_cookie}'
-        }
-        try:
-            response = requests.get(url, headers=headers, impersonate="chrome110", proxies =self.proxies, timeout=400)
-            res = json.loads(response.text)
-            uuid = res[0]['uuid']
-        except:
-            if "App unavailable" in response.text:
-                logger.error("IP error: The IP is not allowed to be used on Claude")
-                self.error = "ip所在地区不被claude支持"
-            elif "Invalid authorization" in response.text:
-                logger.error("Cookie error: Invalid authorization of claude, check cookie please.")
-                self.error = "无法通过claude身份验证，请检查cookie"
-            return None
-        return uuid
-
-    def conversation_share_check(self,session_id):
-        if conf().get("claude_uuid") is not None and conf().get("claude_uuid") != "":
-            con_uuid = conf().get("claude_uuid")
-            return con_uuid
-        if session_id not in self.con_uuid_dic:
-            self.con_uuid_dic[session_id] = self.generate_uuid()
-            self.create_new_chat(self.con_uuid_dic[session_id])
-        return self.con_uuid_dic[session_id]
-
-    def check_cookie(self):
-        flag = self.get_organization_id()
-        return flag
-
-    def create_new_chat(self, con_uuid):
-        """
-        新建claude对话实体
-        :param con_uuid: 对话id
-        :return:
-        """
-        url = f"https://claude.ai/api/organizations/{self.org_uuid}/chat_conversations"
-        payload = json.dumps({"uuid": con_uuid, "name": ""})
-        headers = {
-            'User-Agent':
-                'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0',
-            'Accept-Language': 'en-US,en;q=0.5',
-            'Referer': 'https://claude.ai/chats',
-            'Content-Type': 'application/json',
-            'Origin': 'https://claude.ai',
-            'DNT': '1',
-            'Connection': 'keep-alive',
-            'Cookie': self.claude_api_cookie,
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-            'TE': 'trailers'
-        }
-        response = requests.post(url, headers=headers, data=payload, impersonate="chrome110", proxies=self.proxies, timeout=400)
-        # Returns JSON of the newly created conversation information
-        return response.json()
-        
-    def _chat(self, query, context, retry_count=0) -> Reply:
-        """
-        发起对话请求
-        :param query: 请求提示词
-        :param context: 对话上下文
-        :param retry_count: 当前递归重试次数
-        :return: 回复
-        """
-        if retry_count >= 2:
-            # exit from retry 2 times
-            logger.warn("[CLAUDEAI] failed after maximum number of retry times")
-            return Reply(ReplyType.ERROR, "请再问我一次吧")
-
-        try:
-            session_id = context["session_id"]
-            if self.org_uuid is None:
-                return Reply(ReplyType.ERROR, self.error)
-
-            session = self.sessions.session_query(query, session_id)
-            con_uuid = self.conversation_share_check(session_id)
-
-            model = conf().get("model") or "gpt-3.5-turbo"
-            # remove system message
-            if session.messages[0].get("role") == "system":
-                if model == "wenxin" or model == "claude":
-                    session.messages.pop(0)
-            logger.info(f"[CLAUDEAI] query={query}")
-
-            # do http request
-            base_url = "https://claude.ai"
-            payload = json.dumps({
-                "completion": {
-                    "prompt": f"{query}",
-                    "timezone": "Asia/Kolkata",
-                    "model": "claude-2"
-                },
-                "organization_uuid": f"{self.org_uuid}",
-                "conversation_uuid": f"{con_uuid}",
-                "text": f"{query}",
-                "attachments": []
-            })
-            headers = {
-                'User-Agent':
-                    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/115.0',
-                'Accept': 'text/event-stream, text/event-stream',
-                'Accept-Language': 'en-US,en;q=0.5',
-                'Referer': 'https://claude.ai/chats',
-                'Content-Type': 'application/json',
-                'Origin': 'https://claude.ai',
-                'DNT': '1',
-                'Connection': 'keep-alive',
-                'Cookie': f'{self.claude_api_cookie}',
-                'Sec-Fetch-Dest': 'empty',
-                'Sec-Fetch-Mode': 'cors',
-                'Sec-Fetch-Site': 'same-origin',
-                'TE': 'trailers'
-            }
-
-            res = requests.post(base_url + "/api/append_message", headers=headers, data=payload,impersonate="chrome110",proxies= self.proxies,timeout=400)
-            if res.status_code == 200 or "pemission" in res.text:
-                # execute success
-                decoded_data = res.content.decode("utf-8")
-                decoded_data = re.sub('\n+', '\n', decoded_data).strip()
-                data_strings = decoded_data.split('\n')
-                completions = []
-                for data_string in data_strings:
-                    json_str = data_string[6:].strip()
-                    data = json.loads(json_str)
-                    if 'completion' in data:
-                        completions.append(data['completion'])
-
-                reply_content = ''.join(completions)
-
-                if "rate limi" in reply_content:
-                    logger.error("rate limit error: The conversation has reached the system speed limit and is synchronized with Cladue. Please go to the official website to check the lifting time")
-                    return Reply(ReplyType.ERROR, "对话达到系统速率限制，与cladue同步，请进入官网查看解除限制时间")
-                logger.info(f"[CLAUDE] reply={reply_content}, total_tokens=invisible")
-                self.sessions.session_reply(reply_content, session_id, 100)
-                return Reply(ReplyType.TEXT, reply_content)
-            else:
-                flag = self.check_cookie()
-                if flag == None:
-                    return Reply(ReplyType.ERROR, self.error)
-
-                response = res.json()
-                error = response.get("error")
-                logger.error(f"[CLAUDE] chat failed, status_code={res.status_code}, "
-                             f"msg={error.get('message')}, type={error.get('type')}, detail: {res.text}, uuid: {con_uuid}")
-
-                if res.status_code >= 500:
-                    # server error, need retry
-                    time.sleep(2)
-                    logger.warn(f"[CLAUDE] do retry, times={retry_count}")
-                    return self._chat(query, context, retry_count + 1)
-                return Reply(ReplyType.ERROR, "提问太快啦，请休息一下再问我吧")
-
-        except Exception as e:
-            logger.exception(e)
-            # retry
-            time.sleep(2)
-            logger.warn(f"[CLAUDE] do retry, times={retry_count}")
-            return self._chat(query, context, retry_count + 1)
--- a/bot/claude/claude_ai_session.py
+++ b/bot/claude/claude_ai_session.py
@@ -1,9 +0,0 @@
-from bot.session_manager import Session
-
-
-class ClaudeAiSession(Session):
-    def __init__(self, session_id, system_prompt=None, model="claude"):
-        super().__init__(session_id, system_prompt)
-        self.model = model
-        # claude逆向不支持role prompt
-        # self.reset()
--- a/bot/claudeapi/claude_api_bot.py
+++ b/bot/claudeapi/claude_api_bot.py
@@ -1,135 +0,0 @@
-# encoding:utf-8
-
-import time
-
-import openai
-import openai.error
-import anthropic
-
-from bot.bot import Bot
-from bot.openai.open_ai_image import OpenAIImage
-from bot.chatgpt.chat_gpt_session import ChatGPTSession
-from bot.gemini.google_gemini_bot import GoogleGeminiBot
-from bot.session_manager import SessionManager
-from bridge.context import ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf
-
-user_session = dict()
-
-
-# OpenAI对话模型API (可用)
-class ClaudeAPIBot(Bot, OpenAIImage):
-    def __init__(self):
-        super().__init__()
-        self.claudeClient = anthropic.Anthropic(
-            api_key=conf().get("claude_api_key")
-        )
-        openai.api_key = conf().get("open_ai_api_key")
-        if conf().get("open_ai_api_base"):
-            openai.api_base = conf().get("open_ai_api_base")
-        proxy = conf().get("proxy")
-        if proxy:
-            openai.proxy = proxy
-
-        self.sessions = SessionManager(ChatGPTSession, model=conf().get("model") or "text-davinci-003")
-
-    def reply(self, query, context=None):
-        # acquire reply content
-        if context and context.type:
-            if context.type == ContextType.TEXT:
-                logger.info("[CLAUDE_API] query={}".format(query))
-                session_id = context["session_id"]
-                reply = None
-                if query == "#清除记忆":
-                    self.sessions.clear_session(session_id)
-                    reply = Reply(ReplyType.INFO, "记忆已清除")
-                elif query == "#清除所有":
-                    self.sessions.clear_all_session()
-                    reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-                else:
-                    session = self.sessions.session_query(query, session_id)
-                    result = self.reply_text(session)
-                    logger.info(result)
-                    total_tokens, completion_tokens, reply_content = (
-                        result["total_tokens"],
-                        result["completion_tokens"],
-                        result["content"],
-                    )
-                    logger.debug(
-                        "[CLAUDE_API] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(str(session), session_id, reply_content, completion_tokens)
-                    )
-
-                    if total_tokens == 0:
-                        reply = Reply(ReplyType.ERROR, reply_content)
-                    else:
-                        self.sessions.session_reply(reply_content, session_id, total_tokens)
-                        reply = Reply(ReplyType.TEXT, reply_content)
-                return reply
-            elif context.type == ContextType.IMAGE_CREATE:
-                ok, retstring = self.create_img(query, 0)
-                reply = None
-                if ok:
-                    reply = Reply(ReplyType.IMAGE_URL, retstring)
-                else:
-                    reply = Reply(ReplyType.ERROR, retstring)
-                return reply
-
-    def reply_text(self, session: ChatGPTSession, retry_count=0):
-        try:
-            actual_model = self._model_mapping(conf().get("model"))
-            response = self.claudeClient.messages.create(
-                model=actual_model,
-                max_tokens=1024,
-                # system=conf().get("system"),
-                messages=GoogleGeminiBot.filter_messages(session.messages)
-            )
-            # response = openai.Completion.create(prompt=str(session), **self.args)
-            res_content = response.content[0].text.strip().replace("<|endoftext|>", "")
-            total_tokens = response.usage.input_tokens+response.usage.output_tokens
-            completion_tokens = response.usage.output_tokens
-            logger.info("[CLAUDE_API] reply={}".format(res_content))
-            return {
-                "total_tokens": total_tokens,
-                "completion_tokens": completion_tokens,
-                "content": res_content,
-            }
-        except Exception as e:
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if isinstance(e, openai.error.RateLimitError):
-                logger.warn("[CLAUDE_API] RateLimitError: {}".format(e))
-                result["content"] = "提问太快啦，请休息一下再问我吧"
-                if need_retry:
-                    time.sleep(20)
-            elif isinstance(e, openai.error.Timeout):
-                logger.warn("[CLAUDE_API] Timeout: {}".format(e))
-                result["content"] = "我没有收到你的消息"
-                if need_retry:
-                    time.sleep(5)
-            elif isinstance(e, openai.error.APIConnectionError):
-                logger.warn("[CLAUDE_API] APIConnectionError: {}".format(e))
-                need_retry = False
-                result["content"] = "我连接不到你的网络"
-            else:
-                logger.warn("[CLAUDE_API] Exception: {}".format(e))
-                need_retry = False
-                self.sessions.clear_session(session.session_id)
-
-            if need_retry:
-                logger.warn("[CLAUDE_API] 第{}次重试".format(retry_count + 1))
-                return self.reply_text(session, retry_count + 1)
-            else:
-                return result
-
-    def _model_mapping(self, model) -> str:
-        if model == "claude-3-opus":
-            return "claude-3-opus-20240229"
-        elif model == "claude-3-sonnet":
-            return "claude-3-sonnet-20240229"
-        elif model == "claude-3-haiku":
-            return "claude-3-haiku-20240307"
-        elif model == "claude-3.5-sonnet":
-            return "claude-3-5-sonnet-20240620"
-        return model
--- a/bot/dashscope/dashscope_bot.py
+++ b/bot/dashscope/dashscope_bot.py
@@ -1,117 +0,0 @@
-# encoding:utf-8
-
-from bot.bot import Bot
-from bot.session_manager import SessionManager
-from bridge.context import ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf, load_config
-from .dashscope_session import DashscopeSession
-import os
-import dashscope
-from http import HTTPStatus
-
-
-
-dashscope_models = {
-    "qwen-turbo": dashscope.Generation.Models.qwen_turbo,
-    "qwen-plus": dashscope.Generation.Models.qwen_plus,
-    "qwen-max": dashscope.Generation.Models.qwen_max,
-    "qwen-bailian-v1": dashscope.Generation.Models.bailian_v1
-}
-# ZhipuAI对话模型API
-class DashscopeBot(Bot):
-    def __init__(self):
-        super().__init__()
-        self.sessions = SessionManager(DashscopeSession, model=conf().get("model") or "qwen-plus")
-        self.model_name = conf().get("model") or "qwen-plus"
-        self.api_key = conf().get("dashscope_api_key")
-        os.environ["DASHSCOPE_API_KEY"] = self.api_key
-        self.client = dashscope.Generation
-
-    def reply(self, query, context=None):
-        # acquire reply content
-        if context.type == ContextType.TEXT:
-            logger.info("[DASHSCOPE] query={}".format(query))
-
-            session_id = context["session_id"]
-            reply = None
-            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
-            if query in clear_memory_commands:
-                self.sessions.clear_session(session_id)
-                reply = Reply(ReplyType.INFO, "记忆已清除")
-            elif query == "#清除所有":
-                self.sessions.clear_all_session()
-                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-            elif query == "#更新配置":
-                load_config()
-                reply = Reply(ReplyType.INFO, "配置已更新")
-            if reply:
-                return reply
-            session = self.sessions.session_query(query, session_id)
-            logger.debug("[DASHSCOPE] session query={}".format(session.messages))
-
-            reply_content = self.reply_text(session)
-            logger.debug(
-                "[DASHSCOPE] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
-                    session.messages,
-                    session_id,
-                    reply_content["content"],
-                    reply_content["completion_tokens"],
-                )
-            )
-            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-            elif reply_content["completion_tokens"] > 0:
-                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
-                reply = Reply(ReplyType.TEXT, reply_content["content"])
-            else:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[DASHSCOPE] reply {} used 0 tokens.".format(reply_content))
-            return reply
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def reply_text(self, session: DashscopeSession, retry_count=0) -> dict:
-        """
-        call openai's ChatCompletion to get the answer
-        :param session: a conversation session
-        :param session_id: session id
-        :param retry_count: retry count
-        :return: {}
-        """
-        try:
-            dashscope.api_key = self.api_key
-            response = self.client.call(
-                dashscope_models[self.model_name],
-                messages=session.messages,
-                result_format="message"
-            )
-            if response.status_code == HTTPStatus.OK:
-                content = response.output.choices[0]["message"]["content"]
-                return {
-                    "total_tokens": response.usage["total_tokens"],
-                    "completion_tokens": response.usage["output_tokens"],
-                    "content": content,
-                }
-            else:
-                logger.error('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
-                    response.request_id, response.status_code,
-                    response.code, response.message
-                ))
-                result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-                need_retry = retry_count < 2
-                result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-                if need_retry:
-                    return self.reply_text(session, retry_count + 1)
-                else:
-                    return result
-        except Exception as e:
-            logger.exception(e)
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if need_retry:
-                return self.reply_text(session, retry_count + 1)
-            else:
-                return result
--- a/bot/gemini/google_gemini_bot.py
+++ b/bot/gemini/google_gemini_bot.py
@@ -1,81 +0,0 @@
-"""
-Google gemini bot
-
-@author zhayujie
-@Date 2023/12/15
-"""
-# encoding:utf-8
-
-from bot.bot import Bot
-import google.generativeai as genai
-from bot.session_manager import SessionManager
-from bridge.context import ContextType, Context
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf
-from bot.baidu.baidu_wenxin_session import BaiduWenxinSession
-
-
-# OpenAI对话模型API (可用)
-class GoogleGeminiBot(Bot):
-
-    def __init__(self):
-        super().__init__()
-        self.api_key = conf().get("gemini_api_key")
-        # 复用文心的token计算方式
-        self.sessions = SessionManager(BaiduWenxinSession, model=conf().get("model") or "gpt-3.5-turbo")
-        self.model = conf().get("model") or "gemini-pro"
-        if self.model == "gemini":
-            self.model = "gemini-pro"
-    def reply(self, query, context: Context = None) -> Reply:
-        try:
-            if context.type != ContextType.TEXT:
-                logger.warn(f"[Gemini] Unsupported message type, type={context.type}")
-                return Reply(ReplyType.TEXT, None)
-            logger.info(f"[Gemini] query={query}")
-            session_id = context["session_id"]
-            session = self.sessions.session_query(query, session_id)
-            gemini_messages = self._convert_to_gemini_messages(self.filter_messages(session.messages))
-            genai.configure(api_key=self.api_key)
-            model = genai.GenerativeModel(self.model)
-            response = model.generate_content(gemini_messages)
-            reply_text = response.text
-            self.sessions.session_reply(reply_text, session_id)
-            logger.info(f"[Gemini] reply={reply_text}")
-            return Reply(ReplyType.TEXT, reply_text)
-        except Exception as e:
-            logger.error("[Gemini] fetch reply error, may contain unsafe content")
-            logger.error(e)
-            return Reply(ReplyType.ERROR, "invoke [Gemini] api failed!")
-
-    def _convert_to_gemini_messages(self, messages: list):
-        res = []
-        for msg in messages:
-            if msg.get("role") == "user":
-                role = "user"
-            elif msg.get("role") == "assistant":
-                role = "model"
-            else:
-                continue
-            res.append({
-                "role": role,
-                "parts": [{"text": msg.get("content")}]
-            })
-        return res
-
-    @staticmethod
-    def filter_messages(messages: list):
-        res = []
-        turn = "user"
-        if not messages:
-            return res
-        for i in range(len(messages) - 1, -1, -1):
-            message = messages[i]
-            if message.get("role") != turn:
-                continue
-            res.insert(0, message)
-            if turn == "user":
-                turn = "assistant"
-            elif turn == "assistant":
-                turn = "user"
-        return res
--- a/bot/minimax/minimax_bot.py
+++ b/bot/minimax/minimax_bot.py
@@ -1,151 +0,0 @@
-# encoding:utf-8
-
-import time
-
-import openai
-import openai.error
-from bot.bot import Bot
-from bot.minimax.minimax_session import MinimaxSession
-from bot.session_manager import SessionManager
-from bridge.context import Context, ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf, load_config
-from bot.chatgpt.chat_gpt_session import ChatGPTSession
-import requests
-from common import const
-
-
-# ZhipuAI对话模型API
-class MinimaxBot(Bot):
-    def __init__(self):
-        super().__init__()
-        self.args = {
-            "model": conf().get("model") or "abab6.5",  # 对话模型的名称
-            "temperature": conf().get("temperature", 0.3),  # 如果设置，值域须为 [0, 1] 我们推荐 0.3，以达到较合适的效果。
-            "top_p": conf().get("top_p", 0.95),  # 使用默认值
-        }
-        self.api_key = conf().get("Minimax_api_key")
-        self.group_id = conf().get("Minimax_group_id")
-        self.base_url = conf().get("Minimax_base_url", f"https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId={self.group_id}")
-        # tokens_to_generate/bot_setting/reply_constraints可自行修改
-        self.request_body = {
-            "model": self.args["model"],
-            "tokens_to_generate": 2048,
-            "reply_constraints": {"sender_type": "BOT", "sender_name": "MM智能助理"},
-            "messages": [],
-            "bot_setting": [
-                {
-                    "bot_name": "MM智能助理",
-                    "content": "MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。",
-                }
-            ],
-        }
-        self.sessions = SessionManager(MinimaxSession, model=const.MiniMax)
-
-    def reply(self, query, context: Context = None) -> Reply:
-        # acquire reply content
-        logger.info("[Minimax_AI] query={}".format(query))
-        if context.type == ContextType.TEXT:
-            session_id = context["session_id"]
-            reply = None
-            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
-            if query in clear_memory_commands:
-                self.sessions.clear_session(session_id)
-                reply = Reply(ReplyType.INFO, "记忆已清除")
-            elif query == "#清除所有":
-                self.sessions.clear_all_session()
-                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-            elif query == "#更新配置":
-                load_config()
-                reply = Reply(ReplyType.INFO, "配置已更新")
-            if reply:
-                return reply
-            session = self.sessions.session_query(query, session_id)
-            logger.debug("[Minimax_AI] session query={}".format(session))
-
-            model = context.get("Minimax_model")
-            new_args = self.args.copy()
-            if model:
-                new_args["model"] = model
-            # if context.get('stream'):
-            #     # reply in stream
-            #     return self.reply_text_stream(query, new_query, session_id)
-
-            reply_content = self.reply_text(session, args=new_args)
-            logger.debug(
-                "[Minimax_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
-                    session.messages,
-                    session_id,
-                    reply_content["content"],
-                    reply_content["completion_tokens"],
-                )
-            )
-            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-            elif reply_content["completion_tokens"] > 0:
-                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
-                reply = Reply(ReplyType.TEXT, reply_content["content"])
-            else:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[Minimax_AI] reply {} used 0 tokens.".format(reply_content))
-            return reply
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def reply_text(self, session: MinimaxSession, args=None, retry_count=0) -> dict:
-        """
-        call openai's ChatCompletion to get the answer
-        :param session: a conversation session
-        :param session_id: session id
-        :param retry_count: retry count
-        :return: {}
-        """
-        try:
-            headers = {"Content-Type": "application/json", "Authorization": "Bearer " + self.api_key}
-            self.request_body["messages"].extend(session.messages)
-            logger.info("[Minimax_AI] request_body={}".format(self.request_body))
-            # logger.info("[Minimax_AI] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
-            res = requests.post(self.base_url, headers=headers, json=self.request_body)
-
-            # self.request_body["messages"].extend(response.json()["choices"][0]["messages"])
-            if res.status_code == 200:
-                response = res.json()
-                return {
-                    "total_tokens": response["usage"]["total_tokens"],
-                    "completion_tokens": response["usage"]["total_tokens"],
-                    "content": response["reply"],
-                }
-            else:
-                response = res.json()
-                error = response.get("error")
-                logger.error(f"[Minimax_AI] chat failed, status_code={res.status_code}, " f"msg={error.get('message')}, type={error.get('type')}")
-
-                result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
-                need_retry = False
-                if res.status_code >= 500:
-                    # server error, need retry
-                    logger.warn(f"[Minimax_AI] do retry, times={retry_count}")
-                    need_retry = retry_count < 2
-                elif res.status_code == 401:
-                    result["content"] = "授权失败，请检查API Key是否正确"
-                elif res.status_code == 429:
-                    result["content"] = "请求过于频繁，请稍后再试"
-                    need_retry = retry_count < 2
-                else:
-                    need_retry = False
-
-                if need_retry:
-                    time.sleep(3)
-                    return self.reply_text(session, args, retry_count + 1)
-                else:
-                    return result
-        except Exception as e:
-            logger.exception(e)
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if need_retry:
-                return self.reply_text(session, args, retry_count + 1)
-            else:
-                return result
--- a/bot/zhipuai/zhipuai_bot.py
+++ b/bot/zhipuai/zhipuai_bot.py
@@ -1,149 +0,0 @@
-# encoding:utf-8
-
-import time
-
-import openai
-import openai.error
-from bot.bot import Bot
-from bot.zhipuai.zhipu_ai_session import ZhipuAISession
-from bot.zhipuai.zhipu_ai_image import ZhipuAIImage
-from bot.session_manager import SessionManager
-from bridge.context import ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from config import conf, load_config
-from zhipuai import ZhipuAI
-
-
-# ZhipuAI对话模型API
-class ZHIPUAIBot(Bot, ZhipuAIImage):
-    def __init__(self):
-        super().__init__()
-        self.sessions = SessionManager(ZhipuAISession, model=conf().get("model") or "ZHIPU_AI")
-        self.args = {
-            "model": conf().get("model") or "glm-4",  # 对话模型的名称
-            "temperature": conf().get("temperature", 0.9),  # 值在(0,1)之间(智谱AI 的温度不能取 0 或者 1)
-            "top_p": conf().get("top_p", 0.7),  # 值在(0,1)之间(智谱AI 的 top_p 不能取 0 或者 1)
-        }
-        self.client = ZhipuAI(api_key=conf().get("zhipu_ai_api_key"))
-
-    def reply(self, query, context=None):
-        # acquire reply content
-        if context.type == ContextType.TEXT:
-            logger.info("[ZHIPU_AI] query={}".format(query))
-
-            session_id = context["session_id"]
-            reply = None
-            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
-            if query in clear_memory_commands:
-                self.sessions.clear_session(session_id)
-                reply = Reply(ReplyType.INFO, "记忆已清除")
-            elif query == "#清除所有":
-                self.sessions.clear_all_session()
-                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-            elif query == "#更新配置":
-                load_config()
-                reply = Reply(ReplyType.INFO, "配置已更新")
-            if reply:
-                return reply
-            session = self.sessions.session_query(query, session_id)
-            logger.debug("[ZHIPU_AI] session query={}".format(session.messages))
-
-            api_key = context.get("openai_api_key") or openai.api_key
-            model = context.get("gpt_model")
-            new_args = None
-            if model:
-                new_args = self.args.copy()
-                new_args["model"] = model
-            # if context.get('stream'):
-            #     # reply in stream
-            #     return self.reply_text_stream(query, new_query, session_id)
-
-            reply_content = self.reply_text(session, api_key, args=new_args)
-            logger.debug(
-                "[ZHIPU_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
-                    session.messages,
-                    session_id,
-                    reply_content["content"],
-                    reply_content["completion_tokens"],
-                )
-            )
-            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-            elif reply_content["completion_tokens"] > 0:
-                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
-                reply = Reply(ReplyType.TEXT, reply_content["content"])
-            else:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[ZHIPU_AI] reply {} used 0 tokens.".format(reply_content))
-            return reply
-        elif context.type == ContextType.IMAGE_CREATE:
-            ok, retstring = self.create_img(query, 0)
-            reply = None
-            if ok:
-                reply = Reply(ReplyType.IMAGE_URL, retstring)
-            else:
-                reply = Reply(ReplyType.ERROR, retstring)
-            return reply
-
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def reply_text(self, session: ZhipuAISession, api_key=None, args=None, retry_count=0) -> dict:
-        """
-        call openai's ChatCompletion to get the answer
-        :param session: a conversation session
-        :param session_id: session id
-        :param retry_count: retry count
-        :return: {}
-        """
-        try:
-            # if conf().get("rate_limit_chatgpt") and not self.tb4chatgpt.get_token():
-            #     raise openai.error.RateLimitError("RateLimitError: rate limit exceeded")
-            # if api_key == None, the default openai.api_key will be used
-            if args is None:
-                args = self.args
-            # response = openai.ChatCompletion.create(api_key=api_key, messages=session.messages, **args)
-            response = self.client.chat.completions.create(messages=session.messages, **args)
-            # logger.debug("[ZHIPU_AI] response={}".format(response))
-            # logger.info("[ZHIPU_AI] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
-
-            return {
-                "total_tokens": response.usage.total_tokens,
-                "completion_tokens": response.usage.completion_tokens,
-                "content": response.choices[0].message.content,
-            }
-        except Exception as e:
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if isinstance(e, openai.error.RateLimitError):
-                logger.warn("[ZHIPU_AI] RateLimitError: {}".format(e))
-                result["content"] = "提问太快啦，请休息一下再问我吧"
-                if need_retry:
-                    time.sleep(20)
-            elif isinstance(e, openai.error.Timeout):
-                logger.warn("[ZHIPU_AI] Timeout: {}".format(e))
-                result["content"] = "我没有收到你的消息"
-                if need_retry:
-                    time.sleep(5)
-            elif isinstance(e, openai.error.APIError):
-                logger.warn("[ZHIPU_AI] Bad Gateway: {}".format(e))
-                result["content"] = "请再问我一次"
-                if need_retry:
-                    time.sleep(10)
-            elif isinstance(e, openai.error.APIConnectionError):
-                logger.warn("[ZHIPU_AI] APIConnectionError: {}".format(e))
-                result["content"] = "我连接不到你的网络"
-                if need_retry:
-                    time.sleep(5)
-            else:
-                logger.exception("[ZHIPU_AI] Exception: {}".format(e), e)
-                need_retry = False
-                self.sessions.clear_session(session.session_id)
-
-            if need_retry:
-                logger.warn("[ZHIPU_AI] 第{}次重试".format(retry_count + 1))
-                return self.reply_text(session, api_key, args, retry_count + 1)
-            else:
-                return result
--- a/bridge/agent_bridge.py
+++ b/bridge/agent_bridge.py
@@ -0,0 +1,530 @@
+"""
+Agent Bridge - Integrates Agent system with existing COW bridge
+"""
+
+import os
+from typing import Optional, List
+
+from agent.protocol import Agent, LLMModel, LLMRequest
+from bridge.agent_event_handler import AgentEventHandler
+from bridge.agent_initializer import AgentInitializer
+from bridge.bridge import Bridge
+from bridge.context import Context
+from bridge.reply import Reply, ReplyType
+from common import const
+from common.log import logger
+from models.openai_compatible_bot import OpenAICompatibleBot
+
+
+def add_openai_compatible_support(bot_instance):
+    """
+    Dynamically add OpenAI-compatible tool calling support to a bot instance.
+    
+    This allows any bot to gain tool calling capability without modifying its code,
+    as long as it uses OpenAI-compatible API format.
+    
+    Note: Some bots like ZHIPUAIBot have native tool calling support and don't need enhancement.
+    """
+    if hasattr(bot_instance, 'call_with_tools'):
+        # Bot already has tool calling support (e.g., ZHIPUAIBot)
+        logger.info(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
+        return bot_instance
+
+    # Create a temporary mixin class that combines the bot with OpenAI compatibility
+    class EnhancedBot(bot_instance.__class__, OpenAICompatibleBot):
+        """Dynamically enhanced bot with OpenAI-compatible tool calling"""
+
+        def get_api_config(self):
+            """
+            Infer API config from common configuration patterns.
+            Most OpenAI-compatible bots use similar configuration.
+            """
+            from config import conf
+
+            return {
+                'api_key': conf().get("open_ai_api_key"),
+                'api_base': conf().get("open_ai_api_base"),
+                'model': conf().get("model", "gpt-3.5-turbo"),
+                'default_temperature': conf().get("temperature", 0.9),
+                'default_top_p': conf().get("top_p", 1.0),
+                'default_frequency_penalty': conf().get("frequency_penalty", 0.0),
+                'default_presence_penalty': conf().get("presence_penalty", 0.0),
+            }
+
+    # Change the bot's class to the enhanced version
+    bot_instance.__class__ = EnhancedBot
+    logger.info(
+        f"[AgentBridge] Enhanced {bot_instance.__class__.__bases__[0].__name__} with OpenAI-compatible tool calling")
+
+    return bot_instance
+
+
+class AgentLLMModel(LLMModel):
+    """
+    LLM Model adapter that uses COW's existing bot infrastructure
+    """
+
+    def __init__(self, bridge: Bridge, bot_type: str = "chat"):
+        # Get model name directly from config
+        from config import conf
+        model_name = conf().get("model", const.GPT_41)
+        super().__init__(model=model_name)
+        self.bridge = bridge
+        self.bot_type = bot_type
+        self._bot = None
+        self._use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
+    
+    @property
+    def bot(self):
+        """Lazy load the bot and enhance it with tool calling if needed"""
+        if self._bot is None:
+            # If use_linkai is enabled, use LinkAI bot directly
+            if self._use_linkai:
+                self._bot = self.bridge.find_chat_bot(const.LINKAI)
+            else:
+                self._bot = self.bridge.get_bot(self.bot_type)
+                # Automatically add tool calling support if not present
+                self._bot = add_openai_compatible_support(self._bot)
+            
+            # Log bot info
+            bot_name = type(self._bot).__name__
+        return self._bot
+
+    def call(self, request: LLMRequest):
+        """
+        Call the model using COW's bot infrastructure
+        """
+        try:
+            # For non-streaming calls, we'll use the existing reply method
+            # This is a simplified implementation
+            if hasattr(self.bot, 'call_with_tools'):
+                # Use tool-enabled call if available
+                kwargs = {
+                    'messages': request.messages,
+                    'tools': getattr(request, 'tools', None),
+                    'stream': False,
+                    'model': self.model  # Pass model parameter
+                }
+                # 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
+                
+                response = self.bot.call_with_tools(**kwargs)
+                return self._format_response(response)
+            else:
+                # Fallback to regular call
+                # This would need to be implemented based on your specific needs
+                raise NotImplementedError("Regular call not implemented yet")
+                
+        except Exception as e:
+            logger.error(f"AgentLLMModel call error: {e}")
+            raise
+    
+    def call_stream(self, request: LLMRequest):
+        """
+        Call the model with streaming using COW's bot infrastructure
+        """
+        try:
+            if hasattr(self.bot, 'call_with_tools'):
+                # Use tool-enabled streaming call if available
+                # Extract system prompt if present
+                system_prompt = getattr(request, 'system', None)
+                
+                # Build kwargs for call_with_tools
+                kwargs = {
+                    'messages': request.messages,
+                    'tools': getattr(request, 'tools', None),
+                    'stream': True,
+                    'model': self.model  # Pass model parameter
+                }
+                
+                # Only pass max_tokens if explicitly set, let the bot use its default
+                if request.max_tokens is not None:
+                    kwargs['max_tokens'] = request.max_tokens
+                
+                # Add system prompt if present
+                if system_prompt:
+                    kwargs['system'] = system_prompt
+                
+                stream = self.bot.call_with_tools(**kwargs)
+                
+                # Convert stream format to our expected format
+                for chunk in stream:
+                    yield self._format_stream_chunk(chunk)
+            else:
+                bot_type = type(self.bot).__name__
+                raise NotImplementedError(f"Bot {bot_type} does not support call_with_tools. Please add the method.")
+                
+        except Exception as e:
+            logger.error(f"AgentLLMModel call_stream error: {e}", exc_info=True)
+            raise
+    
+    def _format_response(self, response):
+        """Format Claude response to our expected format"""
+        # This would need to be implemented based on Claude's response format
+        return response
+    
+    def _format_stream_chunk(self, chunk):
+        """Format Claude stream chunk to our expected format"""
+        # This would need to be implemented based on Claude's stream format
+        return chunk
+
+
+class AgentBridge:
+    """
+    Bridge class that integrates super Agent with COW
+    Manages multiple agent instances per session for conversation isolation
+    """
+    
+    def __init__(self, bridge: Bridge):
+        self.bridge = bridge
+        self.agents = {}  # session_id -> Agent instance mapping
+        self.default_agent = None  # For backward compatibility (no session_id)
+        self.agent: Optional[Agent] = None
+        self.scheduler_initialized = False
+        
+        # Create helper instances
+        self.initializer = AgentInitializer(bridge, self)
+    def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
+        """
+        Create the super agent with COW integration
+        
+        Args:
+            system_prompt: System prompt
+            tools: List of tools (optional)
+            **kwargs: Additional agent parameters
+            
+        Returns:
+            Agent instance
+        """
+        # Create LLM model that uses COW's bot infrastructure
+        model = AgentLLMModel(self.bridge)
+        
+        # Default tools if none provided
+        if tools is None:
+            # Use ToolManager to load all available tools
+            from agent.tools import ToolManager
+            tool_manager = ToolManager()
+            tool_manager.load_tools()
+            
+            tools = []
+            for tool_name in tool_manager.tool_classes.keys():
+                try:
+                    tool = tool_manager.create_tool(tool_name)
+                    if tool:
+                        tools.append(tool)
+                except Exception as e:
+                    logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
+        
+        # Create agent instance
+        agent = Agent(
+            system_prompt=system_prompt,
+            description=kwargs.get("description", "AI Super Agent"),
+            model=model,
+            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
+            max_context_tokens=kwargs.get("max_context_tokens"),
+            context_reserve_tokens=kwargs.get("context_reserve_tokens")
+        )
+
+        # Log skill loading details
+        if agent.skill_manager:
+            logger.debug(f"[AgentBridge] SkillManager initialized with {len(agent.skill_manager.skills)} skills")
+
+        return agent
+    
+    def get_agent(self, session_id: str = None) -> Optional[Agent]:
+        """
+        Get agent instance for the given session
+        
+        Args:
+            session_id: Session identifier (e.g., user_id). If None, returns default agent.
+        
+        Returns:
+            Agent instance for this session
+        """
+        # If no session_id, use default agent (backward compatibility)
+        if session_id is None:
+            if self.default_agent is None:
+                self._init_default_agent()
+            return self.default_agent
+        
+        # Check if agent exists for this session
+        if session_id not in self.agents:
+            self._init_agent_for_session(session_id)
+        
+        return self.agents[session_id]
+    
+    def _init_default_agent(self):
+        """Initialize default super agent"""
+        agent = self.initializer.initialize_agent(session_id=None)
+        self.default_agent = agent
+    
+    def _init_agent_for_session(self, session_id: str):
+        """Initialize agent for a specific session"""
+        agent = self.initializer.initialize_agent(session_id=session_id)
+        self.agents[session_id] = agent
+    
+    def agent_reply(self, query: str, context: Context = None, 
+                   on_event=None, clear_history: bool = False) -> Reply:
+        """
+        Use super agent to reply to a query
+        
+        Args:
+            query: User query
+            context: COW context (optional, contains session_id for user isolation)
+            on_event: Event callback (optional)
+            clear_history: Whether to clear conversation history
+            
+        Returns:
+            Reply object
+        """
+        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")
+            
+            # Get agent for this session (will auto-initialize if needed)
+            agent = self.get_agent(session_id=session_id)
+            if not agent:
+                return Reply(ReplyType.ERROR, "Failed to initialize super agent")
+            
+            # Create event handler for logging and channel communication
+            event_handler = AgentEventHandler(context=context, original_callback=on_event)
+            
+            # Filter tools based on context
+            original_tools = agent.tools
+            filtered_tools = original_tools
+            
+            # If this is a scheduled task execution, exclude scheduler tool to prevent recursion
+            if context and context.get("is_scheduled_task"):
+                filtered_tools = [tool for tool in agent.tools if tool.name != "scheduler"]
+                agent.tools = filtered_tools
+                logger.info(f"[AgentBridge] Scheduled task execution: excluded scheduler tool ({len(filtered_tools)}/{len(original_tools)} tools)")
+            else:
+                # Attach context to scheduler tool if present
+                if context and agent.tools:
+                    for tool in agent.tools:
+                        if tool.name == "scheduler":
+                            try:
+                                from agent.tools.scheduler.integration import attach_scheduler_to_tool
+                                attach_scheduler_to_tool(tool, context)
+                            except Exception as e:
+                                logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
+                            break
+            
+            try:
+                # Use agent's run_stream method with event handler
+                response = agent.run_stream(
+                    user_message=query,
+                    on_event=event_handler.handle_event,
+                    clear_history=clear_history
+                )
+            finally:
+                # Restore original tools
+                if context and context.get("is_scheduled_task"):
+                    agent.tools = original_tools
+                
+                # Log execution summary
+                event_handler.log_summary()
+            
+            # Check if there are files to send (from read tool)
+            if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
+                files_to_send = agent.stream_executor.files_to_send
+                if files_to_send:
+                    # Send the first file (for now, handle one file at a time)
+                    file_info = files_to_send[0]
+                    logger.info(f"[AgentBridge] Sending file: {file_info.get('path')}")
+                    
+                    # Clear files_to_send for next request
+                    agent.stream_executor.files_to_send = []
+                    
+                    # Return file reply based on file type
+                    return self._create_file_reply(file_info, response, context)
+            
+            return Reply(ReplyType.TEXT, response)
+            
+        except Exception as e:
+            logger.error(f"Agent reply error: {e}")
+            return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
+    
+    def _create_file_reply(self, file_info: dict, text_response: str, context: Context = None) -> Reply:
+        """
+        Create a reply for sending files
+        
+        Args:
+            file_info: File metadata from read tool
+            text_response: Text response from agent
+            context: Context object
+            
+        Returns:
+            Reply object for file sending
+        """
+        file_type = file_info.get("file_type", "file")
+        file_path = file_info.get("path")
+        
+        # For images, use IMAGE_URL type (channel will handle upload)
+        if file_type == "image":
+            # Convert local path to file:// URL for channel processing
+            file_url = f"file://{file_path}"
+            logger.info(f"[AgentBridge] Sending image: {file_url}")
+            reply = Reply(ReplyType.IMAGE_URL, file_url)
+            # Attach text message if present (for channels that support text+image)
+            if text_response:
+                reply.text_content = text_response  # Store accompanying text
+            return reply
+        
+        # For all file types (document, video, audio), use FILE type
+        if file_type in ["document", "video", "audio"]:
+            file_url = f"file://{file_path}"
+            logger.info(f"[AgentBridge] Sending {file_type}: {file_url}")
+            reply = Reply(ReplyType.FILE, file_url)
+            reply.file_name = file_info.get("file_name", os.path.basename(file_path))
+            # Attach text message if present
+            if text_response:
+                reply.text_content = text_response
+            return reply
+        
+        # For other unknown file types, return text with file info
+        message = text_response or file_info.get("message", "文件已准备")
+        message += f"\n\n[文件: {file_info.get('file_name', file_path)}]"
+        return Reply(ReplyType.TEXT, message)
+    
+    def _migrate_config_to_env(self, workspace_root: str):
+        """
+        Migrate API keys from config.json to .env file if not already set
+        
+        Args:
+            workspace_root: Workspace directory path (not used, kept for compatibility)
+        """
+        from config import conf
+        import os
+        
+        # Mapping from config.json keys to environment variable names
+        key_mapping = {
+            "open_ai_api_key": "OPENAI_API_KEY",
+            "open_ai_api_base": "OPENAI_API_BASE",
+            "gemini_api_key": "GEMINI_API_KEY",
+            "claude_api_key": "CLAUDE_API_KEY",
+            "linkai_api_key": "LINKAI_API_KEY",
+        }
+        
+        # Use fixed secure location for .env file
+        env_file = os.path.expanduser("~/.cow/.env")
+        
+        # Read existing env vars from .env file
+        existing_env_vars = {}
+        if os.path.exists(env_file):
+            try:
+                with open(env_file, 'r', encoding='utf-8') as f:
+                    for line in f:
+                        line = line.strip()
+                        if line and not line.startswith('#') and '=' in line:
+                            key, _ = line.split('=', 1)
+                            existing_env_vars[key.strip()] = True
+            except Exception as e:
+                logger.warning(f"[AgentBridge] Failed to read .env file: {e}")
+        
+        # Check which keys need to be migrated
+        keys_to_migrate = {}
+        for config_key, env_key in key_mapping.items():
+            # Skip if already in .env file
+            if env_key in existing_env_vars:
+                continue
+            
+            # Get value from config.json
+            value = conf().get(config_key, "")
+            if value and value.strip():  # Only migrate non-empty values
+                keys_to_migrate[env_key] = value.strip()
+        
+        # Log summary if there are keys to skip
+        if existing_env_vars:
+            logger.debug(f"[AgentBridge] {len(existing_env_vars)} env vars already in .env")
+        
+        # Write new keys to .env file
+        if keys_to_migrate:
+            try:
+                # Ensure ~/.cow directory and .env file exist
+                env_dir = os.path.dirname(env_file)
+                if not os.path.exists(env_dir):
+                    os.makedirs(env_dir, exist_ok=True)
+                if not os.path.exists(env_file):
+                    open(env_file, 'a').close()
+                
+                # Append new keys
+                with open(env_file, 'a', encoding='utf-8') as f:
+                    f.write('\n# Auto-migrated from config.json\n')
+                    for key, value in keys_to_migrate.items():
+                        f.write(f'{key}={value}\n')
+                        # Also set in current process
+                        os.environ[key] = value
+                
+                logger.info(f"[AgentBridge] Migrated {len(keys_to_migrate)} API keys from config.json to .env: {list(keys_to_migrate.keys())}")
+            except Exception as e:
+                logger.warning(f"[AgentBridge] Failed to migrate API keys: {e}")
+    
+    def clear_session(self, session_id: str):
+        """
+        Clear a specific session's agent and conversation history
+        
+        Args:
+            session_id: Session identifier to clear
+        """
+        if session_id in self.agents:
+            logger.info(f"[AgentBridge] Clearing session: {session_id}")
+            del self.agents[session_id]
+    
+    def clear_all_sessions(self):
+        """Clear all agent sessions"""
+        logger.info(f"[AgentBridge] Clearing all sessions ({len(self.agents)} total)")
+        self.agents.clear()
+        self.default_agent = None
+    
+    def refresh_all_skills(self) -> int:
+        """
+        Refresh skills in all agent instances after environment variable changes.
+        This allows hot-reload of skills without restarting the agent.
+        
+        Returns:
+            Number of agent instances refreshed
+        """
+        import os
+        from dotenv import load_dotenv
+        from config import conf
+        
+        # Reload environment variables from .env file
+        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        env_file = os.path.join(workspace_root, '.env')
+        
+        if os.path.exists(env_file):
+            load_dotenv(env_file, override=True)
+            logger.info(f"[AgentBridge] Reloaded environment variables from {env_file}")
+        
+        refreshed_count = 0
+        
+        # Refresh default agent
+        if self.default_agent and hasattr(self.default_agent, 'skill_manager'):
+            self.default_agent.skill_manager.refresh_skills()
+            refreshed_count += 1
+            logger.info("[AgentBridge] Refreshed skills in default agent")
+        
+        # Refresh all session agents
+        for session_id, agent in self.agents.items():
+            if hasattr(agent, 'skill_manager'):
+                agent.skill_manager.refresh_skills()
+                refreshed_count += 1
+        
+        if refreshed_count > 0:
+            logger.info(f"[AgentBridge] Refreshed skills in {refreshed_count} agent instance(s)")
+        
+        return refreshed_count
--- a/bridge/agent_event_handler.py
+++ b/bridge/agent_event_handler.py
@@ -0,0 +1,115 @@
+"""
+Agent Event Handler - Handles agent events and thinking process output
+"""
+
+from common.log import logger
+
+
+class AgentEventHandler:
+    """
+    Handles agent events and optionally sends intermediate messages to channel
+    """
+    
+    def __init__(self, context=None, original_callback=None):
+        """
+        Initialize event handler
+        
+        Args:
+            context: COW context (for accessing channel)
+            original_callback: Original event callback to chain
+        """
+        self.context = context
+        self.original_callback = original_callback
+        
+        # Get channel for sending intermediate messages
+        self.channel = None
+        if context:
+            self.channel = context.kwargs.get("channel") if hasattr(context, "kwargs") else None
+        
+        # Track current thinking for channel output
+        self.current_thinking = ""
+        self.turn_number = 0
+    
+    def handle_event(self, event):
+        """
+        Main event handler
+        
+        Args:
+            event: Event dict with type and data
+        """
+        event_type = event.get("type")
+        data = event.get("data", {})
+        
+        # Dispatch to specific handlers
+        if event_type == "turn_start":
+            self._handle_turn_start(data)
+        elif event_type == "message_update":
+            self._handle_message_update(data)
+        elif event_type == "message_end":
+            self._handle_message_end(data)
+        elif event_type == "tool_execution_start":
+            self._handle_tool_execution_start(data)
+        elif event_type == "tool_execution_end":
+            self._handle_tool_execution_end(data)
+        
+        # Call original callback if provided
+        if self.original_callback:
+            self.original_callback(event)
+    
+    def _handle_turn_start(self, data):
+        """Handle turn start event"""
+        self.turn_number = data.get("turn", 0)
+        self.has_tool_calls_in_turn = False
+        self.current_thinking = ""
+    
+    def _handle_message_update(self, data):
+        """Handle message update event (streaming text)"""
+        delta = data.get("delta", "")
+        self.current_thinking += delta
+    
+    def _handle_message_end(self, data):
+        """Handle message end event"""
+        tool_calls = data.get("tool_calls", [])
+        
+        # Only send thinking process if followed by tool calls
+        if tool_calls:
+            if self.current_thinking.strip():
+                logger.debug(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
+                # Send thinking process to channel
+                self._send_to_channel(f"{self.current_thinking.strip()}")
+        else:
+            # No tool calls = final response (logged at agent_stream level)
+            if self.current_thinking.strip():
+                logger.debug(f"💬 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
+        
+        self.current_thinking = ""
+    
+    def _handle_tool_execution_start(self, data):
+        """Handle tool execution start event - logged by agent_stream.py"""
+        pass
+    
+    def _handle_tool_execution_end(self, data):
+        """Handle tool execution end event - logged by agent_stream.py"""
+        pass
+    
+    def _send_to_channel(self, message):
+        """
+        Try to send message to channel
+        
+        Args:
+            message: Message to send
+        """
+        if self.channel:
+            try:
+                from bridge.reply import Reply, ReplyType
+                # Create a Reply object for the message
+                reply = Reply(ReplyType.TEXT, message)
+                self.channel._send(reply, self.context)
+            except Exception as e:
+                logger.debug(f"[AgentEventHandler] Failed to send to channel: {e}")
+    
+    def log_summary(self):
+        """Log execution summary - simplified"""
+        # Summary removed as per user request
+        # Real-time logging during execution is sufficient
+        pass
--- a/bridge/agent_initializer.py
+++ b/bridge/agent_initializer.py
@@ -0,0 +1,375 @@
+"""
+Agent Initializer - Handles agent initialization logic
+"""
+
+import os
+import asyncio
+import datetime
+import time
+from typing import Optional, List
+
+from agent.protocol import Agent
+from agent.tools import ToolManager
+from common.log import logger
+
+
+class AgentInitializer:
+    """
+    Handles agent initialization including:
+    - Workspace setup
+    - Memory system initialization  
+    - Tool loading
+    - System prompt building
+    """
+    
+    def __init__(self, bridge, agent_bridge):
+        """
+        Initialize agent initializer
+        
+        Args:
+            bridge: COW bridge instance
+            agent_bridge: AgentBridge instance (for create_agent method)
+        """
+        self.bridge = bridge
+        self.agent_bridge = agent_bridge
+    
+    def initialize_agent(self, session_id: Optional[str] = None) -> Agent:
+        """
+        Initialize agent for a session
+        
+        Args:
+            session_id: Session ID (None for default agent)
+        
+        Returns:
+            Initialized agent instance
+        """
+        from config import conf
+        
+        # Get workspace from config
+        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+        
+        # Migrate API keys
+        self._migrate_config_to_env(workspace_root)
+        
+        # Load environment variables
+        self._load_env_file()
+        
+        # Initialize workspace
+        from agent.prompt import ensure_workspace, load_context_files, PromptBuilder
+        workspace_files = ensure_workspace(workspace_root, create_templates=True)
+        
+        if session_id is None:
+            logger.info(f"[AgentInitializer] Workspace initialized at: {workspace_root}")
+        
+        # Setup memory system
+        memory_manager, memory_tools = self._setup_memory_system(workspace_root, session_id)
+        
+        # Load tools
+        tools = self._load_tools(workspace_root, memory_manager, memory_tools, session_id)
+        
+        # Initialize scheduler if needed
+        self._initialize_scheduler(tools, session_id)
+        
+        # Load context files
+        context_files = load_context_files(workspace_root)
+        
+        # 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)
+        
+        system_prompt = prompt_builder.build(
+            tools=tools,
+            context_files=context_files,
+            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)
+        max_context_tokens = conf().get("agent_max_context_tokens", 50000)
+        
+        # Create agent
+        agent = self.agent_bridge.create_agent(
+            system_prompt=system_prompt,
+            tools=tools,
+            max_steps=max_steps,
+            output_mode="logger",
+            workspace_dir=workspace_root,
+            skill_manager=skill_manager,
+            enable_skills=True,
+            max_context_tokens=max_context_tokens
+        )
+        
+        # Attach memory manager
+        if memory_manager:
+            agent.memory_manager = memory_manager
+        
+        return agent
+    
+    def _load_env_file(self):
+        """Load environment variables from .env file"""
+        env_file = os.path.expanduser("~/.cow/.env")
+        if os.path.exists(env_file):
+            try:
+                from dotenv import load_dotenv
+                load_dotenv(env_file, override=True)
+            except ImportError:
+                logger.warning("[AgentInitializer] python-dotenv not installed")
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to load .env file: {e}")
+    
+    def _setup_memory_system(self, workspace_root: str, session_id: Optional[str] = None):
+        """
+        Setup memory system
+        
+        Returns:
+            (memory_manager, memory_tools) tuple
+        """
+        memory_manager = None
+        memory_tools = []
+        
+        try:
+            from agent.memory import MemoryManager, MemoryConfig, create_embedding_provider
+            from agent.tools import MemorySearchTool, MemoryGetTool
+            from config import conf
+            
+            # Get OpenAI config
+            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(
+                        provider="openai",
+                        model="text-embedding-3-small",
+                        api_key=openai_api_key,
+                        api_base=openai_api_base or "https://api.openai.com/v1"
+                    )
+                    if session_id is None:
+                        logger.info("[AgentInitializer] OpenAI embedding initialized")
+                except Exception as e:
+                    logger.warning(f"[AgentInitializer] OpenAI embedding failed: {e}")
+            
+            # Create memory manager
+            memory_config = MemoryConfig(workspace_root=workspace_root)
+            memory_manager = MemoryManager(memory_config, embedding_provider=embedding_provider)
+            
+            # Sync memory
+            self._sync_memory(memory_manager, session_id)
+            
+            # Create memory tools
+            memory_tools = [
+                MemorySearchTool(memory_manager),
+                MemoryGetTool(memory_manager)
+            ]
+            
+            if session_id is None:
+                logger.info("[AgentInitializer] Memory system initialized")
+        
+        except Exception as e:
+            logger.warning(f"[AgentInitializer] Memory system not available: {e}")
+        
+        return memory_manager, memory_tools
+    
+    def _sync_memory(self, memory_manager, session_id: Optional[str] = None):
+        """Sync memory database"""
+        try:
+            loop = asyncio.get_event_loop()
+            if loop.is_closed():
+                raise RuntimeError("Event loop is closed")
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+        
+        try:
+            if loop.is_running():
+                asyncio.create_task(memory_manager.sync())
+            else:
+                loop.run_until_complete(memory_manager.sync())
+        except Exception as e:
+            logger.warning(f"[AgentInitializer] Memory sync failed: {e}")
+    
+    def _load_tools(self, workspace_root: str, memory_manager, memory_tools: List, session_id: Optional[str] = None):
+        """Load all tools"""
+        tool_manager = ToolManager()
+        tool_manager.load_tools()
+        
+        tools = []
+        file_config = {
+            "cwd": workspace_root,
+            "memory_manager": memory_manager
+        } if memory_manager else {"cwd": workspace_root}
+        
+        for tool_name in tool_manager.tool_classes.keys():
+            try:
+                # Special handling for EnvConfig tool
+                if tool_name == "env_config":
+                    from agent.tools import EnvConfig
+                    tool = EnvConfig({"agent_bridge": self.agent_bridge})
+                else:
+                    tool = tool_manager.create_tool(tool_name)
+                
+                if tool:
+                    # Apply workspace config to file operation tools
+                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']:
+                        tool.config = file_config
+                        tool.cwd = file_config.get("cwd", getattr(tool, 'cwd', None))
+                        if 'memory_manager' in file_config:
+                            tool.memory_manager = file_config['memory_manager']
+                    tools.append(tool)
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to load tool {tool_name}: {e}")
+        
+        # Add memory tools
+        if memory_tools:
+            tools.extend(memory_tools)
+            if session_id is None:
+                logger.info(f"[AgentInitializer] Added {len(memory_tools)} memory tools")
+        
+        if session_id is None:
+            logger.info(f"[AgentInitializer] Loaded {len(tools)} tools: {[t.name for t in tools]}")
+        
+        return tools
+    
+    def _initialize_scheduler(self, tools: List, session_id: Optional[str] = None):
+        """Initialize scheduler service if needed"""
+        if not self.agent_bridge.scheduler_initialized:
+            try:
+                from agent.tools.scheduler.integration import init_scheduler
+                if init_scheduler(self.agent_bridge):
+                    self.agent_bridge.scheduler_initialized = True
+                    if session_id is None:
+                        logger.info("[AgentInitializer] Scheduler service initialized")
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to initialize scheduler: {e}")
+        
+        # Inject scheduler dependencies
+        if self.agent_bridge.scheduler_initialized:
+            try:
+                from agent.tools.scheduler.integration import get_task_store, get_scheduler_service
+                from agent.tools import SchedulerTool
+                from config import conf
+                
+                task_store = get_task_store()
+                scheduler_service = get_scheduler_service()
+                
+                for tool in tools:
+                    if isinstance(tool, SchedulerTool):
+                        tool.task_store = task_store
+                        tool.scheduler_service = scheduler_service
+                        if not tool.config:
+                            tool.config = {}
+                        tool.config["channel_type"] = conf().get("channel_type", "unknown")
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to inject scheduler dependencies: {e}")
+    
+    def _initialize_skill_manager(self, workspace_root: str, session_id: Optional[str] = None):
+        """Initialize skill manager"""
+        try:
+            from agent.skills import SkillManager
+            skill_manager = SkillManager(workspace_dir=workspace_root)
+            return skill_manager
+        except Exception as e:
+            logger.warning(f"[AgentInitializer] Failed to initialize SkillManager: {e}")
+            return None
+    
+    def _get_runtime_info(self, workspace_root: str):
+        """Get runtime information"""
+        from config import conf
+        
+        now = datetime.datetime.now()
+        
+        # Get timezone info
+        try:
+            offset = -time.timezone if not time.daylight else -time.altzone
+            hours = offset // 3600
+            minutes = (offset % 3600) // 60
+            timezone_name = f"UTC{hours:+03d}:{minutes:02d}" if minutes else f"UTC{hours:+03d}"
+        except Exception:
+            timezone_name = "UTC"
+        
+        # Chinese weekday mapping
+        weekday_map = {
+            'Monday': '星期一', 'Tuesday': '星期二', 'Wednesday': '星期三',
+            'Thursday': '星期四', 'Friday': '星期五', 'Saturday': '星期六', 'Sunday': '星期日'
+        }
+        weekday_zh = weekday_map.get(now.strftime("%A"), now.strftime("%A"))
+        
+        return {
+            "model": conf().get("model", "unknown"),
+            "workspace": workspace_root,
+            "channel": conf().get("channel_type", "unknown"),
+            "current_time": now.strftime("%Y-%m-%d %H:%M:%S"),
+            "weekday": weekday_zh,
+            "timezone": timezone_name
+        }
+    
+    def _migrate_config_to_env(self, workspace_root: str):
+        """Migrate API keys from config.json to .env file"""
+        from config import conf
+        
+        key_mapping = {
+            "open_ai_api_key": "OPENAI_API_KEY",
+            "open_ai_api_base": "OPENAI_API_BASE",
+            "gemini_api_key": "GEMINI_API_KEY",
+            "claude_api_key": "CLAUDE_API_KEY",
+            "linkai_api_key": "LINKAI_API_KEY",
+        }
+        
+        env_file = os.path.expanduser("~/.cow/.env")
+        
+        # Read existing env vars
+        existing_env_vars = {}
+        if os.path.exists(env_file):
+            try:
+                with open(env_file, 'r', encoding='utf-8') as f:
+                    for line in f:
+                        line = line.strip()
+                        if line and not line.startswith('#') and '=' in line:
+                            key, _ = line.split('=', 1)
+                            existing_env_vars[key.strip()] = True
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to read .env file: {e}")
+        
+        # Check which keys need migration
+        keys_to_migrate = {}
+        for config_key, env_key in key_mapping.items():
+            if env_key in existing_env_vars:
+                continue
+            value = conf().get(config_key, "")
+            if value and value.strip():
+                keys_to_migrate[env_key] = value.strip()
+        
+        # Write new keys
+        if keys_to_migrate:
+            try:
+                env_dir = os.path.dirname(env_file)
+                if not os.path.exists(env_dir):
+                    os.makedirs(env_dir, exist_ok=True)
+                if not os.path.exists(env_file):
+                    open(env_file, 'a').close()
+                
+                with open(env_file, 'a', encoding='utf-8') as f:
+                    f.write('\n# Auto-migrated from config.json\n')
+                    for key, value in keys_to_migrate.items():
+                        f.write(f'{key}={value}\n')
+                        os.environ[key] = value
+                
+                logger.info(f"[AgentInitializer] Migrated {len(keys_to_migrate)} API keys to .env: {list(keys_to_migrate.keys())}")
+            except Exception as e:
+                logger.warning(f"[AgentInitializer] Failed to migrate API keys: {e}")
--- a/bridge/bridge.py
+++ b/bridge/bridge.py
@@ -1,4 +1,4 @@
-from bot.bot_factory import create_bot
+from models.bot_factory import create_bot
 from bridge.context import Context
 from bridge.reply import Reply
 from common import const
@@ -23,7 +23,7 @@ class Bridge(object):
        if bot_type:
            self.btype["chat"] = bot_type
        else:
-            model_type = conf().get("model") or const.GPT35
+            model_type = conf().get("model") or const.GPT_41_MINI
            if model_type in ["text-davinci-003"]:
                self.btype["chat"] = const.OPEN_AI
            if conf().get("use_azure_chatgpt", False):
@@ -36,20 +36,24 @@ class Bridge(object):
                self.btype["chat"] = const.QWEN
            if model_type in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
                self.btype["chat"] = const.QWEN_DASHSCOPE
+            # Support Qwen3 and other DashScope models
+            if model_type and (model_type.startswith("qwen") or model_type.startswith("qwq") or model_type.startswith("qvq")):
+                self.btype["chat"] = const.QWEN_DASHSCOPE
            if model_type and model_type.startswith("gemini"):
                self.btype["chat"] = const.GEMINI
-            if model_type in [const.ZHIPU_AI]:
+            if model_type and model_type.startswith("glm"):
                self.btype["chat"] = const.ZHIPU_AI
-            if model_type and model_type.startswith("claude-3"):
+            if model_type and model_type.startswith("claude"):
                self.btype["chat"] = const.CLAUDEAPI

-            if model_type in ["claude"]:
-                self.btype["chat"] = const.CLAUDEAI
-
-            if model_type in ["moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
+            if model_type in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
                self.btype["chat"] = const.MOONSHOT

-            if model_type in ["abab6.5-chat"]:
+            if model_type in [const.MODELSCOPE]:
+                self.btype["chat"] = const.MODELSCOPE
+            
+            # MiniMax models
+            if model_type and (model_type in ["abab6.5-chat", "abab6.5"] or model_type.lower().startswith("minimax")):
                self.btype["chat"] = const.MiniMax

            if conf().get("use_linkai") and conf().get("linkai_api_key"):
@@ -61,6 +65,7 @@ class Bridge(object):

        self.bots = {}
        self.chat_bots = {}
+        self._agent_bridge = None

    # 模型对应的接口
    def get_bot(self, typename):
@@ -101,3 +106,29 @@ class Bridge(object):
        重置bot路由
        """
        self.__init__()
+
+    def get_agent_bridge(self):
+        """
+        Get agent bridge for agent-based conversations
+        """
+        if self._agent_bridge is None:
+            from bridge.agent_bridge import AgentBridge
+            self._agent_bridge = AgentBridge(self)
+        return self._agent_bridge
+
+    def fetch_agent_reply(self, query: str, context: Context = None,
+                          on_event=None, clear_history: bool = False) -> Reply:
+        """
+        Use super agent to handle the query
+
+        Args:
+            query: User query
+            context: Context object
+            on_event: Event callback for streaming
+            clear_history: Whether to clear conversation history
+
+        Returns:
+            Reply object
+        """
+        agent_bridge = self.get_agent_bridge()
+        return agent_bridge.agent_reply(query, context, on_event, clear_history)
--- a/channel/channel.py
+++ b/channel/channel.py
@@ -5,6 +5,8 @@ Message sending channel abstract class
 from bridge.bridge import Bridge
 from bridge.context import Context
 from bridge.reply import *
+from common.log import logger
+from config import conf


 class Channel(object):
@@ -35,7 +37,34 @@ class Channel(object):
        raise NotImplementedError

    def build_reply_content(self, query, context: Context = None) -> Reply:
-        return Bridge().fetch_reply_content(query, context)
+        """
+        Build reply content, using agent if enabled in config
+        """
+        # Check if agent mode is enabled
+        use_agent = conf().get("agent", False)
+
+        if use_agent:
+            try:
+                logger.info("[Channel] Using agent mode")
+
+                # Add channel_type to context if not present
+                if context and "channel_type" not in context:
+                    context["channel_type"] = self.channel_type
+
+                # Use agent bridge to handle the query
+                return Bridge().fetch_agent_reply(
+                    query=query,
+                    context=context,
+                    on_event=None,
+                    clear_history=False
+                )
+            except Exception as e:
+                logger.error(f"[Channel] Agent mode failed, fallback to normal mode: {e}")
+                # Fallback to normal mode if agent fails
+                return Bridge().fetch_reply_content(query, context)
+        else:
+            # Normal mode
+            return Bridge().fetch_reply_content(query, context)

    def build_voice_to_text(self, voice_file) -> Reply:
        return Bridge().fetch_voice_to_text(voice_file)
--- a/channel/channel_factory.py
+++ b/channel/channel_factory.py
@@ -18,9 +18,15 @@ def create_channel(channel_type) -> Channel:
    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":
        from channel.terminal.terminal_channel import TerminalChannel
        ch = TerminalChannel()
+    elif channel_type == 'web':
+        from channel.web.web_channel import WebChannel
+        ch = WebChannel()
    elif channel_type == "wechatmp":
        from channel.wechatmp.wechatmp_channel import WechatMPChannel
        ch = WechatMPChannel(passive_reply=True)
--- a/channel/chat_channel.py
+++ b/channel/chat_channel.py
@@ -64,15 +64,22 @@ class ChatChannel(Channel):
                        check_contain(group_name, group_name_keyword_white_list),
                    ]
                ):
-                    group_chat_in_one_session = conf().get("group_chat_in_one_session", [])
-                    session_id = cmsg.actual_user_id
-                    if any(
-                        [
-                            group_name in group_chat_in_one_session,
-                            "ALL_GROUP" in group_chat_in_one_session,
-                        ]
-                    ):
+                    # Check global group_shared_session config first
+                    group_shared_session = conf().get("group_shared_session", True)
+                    if group_shared_session:
+                        # All users in the group share the same session
                        session_id = group_id
+                    else:
+                        # Check group-specific whitelist (legacy behavior)
+                        group_chat_in_one_session = conf().get("group_chat_in_one_session", [])
+                        session_id = cmsg.actual_user_id
+                        if any(
+                            [
+                                group_name in group_chat_in_one_session,
+                                "ALL_GROUP" in group_chat_in_one_session,
+                            ]
+                        ):
+                            session_id = group_id
                else:
                    logger.debug(f"No need reply, groupName not in whitelist, group_name={group_name}")
                    return None
@@ -146,6 +153,7 @@ class ChatChannel(Channel):
                elif context["origin_ctype"] == ContextType.VOICE:  # 如果源消息是私聊的语音消息，允许不匹配前缀，放宽条件
                    pass
                else:
+                    logger.info("[chat_channel]receive single chat msg, but checkprefix didn't match")
                    return None
            content = content.strip()
            img_match_prefix = check_prefix(content, conf().get("image_create_prefix",[""]))
@@ -165,11 +173,11 @@ class ChatChannel(Channel):
    def _handle(self, context: Context):
        if context is None or not context.content:
            return
-        logger.debug("[chat_channel] ready to handle context: {}".format(context))
+        logger.debug("[chat_channel] handling context: {}".format(context))
        # reply的构建步骤
        reply = self._generate_reply(context)

-        logger.debug("[chat_channel] ready to decorate reply: {}".format(reply))
+        logger.debug("[chat_channel] decorating reply: {}".format(reply))

        # reply的包装步骤
        if reply and reply.content:
@@ -187,7 +195,7 @@ class ChatChannel(Channel):
        )
        reply = e_context["reply"]
        if not e_context.is_pass():
-            logger.debug("[chat_channel] ready to handle context: type={}, content={}".format(context.type, context.content))
+            logger.debug("[chat_channel] type={}, content={}".format(context.type, context.content))
            if context.type == ContextType.TEXT or context.type == ContextType.IMAGE_CREATE:  # 文字和图片消息
                context["channel"] = e_context["channel"]
                reply = super().build_reply_content(context.content, context)
@@ -281,7 +289,100 @@ class ChatChannel(Channel):
            )
            reply = e_context["reply"]
            if not e_context.is_pass() and reply and reply.type:
-                logger.debug("[chat_channel] ready to send reply: {}, context: {}".format(reply, context))
+                logger.debug("[chat_channel] sending reply: {}, context: {}".format(reply, context))
+                
+                # 如果是文本回复，尝试提取并发送图片
+                if reply.type == ReplyType.TEXT:
+                    self._extract_and_send_images(reply, context)
+                # 如果是图片回复但带有文本内容，先发文本再发图片
+                elif reply.type == ReplyType.IMAGE_URL and hasattr(reply, 'text_content') and reply.text_content:
+                    # 先发送文本
+                    text_reply = Reply(ReplyType.TEXT, reply.text_content)
+                    self._send(text_reply, context)
+                    # 短暂延迟后发送图片
+                    time.sleep(0.3)
+                    self._send(reply, context)
+                else:
+                    self._send(reply, context)
+    
+    def _extract_and_send_images(self, reply: Reply, context: Context):
+        """
+        从文本回复中提取图片/视频URL并单独发送
+        支持格式：[图片: /path/to/image.png], [视频: /path/to/video.mp4], ![](url), <img src="url">
+        最多发送5个媒体文件
+        """
+        content = reply.content
+        media_items = []  # [(url, type), ...]
+        
+        # 正则提取各种格式的媒体URL
+        patterns = [
+            (r'\[图片:\s*([^\]]+)\]', 'image'),   # [图片: /path/to/image.png]
+            (r'\[视频:\s*([^\]]+)\]', 'video'),   # [视频: /path/to/video.mp4]
+            (r'!\[.*?\]\(([^\)]+)\)', 'image'),   # ![alt](url) - 默认图片
+            (r'<img[^>]+src=["\']([^"\']+)["\']', 'image'),  # <img src="url">
+            (r'<video[^>]+src=["\']([^"\']+)["\']', 'video'),  # <video src="url">
+            (r'https?://[^\s]+\.(?:jpg|jpeg|png|gif|webp)', 'image'),  # 直接的图片URL
+            (r'https?://[^\s]+\.(?:mp4|avi|mov|wmv|flv)', 'video'),  # 直接的视频URL
+        ]
+        
+        for pattern, media_type in patterns:
+            matches = re.findall(pattern, content, re.IGNORECASE)
+            for match in matches:
+                media_items.append((match, media_type))
+        
+        # 去重（保持顺序）并限制最多5个
+        seen = set()
+        unique_items = []
+        for url, mtype in media_items:
+            if url not in seen:
+                seen.add(url)
+                unique_items.append((url, mtype))
+        media_items = unique_items[:5]
+        
+        if media_items:
+            logger.info(f"[chat_channel] Extracted {len(media_items)} media item(s) from reply")
+            
+            # 先发送文本（保持原文本不变）
+            logger.info(f"[chat_channel] Sending text content before media: {reply.content[:100]}...")
+            self._send(reply, context)
+            logger.info(f"[chat_channel] Text sent, now sending {len(media_items)} media item(s)")
+            
+            # 然后逐个发送媒体文件
+            for i, (url, media_type) in enumerate(media_items):
+                try:
+                    # 判断是本地文件还是URL
+                    if url.startswith(('http://', 'https://')):
+                        # 网络资源
+                        if media_type == 'video':
+                            # 视频使用 FILE 类型发送
+                            media_reply = Reply(ReplyType.FILE, url)
+                            media_reply.file_name = os.path.basename(url)
+                        else:
+                            # 图片使用 IMAGE_URL 类型
+                            media_reply = Reply(ReplyType.IMAGE_URL, url)
+                    elif os.path.exists(url):
+                        # 本地文件
+                        if media_type == 'video':
+                            # 视频使用 FILE 类型，转换为 file:// URL
+                            media_reply = Reply(ReplyType.FILE, f"file://{url}")
+                            media_reply.file_name = os.path.basename(url)
+                        else:
+                            # 图片使用 IMAGE_URL 类型，转换为 file:// URL
+                            media_reply = Reply(ReplyType.IMAGE_URL, f"file://{url}")
+                    else:
+                        logger.warning(f"[chat_channel] Media file not found or invalid URL: {url}")
+                        continue
+                    
+                    # 发送媒体文件（添加小延迟避免频率限制）
+                    if i > 0:
+                        time.sleep(0.5)
+                    self._send(media_reply, context)
+                    logger.info(f"[chat_channel] Sent {media_type} {i+1}/{len(media_items)}: {url[:50]}...")
+                    
+                except Exception as e:
+                    logger.error(f"[chat_channel] Failed to send {media_type} {url}: {e}")
+        else:
+            # 没有媒体文件，正常发送文本
                self._send(reply, context)

    def _send(self, reply: Reply, context: Context, retry_cnt=0):
@@ -337,24 +438,27 @@ class ChatChannel(Channel):
        while True:
            with self.lock:
                session_ids = list(self.sessions.keys())
-                for session_id in session_ids:
+            for session_id in session_ids:
+                with self.lock:
                    context_queue, semaphore = self.sessions[session_id]
-                    if semaphore.acquire(blocking=False):  # 等线程处理完毕才能删除
-                        if not context_queue.empty():
-                            context = context_queue.get()
-                            logger.debug("[chat_channel] consume context: {}".format(context))
-                            future: Future = handler_pool.submit(self._handle, context)
-                            future.add_done_callback(self._thread_pool_callback(session_id, context=context))
+                if semaphore.acquire(blocking=False):  # 等线程处理完毕才能删除
+                    if not context_queue.empty():
+                        context = context_queue.get()
+                        logger.debug("[chat_channel] consume context: {}".format(context))
+                        future: Future = handler_pool.submit(self._handle, context)
+                        future.add_done_callback(self._thread_pool_callback(session_id, context=context))
+                        with self.lock:
                            if session_id not in self.futures:
                                self.futures[session_id] = []
                            self.futures[session_id].append(future)
-                        elif semaphore._initial_value == semaphore._value + 1:  # 除了当前，没有任务再申请到信号量，说明所有任务都处理完毕
+                    elif semaphore._initial_value == semaphore._value + 1:  # 除了当前，没有任务再申请到信号量，说明所有任务都处理完毕
+                        with self.lock:
                            self.futures[session_id] = [t for t in self.futures[session_id] if not t.done()]
                            assert len(self.futures[session_id]) == 0, "thread pool error"
                            del self.sessions[session_id]
-                        else:
-                            semaphore.release()
-            time.sleep(0.1)
+                    else:
+                        semaphore.release()
+            time.sleep(0.2)

    # 取消session_id对应的所有任务，只能取消排队的消息和已提交线程池但未执行的任务
    def cancel_session(self, session_id):
--- a/channel/dingtalk/dingtalk_channel.py
+++ b/channel/dingtalk/dingtalk_channel.py
@@ -8,7 +8,9 @@ import copy
 import json
 # -*- coding=utf-8 -*-
 import logging
+import os
 import time
+import requests

 import dingtalk_stream
 from dingtalk_stream import AckMessage
@@ -100,23 +102,377 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
        super(dingtalk_stream.ChatbotHandler, self).__init__()
        self.logger = self.setup_logger()
        # 历史消息id暂存，用于幂等控制
-        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds"))
-        logger.info("[DingTalk] client_id={}, client_secret={} ".format(
+        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
+        logger.debug("[DingTalk] client_id={}, client_secret={} ".format(
            self.dingtalk_client_id, self.dingtalk_client_secret))
        # 无需群校验和前缀
        conf()["group_name_white_list"] = ["ALL_GROUP"]
        # 单聊无需前缀
        conf()["single_chat_prefix"] = [""]
+        # Access token cache
+        self._access_token = None
+        self._access_token_expires_at = 0
+        # Robot code cache (extracted from incoming messages)
+        self._robot_code = None

    def startup(self):
        credential = dingtalk_stream.Credential(self.dingtalk_client_id, self.dingtalk_client_secret)
        client = dingtalk_stream.DingTalkStreamClient(credential)
        client.register_callback_handler(dingtalk_stream.chatbot.ChatbotMessage.TOPIC, self)
+        logger.info("[DingTalk] ✅ Stream connected, ready to receive messages")
        client.start_forever()
+    
+    def get_access_token(self):
+        """
+        获取企业内部应用的 access_token
+        文档: https://open.dingtalk.com/document/orgapp/obtain-orgapp-token
+        """
+        current_time = time.time()
+        
+        # 如果 token 还没过期，直接返回缓存的 token
+        if self._access_token and current_time < self._access_token_expires_at:
+            return self._access_token
+        
+        # 获取新的 access_token
+        url = "https://api.dingtalk.com/v1.0/oauth2/accessToken"
+        headers = {"Content-Type": "application/json"}
+        data = {
+            "appKey": self.dingtalk_client_id,
+            "appSecret": self.dingtalk_client_secret
+        }
+        
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=10)
+            result = response.json()
+            
+            if response.status_code == 200 and "accessToken" in result:
+                self._access_token = result["accessToken"]
+                # Token 有效期为 2 小时，提前 5 分钟刷新
+                self._access_token_expires_at = current_time + result.get("expireIn", 7200) - 300
+                logger.info("[DingTalk] Access token refreshed successfully")
+                return self._access_token
+            else:
+                logger.error(f"[DingTalk] Failed to get access token: {result}")
+                return None
+        except Exception as e:
+            logger.error(f"[DingTalk] Error getting access token: {e}")
+            return None
+    
+    def send_single_message(self, user_id: str, content: str, robot_code: str) -> bool:
+        """
+        Send message to single user (private chat)
+        API: https://open.dingtalk.com/document/orgapp/chatbots-send-one-on-one-chat-messages-in-batches
+        """
+        access_token = self.get_access_token()
+        if not access_token:
+            logger.error("[DingTalk] Failed to send single message: Access token not available.")
+            return False
+
+        if not robot_code:
+            logger.error("[DingTalk] Cannot send single message: robot_code is required")
+            return False
+
+        url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
+        headers = {
+            "x-acs-dingtalk-access-token": access_token,
+            "Content-Type": "application/json"
+        }
+        data = {
+            "msgParam": json.dumps({"content": content}),
+            "msgKey": "sampleText",
+            "userIds": [user_id],
+            "robotCode": robot_code
+        }
+
+        logger.info(f"[DingTalk] Sending single message to user {user_id} with robot_code {robot_code}")
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=10)
+            result = response.json()
+            
+            if response.status_code == 200 and result.get("processQueryKey"):
+                logger.info(f"[DingTalk] Single message sent successfully to {user_id}")
+                return True
+            else:
+                logger.error(f"[DingTalk] Failed to send single message: {result}")
+                return False
+        except Exception as e:
+            logger.error(f"[DingTalk] Error sending single message: {e}")
+            return False
+    
+    def send_group_message(self, conversation_id: str, content: str, robot_code: str = None):
+        """
+        主动发送群消息
+        文档: https://open.dingtalk.com/document/orgapp/the-robot-sends-a-group-message
+        
+        Args:
+            conversation_id: 会话ID (openConversationId)
+            content: 消息内容
+            robot_code: 机器人编码，默认使用 dingtalk_client_id
+        """
+        access_token = self.get_access_token()
+        if not access_token:
+            logger.error("[DingTalk] Cannot send group message: no access token")
+            return False
+        
+        # Validate robot_code
+        if not robot_code:
+            logger.error("[DingTalk] Cannot send group message: robot_code is required")
+            return False
+        
+        url = "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+        headers = {
+            "x-acs-dingtalk-access-token": access_token,
+            "Content-Type": "application/json"
+        }
+        data = {
+            "msgParam": json.dumps({"content": content}),
+            "msgKey": "sampleText",
+            "openConversationId": conversation_id,
+            "robotCode": robot_code
+        }
+        
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=10)
+            result = response.json()
+            
+            if response.status_code == 200:
+                logger.info(f"[DingTalk] Group message sent successfully to {conversation_id}")
+                return True
+            else:
+                logger.error(f"[DingTalk] Failed to send group message: {result}")
+                return False
+        except Exception as e:
+            logger.error(f"[DingTalk] Error sending group message: {e}")
+            return False
+    
+    def upload_media(self, file_path: str, media_type: str = "image") -> str:
+        """
+        上传媒体文件到钉钉
+        
+        Args:
+            file_path: 本地文件路径或URL
+            media_type: 媒体类型 (image, video, voice, file)
+        
+        Returns:
+            media_id，如果上传失败返回 None
+        """
+        access_token = self.get_access_token()
+        if not access_token:
+            logger.error("[DingTalk] Cannot upload media: no access token")
+            return None
+        
+        # 处理 file:// URL
+        if file_path.startswith("file://"):
+            file_path = file_path[7:]
+        
+        # 如果是 HTTP URL，先下载
+        if file_path.startswith("http://") or file_path.startswith("https://"):
+            try:
+                import uuid
+                response = requests.get(file_path, timeout=(5, 60))
+                if response.status_code != 200:
+                    logger.error(f"[DingTalk] Failed to download file from URL: {file_path}")
+                    return None
+                
+                # 保存到临时文件
+                file_name = os.path.basename(file_path) or f"media_{uuid.uuid4()}"
+                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                tmp_dir = os.path.join(workspace_root, "tmp")
+                os.makedirs(tmp_dir, exist_ok=True)
+                temp_file = os.path.join(tmp_dir, file_name)
+                
+                with open(temp_file, "wb") as f:
+                    f.write(response.content)
+                
+                file_path = temp_file
+                logger.info(f"[DingTalk] Downloaded file to {file_path}")
+            except Exception as e:
+                logger.error(f"[DingTalk] Error downloading file: {e}")
+                return None
+        
+        if not os.path.exists(file_path):
+            logger.error(f"[DingTalk] File not found: {file_path}")
+            return None
+        
+        # 上传到钉钉
+        # 钉钉上传媒体文件 API: https://open.dingtalk.com/document/orgapp/upload-media-files
+        url = "https://oapi.dingtalk.com/media/upload"
+        params = {
+            "access_token": access_token,
+            "type": media_type
+        }
+        
+        try:
+            with open(file_path, "rb") as f:
+                files = {"media": (os.path.basename(file_path), f)}
+                response = requests.post(url, params=params, files=files, timeout=(5, 60))
+                result = response.json()
+                
+                if result.get("errcode") == 0:
+                    media_id = result.get("media_id")
+                    logger.info(f"[DingTalk] Media uploaded successfully, media_id={media_id}")
+                    return media_id
+                else:
+                    logger.error(f"[DingTalk] Failed to upload media: {result}")
+                    return None
+        except Exception as e:
+            logger.error(f"[DingTalk] Error uploading media: {e}")
+            return None
+    
+    def send_image_with_media_id(self, access_token: str, media_id: str, incoming_message, is_group: bool) -> bool:
+        """
+        发送图片消息（使用 media_id）
+        
+        Args:
+            access_token: 访问令牌
+            media_id: 媒体ID
+            incoming_message: 钉钉消息对象
+            is_group: 是否为群聊
+        
+        Returns:
+            是否发送成功
+        """
+        headers = {
+            "x-acs-dingtalk-access-token": access_token,
+            'Content-Type': 'application/json'
+        }
+        
+        msg_param = {
+            "photoURL": media_id  # 钉钉图片消息使用 photoURL 字段
+        }
+        
+        body = {
+            "robotCode": incoming_message.robot_code,
+            "msgKey": "sampleImageMsg",
+            "msgParam": json.dumps(msg_param),
+        }
+        
+        if is_group:
+            # 群聊
+            url = "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+            body["openConversationId"] = incoming_message.conversation_id
+        else:
+            # 单聊
+            url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
+            body["userIds"] = [incoming_message.sender_staff_id]
+        
+        try:
+            response = requests.post(url=url, headers=headers, json=body, timeout=10)
+            result = response.json()
+            
+            logger.info(f"[DingTalk] Image send result: {response.text}")
+            
+            if response.status_code == 200:
+                return True
+            else:
+                logger.error(f"[DingTalk] Send image error: {response.text}")
+                return False
+        except Exception as e:
+            logger.error(f"[DingTalk] Send image exception: {e}")
+            return False
+
+    def send_image_message(self, receiver: str, media_id: str, is_group: bool, robot_code: str) -> bool:
+        """
+        发送图片消息
+        
+        Args:
+            receiver: 接收者ID (user_id 或 conversation_id)
+            media_id: 媒体ID
+            is_group: 是否为群聊
+            robot_code: 机器人编码
+        
+        Returns:
+            是否发送成功
+        """
+        access_token = self.get_access_token()
+        if not access_token:
+            logger.error("[DingTalk] Cannot send image: no access token")
+            return False
+        
+        if not robot_code:
+            logger.error("[DingTalk] Cannot send image: robot_code is required")
+            return False
+        
+        if is_group:
+            # 发送群聊图片
+            url = "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+            headers = {
+                "x-acs-dingtalk-access-token": access_token,
+                "Content-Type": "application/json"
+            }
+            data = {
+                "msgParam": json.dumps({"mediaId": media_id}),
+                "msgKey": "sampleImageMsg",
+                "openConversationId": receiver,
+                "robotCode": robot_code
+            }
+        else:
+            # 发送单聊图片
+            url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
+            headers = {
+                "x-acs-dingtalk-access-token": access_token,
+                "Content-Type": "application/json"
+            }
+            data = {
+                "msgParam": json.dumps({"mediaId": media_id}),
+                "msgKey": "sampleImageMsg",
+                "userIds": [receiver],
+                "robotCode": robot_code
+            }
+        
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=10)
+            result = response.json()
+            
+            if response.status_code == 200:
+                logger.info(f"[DingTalk] Image message sent successfully")
+                return True
+            else:
+                logger.error(f"[DingTalk] Failed to send image message: {result}")
+                return False
+        except Exception as e:
+            logger.error(f"[DingTalk] Error sending image message: {e}")
+            return False
+    
+    def get_image_download_url(self, download_code: str) -> str:
+        """
+        获取图片下载地址
+        返回一个特殊的 URL 格式：dingtalk://download/{robot_code}:{download_code}
+        后续会在 download_image_file 中使用新版 API 下载
+        """
+        # 获取 robot_code
+        if not hasattr(self, '_robot_code_cache'):
+            self._robot_code_cache = None
+        
+        robot_code = self._robot_code_cache
+        
+        if not robot_code:
+            logger.error("[DingTalk] robot_code not available for image download")
+            return None
+        
+        # 返回一个特殊的 URL，包含 robot_code 和 download_code
+        logger.info(f"[DingTalk] Successfully got image download URL for code: {download_code}")
+        return f"dingtalk://download/{robot_code}:{download_code}"

    async def process(self, callback: dingtalk_stream.CallbackMessage):
        try:
            incoming_message = dingtalk_stream.ChatbotMessage.from_dict(callback.data)
+            
+            # 缓存 robot_code，用于后续图片下载
+            if hasattr(incoming_message, 'robot_code'):
+                self._robot_code_cache = incoming_message.robot_code
+            
+            # Debug: 打印完整的 event 数据
+            logger.debug(f"[DingTalk] ===== Incoming Message Debug =====")
+            logger.debug(f"[DingTalk] callback.data keys: {callback.data.keys() if hasattr(callback.data, 'keys') else 'N/A'}")
+            logger.debug(f"[DingTalk] incoming_message attributes: {dir(incoming_message)}")
+            logger.debug(f"[DingTalk] robot_code: {getattr(incoming_message, 'robot_code', 'N/A')}")
+            logger.debug(f"[DingTalk] chatbot_corp_id: {getattr(incoming_message, 'chatbot_corp_id', 'N/A')}")
+            logger.debug(f"[DingTalk] chatbot_user_id: {getattr(incoming_message, 'chatbot_user_id', 'N/A')}")
+            logger.debug(f"[DingTalk] conversation_id: {getattr(incoming_message, 'conversation_id', 'N/A')}")
+            logger.debug(f"[DingTalk] Raw callback.data: {callback.data}")
+            logger.debug(f"[DingTalk] =====================================")
+            
            image_download_handler = self  # 传入方法所在的类实例
            dingtalk_msg = DingTalkMessage(incoming_message, image_download_handler)

@@ -126,7 +482,8 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
                self.handle_single(dingtalk_msg)
            return AckMessage.STATUS_OK, 'OK'
        except Exception as e:
-            logger.error(f"dingtalk process error={e}")
+            logger.error(f"[DingTalk] process error: {e}")
+            logger.exception(e)  # 打印完整堆栈跟踪
            return AckMessage.STATUS_SYSTEM_EXCEPTION, 'ERROR'

    @time_checker
@@ -145,6 +502,43 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
            logger.debug("[DingTalk]receive text msg: {}".format(cmsg.content))
        else:
            logger.debug("[DingTalk]receive other msg: {}".format(cmsg.content))
+        
+        # 处理文件缓存逻辑
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+        
+        # 单聊的 session_id 就是 sender_id
+        session_id = cmsg.from_user_id
+        
+        # 如果是单张图片消息，缓存起来
+        if cmsg.ctype == ContextType.IMAGE:
+            if hasattr(cmsg, 'image_path') and cmsg.image_path:
+                file_cache.add(session_id, cmsg.image_path, file_type='image')
+                logger.info(f"[DingTalk] Image cached for session {session_id}, waiting for user query...")
+            # 单张图片不直接处理，等待用户提问
+            return
+        
+        # 如果是文本消息，检查是否有缓存的文件
+        if cmsg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                # 将缓存的文件附加到文本消息中
+                file_refs = []
+                for file_info in cached_files:
+                    file_path = file_info['path']
+                    file_type = file_info['type']
+                    if file_type == 'image':
+                        file_refs.append(f"[图片: {file_path}]")
+                    elif file_type == 'video':
+                        file_refs.append(f"[视频: {file_path}]")
+                    else:
+                        file_refs.append(f"[文件: {file_path}]")
+                
+                cmsg.content = cmsg.content + "\n" + "\n".join(file_refs)
+                logger.info(f"[DingTalk] Attached {len(cached_files)} cached file(s) to user query")
+                # 清除缓存
+                file_cache.clear(session_id)
+        
        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=False, msg=cmsg)
        if context:
            self.produce(context)
@@ -166,6 +560,46 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
            logger.debug("[DingTalk]receive text msg: {}".format(cmsg.content))
        else:
            logger.debug("[DingTalk]receive other msg: {}".format(cmsg.content))
+        
+        # 处理文件缓存逻辑
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+        
+        # 群聊的 session_id
+        if conf().get("group_shared_session", True):
+            session_id = cmsg.other_user_id  # conversation_id
+        else:
+            session_id = cmsg.from_user_id + "_" + cmsg.other_user_id
+        
+        # 如果是单张图片消息，缓存起来
+        if cmsg.ctype == ContextType.IMAGE:
+            if hasattr(cmsg, 'image_path') and cmsg.image_path:
+                file_cache.add(session_id, cmsg.image_path, file_type='image')
+                logger.info(f"[DingTalk] Image cached for session {session_id}, waiting for user query...")
+            # 单张图片不直接处理，等待用户提问
+            return
+        
+        # 如果是文本消息，检查是否有缓存的文件
+        if cmsg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                # 将缓存的文件附加到文本消息中
+                file_refs = []
+                for file_info in cached_files:
+                    file_path = file_info['path']
+                    file_type = file_info['type']
+                    if file_type == 'image':
+                        file_refs.append(f"[图片: {file_path}]")
+                    elif file_type == 'video':
+                        file_refs.append(f"[视频: {file_path}]")
+                    else:
+                        file_refs.append(f"[文件: {file_path}]")
+                
+                cmsg.content = cmsg.content + "\n" + "\n".join(file_refs)
+                logger.info(f"[DingTalk] Attached {len(cached_files)} cached file(s) to user query")
+                # 清除缓存
+                file_cache.clear(session_id)
+        
        context = self._compose_context(cmsg.ctype, cmsg.content, isgroup=True, msg=cmsg)
        context['no_need_at'] = True
        if context:
@@ -173,32 +607,228 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):


    def send(self, reply: Reply, context: Context):
+        logger.info(f"[DingTalk] send() called with reply.type={reply.type}, content_length={len(str(reply.content))}")
        receiver = context["receiver"]
-        isgroup = context.kwargs['msg'].is_group
-        incoming_message = context.kwargs['msg'].incoming_message
-
-        if conf().get("dingtalk_card_enabled"):
-            logger.info("[Dingtalk] sendMsg={}, receiver={}".format(reply, receiver))
-            def reply_with_text():
-                self.reply_text(reply.content, incoming_message)
-            def reply_with_at_text():
-                self.reply_text("📢 您有一条新的消息，请查看。", incoming_message)
-            def reply_with_ai_markdown():
-                button_list, markdown_content = self.generate_button_markdown_content(context, reply)
-                self.reply_ai_markdown_button(incoming_message, markdown_content, button_list, "", "📌 内容由AI生成", "",[incoming_message.sender_staff_id])
-
-            if reply.type in [ReplyType.IMAGE_URL, ReplyType.IMAGE, ReplyType.TEXT]:
-                if isgroup:
-                    reply_with_ai_markdown()
-                    reply_with_at_text()
-                else:
-                    reply_with_ai_markdown()
+        
+        # Check if msg exists (for scheduled tasks, msg might be None)
+        msg = context.kwargs.get('msg')
+        if msg is None:
+            # 定时任务场景：使用主动发送 API
+            is_group = context.get("isgroup", False)
+            logger.info(f"[DingTalk] Sending scheduled task message to {receiver} (is_group={is_group})")
+            
+            # 使用缓存的 robot_code 或配置的值
+            robot_code = self._robot_code or conf().get("dingtalk_robot_code")
+            logger.info(f"[DingTalk] Using robot_code: {robot_code}, cached: {self._robot_code}, config: {conf().get('dingtalk_robot_code')}")
+            
+            if not robot_code:
+                logger.error(f"[DingTalk] Cannot send scheduled task: robot_code not available. Please send at least one message to the bot first, or configure dingtalk_robot_code in config.json")
+                return
+            
+            # 根据是否群聊选择不同的 API
+            if is_group:
+                success = self.send_group_message(receiver, reply.content, robot_code)
            else:
-                # 暂不支持其它类型消息回复
-                reply_with_text()
-        else:
-            self.reply_text(reply.content, incoming_message)
+                # 单聊场景：尝试从 context 中获取 dingtalk_sender_staff_id
+                sender_staff_id = context.get("dingtalk_sender_staff_id")
+                if not sender_staff_id:
+                    logger.error(f"[DingTalk] Cannot send single chat scheduled message: sender_staff_id not available in context")
+                    return
+                
+                logger.info(f"[DingTalk] Sending single message to staff_id: {sender_staff_id}")
+                success = self.send_single_message(sender_staff_id, reply.content, robot_code)
+            
+            if not success:
+                logger.error(f"[DingTalk] Failed to send scheduled task message")
+            return
+        
+        # 从正常消息中提取并缓存 robot_code
+        if hasattr(msg, 'robot_code'):
+            robot_code = msg.robot_code
+            if robot_code and robot_code != self._robot_code:
+                self._robot_code = robot_code
+                logger.info(f"[DingTalk] Cached robot_code: {robot_code}")
+        
+        isgroup = msg.is_group
+        incoming_message = msg.incoming_message
+        robot_code = self._robot_code or conf().get("dingtalk_robot_code")
+        
+        # 处理图片和视频发送
+        if reply.type == ReplyType.IMAGE_URL:
+            logger.info(f"[DingTalk] Sending image: {reply.content}")
+            
+            # 如果有附加的文本内容，先发送文本
+            if hasattr(reply, 'text_content') and reply.text_content:
+                self.reply_text(reply.text_content, incoming_message)
+                import time
+                time.sleep(0.3)  # 短暂延迟，确保文本先到达
+            
+            media_id = self.upload_media(reply.content, media_type="image")
+            if media_id:
+                # 使用主动发送 API 发送图片
+                access_token = self.get_access_token()
+                if access_token:
+                    success = self.send_image_with_media_id(
+                        access_token,
+                        media_id,
+                        incoming_message,
+                        isgroup
+                    )
+                    if not success:
+                        logger.error("[DingTalk] Failed to send image message")
+                        self.reply_text("抱歉，图片发送失败", incoming_message)
+                else:
+                    logger.error("[DingTalk] Cannot get access token")
+                    self.reply_text("抱歉，图片发送失败（无法获取token）", incoming_message)
+            else:
+                logger.error("[DingTalk] Failed to upload image")
+                self.reply_text("抱歉，图片上传失败", incoming_message)
+            return
+        
+        elif reply.type == ReplyType.FILE:
+            # 如果有附加的文本内容，先发送文本
+            if hasattr(reply, 'text_content') and reply.text_content:
+                self.reply_text(reply.text_content, incoming_message)
+                import time
+                time.sleep(0.3)  # 短暂延迟，确保文本先到达
+            
+            # 判断是否为视频文件
+            file_path = reply.content
+            if file_path.startswith("file://"):
+                file_path = file_path[7:]
+            
+            is_video = file_path.lower().endswith(('.mp4', '.avi', '.mov', '.wmv', '.flv'))
+            
+            access_token = self.get_access_token()
+            if not access_token:
+                logger.error("[DingTalk] Cannot get access token")
+                self.reply_text("抱歉，文件发送失败（无法获取token）", incoming_message)
+                return
+            
+            if is_video:
+                logger.info(f"[DingTalk] Sending video: {reply.content}")
+                media_id = self.upload_media(reply.content, media_type="video")
+                if media_id:
+                    # 发送视频消息
+                    msg_param = {
+                        "duration": "30",  # TODO: 获取实际视频时长
+                        "videoMediaId": media_id,
+                        "videoType": "mp4",
+                        "height": "400",
+                        "width": "600",
+                    }
+                    success = self._send_file_message(
+                        access_token,
+                        incoming_message,
+                        "sampleVideo",
+                        msg_param,
+                        isgroup
+                    )
+                    if not success:
+                        self.reply_text("抱歉，视频发送失败", incoming_message)
+                else:
+                    logger.error("[DingTalk] Failed to upload video")
+                    self.reply_text("抱歉，视频上传失败", incoming_message)
+            else:
+                # 其他文件类型
+                logger.info(f"[DingTalk] Sending file: {reply.content}")
+                media_id = self.upload_media(reply.content, media_type="file")
+                if media_id:
+                    file_name = os.path.basename(file_path)
+                    file_base, file_extension = os.path.splitext(file_name)
+                    msg_param = {
+                        "mediaId": media_id,
+                        "fileName": file_name,
+                        "fileType": file_extension[1:] if file_extension else "file"
+                    }
+                    success = self._send_file_message(
+                        access_token,
+                        incoming_message,
+                        "sampleFile",
+                        msg_param,
+                        isgroup
+                    )
+                    if not success:
+                        self.reply_text("抱歉，文件发送失败", incoming_message)
+                else:
+                    logger.error("[DingTalk] Failed to upload file")
+                    self.reply_text("抱歉，文件上传失败", incoming_message)
+            return
+        
+        # 处理文本消息
+        elif reply.type == ReplyType.TEXT:
+            logger.info(f"[DingTalk] Sending text message, length={len(reply.content)}")
+            if conf().get("dingtalk_card_enabled"):
+                logger.info("[Dingtalk] sendMsg={}, receiver={}".format(reply, receiver))
+                def reply_with_text():
+                    self.reply_text(reply.content, incoming_message)
+                def reply_with_at_text():
+                    self.reply_text("📢 您有一条新的消息，请查看。", incoming_message)
+                def reply_with_ai_markdown():
+                    button_list, markdown_content = self.generate_button_markdown_content(context, reply)
+                    self.reply_ai_markdown_button(incoming_message, markdown_content, button_list, "", "📌 内容由AI生成", "",[incoming_message.sender_staff_id])

+                if reply.type in [ReplyType.IMAGE_URL, ReplyType.IMAGE, ReplyType.TEXT]:
+                    if isgroup:
+                        reply_with_ai_markdown()
+                        reply_with_at_text()
+                    else:
+                        reply_with_ai_markdown()
+                else:
+                    # 暂不支持其它类型消息回复
+                    reply_with_text()
+            else:
+                self.reply_text(reply.content, incoming_message)
+            return
+    
+    def _send_file_message(self, access_token: str, incoming_message, msg_key: str, msg_param: dict, is_group: bool) -> bool:
+        """
+        发送文件/视频消息的通用方法
+        
+        Args:
+            access_token: 访问令牌
+            incoming_message: 钉钉消息对象
+            msg_key: 消息类型 (sampleFile, sampleVideo, sampleAudio)
+            msg_param: 消息参数
+            is_group: 是否为群聊
+        
+        Returns:
+            是否发送成功
+        """
+        headers = {
+            "x-acs-dingtalk-access-token": access_token,
+            'Content-Type': 'application/json'
+        }
+        
+        body = {
+            "robotCode": incoming_message.robot_code,
+            "msgKey": msg_key,
+            "msgParam": json.dumps(msg_param),
+        }
+        
+        if is_group:
+            # 群聊
+            url = "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+            body["openConversationId"] = incoming_message.conversation_id
+        else:
+            # 单聊
+            url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
+            body["userIds"] = [incoming_message.sender_staff_id]
+        
+        try:
+            response = requests.post(url=url, headers=headers, json=body, timeout=10)
+            result = response.json()
+            
+            logger.info(f"[DingTalk] File send result: {response.text}")
+            
+            if response.status_code == 200:
+                return True
+            else:
+                logger.error(f"[DingTalk] Send file error: {response.text}")
+                return False
+        except Exception as e:
+            logger.error(f"[DingTalk] Send file exception: {e}")
+            return False

    def generate_button_markdown_content(self, context, reply):
        image_url = context.kwargs.get("image_url")
--- a/channel/dingtalk/dingtalk_message.py
+++ b/channel/dingtalk/dingtalk_message.py
@@ -1,4 +1,5 @@
 import os
+import re

 import requests
 from dingtalk_stream import ChatbotMessage
@@ -8,6 +9,7 @@ from channel.chat_message import ChatMessage
 # -*- coding=utf-8 -*-
 from common.log import logger
 from common.tmp_dir import TmpDir
+from config import conf


 class DingTalkMessage(ChatMessage):
@@ -22,6 +24,7 @@ class DingTalkMessage(ChatMessage):
        self.create_time = event.create_at
        self.image_content = event.image_content
        self.rich_text_content = event.rich_text_content
+        self.robot_code = event.robot_code  # 机器人编码
        if event.conversation_type == "1":
            self.is_group = False
        else:
@@ -36,15 +39,67 @@ class DingTalkMessage(ChatMessage):
            self.content = event.extensions['content']['recognition'].strip()
            self.ctype = ContextType.TEXT
        elif (self.message_type == 'picture') or (self.message_type == 'richText'):
-            self.ctype = ContextType.IMAGE
            # 钉钉图片类型或富文本类型消息处理
            image_list = event.get_image_list()
-            if len(image_list) > 0:
+            
+            if self.message_type == 'picture' and len(image_list) > 0:
+                # 单张图片消息：下载到工作空间，用于文件缓存
+                self.ctype = ContextType.IMAGE
                download_code = image_list[0]
                download_url = image_download_handler.get_image_download_url(download_code)
-                self.content = download_image_file(download_url, TmpDir().path())
+                
+                # 下载到工作空间 tmp 目录
+                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                tmp_dir = os.path.join(workspace_root, "tmp")
+                os.makedirs(tmp_dir, exist_ok=True)
+                
+                image_path = download_image_file(download_url, tmp_dir)
+                if image_path:
+                    self.content = image_path
+                    self.image_path = image_path  # 保存图片路径用于缓存
+                    logger.info(f"[DingTalk] Downloaded single image to {image_path}")
+                else:
+                    self.content = "[图片下载失败]"
+                    self.image_path = None
+            
+            elif self.message_type == 'richText' and len(image_list) > 0:
+                # 富文本消息：下载所有图片并附加到文本中
+                self.ctype = ContextType.TEXT
+                
+                # 下载到工作空间 tmp 目录
+                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                tmp_dir = os.path.join(workspace_root, "tmp")
+                os.makedirs(tmp_dir, exist_ok=True)
+                
+                # 提取富文本中的文本内容
+                text_content = ""
+                if self.rich_text_content:
+                    # rich_text_content 是一个 RichTextContent 对象，需要从中提取文本
+                    text_list = event.get_text_list()
+                    if text_list:
+                        text_content = "".join(text_list).strip()
+                
+                # 下载所有图片
+                image_paths = []
+                for download_code in image_list:
+                    download_url = image_download_handler.get_image_download_url(download_code)
+                    image_path = download_image_file(download_url, tmp_dir)
+                    if image_path:
+                        image_paths.append(image_path)
+                
+                # 构建消息内容：文本 + 图片路径
+                content_parts = []
+                if text_content:
+                    content_parts.append(text_content)
+                for img_path in image_paths:
+                    content_parts.append(f"[图片: {img_path}]")
+                
+                self.content = "\n".join(content_parts) if content_parts else "[富文本消息]"
+                logger.info(f"[DingTalk] Received richText with {len(image_paths)} image(s): {self.content}")
            else:
-                logger.debug(f"[Dingtalk] messageType :{self.message_type} , imageList isEmpty")
+                self.ctype = ContextType.IMAGE
+                self.content = "[未找到图片]"
+                logger.debug(f"[DingTalk] messageType: {self.message_type}, imageList isEmpty")

        if self.is_group:
            self.from_user_id = event.conversation_id
@@ -58,27 +113,131 @@ class DingTalkMessage(ChatMessage):


 def download_image_file(image_url, temp_dir):
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'
-    }
-    # 设置代理
-    # self.proxies
-    # , proxies=self.proxies
-    response = requests.get(image_url, headers=headers, stream=True, timeout=60 * 5)
-    if response.status_code == 200:
-
-        # 生成文件名
-        file_name = image_url.split("/")[-1].split("?")[0]
-
-        # 检查临时目录是否存在，如果不存在则创建
-        if not os.path.exists(temp_dir):
-            os.makedirs(temp_dir)
-
-        # 将文件保存到临时目录
-        file_path = os.path.join(temp_dir, file_name)
-        with open(file_path, 'wb') as file:
-            file.write(response.content)
-        return file_path
+    """
+    下载图片文件
+    支持两种方式：
+    1. 普通 HTTP(S) URL
+    2. 钉钉 downloadCode: dingtalk://download/{download_code}
+    """
+    # 检查临时目录是否存在，如果不存在则创建
+    if not os.path.exists(temp_dir):
+        os.makedirs(temp_dir)
+    
+    # 处理钉钉 downloadCode
+    if image_url.startswith("dingtalk://download/"):
+        download_code = image_url.replace("dingtalk://download/", "")
+        logger.info(f"[DingTalk] Downloading image with downloadCode: {download_code[:20]}...")
+        
+        # 需要从外部传入 access_token，这里先用一个临时方案
+        # 从 config 获取 dingtalk_client_id 和 dingtalk_client_secret
+        from config import conf
+        client_id = conf().get("dingtalk_client_id")
+        client_secret = conf().get("dingtalk_client_secret")
+        
+        if not client_id or not client_secret:
+            logger.error("[DingTalk] Missing dingtalk_client_id or dingtalk_client_secret")
+            return None
+        
+        # 解析 robot_code 和 download_code
+        parts = download_code.split(":", 1)
+        if len(parts) != 2:
+            logger.error(f"[DingTalk] Invalid download_code format (expected robot_code:download_code): {download_code[:50]}")
+            return None
+        
+        robot_code, actual_download_code = parts
+        
+        # 获取 access_token（使用新版 API）
+        token_url = "https://api.dingtalk.com/v1.0/oauth2/accessToken"
+        token_headers = {
+            "Content-Type": "application/json"
+        }
+        token_body = {
+            "appKey": client_id,
+            "appSecret": client_secret
+        }
+        
+        try:
+            token_response = requests.post(token_url, json=token_body, headers=token_headers, timeout=10)
+            
+            if token_response.status_code == 200:
+                token_data = token_response.json()
+                access_token = token_data.get("accessToken")
+                
+                if not access_token:
+                    logger.error(f"[DingTalk] Failed to get access token: {token_data}")
+                    return None
+                
+                # 获取下载 URL（使用新版 API）
+                download_api_url = "https://api.dingtalk.com/v1.0/robot/messageFiles/download"
+                download_headers = {
+                    "x-acs-dingtalk-access-token": access_token,
+                    "Content-Type": "application/json"
+                }
+                download_body = {
+                    "downloadCode": actual_download_code,
+                    "robotCode": robot_code
+                }
+                
+                download_response = requests.post(download_api_url, json=download_body, headers=download_headers, timeout=10)
+                
+                if download_response.status_code == 200:
+                    download_data = download_response.json()
+                    download_url = download_data.get("downloadUrl")
+                    
+                    if not download_url:
+                        logger.error(f"[DingTalk] No downloadUrl in response: {download_data}")
+                        return None
+                    
+                    # 从 downloadUrl 下载实际图片
+                    image_response = requests.get(download_url, stream=True, timeout=60)
+                    
+                    if image_response.status_code == 200:
+                        # 生成文件名（使用 download_code 的 hash，避免特殊字符）
+                        import hashlib
+                        file_hash = hashlib.md5(actual_download_code.encode()).hexdigest()[:16]
+                        file_name = f"{file_hash}.png"
+                        file_path = os.path.join(temp_dir, file_name)
+                        
+                        with open(file_path, 'wb') as file:
+                            file.write(image_response.content)
+                        
+                        logger.info(f"[DingTalk] Image downloaded successfully: {file_path}")
+                        return file_path
+                    else:
+                        logger.error(f"[DingTalk] Failed to download image from URL: {image_response.status_code}")
+                        return None
+                else:
+                    logger.error(f"[DingTalk] Failed to get download URL: {download_response.status_code}, {download_response.text}")
+                    return None
+            else:
+                logger.error(f"[DingTalk] Failed to get access token: {token_response.status_code}, {token_response.text}")
+                return None
+        except Exception as e:
+            logger.error(f"[DingTalk] Exception downloading image: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            return None
+    
+    # 普通 HTTP(S) URL
    else:
-        logger.info(f"[Dingtalk] Failed to download image file, {response.content}")
-        return None
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'
+        }
+        
+        try:
+            response = requests.get(image_url, headers=headers, stream=True, timeout=60 * 5)
+            if response.status_code == 200:
+                # 生成文件名
+                file_name = image_url.split("/")[-1].split("?")[0]
+                
+                # 将文件保存到临时目录
+                file_path = os.path.join(temp_dir, file_name)
+                with open(file_path, 'wb') as file:
+                    file.write(response.content)
+                return file_path
+            else:
+                logger.info(f"[Dingtalk] Failed to download image file, {response.content}")
+                return None
+        except Exception as e:
+            logger.error(f"[Dingtalk] Exception downloading image: {e}")
+            return None
--- a/channel/feishu/README.md
+++ b/channel/feishu/README.md
@@ -0,0 +1,167 @@
+# 飞书Channel使用说明
+
+飞书Channel支持两种事件接收模式，可以根据部署环境灵活选择。
+
+## 模式对比
+
+| 模式 | 适用场景 | 优点 | 缺点 |
+|------|---------|------|------|
+| **webhook** | 生产环境 | 稳定可靠，官方推荐 | 需要公网IP或域名 |
+| **websocket** | 本地开发 | 无需公网IP，开发便捷 | 需要额外依赖 |
+
+## 配置说明
+
+### 基础配置
+
+在 `config.json` 中添加以下配置:
+
+```json
+{
+  "channel_type": "feishu",
+  "feishu_app_id": "cli_xxxxx",
+  "feishu_app_secret": "your_app_secret",
+  "feishu_token": "your_verification_token",
+  "feishu_bot_name": "你的机器人名称",
+  "feishu_event_mode": "webhook",
+  "feishu_port": 9891
+}
+```
+
+### 配置项说明
+
+- `feishu_app_id`: 飞书应用的App ID
+- `feishu_app_secret`: 飞书应用的App Secret
+- `feishu_token`: 事件订阅的Verification Token
+- `feishu_bot_name`: 机器人名称(用于群聊@判断)
+- `feishu_event_mode`: 事件接收模式，可选值:
+  - `"websocket"`: 长连接模式(默认)
+  - `"webhook"`: HTTP服务器模式
+- `feishu_port`: webhook模式下的HTTP服务端口(默认9891)
+
+## 模式一: Webhook模式(推荐生产环境)
+
+### 1. 配置
+
+```json
+{
+  "feishu_event_mode": "webhook",
+  "feishu_port": 9891
+}
+```
+
+### 2. 启动服务
+
+```bash
+python3 app.py
+```
+
+服务将在 `http://0.0.0.0:9891` 启动。
+
+### 3. 配置飞书应用
+
+1. 登录[飞书开放平台](https://open.feishu.cn/)
+2. 进入应用详情 -> 事件订阅
+3. 选择 **将事件发送至开发者服务器**
+4. 填写请求地址: `http://your-domain:9891/`
+5. 添加事件: `im.message.receive_v1` (接收消息v2.0)
+6. 保存配置
+
+### 4. 注意事项
+
+- 需要有公网IP或域名
+- 确保防火墙开放对应端口
+- 建议使用HTTPS(需要配置反向代理)
+
+## 模式二: WebSocket模式(推荐本地开发)
+
+### 1. 安装依赖
+
+```bash
+pip install lark-oapi
+```
+
+### 2. 配置
+
+```json
+{
+  "feishu_event_mode": "websocket"
+}
+```
+
+### 3. 启动服务
+
+```bash
+python3 app.py
+```
+
+程序将自动建立与飞书开放平台的长连接。
+
+### 4. 配置飞书应用
+
+1. 登录[飞书开放平台](https://open.feishu.cn/)
+2. 进入应用详情 -> 事件订阅
+3. 选择 **使用长连接接收事件**
+4. 添加事件: `im.message.receive_v1` (接收消息v2.0)
+5. 保存配置
+
+### 5. 注意事项
+
+- 无需公网IP
+- 需要能访问公网(建立WebSocket连接)
+- 每个应用最多50个连接
+- 集群模式下消息随机分发到一个客户端
+
+## 平滑迁移
+
+从webhook模式切换到websocket模式(或反向切换):
+
+1. 修改 `config.json` 中的 `feishu_event_mode`
+2. 如果切换到websocket模式，安装 `lark-oapi` 依赖
+3. 重启服务
+4. 在飞书开放平台修改事件订阅方式
+
+**重要**: 同一时间只能使用一种模式，否则会导致消息重复接收。
+
+## 消息去重机制
+
+两种模式都使用相同的消息去重机制:
+
+- 使用 `ExpiredDict` 存储已处理的消息ID
+- 过期时间: 7.1小时
+- 确保消息不会重复处理
+
+## 故障排查
+
+### WebSocket模式连接失败
+
+```
+[FeiShu] lark_oapi not installed
+```
+
+**解决**: 安装依赖 `pip install lark-oapi`
+
+### Webhook模式端口被占用
+
+```
+Address already in use
+```
+
+**解决**: 修改 `feishu_port` 配置或关闭占用端口的进程
+
+### 收不到消息
+
+1. 检查飞书应用的事件订阅配置
+2. 确认已添加 `im.message.receive_v1` 事件
+3. 检查应用权限: 需要 `im:message` 权限
+4. 查看日志中的错误信息
+
+## 开发建议
+
+- **本地开发**: 使用websocket模式，快速迭代
+- **测试环境**: 可以使用webhook模式 + 内网穿透工具(如ngrok)
+- **生产环境**: 使用webhook模式，配置正式域名和HTTPS
+
+## 参考文档
+
+- [飞书开放平台 - 事件订阅](https://open.feishu.cn/document/ukTMukTMukTM/uUTNz4SN1MjL1UzM)
+- [飞书SDK - Python](https://github.com/larksuite/oapi-sdk-python)
--- a/channel/feishu/feishu_channel.py
+++ b/channel/feishu/feishu_channel.py
@@ -1,48 +1,80 @@
 """
 飞书通道接入

+支持两种事件接收模式:
+1. webhook模式: 通过HTTP服务器接收事件(需要公网IP)
+2. websocket模式: 通过长连接接收事件(本地开发友好)
+
+通过配置项 feishu_event_mode 选择模式: "webhook" 或 "websocket"
+
@author Saboteur7
@Date 2023/11/19
 """

+import json
+import os
+import threading
 # -*- coding=utf-8 -*-
 import uuid

 import requests
 import web
-from channel.feishu.feishu_message import FeishuMessage
+
 from bridge.context import Context
+from bridge.context import ContextType
 from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.feishu.feishu_message import FeishuMessage
+from common import utils
+from common.expired_dict import ExpiredDict
 from common.log import logger
 from common.singleton import singleton
 from config import conf
-from common.expired_dict import ExpiredDict
-from bridge.context import ContextType
-from channel.chat_channel import ChatChannel, check_prefix
-from common import utils
-import json
-import os

 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
+
+    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")
+

@singleton
 class FeiShuChanel(ChatChannel):
    feishu_app_id = conf().get('feishu_app_id')
    feishu_app_secret = conf().get('feishu_app_secret')
    feishu_token = conf().get('feishu_token')
+    feishu_event_mode = conf().get('feishu_event_mode', 'websocket')  # webhook 或 websocket

    def __init__(self):
        super().__init__()
        # 历史消息id暂存，用于幂等控制
        self.receivedMsgs = ExpiredDict(60 * 60 * 7.1)
-        logger.info("[FeiShu] app_id={}, app_secret={} verification_token={}".format(
-            self.feishu_app_id, self.feishu_app_secret, self.feishu_token))
+        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
        conf()["group_name_white_list"] = ["ALL_GROUP"]
        conf()["single_chat_prefix"] = [""]

+        # 验证配置
+        if self.feishu_event_mode == 'websocket' and not LARK_SDK_AVAILABLE:
+            logger.error("[FeiShu] websocket mode requires lark_oapi. Please install: pip install lark-oapi")
+            raise Exception("lark_oapi not installed")
+
    def startup(self):
+        if self.feishu_event_mode == 'websocket':
+            self._startup_websocket()
+        else:
+            self._startup_webhook()
+
+    def _startup_webhook(self):
+        """启动HTTP服务器接收事件(webhook模式)"""
+        logger.debug("[FeiShu] Starting in webhook mode...")
        urls = (
            '/', 'channel.feishu.feishu_channel.FeishuController'
        )
@@ -50,6 +82,151 @@ class FeiShuChanel(ChatChannel):
        port = conf().get("feishu_port", 9891)
        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))

+    def _startup_websocket(self):
+        """启动长连接接收事件(websocket模式)"""
+        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", {})
+
+                # 处理消息
+                self._handle_message_event(event)
+
+            except Exception as e:
+                logger.error(f"[FeiShu] websocket handle message error: {e}", exc_info=True)
+
+        # 构建事件分发器
+        event_handler = lark.EventDispatcherHandler.builder("", "") \
+            .register_p2_im_message_receive_v1(handle_message_event) \
+            .build()
+
+        # 创建长连接客户端
+        ws_client = lark.ws.Client(
+            self.feishu_app_id,
+            self.feishu_app_secret,
+            event_handler=event_handler,
+            log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.INFO
+        )
+
+        # 在新线程中启动客户端，避免阻塞主线程
+        def start_client():
+            try:
+                logger.debug("[FeiShu] Websocket client starting...")
+                ws_client.start()
+            except Exception as e:
+                logger.error(f"[FeiShu] Websocket client error: {e}", exc_info=True)
+
+        ws_thread = threading.Thread(target=start_client, daemon=True)
+        ws_thread.start()
+
+        # 保持主线程运行
+        logger.info("[FeiShu] ✅ Websocket connected, ready to receive messages")
+        ws_thread.join()
+
+    def _handle_message_event(self, event: dict):
+        """
+        处理消息事件的核心逻辑
+        webhook和websocket模式共用此方法
+        """
+        if not event.get("message") or not event.get("sender"):
+            logger.warning(f"[FeiShu] invalid message, event={event}")
+            return
+
+        msg = event.get("message")
+
+        # 幂等判断
+        msg_id = msg.get("message_id")
+        if self.receivedMsgs.get(msg_id):
+            logger.warning(f"[FeiShu] repeat msg filtered, msg_id={msg_id}")
+            return
+        self.receivedMsgs[msg_id] = True
+
+        is_group = False
+        chat_type = msg.get("chat_type")
+
+        if chat_type == "group":
+            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
+            # 群聊
+            is_group = True
+            receive_id_type = "chat_id"
+        elif chat_type == "p2p":
+            receive_id_type = "open_id"
+        else:
+            logger.warning("[FeiShu] message ignore")
+            return
+
+        # 构造飞书消息对象
+        feishu_msg = FeishuMessage(event, is_group=is_group, access_token=self.fetch_access_token())
+        if not feishu_msg:
+            return
+
+        # 处理文件缓存逻辑
+        from channel.file_cache import get_file_cache
+        file_cache = get_file_cache()
+        
+        # 获取 session_id（用于缓存关联）
+        if is_group:
+            if conf().get("group_shared_session", True):
+                session_id = msg.get("chat_id")  # 群共享会话
+            else:
+                session_id = feishu_msg.from_user_id + "_" + msg.get("chat_id")
+        else:
+            session_id = feishu_msg.from_user_id
+        
+        # 如果是单张图片消息，缓存起来
+        if feishu_msg.ctype == ContextType.IMAGE:
+            if hasattr(feishu_msg, 'image_path') and feishu_msg.image_path:
+                file_cache.add(session_id, feishu_msg.image_path, file_type='image')
+                logger.info(f"[FeiShu] Image cached for session {session_id}, waiting for user query...")
+            # 单张图片不直接处理，等待用户提问
+            return
+        
+        # 如果是文本消息，检查是否有缓存的文件
+        if feishu_msg.ctype == ContextType.TEXT:
+            cached_files = file_cache.get(session_id)
+            if cached_files:
+                # 将缓存的文件附加到文本消息中
+                file_refs = []
+                for file_info in cached_files:
+                    file_path = file_info['path']
+                    file_type = file_info['type']
+                    if file_type == 'image':
+                        file_refs.append(f"[图片: {file_path}]")
+                    elif file_type == 'video':
+                        file_refs.append(f"[视频: {file_path}]")
+                    else:
+                        file_refs.append(f"[文件: {file_path}]")
+                
+                feishu_msg.content = feishu_msg.content + "\n" + "\n".join(file_refs)
+                logger.info(f"[FeiShu] Attached {len(cached_files)} cached file(s) to user query")
+                # 清除缓存
+                file_cache.clear(session_id)
+
+        context = self._compose_context(
+            feishu_msg.ctype,
+            feishu_msg.content,
+            isgroup=is_group,
+            msg=feishu_msg,
+            receive_id_type=receive_id_type,
+            no_need_at=True
+        )
+        if context:
+            self.produce(context)
+        logger.debug(f"[FeiShu] query={feishu_msg.content}, type={feishu_msg.ctype}")
+
    def send(self, reply: Reply, context: Context):
        msg = context.get("msg")
        is_group = context["isgroup"]
@@ -62,32 +239,79 @@ class FeiShuChanel(ChatChannel):
            "Content-Type": "application/json",
        }
        msg_type = "text"
-        logger.info(f"[FeiShu] start send reply message, type={context.type}, content={reply.content}")
+        logger.debug(f"[FeiShu] sending reply, type={context.type}, content={reply.content[:100]}...")
        reply_content = reply.content
        content_key = "text"
        if reply.type == ReplyType.IMAGE_URL:
            # 图片上传
            reply_content = self._upload_image_url(reply.content, access_token)
            if not reply_content:
-                logger.warning("[FeiShu] upload file failed")
+                logger.warning("[FeiShu] upload image failed")
                return
            msg_type = "image"
            content_key = "image_key"
-        if is_group:
-            # 群聊中直接回复
+        elif reply.type == ReplyType.FILE:
+            # 如果有附加的文本内容，先发送文本
+            if hasattr(reply, 'text_content') and reply.text_content:
+                logger.info(f"[FeiShu] Sending text before file: {reply.text_content[:50]}...")
+                text_reply = Reply(ReplyType.TEXT, reply.text_content)
+                self._send(text_reply, context)
+                import time
+                time.sleep(0.3)  # 短暂延迟，确保文本先到达
+            
+            # 判断是否为视频文件
+            file_path = reply.content
+            if file_path.startswith("file://"):
+                file_path = file_path[7:]
+            
+            is_video = file_path.lower().endswith(('.mp4', '.avi', '.mov', '.wmv', '.flv'))
+            
+            if is_video:
+                # 视频上传（包含duration信息）
+                upload_data = self._upload_video_url(reply.content, access_token)
+                if not upload_data or not upload_data.get('file_key'):
+                    logger.warning("[FeiShu] upload video failed")
+                    return
+                
+                # 视频使用 media 类型（根据官方文档）
+                # 错误码 230055 说明：上传 mp4 时必须使用 msg_type="media"
+                msg_type = "media"
+                reply_content = upload_data  # 完整的上传响应数据（包含file_key和duration）
+                logger.info(f"[FeiShu] Sending video: file_key={upload_data.get('file_key')}, duration={upload_data.get('duration')}ms")
+                content_key = None  # 直接序列化整个对象
+            else:
+                # 其他文件使用 file 类型
+                file_key = self._upload_file_url(reply.content, access_token)
+                if not file_key:
+                    logger.warning("[FeiShu] upload file failed")
+                    return
+                reply_content = file_key
+                msg_type = "file"
+                content_key = "file_key"
+        
+        # Check if we can reply to an existing message (need msg_id)
+        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})
+        logger.debug(f"[FeiShu] Sending message: msg_type={msg_type}, content={content_json[:200]}")
+        
+        if can_reply:
+            # 群聊中回复已有消息
            url = f"https://open.feishu.cn/open-apis/im/v1/messages/{msg.msg_id}/reply"
            data = {
                "msg_type": msg_type,
-                "content": json.dumps({content_key: reply_content})
+                "content": content_json
            }
            res = requests.post(url=url, headers=headers, json=data, timeout=(5, 10))
        else:
+            # 发送新消息（私聊或群聊中无msg_id的情况，如定时任务）
            url = "https://open.feishu.cn/open-apis/im/v1/messages"
            params = {"receive_id_type": context.get("receive_id_type") or "open_id"}
            data = {
                "receive_id": context.get("receiver"),
                "msg_type": msg_type,
-                "content": json.dumps({content_key: reply_content})
+                "content": content_json
            }
            res = requests.post(url=url, headers=headers, params=params, json=data, timeout=(5, 10))
        res = res.json()
@@ -120,7 +344,34 @@ class FeiShuChanel(ChatChannel):


    def _upload_image_url(self, img_url, access_token):
-        logger.debug(f"[WX] start download image, img_url={img_url}")
+        logger.debug(f"[FeiShu] start process image, img_url={img_url}")
+        
+        # Check if it's a local file path (file:// protocol)
+        if img_url.startswith("file://"):
+            local_path = img_url[7:]  # Remove "file://" prefix
+            logger.info(f"[FeiShu] uploading local file: {local_path}")
+            
+            if not os.path.exists(local_path):
+                logger.error(f"[FeiShu] local file not found: {local_path}")
+                return None
+            
+            # Upload directly from local file
+            upload_url = "https://open.feishu.cn/open-apis/im/v1/images"
+            data = {'image_type': 'message'}
+            headers = {'Authorization': f'Bearer {access_token}'}
+            
+            with open(local_path, "rb") as file:
+                upload_response = requests.post(upload_url, files={"image": file}, data=data, headers=headers)
+                logger.info(f"[FeiShu] upload file, res={upload_response.content}")
+                
+                response_data = upload_response.json()
+                if response_data.get("code") == 0:
+                    return response_data.get("data").get("image_key")
+                else:
+                    logger.error(f"[FeiShu] upload failed: {response_data}")
+                    return None
+        
+        # Original logic for HTTP URLs
        response = requests.get(img_url)
        suffix = utils.get_path_suffix(img_url)
        temp_name = str(uuid.uuid4()) + "." + suffix
@@ -143,9 +394,290 @@ class FeiShuChanel(ChatChannel):
            os.remove(temp_name)
            return upload_response.json().get("data").get("image_key")

+    def _get_video_duration(self, file_path: str) -> int:
+        """
+        获取视频时长（毫秒）
+        
+        Args:
+            file_path: 视频文件路径
+        
+        Returns:
+            视频时长（毫秒），如果获取失败返回0
+        """
+        try:
+            import subprocess
+            
+            # 使用 ffprobe 获取视频时长
+            cmd = [
+                'ffprobe',
+                '-v', 'error',
+                '-show_entries', 'format=duration',
+                '-of', 'default=noprint_wrappers=1:nokey=1',
+                file_path
+            ]
+            
+            result = subprocess.run(cmd, capture_output=True, text=True, timeout=10)
+            if result.returncode == 0:
+                duration_seconds = float(result.stdout.strip())
+                duration_ms = int(duration_seconds * 1000)
+                logger.info(f"[FeiShu] Video duration: {duration_seconds:.2f}s ({duration_ms}ms)")
+                return duration_ms
+            else:
+                logger.warning(f"[FeiShu] Failed to get video duration via ffprobe: {result.stderr}")
+                return 0
+        except FileNotFoundError:
+            logger.warning("[FeiShu] ffprobe not found, video duration will be 0. Install ffmpeg to fix this.")
+            return 0
+        except Exception as e:
+            logger.warning(f"[FeiShu] Failed to get video duration: {e}")
+            return 0
+
+    def _upload_video_url(self, video_url, access_token):
+        """
+        Upload video to Feishu and return video info (file_key and duration)
+        Supports:
+        - file:// URLs for local files
+        - http(s):// URLs (download then upload)
+        
+        Returns:
+            dict with 'file_key' and 'duration' (milliseconds), or None if failed
+        """
+        local_path = None
+        temp_file = None
+        
+        try:
+            # For file:// URLs (local files), upload directly
+            if video_url.startswith("file://"):
+                local_path = video_url[7:]  # Remove file:// prefix
+                if not os.path.exists(local_path):
+                    logger.error(f"[FeiShu] local video file not found: {local_path}")
+                    return None
+            else:
+                # For HTTP URLs, download first
+                logger.info(f"[FeiShu] Downloading video from URL: {video_url}")
+                response = requests.get(video_url, timeout=(5, 60))
+                if response.status_code != 200:
+                    logger.error(f"[FeiShu] download video failed, status={response.status_code}")
+                    return None
+                
+                # Save to temp file
+                import uuid
+                file_name = os.path.basename(video_url) or "video.mp4"
+                temp_file = str(uuid.uuid4()) + "_" + file_name
+                
+                with open(temp_file, "wb") as file:
+                    file.write(response.content)
+                
+                logger.info(f"[FeiShu] Video downloaded, size={len(response.content)} bytes")
+                local_path = temp_file
+            
+            # Get video duration
+            duration = self._get_video_duration(local_path)
+            
+            # Upload to Feishu
+            file_name = os.path.basename(local_path)
+            file_ext = os.path.splitext(file_name)[1].lower()
+            file_type_map = {'.mp4': 'mp4'}
+            file_type = file_type_map.get(file_ext, 'mp4')
+            
+            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
+            data = {
+                'file_type': file_type, 
+                'file_name': file_name
+            }
+            # Add duration only if available (required for video/audio)
+            if duration:
+                data['duration'] = duration  # Must be int, not string
+            
+            headers = {'Authorization': f'Bearer {access_token}'}
+            
+            logger.info(f"[FeiShu] Uploading video: file_name={file_name}, duration={duration}ms")
+            
+            with open(local_path, "rb") as file:
+                upload_response = requests.post(
+                    upload_url, 
+                    files={"file": file}, 
+                    data=data, 
+                    headers=headers, 
+                    timeout=(5, 60)
+                )
+                logger.info(f"[FeiShu] upload video response, status={upload_response.status_code}, res={upload_response.content}")
+                
+                response_data = upload_response.json()
+                if response_data.get("code") == 0:
+                    # Add duration to the response data (API doesn't return it)
+                    upload_data = response_data.get("data")
+                    upload_data['duration'] = duration  # Add our calculated duration
+                    logger.info(f"[FeiShu] Upload complete: file_key={upload_data.get('file_key')}, duration={duration}ms")
+                    return upload_data
+                else:
+                    logger.error(f"[FeiShu] upload video failed: {response_data}")
+                    return None
+        
+        except Exception as e:
+            logger.error(f"[FeiShu] upload video exception: {e}")
+            return None
+        
+        finally:
+            # Clean up temp file
+            if temp_file and os.path.exists(temp_file):
+                try:
+                    os.remove(temp_file)
+                except Exception as e:
+                    logger.warning(f"[FeiShu] Failed to remove temp file {temp_file}: {e}")
+
+    def _upload_file_url(self, file_url, access_token):
+        """
+        Upload file to Feishu
+        Supports both local files (file://) and HTTP URLs
+        """
+        logger.debug(f"[FeiShu] start process file, file_url={file_url}")
+        
+        # Check if it's a local file path (file:// protocol)
+        if file_url.startswith("file://"):
+            local_path = file_url[7:]  # Remove "file://" prefix
+            logger.info(f"[FeiShu] uploading local file: {local_path}")
+            
+            if not os.path.exists(local_path):
+                logger.error(f"[FeiShu] local file not found: {local_path}")
+                return None
+            
+            # Get file info
+            file_name = os.path.basename(local_path)
+            file_ext = os.path.splitext(file_name)[1].lower()
+            
+            # Determine file type for Feishu API
+            # Feishu supports: opus, mp4, pdf, doc, xls, ppt, stream (other types)
+            file_type_map = {
+                '.opus': 'opus',
+                '.mp4': 'mp4',
+                '.pdf': 'pdf',
+                '.doc': 'doc', '.docx': 'doc',
+                '.xls': 'xls', '.xlsx': 'xls',
+                '.ppt': 'ppt', '.pptx': 'ppt',
+            }
+            file_type = file_type_map.get(file_ext, 'stream')  # Default to stream for other types
+            
+            # Upload file to Feishu
+            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
+            data = {'file_type': file_type, 'file_name': file_name}
+            headers = {'Authorization': f'Bearer {access_token}'}
+            
+            try:
+                with open(local_path, "rb") as file:
+                    upload_response = requests.post(
+                        upload_url, 
+                        files={"file": file}, 
+                        data=data, 
+                        headers=headers,
+                        timeout=(5, 30)  # 5s connect, 30s read timeout
+                    )
+                    logger.info(f"[FeiShu] upload file response, status={upload_response.status_code}, res={upload_response.content}")
+                    
+                    response_data = upload_response.json()
+                    if response_data.get("code") == 0:
+                        return response_data.get("data").get("file_key")
+                    else:
+                        logger.error(f"[FeiShu] upload file failed: {response_data}")
+                        return None
+            except Exception as e:
+                logger.error(f"[FeiShu] upload file exception: {e}")
+                return None
+        
+        # For HTTP URLs, download first then upload
+        try:
+            response = requests.get(file_url, timeout=(5, 30))
+            if response.status_code != 200:
+                logger.error(f"[FeiShu] download file failed, status={response.status_code}")
+                return None
+            
+            # Save to temp file
+            import uuid
+            file_name = os.path.basename(file_url)
+            temp_name = str(uuid.uuid4()) + "_" + file_name
+            
+            with open(temp_name, "wb") as file:
+                file.write(response.content)
+            
+            # Upload
+            file_ext = os.path.splitext(file_name)[1].lower()
+            file_type_map = {
+                '.opus': 'opus', '.mp4': 'mp4', '.pdf': 'pdf',
+                '.doc': 'doc', '.docx': 'doc',
+                '.xls': 'xls', '.xlsx': 'xls',
+                '.ppt': 'ppt', '.pptx': 'ppt',
+            }
+            file_type = file_type_map.get(file_ext, 'stream')
+            
+            upload_url = "https://open.feishu.cn/open-apis/im/v1/files"
+            data = {'file_type': file_type, 'file_name': file_name}
+            headers = {'Authorization': f'Bearer {access_token}'}
+            
+            with open(temp_name, "rb") as file:
+                upload_response = requests.post(upload_url, files={"file": file}, data=data, headers=headers)
+                logger.info(f"[FeiShu] upload file, res={upload_response.content}")
+                
+                response_data = upload_response.json()
+                os.remove(temp_name)  # Clean up temp file
+                
+                if response_data.get("code") == 0:
+                    return response_data.get("data").get("file_key")
+                else:
+                    logger.error(f"[FeiShu] upload file failed: {response_data}")
+                    return None
+        except Exception as e:
+            logger.error(f"[FeiShu] upload file from URL exception: {e}")
+            return None
+
+    def _compose_context(self, ctype: ContextType, content, **kwargs):
+        context = Context(ctype, content)
+        context.kwargs = kwargs
+        if "origin_ctype" not in context:
+            context["origin_ctype"] = ctype
+
+        cmsg = context["msg"]
+        
+        # Set session_id based on chat type
+        if cmsg.is_group:
+            # Group chat: check if group_shared_session is enabled
+            if conf().get("group_shared_session", True):
+                # All users in the group share the same session context
+                context["session_id"] = cmsg.other_user_id  # group_id
+            else:
+                # Each user has their own session within the group
+                # This ensures:
+                # - Same user in different groups have separate conversation histories
+                # - Same user in private chat and group chat have separate histories
+                context["session_id"] = f"{cmsg.from_user_id}:{cmsg.other_user_id}"
+        else:
+            # Private chat: use user_id only
+            context["session_id"] = cmsg.from_user_id
+        
+        context["receiver"] = cmsg.other_user_id
+
+        if ctype == ContextType.TEXT:
+            # 1.文本请求
+            # 图片生成处理
+            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()
+
+        elif context.type == ContextType.VOICE:
+            # 2.语音请求
+            if "desire_rtype" not in context and conf().get("voice_reply_voice"):
+                context["desire_rtype"] = ReplyType.VOICE
+
+        return context


 class FeishuController:
+    """
+    HTTP服务器控制器，用于webhook模式
+    """
    # 类常量
    FAILED_MSG = '{"success": false}'
    SUCCESS_MSG = '{"success": true}'
@@ -175,80 +707,10 @@ class FeishuController:
            # 处理消息事件
            event = request.get("event")
            if header.get("event_type") == self.MESSAGE_RECEIVE_TYPE and event:
-                if not event.get("message") or not event.get("sender"):
-                    logger.warning(f"[FeiShu] invalid message, msg={request}")
-                    return self.FAILED_MSG
-                msg = event.get("message")
+                channel._handle_message_event(event)

-                # 幂等判断
-                if channel.receivedMsgs.get(msg.get("message_id")):
-                    logger.warning(f"[FeiShu] repeat msg filtered, event_id={header.get('event_id')}")
-                    return self.SUCCESS_MSG
-                channel.receivedMsgs[msg.get("message_id")] = True
-
-                is_group = False
-                chat_type = msg.get("chat_type")
-                if chat_type == "group":
-                    if not msg.get("mentions") and msg.get("message_type") == "text":
-                        # 群聊中未@不响应
-                        return self.SUCCESS_MSG
-                    if 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 self.SUCCESS_MSG
-                    # 群聊
-                    is_group = True
-                    receive_id_type = "chat_id"
-                elif chat_type == "p2p":
-                    receive_id_type = "open_id"
-                else:
-                    logger.warning("[FeiShu] message ignore")
-                    return self.SUCCESS_MSG
-                # 构造飞书消息对象
-                feishu_msg = FeishuMessage(event, is_group=is_group, access_token=channel.fetch_access_token())
-                if not feishu_msg:
-                    return self.SUCCESS_MSG
-
-                context = self._compose_context(
-                    feishu_msg.ctype,
-                    feishu_msg.content,
-                    isgroup=is_group,
-                    msg=feishu_msg,
-                    receive_id_type=receive_id_type,
-                    no_need_at=True
-                )
-                if context:
-                    channel.produce(context)
-                logger.info(f"[FeiShu] query={feishu_msg.content}, type={feishu_msg.ctype}")
            return self.SUCCESS_MSG

        except Exception as e:
            logger.error(e)
            return self.FAILED_MSG
-
-    def _compose_context(self, ctype: ContextType, content, **kwargs):
-        context = Context(ctype, content)
-        context.kwargs = kwargs
-        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:
-            # 1.文本请求
-            # 图片生成处理
-            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()
-
-        elif context.type == ContextType.VOICE:
-            # 2.语音请求
-            if "desire_rtype" not in context and conf().get("voice_reply_voice"):
-                context["desire_rtype"] = ReplyType.VOICE
-
-        return context
--- a/channel/feishu/feishu_message.py
+++ b/channel/feishu/feishu_message.py
@@ -1,10 +1,12 @@
 from bridge.context import ContextType
 from channel.chat_message import ChatMessage
 import json
+import os
 import requests
 from common.log import logger
 from common.tmp_dir import TmpDir
 from common import utils
+from config import conf


 class FeishuMessage(ChatMessage):
@@ -22,6 +24,119 @@ class FeishuMessage(ChatMessage):
            self.ctype = ContextType.TEXT
            content = json.loads(msg.get('content'))
            self.content = content.get("text").strip()
+        elif msg_type == "image":
+            # 单张图片消息：下载并缓存，等待用户提问时一起发送
+            self.ctype = ContextType.IMAGE
+            content = json.loads(msg.get("content"))
+            image_key = content.get("image_key")
+            
+            # 下载图片到工作空间临时目录
+            workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+            tmp_dir = os.path.join(workspace_root, "tmp")
+            os.makedirs(tmp_dir, exist_ok=True)
+            image_path = os.path.join(tmp_dir, f"{image_key}.png")
+            
+            # 下载图片
+            url = f"https://open.feishu.cn/open-apis/im/v1/messages/{msg.get('message_id')}/resources/{image_key}"
+            headers = {"Authorization": "Bearer " + access_token}
+            params = {"type": "image"}
+            response = requests.get(url=url, headers=headers, params=params)
+            
+            if response.status_code == 200:
+                with open(image_path, "wb") as f:
+                    f.write(response.content)
+                logger.info(f"[FeiShu] Downloaded single image, key={image_key}, path={image_path}")
+                self.content = image_path
+                self.image_path = image_path  # 保存图片路径
+            else:
+                logger.error(f"[FeiShu] Failed to download single image, key={image_key}, status={response.status_code}")
+                self.content = f"[图片下载失败: {image_key}]"
+                self.image_path = None
+        elif msg_type == "post":
+            # 富文本消息，可能包含图片、文本等多种元素
+            content = json.loads(msg.get("content"))
+            
+            # 飞书富文本消息结构：content 直接包含 title 和 content 数组
+            # 不是嵌套在 post 字段下
+            title = content.get("title", "")
+            content_list = content.get("content", [])
+            
+            logger.info(f"[FeiShu] Post message - title: '{title}', content_list length: {len(content_list)}")
+            
+            # 收集所有图片和文本
+            image_keys = []
+            text_parts = []
+            
+            if title:
+                text_parts.append(title)
+            
+            for block in content_list:
+                logger.debug(f"[FeiShu] Processing block: {block}")
+                # block 本身就是元素列表
+                if not isinstance(block, list):
+                    continue
+                    
+                for element in block:
+                    element_tag = element.get("tag")
+                    logger.debug(f"[FeiShu] Element tag: {element_tag}, element: {element}")
+                    if element_tag == "img":
+                        # 找到图片元素
+                        image_key = element.get("image_key")
+                        if image_key:
+                            image_keys.append(image_key)
+                    elif element_tag == "text":
+                        # 文本元素
+                        text_content = element.get("text", "")
+                        if text_content:
+                            text_parts.append(text_content)
+            
+            logger.info(f"[FeiShu] Parsed - images: {len(image_keys)}, text_parts: {text_parts}")
+            
+            # 富文本消息统一作为文本消息处理
+            self.ctype = ContextType.TEXT
+            
+            if image_keys:
+                # 如果包含图片，下载并在文本中引用本地路径
+                workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+                tmp_dir = os.path.join(workspace_root, "tmp")
+                os.makedirs(tmp_dir, exist_ok=True)
+                
+                # 保存图片路径映射
+                self.image_paths = {}
+                for image_key in image_keys:
+                    image_path = os.path.join(tmp_dir, f"{image_key}.png")
+                    self.image_paths[image_key] = image_path
+                
+                def _download_images():
+                    for image_key, image_path in self.image_paths.items():
+                        url = f"https://open.feishu.cn/open-apis/im/v1/messages/{self.msg_id}/resources/{image_key}"
+                        headers = {"Authorization": "Bearer " + access_token}
+                        params = {"type": "image"}
+                        response = requests.get(url=url, headers=headers, params=params)
+                        if response.status_code == 200:
+                            with open(image_path, "wb") as f:
+                                f.write(response.content)
+                            logger.info(f"[FeiShu] Image downloaded from post message, key={image_key}, path={image_path}")
+                        else:
+                            logger.error(f"[FeiShu] Failed to download image from post, key={image_key}, status={response.status_code}")
+                
+                # 立即下载图片，不使用延迟下载
+                # 因为 TEXT 类型消息不会调用 prepare()
+                _download_images()
+                
+                # 构建消息内容：文本 + 图片路径
+                content_parts = []
+                if text_parts:
+                    content_parts.append("\n".join(text_parts).strip())
+                for image_key, image_path in self.image_paths.items():
+                    content_parts.append(f"[图片: {image_path}]")
+                
+                self.content = "\n".join(content_parts)
+                logger.info(f"[FeiShu] Received post message with {len(image_keys)} image(s) and text: {self.content}")
+            else:
+                # 纯文本富文本消息
+                self.content = "\n".join(text_parts).strip() if text_parts else "[富文本消息]"
+                logger.info(f"[FeiShu] Received post message (text only): {self.content}")
        elif msg_type == "file":
            self.ctype = ContextType.FILE
            content = json.loads(msg.get("content"))
--- a/channel/file_cache.py
+++ b/channel/file_cache.py
@@ -0,0 +1,100 @@
+"""
+文件缓存管理器
+用于缓存单独发送的文件消息（图片、视频、文档等），在用户提问时自动附加
+"""
+import time
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class FileCache:
+    """文件缓存管理器，按 session_id 缓存文件，TTL=2分钟"""
+    
+    def __init__(self, ttl=120):
+        """
+        Args:
+            ttl: 缓存过期时间（秒），默认2分钟
+        """
+        self.cache = {}
+        self.ttl = ttl
+    
+    def add(self, session_id: str, file_path: str, file_type: str = "image"):
+        """
+        添加文件到缓存
+        
+        Args:
+            session_id: 会话ID
+            file_path: 文件本地路径
+            file_type: 文件类型（image, video, file 等）
+        """
+        if session_id not in self.cache:
+            self.cache[session_id] = {
+                'files': [],
+                'timestamp': time.time()
+            }
+        
+        # 添加文件（去重）
+        file_info = {'path': file_path, 'type': file_type}
+        if file_info not in self.cache[session_id]['files']:
+            self.cache[session_id]['files'].append(file_info)
+            logger.info(f"[FileCache] Added {file_type} to cache for session {session_id}: {file_path}")
+    
+    def get(self, session_id: str) -> list:
+        """
+        获取缓存的文件列表
+        
+        Args:
+            session_id: 会话ID
+        
+        Returns:
+            文件信息列表 [{'path': '...', 'type': 'image'}, ...]，如果没有或已过期返回空列表
+        """
+        if session_id not in self.cache:
+            return []
+        
+        item = self.cache[session_id]
+        
+        # 检查是否过期
+        if time.time() - item['timestamp'] > self.ttl:
+            logger.info(f"[FileCache] Cache expired for session {session_id}, clearing...")
+            del self.cache[session_id]
+            return []
+        
+        return item['files']
+    
+    def clear(self, session_id: str):
+        """
+        清除指定会话的缓存
+        
+        Args:
+            session_id: 会话ID
+        """
+        if session_id in self.cache:
+            logger.info(f"[FileCache] Cleared cache for session {session_id}")
+            del self.cache[session_id]
+    
+    def cleanup_expired(self):
+        """清理所有过期的缓存"""
+        current_time = time.time()
+        expired_sessions = []
+        
+        for session_id, item in self.cache.items():
+            if current_time - item['timestamp'] > self.ttl:
+                expired_sessions.append(session_id)
+        
+        for session_id in expired_sessions:
+            del self.cache[session_id]
+            logger.debug(f"[FileCache] Cleaned up expired cache for session {session_id}")
+        
+        if expired_sessions:
+            logger.info(f"[FileCache] Cleaned up {len(expired_sessions)} expired cache(s)")
+
+
+# 全局单例
+_file_cache = FileCache()
+
+
+def get_file_cache() -> FileCache:
+    """获取全局文件缓存实例"""
+    return _file_cache
--- a/channel/web/README.md
+++ b/channel/web/README.md
@@ -0,0 +1,10 @@
+# Web Channel
+
+提供了一个默认的AI对话页面，可展示文本、图片等消息交互，支持markdown语法渲染，兼容插件执行。
+
+# 使用说明
+
+ - 在 `config.json` 配置文件中的 `channel_type` 字段填入 `web`
+ - 程序运行后将监听9899端口，浏览器访问 http://localhost:9899/chat 即可使用
+ - 监听端口可以在配置文件 `web_port` 中自定义
+ - 对于Docker运行方式，如果需要外部访问，需要在 `docker-compose.yml` 中通过 ports配置将端口监听映射到宿主机
--- a/channel/web/chat.html
+++ b/channel/web/chat.html
--- a/channel/web/static/axios.min.js
+++ b/channel/web/static/axios.min.js
--- a/channel/web/static/favicon.ico
+++ b/channel/web/static/favicon.ico
--- a/channel/web/static/github.png
+++ b/channel/web/static/github.png
--- a/channel/web/static/logo.jpg
+++ b/channel/web/static/logo.jpg
--- a/channel/web/web_channel.py
+++ b/channel/web/web_channel.py
@@ -0,0 +1,332 @@
+import sys
+import time
+import web
+import json
+import uuid
+import io
+from queue import Queue, Empty
+from bridge.context import *
+from bridge.reply import Reply, ReplyType
+from channel.chat_channel import ChatChannel, check_prefix
+from channel.chat_message import ChatMessage
+from common.log import logger
+from common.singleton import singleton
+from config import conf
+import os
+import mimetypes  # 添加这行来处理MIME类型
+import threading
+import logging
+
+class WebMessage(ChatMessage):
+    def __init__(
+        self,
+        msg_id,
+        content,
+        ctype=ContextType.TEXT,
+        from_user_id="User",
+        to_user_id="Chatgpt",
+        other_user_id="Chatgpt",
+    ):
+        self.msg_id = msg_id
+        self.ctype = ctype
+        self.content = content
+        self.from_user_id = from_user_id
+        self.to_user_id = to_user_id
+        self.other_user_id = other_user_id
+
+
+@singleton
+class WebChannel(ChatChannel):
+    NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE]
+    _instance = None
+    
+    # def __new__(cls):
+    #     if cls._instance is None:
+    #         cls._instance = super(WebChannel, cls).__new__(cls)
+    #     return cls._instance
+
+    def __init__(self):
+        super().__init__()
+        self.msg_id_counter = 0  # 添加消息ID计数器
+        self.session_queues = {}  # 存储session_id到队列的映射
+        self.request_to_session = {}  # 存储request_id到session_id的映射
+
+
+    def _generate_msg_id(self):
+        """生成唯一的消息ID"""
+        self.msg_id_counter += 1
+        return str(int(time.time())) + str(self.msg_id_counter)
+
+    def _generate_request_id(self):
+        """生成唯一的请求ID"""
+        return str(uuid.uuid4())
+
+    def send(self, reply: Reply, context: Context):
+        try:
+            if reply.type in self.NOT_SUPPORT_REPLYTYPE:
+                logger.warning(f"Web channel doesn't support {reply.type} yet")
+                return
+
+            if reply.type == ReplyType.IMAGE_URL:
+                time.sleep(0.5)
+
+            # 获取请求ID和会话ID
+            request_id = context.get("request_id", None)
+            
+            if not request_id:
+                logger.error("No request_id found in context, cannot send message")
+                return
+                
+            # 通过request_id获取session_id
+            session_id = self.request_to_session.get(request_id)
+            if not session_id:
+                logger.error(f"No session_id found for request {request_id}")
+                return
+            
+            # 检查是否有会话队列
+            if session_id in self.session_queues:
+                # 创建响应数据，包含请求ID以区分不同请求的响应
+                response_data = {
+                    "type": str(reply.type),
+                    "content": reply.content,
+                    "timestamp": time.time(),
+                    "request_id": request_id
+                }
+                self.session_queues[session_id].put(response_data)
+                logger.debug(f"Response sent to queue for session {session_id}, request {request_id}")
+            else:
+                logger.warning(f"No response queue found for session {session_id}, response dropped")
+            
+        except Exception as e:
+            logger.error(f"Error in send method: {e}")
+
+    def post_message(self):
+        """
+        Handle incoming messages from users via POST request.
+        Returns a request_id for tracking this specific request.
+        """
+        try:
+            data = web.data()  # 获取原始POST数据
+            json_data = json.loads(data)
+            session_id = json_data.get('session_id', f'session_{int(time.time())}')
+            prompt = json_data.get('message', '')
+            
+            # 生成请求ID
+            request_id = self._generate_request_id()
+            
+            # 将请求ID与会话ID关联
+            self.request_to_session[request_id] = session_id
+            
+            # 确保会话队列存在
+            if session_id not in self.session_queues:
+                self.session_queues[session_id] = Queue()
+            
+            # Web channel 不需要前缀，确保消息能通过前缀检查
+            trigger_prefixs = conf().get("single_chat_prefix", [""])
+            if check_prefix(prompt, trigger_prefixs) is None:
+                # 如果没有匹配到前缀，给消息加上第一个前缀
+                if trigger_prefixs:
+                    prompt = trigger_prefixs[0] + prompt
+                    logger.debug(f"[WebChannel] Added prefix to message: {prompt}")
+            
+            # 创建消息对象
+            msg = WebMessage(self._generate_msg_id(), prompt)
+            msg.from_user_id = session_id  # 使用会话ID作为用户ID
+            
+            # 创建上下文，明确指定 isgroup=False
+            context = self._compose_context(ContextType.TEXT, prompt, msg=msg, isgroup=False)
+            
+            # 检查 context 是否为 None（可能被插件过滤等）
+            if context is None:
+                logger.warning(f"[WebChannel] Context is None for session {session_id}, message may be filtered")
+                return json.dumps({"status": "error", "message": "Message was filtered"})
+
+            # 覆盖必要的字段（_compose_context 会设置默认值，但我们需要使用实际的 session_id）
+            context["session_id"] = session_id
+            context["receiver"] = session_id
+            context["request_id"] = request_id
+            
+            # 异步处理消息 - 只传递上下文
+            threading.Thread(target=self.produce, args=(context,)).start()
+            
+            # 返回请求ID
+            return json.dumps({"status": "success", "request_id": request_id})
+            
+        except Exception as e:
+            logger.error(f"Error processing message: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+    def poll_response(self):
+        """
+        Poll for responses using the session_id.
+        """
+        try:
+            data = web.data()
+            json_data = json.loads(data)
+            session_id = json_data.get('session_id')
+            
+            if not session_id or session_id not in self.session_queues:
+                return json.dumps({"status": "error", "message": "Invalid session ID"})
+            
+            # 尝试从队列获取响应，不等待
+            try:
+                # 使用peek而不是get，这样如果前端没有成功处理，下次还能获取到
+                response = self.session_queues[session_id].get(block=False)
+                
+                # 返回响应，包含请求ID以区分不同请求
+                return json.dumps({
+                    "status": "success", 
+                    "has_content": True,
+                    "content": response["content"],
+                    "request_id": response["request_id"],
+                    "timestamp": response["timestamp"]
+                })
+                
+            except Empty:
+                # 没有新响应
+                return json.dumps({"status": "success", "has_content": False})
+                
+        except Exception as e:
+            logger.error(f"Error polling response: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+    def chat_page(self):
+        """Serve the chat HTML page."""
+        file_path = os.path.join(os.path.dirname(__file__), 'chat.html')  # 使用绝对路径
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+
+    def startup(self):
+        port = conf().get("web_port", 9899)
+        
+        # 打印可用渠道类型提示
+        logger.info("[WebChannel] 当前channel为web，可修改 config.json 配置文件中的 channel_type 字段进行切换。全部可用类型为：")
+        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(f"[WebChannel] 🌐 本地访问: http://localhost:{port}/chat")
+        logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port}/chat (请将YOUR_IP替换为服务器IP)")
+        logger.info("[WebChannel] ✅ Web对话网页已运行")
+        
+        # 确保静态文件目录存在
+        static_dir = os.path.join(os.path.dirname(__file__), 'static')
+        if not os.path.exists(static_dir):
+            os.makedirs(static_dir)
+            logger.debug(f"[WebChannel] Created static directory: {static_dir}")
+        
+        urls = (
+            '/', 'RootHandler',
+            '/message', 'MessageHandler',
+            '/poll', 'PollHandler',
+            '/chat', 'ChatHandler',
+            '/config', 'ConfigHandler',
+            '/assets/(.*)', 'AssetsHandler',
+        )
+        app = web.application(urls, globals(), autoreload=False)
+        
+        # 完全禁用web.py的HTTP日志输出
+        web.httpserver.LogMiddleware.log = lambda self, status, environ: None
+        
+        # 配置web.py的日志级别为ERROR
+        logging.getLogger("web").setLevel(logging.ERROR)
+        logging.getLogger("web.httpserver").setLevel(logging.ERROR)
+        
+        # 抑制 web.py 默认的服务器启动消息
+        old_stdout = sys.stdout
+        sys.stdout = io.StringIO()
+        try:
+            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        finally:
+            sys.stdout = old_stdout
+
+
+class RootHandler:
+    def GET(self):
+        # 重定向到/chat
+        raise web.seeother('/chat')
+
+
+class MessageHandler:
+    def POST(self):
+        return WebChannel().post_message()
+
+
+class PollHandler:
+    def POST(self):
+        return WebChannel().poll_response()
+
+
+class ChatHandler:
+    def GET(self):
+        # 正常返回聊天页面
+        file_path = os.path.join(os.path.dirname(__file__), 'chat.html')
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+
+
+class ConfigHandler:
+    def GET(self):
+        """返回前端需要的配置信息"""
+        try:
+            use_agent = conf().get("agent", False)
+            
+            if use_agent:
+                title = "CowAgent"
+                subtitle = "我可以帮你解答问题、管理计算机、创造和执行技能，并通过长期记忆不断成长"
+            else:
+                title = "AI 助手"
+                subtitle = "我可以回答问题、提供信息或者帮助您完成各种任务"
+            
+            return json.dumps({
+                "status": "success",
+                "use_agent": use_agent,
+                "title": title,
+                "subtitle": subtitle
+            })
+        except Exception as e:
+            logger.error(f"Error getting config: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class AssetsHandler:
+    def GET(self, file_path):  # 修改默认参数
+        try:
+            # 如果请求是/static/，需要处理
+            if file_path == '':
+                # 返回目录列表...
+                pass
+
+            # 获取当前文件的绝对路径
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            static_dir = os.path.join(current_dir, 'static')
+
+            full_path = os.path.normpath(os.path.join(static_dir, file_path))
+
+            # 安全检查：确保请求的文件在static目录内
+            if not os.path.abspath(full_path).startswith(os.path.abspath(static_dir)):
+                logger.error(f"Security check failed for path: {full_path}")
+                raise web.notfound()
+
+            if not os.path.exists(full_path) or not os.path.isfile(full_path):
+                logger.error(f"File not found: {full_path}")
+                raise web.notfound()
+
+            # 设置正确的Content-Type
+            content_type = mimetypes.guess_type(full_path)[0]
+            if content_type:
+                web.header('Content-Type', content_type)
+            else:
+                # 默认为二进制流
+                web.header('Content-Type', 'application/octet-stream')
+
+            # 读取并返回文件内容
+            with open(full_path, 'rb') as f:
+                return f.read()
+
+        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()
--- a/channel/wechat/wcf_channel.py
+++ b/channel/wechat/wcf_channel.py
@@ -0,0 +1,179 @@
+# 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 ''
--- a/channel/wechat/wcf_message.py
+++ b/channel/wechat/wcf_message.py
@@ -0,0 +1,58 @@
+# 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()
--- a/channel/wechat/wechat_channel.py
+++ b/channel/wechat/wechat_channel.py
@@ -9,7 +9,6 @@ import json
 import os
 import threading
 import time
-
 import requests

 from bridge.context import *
@@ -21,6 +20,7 @@ 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 *
@@ -100,7 +100,10 @@ def qrCallback(uuid, status, qrcode):
        qr = qrcode.QRCode(border=1)
        qr.add_data(url)
        qr.make(fit=True)
-        qr.print_ascii(invert=True)
+        try:
+            qr.print_ascii(invert=True)
+        except UnicodeEncodeError:
+            print("ASCII QR code printing failed due to encoding issues.")


@singleton
@@ -109,28 +112,40 @@ class WechatChannel(ChatChannel):

    def __init__(self):
        super().__init__()
-        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds"))
+        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
        self.auto_login_times = 0

    def startup(self):
        try:
-            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()
+            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)

@@ -202,7 +217,7 @@ class WechatChannel(ChatChannel):
            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)
+        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)

@@ -210,9 +225,11 @@ class WechatChannel(ChatChannel):
    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:
@@ -229,6 +246,12 @@ class WechatChannel(ChatChannel):
                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
@@ -266,6 +289,7 @@ def _send_login_success():
    except Exception as e:
        pass

+
 def _send_logout():
    try:
        from common.linkai_client import chat_client
@@ -274,6 +298,7 @@ def _send_logout():
    except Exception as e:
        pass

+
 def _send_qr_code(qrcode_list: list):
    try:
        from common.linkai_client import chat_client
@@ -281,3 +306,4 @@ def _send_qr_code(qrcode_list: list):
            chat_client.send_qrcode(qrcode_list)
    except Exception as e:
        pass
+
--- a/channel/wechat/wechat_message.py
+++ b/channel/wechat/wechat_message.py
@@ -14,6 +14,11 @@ class WechatMessage(ChatMessage):
        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"]
@@ -26,30 +31,47 @@ class WechatMessage(ChatMessage):
            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 and ("加入群聊" in itchat_msg["Content"] or "加入了群聊" in itchat_msg["Content"]):
+            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 "加入了群聊" in itchat_msg["Content"]:
-                    self.ctype = ContextType.JOIN_GROUP
-                    self.content = itchat_msg["Content"]
-                    self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[-1]
-                elif "加入群聊" in itchat_msg["Content"]:
-                    self.ctype = ContextType.JOIN_GROUP
+                    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 is_group and ("移出了群聊" in itchat_msg["Content"]):
-                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 "拍了拍我" in 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"]
-                if is_group:
-                    self.actual_user_nickname = re.findall(r"\"(.*?)\"", itchat_msg["Content"])[0]
            else:
                raise NotImplementedError("Unsupported note message: " + itchat_msg["Content"])
        elif itchat_msg["Type"] == ATTACHMENT:
--- a/channel/wechatcom/README.md
+++ b/channel/wechatcom/README.md
@@ -78,8 +78,8 @@ Error code: 60020, message: "not allow to access from your ip, ...from ip: xx.xx

 ~~填写配置后，将部署完成后的网址```**.railway.app/wxcomapp```，填写在上一步的URL中。发送信息后观察日志，把报错的IP加入到可信IP。（每次重启后都需要加入可信IP）~~

-## 测试体验
+~~## 测试体验~~

-AIGC开放社区中已经部署了多个可免费使用的Bot，扫描下方的二维码会自动邀请你来体验。
+~~AIGC开放社区中已经部署了多个可免费使用的Bot，扫描下方的二维码会自动邀请你来体验。~~

-<img width="200" src="../../docs/images/aigcopen.png">
+~~<img width="200" src="../../docs/images/aigcopen.png">~~
--- a/channel/wechatcom/wechatcomapp_channel.py
+++ b/channel/wechatcom/wechatcomapp_channel.py
@@ -1,6 +1,7 @@
 # -*- coding=utf-8 -*-
 import io
 import os
+import sys
 import time

 import requests
@@ -17,7 +18,7 @@ from channel.wechatcom.wechatcomapp_client import WechatComAppClient
 from channel.wechatcom.wechatcomapp_message import WechatComAppMessage
 from common.log import logger
 from common.singleton import singleton
-from common.utils import compress_imgfile, fsize, split_string_by_utf8_length
+from common.utils import compress_imgfile, fsize, split_string_by_utf8_length, convert_webp_to_png, remove_markdown_symbol
 from config import conf, subscribe_msg
 from voice.audio_convert import any_to_amr, split_audio

@@ -35,24 +36,33 @@ class WechatComAppChannel(ChatChannel):
        self.agent_id = conf().get("wechatcomapp_agent_id")
        self.token = conf().get("wechatcomapp_token")
        self.aes_key = conf().get("wechatcomapp_aes_key")
-        print(self.corp_id, self.secret, self.agent_id, self.token, self.aes_key)
        logger.info(
-            "[wechatcom] init: corp_id: {}, secret: {}, agent_id: {}, token: {}, aes_key: {}".format(self.corp_id, self.secret, self.agent_id, self.token, self.aes_key)
+            "[wechatcom] Initializing WeCom app channel, corp_id: {}, agent_id: {}".format(self.corp_id, self.agent_id)
        )
        self.crypto = WeChatCrypto(self.token, self.aes_key, self.corp_id)
        self.client = WechatComAppClient(self.corp_id, self.secret)

    def startup(self):
        # start message listener
-        urls = ("/wxcomapp", "channel.wechatcom.wechatcomapp_channel.Query")
+        urls = ("/wxcomapp/?", "channel.wechatcom.wechatcomapp_channel.Query")
        app = web.application(urls, globals(), autoreload=False)
        port = conf().get("wechatcomapp_port", 9898)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        logger.info("[wechatcom] ✅ WeCom app channel started successfully")
+        logger.info("[wechatcom] 📡 Listening on http://0.0.0.0:{}/wxcomapp/".format(port))
+        logger.info("[wechatcom] 🤖 Ready to receive messages")
+        
+        # Suppress web.py's default server startup message
+        old_stdout = sys.stdout
+        sys.stdout = io.StringIO()
+        try:
+            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        finally:
+            sys.stdout = old_stdout

    def send(self, reply: Reply, context: Context):
        receiver = context["receiver"]
        if reply.type in [ReplyType.TEXT, ReplyType.ERROR, ReplyType.INFO]:
-            reply_text = reply.content
+            reply_text = remove_markdown_symbol(reply.content)
            texts = split_string_by_utf8_length(reply_text, MAX_UTF8_LEN)
            if len(texts) > 1:
                logger.info("[wechatcom] text too long, split into {} parts".format(len(texts)))
@@ -74,6 +84,10 @@ class WechatComAppChannel(ChatChannel):
                    response = self.client.media.upload("voice", open(path, "rb"))
                    logger.debug("[wechatcom] upload voice response: {}".format(response))
                    media_ids.append(response["media_id"])
+            except ImportError as e:
+                logger.error("[wechatcom] voice conversion failed: {}".format(e))
+                logger.error("[wechatcom] please install pydub: pip install pydub")
+                return
            except WeChatClientException as e:
                logger.error("[wechatcom] upload voice failed: {}".format(e))
                return
@@ -99,6 +113,12 @@ class WechatComAppChannel(ChatChannel):
                image_storage = compress_imgfile(image_storage, 10 * 1024 * 1024 - 1)
                logger.info("[wechatcom] image compressed, sz={}".format(fsize(image_storage)))
            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
            try:
                response = self.client.media.upload("image", image_storage)
                logger.debug("[wechatcom] upload image response: {}".format(response))
@@ -156,11 +176,12 @@ class Query:
        logger.debug("[wechatcom] receive message: {}, msg= {}".format(message, msg))
        if msg.type == "event":
            if msg.event == "subscribe":
-                reply_content = subscribe_msg()
-                if reply_content:
-                    reply = create_reply(reply_content, msg).render()
-                    res = channel.crypto.encrypt_message(reply, nonce, timestamp)
-                    return res
+                pass
+                # reply_content = subscribe_msg()
+                # if reply_content:
+                #     reply = create_reply(reply_content, msg).render()
+                #     res = channel.crypto.encrypt_message(reply, nonce, timestamp)
+                #     return res
        else:
            try:
                wechatcom_msg = WechatComAppMessage(msg, client=channel.client)
--- a/channel/wechatcom/wechatcomapp_client.py
+++ b/channel/wechatcom/wechatcomapp_client.py
@@ -1,21 +1,43 @@
+# wechatcomapp_client.py
 import threading
 import time
-
 from wechatpy.enterprise import WeChatClient

-
 class WechatComAppClient(WeChatClient):
    def __init__(self, corp_id, secret, access_token=None, session=None, timeout=None, auto_retry=True):
        super(WechatComAppClient, self).__init__(corp_id, secret, access_token, session, timeout, auto_retry)
        self.fetch_access_token_lock = threading.Lock()
+        self._active_refresh()
+        
+    def _active_refresh(self):
+        """启动主动刷新的后台线程"""
+        def refresh_loop():
+            while True:
+                now = time.time()
+                expires_at = self.session.get(f"{self.corp_id}_expires_at", 0)
+                
+                # 提前10分钟刷新(600秒)
+                if expires_at - now < 600:
+                    with self.fetch_access_token_lock:
+                        # 双重检查避免重复刷新
+                        if self.session.get(f"{self.corp_id}_expires_at", 0) - time.time() < 600:
+                            super(WechatComAppClient, self).fetch_access_token()
+                # 每次检查间隔60秒
+                time.sleep(60)
+                
+        # 启动守护线程
+        refresh_thread = threading.Thread(
+            target=refresh_loop,
+            daemon=True,
+            name="wechatcom_token_refresh_thread"
+        )
+        refresh_thread.start()

-    def fetch_access_token(self):  # 重载父类方法，加锁避免多线程重复获取access_token
+    def fetch_access_token(self):
        with self.fetch_access_token_lock:
            access_token = self.session.get(self.access_token_key)
-            if access_token:
-                if not self.expires_at:
-                    return access_token
-                timestamp = time.time()
-                if self.expires_at - timestamp > 60:
-                    return access_token
-            return super().fetch_access_token()
+            expires_at = self.session.get(f"{self.corp_id}_expires_at", 0)
+            
+            if access_token and expires_at > time.time() + 60:
+                return access_token
+            return super().fetch_access_token()
--- a/channel/wechatmp/wechatmp_channel.py
+++ b/channel/wechatmp/wechatmp_channel.py
@@ -19,9 +19,13 @@ from channel.wechatmp.common import *
 from channel.wechatmp.wechatmp_client import WechatMPClient
 from common.log import logger
 from common.singleton import singleton
-from common.utils import split_string_by_utf8_length
+from common.utils import split_string_by_utf8_length, remove_markdown_symbol
 from config import conf
-from voice.audio_convert import any_to_mp3, split_audio
+
+try:
+    from voice.audio_convert import any_to_mp3, split_audio
+except ImportError as e:
+    logger.debug("import voice.audio_convert failed, voice features will not be supported: {}".format(e))

 # If using SSL, uncomment the following lines, and modify the certificate path.
 # from cheroot.server import HTTPServer
@@ -81,30 +85,35 @@ class WechatMPChannel(ChatChannel):
        receiver = context["receiver"]
        if self.passive_reply:
            if reply.type == ReplyType.TEXT or reply.type == ReplyType.INFO or reply.type == ReplyType.ERROR:
-                reply_text = reply.content
+                reply_text = remove_markdown_symbol(reply.content)
                logger.info("[wechatmp] text cached, receiver {}\n{}".format(receiver, reply_text))
                self.cache_dict[receiver].append(("text", reply_text))
            elif reply.type == ReplyType.VOICE:
-                voice_file_path = reply.content
-                duration, files = split_audio(voice_file_path, 60 * 1000)
-                if len(files) > 1:
-                    logger.info("[wechatmp] voice too long {}s > 60s , split into {} parts".format(duration / 1000.0, len(files)))
+                try:
+                    voice_file_path = reply.content
+                    duration, files = split_audio(voice_file_path, 60 * 1000)
+                    if len(files) > 1:
+                        logger.info("[wechatmp] voice too long {}s > 60s , split into {} parts".format(duration / 1000.0, len(files)))

-                for path in files:
-                    # support: <2M, <60s, mp3/wma/wav/amr
-                    try:
-                        with open(path, "rb") as f:
-                            response = self.client.material.add("voice", f)
-                            logger.debug("[wechatmp] upload voice response: {}".format(response))
-                            f_size = os.fstat(f.fileno()).st_size
-                            time.sleep(1.0 + 2 * f_size / 1024 / 1024)
-                            # todo check media_id
-                    except WeChatClientException as e:
-                        logger.error("[wechatmp] upload voice failed: {}".format(e))
-                        return
-                    media_id = response["media_id"]
-                    logger.info("[wechatmp] voice uploaded, receiver {}, media_id {}".format(receiver, media_id))
-                    self.cache_dict[receiver].append(("voice", media_id))
+                    for path in files:
+                        # support: <2M, <60s, mp3/wma/wav/amr
+                        try:
+                            with open(path, "rb") as f:
+                                response = self.client.material.add("voice", f)
+                                logger.debug("[wechatmp] upload voice response: {}".format(response))
+                                f_size = os.fstat(f.fileno()).st_size
+                                time.sleep(1.0 + 2 * f_size / 1024 / 1024)
+                                # todo check media_id
+                        except WeChatClientException as e:
+                            logger.error("[wechatmp] upload voice failed: {}".format(e))
+                            return
+                        media_id = response["media_id"]
+                        logger.info("[wechatmp] voice uploaded, receiver {}, media_id {}".format(receiver, media_id))
+                        self.cache_dict[receiver].append(("voice", media_id))
+                except ImportError as e:
+                    logger.error("[wechatmp] voice conversion failed: {}".format(e))
+                    logger.error("[wechatmp] please install pydub: pip install pydub")
+                    return

            elif reply.type == ReplyType.IMAGE_URL:  # 从网络下载图片
                img_url = reply.content
@@ -213,6 +222,10 @@ class WechatMPChannel(ChatChannel):
                        logger.debug("[wechatcom] upload voice response: {}".format(response))
                        media_ids.append(response["media_id"])
                        os.remove(path)
+                except ImportError as e:
+                    logger.error("[wechatmp] voice conversion failed: {}".format(e))
+                    logger.error("[wechatmp] please install pydub: pip install pydub")
+                    return
                except WeChatClientException as e:
                    logger.error("[wechatmp] upload voice failed: {}".format(e))
                    return
--- a/common/const.py
+++ b/common/const.py
@@ -1,72 +1,165 @@
-# bot_type
+# 厂商类型
 OPEN_AI = "openAI"
 CHATGPT = "chatGPT"
-BAIDU = "baidu"  # 百度文心一言模型
+BAIDU = "baidu"
 XUNFEI = "xunfei"
 CHATGPTONAZURE = "chatGPTOnAzure"
 LINKAI = "linkai"
-CLAUDEAI = "claude"  # 使用cookie的历史模型
-CLAUDEAPI= "claudeAPI"  # 通过Claude api调用模型
-QWEN = "qwen"  # 旧版通义模型
-QWEN_DASHSCOPE = "dashscope"  # 通义新版sdk和api key
-
-
-GEMINI = "gemini"  # gemini-1.0-pro
+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"
 MOONSHOT = "moonshot"
 MiniMax = "minimax"
+MODELSCOPE = "modelscope"

-
-# model
+# 模型列表
+# Claude (Anthropic)
 CLAUDE3 = "claude-3-opus-20240229"
+CLAUDE_3_OPUS = "claude-3-opus-latest"
+CLAUDE_3_OPUS_0229 = "claude-3-opus-20240229"
+CLAUDE_3_SONNET = "claude-3-sonnet-20240229"
+CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
+CLAUDE_35_SONNET = "claude-3-5-sonnet-latest"  # 带 latest 标签的模型名称，会不断更新指向最新发布的模型
+CLAUDE_35_SONNET_1022 = "claude-3-5-sonnet-20241022"  # 带具体日期的模型名称，会固定为该日期发布的模型
+CLAUDE_35_SONNET_0620 = "claude-3-5-sonnet-20240620"
+CLAUDE_4_OPUS = "claude-opus-4-0"
+CLAUDE_4_SONNET = "claude-sonnet-4-0"    # Claude Sonnet 4.0 - Agent推荐模型
+CLAUDE_4_5_SONNET = "claude-sonnet-4-5"  # Claude Sonnet 4.5 - Agent推荐模型
+
+# Gemini (Google)
+GEMINI_PRO = "gemini-1.0-pro"
+GEMINI_15_flash = "gemini-1.5-flash"
+GEMINI_15_PRO = "gemini-1.5-pro"
+GEMINI_20_flash_exp = "gemini-2.0-flash-exp"  # exp结尾为实验模型，会逐步不再支持
+GEMINI_20_FLASH = "gemini-2.0-flash"  # 正式版模型
+GEMINI_25_FLASH_PRE = "gemini-2.5-flash-preview-05-20"  # preview为预览版模型，主要是新能力体验
+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 - Agent推荐模型
+
+# OpenAI
 GPT35 = "gpt-3.5-turbo"
 GPT35_0125 = "gpt-3.5-turbo-0125"
 GPT35_1106 = "gpt-3.5-turbo-1106"
-
-GPT_4o = "gpt-4o"
+GPT4 = "gpt-4"
+GPT4_06_13 = "gpt-4-0613"
+GPT4_32k = "gpt-4-32k"
+GPT4_32k_06_13 = "gpt-4-32k-0613"
 GPT4_TURBO = "gpt-4-turbo"
 GPT4_TURBO_PREVIEW = "gpt-4-turbo-preview"
-GPT4_TURBO_04_09 = "gpt-4-turbo-2024-04-09"
 GPT4_TURBO_01_25 = "gpt-4-0125-preview"
 GPT4_TURBO_11_06 = "gpt-4-1106-preview"
+GPT4_TURBO_04_09 = "gpt-4-turbo-2024-04-09"
 GPT4_VISION_PREVIEW = "gpt-4-vision-preview"
-
-GPT4 = "gpt-4"
-GPT4_32k = "gpt-4-32k"
-GPT4_06_13 = "gpt-4-0613"
-GPT4_32k_06_13 = "gpt-4-32k-0613"
-
+GPT_4o = "gpt-4o"
+GPT_4O_0806 = "gpt-4o-2024-08-06"
+GPT_4o_MINI = "gpt-4o-mini"
+GPT_41 = "gpt-4.1"
+GPT_41_MINI = "gpt-4.1-mini"
+GPT_41_NANO = "gpt-4.1-nano"
+GPT_5 = "gpt-5"
+GPT_5_MINI = "gpt-5-mini"
+GPT_5_NANO = "gpt-5-nano"
+O1 = "o1-preview"
+O1_MINI = "o1-mini"
 WHISPER_1 = "whisper-1"
 TTS_1 = "tts-1"
 TTS_1_HD = "tts-1-hd"

-WEN_XIN = "wenxin"
-WEN_XIN_4 = "wenxin-4"
+# DeepSeek
+DEEPSEEK_CHAT = "deepseek-chat"  # DeepSeek-V3对话模型
+DEEPSEEK_REASONER = "deepseek-reasoner"  # DeepSeek-R1模型

+# Qwen (通义千问 - 阿里云)
+QWEN = "qwen"
 QWEN_TURBO = "qwen-turbo"
 QWEN_PLUS = "qwen-plus"
 QWEN_MAX = "qwen-max"
+QWEN_LONG = "qwen-long"
+QWEN3_MAX = "qwen3-max"  # Qwen3 Max - Agent推荐模型
+QWQ_PLUS = "qwq-plus"

+# MiniMax
+MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1 - Agent推荐模型
+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_4 = "glm-4"
+GLM_4_PLUS = "glm-4-plus"
+GLM_4_flash = "glm-4-flash"
+GLM_4_LONG = "glm-4-long"
+GLM_4_ALLTOOLS = "glm-4-alltools"
+GLM_4_0520 = "glm-4-0520"
+GLM_4_AIR = "glm-4-air"
+GLM_4_AIRX = "glm-4-airx"
+GLM_4_7 = "glm-4.7"  # 智谱 GLM-4.7 - Agent推荐模型
+
+# Kimi (Moonshot)
+MOONSHOT = "moonshot"
+
+# 其他模型
+WEN_XIN = "wenxin"
+WEN_XIN_4 = "wenxin-4"
+XUNFEI = "xunfei"
 LINKAI_35 = "linkai-3.5"
 LINKAI_4_TURBO = "linkai-4-turbo"
 LINKAI_4o = "linkai-4o"
+MODELSCOPE = "modelscope"

-GEMINI_PRO = "gemini-1.0-pro"
-GEMINI_15_flash = "gemini-1.5-flash"
-GEMINI_15_PRO = "gemini-1.5-pro"
+GITEE_AI_MODEL_LIST = ["Yi-34B-Chat", "InternVL2-8B", "deepseek-coder-33B-instruct", "InternVL2.5-26B", "Qwen2-VL-72B", "Qwen2.5-32B-Instruct", "glm-4-9b-chat", "codegeex4-all-9b", "Qwen2.5-Coder-32B-Instruct", "Qwen2.5-72B-Instruct", "Qwen2.5-7B-Instruct", "Qwen2-72B-Instruct", "Qwen2-7B-Instruct", "code-raccoon-v1", "Qwen2.5-14B-Instruct"]
+
+MODELSCOPE_MODEL_LIST = ["LLM-Research/c4ai-command-r-plus-08-2024","mistralai/Mistral-Small-Instruct-2409","mistralai/Ministral-8B-Instruct-2410","mistralai/Mistral-Large-Instruct-2407",
+                          "Qwen/Qwen2.5-Coder-32B-Instruct","Qwen/Qwen2.5-Coder-14B-Instruct","Qwen/Qwen2.5-Coder-7B-Instruct","Qwen/Qwen2.5-72B-Instruct","Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct","Qwen/QwQ-32B-Preview",
+                          "LLM-Research/Llama-3.3-70B-Instruct","opencompass/CompassJudger-1-32B-Instruct","Qwen/QVQ-72B-Preview","LLM-Research/Meta-Llama-3.1-405B-Instruct","LLM-Research/Meta-Llama-3.1-8B-Instruct","Qwen/Qwen2-VL-7B-Instruct","LLM-Research/Meta-Llama-3.1-70B-Instruct",
+                          "Qwen/Qwen2.5-14B-Instruct-1M","Qwen/Qwen2.5-7B-Instruct-1M","Qwen/Qwen2.5-VL-3B-Instruct","Qwen/Qwen2.5-VL-7B-Instruct","Qwen/Qwen2.5-VL-72B-Instruct","deepseek-ai/DeepSeek-R1-Distill-Llama-70B","deepseek-ai/DeepSeek-R1-Distill-Llama-8B","deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+                          "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B","deepseek-ai/DeepSeek-R1-Distill-Qwen-7B","deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B","deepseek-ai/DeepSeek-R1","deepseek-ai/DeepSeek-V3","Qwen/QwQ-32B"]

 MODEL_LIST = [
+              # Claude
+              CLAUDE3, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, 
+              CLAUDE_35_SONNET, CLAUDE_35_SONNET_1022, CLAUDE_35_SONNET_0620, CLAUDE_3_SONNET, CLAUDE_3_HAIKU, 
+              "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3.5-sonnet",
+              
+              # Gemini
+              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
              GPT35, GPT35_0125, GPT35_1106, "gpt-3.5-turbo-16k",
-              GPT_4o, GPT4_TURBO, GPT4_TURBO_PREVIEW, GPT4_TURBO_01_25, GPT4_TURBO_11_06, GPT4, GPT4_32k, GPT4_06_13, GPT4_32k_06_13,
-              WEN_XIN, WEN_XIN_4,
-              XUNFEI, ZHIPU_AI, MOONSHOT, MiniMax,
-              GEMINI, GEMINI_PRO, GEMINI_15_flash, GEMINI_15_PRO,
-              "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3-opus-20240229", "claude-3.5-sonnet",
-              "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
-              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX,
-              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o
+              GPT4, GPT4_06_13, GPT4_32k, GPT4_32k_06_13,
+              GPT4_TURBO, GPT4_TURBO_PREVIEW, GPT4_TURBO_01_25, GPT4_TURBO_11_06, GPT4_TURBO_04_09,
+              GPT_4o, GPT_4O_0806, GPT_4o_MINI,
+              GPT_41, GPT_41_MINI, GPT_41_NANO,
+              GPT_5, GPT_5_MINI, GPT_5_NANO,
+              O1, O1_MINI,
+              
+              # DeepSeek
+              DEEPSEEK_CHAT, DEEPSEEK_REASONER,
+              
+              # Qwen
+              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX,
+              
+              # MiniMax
+              MiniMax, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
+              
+              # GLM
+              ZHIPU_AI, 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
+              MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
+              
+              # 其他模型
+              WEN_XIN, WEN_XIN_4, XUNFEI,
+              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o,
+              MODELSCOPE
            ]

+MODEL_LIST = MODEL_LIST + GITEE_AI_MODEL_LIST + MODELSCOPE_MODEL_LIST
 # channel
 FEISHU = "feishu"
 DINGTALK = "dingtalk"
--- a/common/linkai_client.py
+++ b/common/linkai_client.py
@@ -2,7 +2,7 @@ from bridge.context import Context, ContextType
 from bridge.reply import Reply, ReplyType
 from common.log import logger
 from linkai import LinkAIClient, PushMsg
-from config import conf, pconf, plugin_config, available_setting
+from config import conf, pconf, plugin_config, available_setting, write_plugin_config
 from plugins import PluginManager
 import time

@@ -42,11 +42,19 @@ class ChatClient(LinkAIClient):
        if reply_voice_mode:
            if reply_voice_mode == "voice_reply_voice":
                local_config["voice_reply_voice"] = True
+                local_config["always_reply_voice"] = False
            elif reply_voice_mode == "always_reply_voice":
                local_config["always_reply_voice"] = True
+                local_config["voice_reply_voice"] = True
+            elif reply_voice_mode == "no_reply_voice":
+                local_config["always_reply_voice"] = False
+                local_config["voice_reply_voice"] = False

-        if config.get("admin_password") and plugin_config.get("Godcmd"):
-            plugin_config["Godcmd"]["password"] = config.get("admin_password")
+        if config.get("admin_password"):
+            if not pconf("Godcmd"):
+                write_plugin_config({"Godcmd": {"password": config.get("admin_password"), "admin_users": []} })
+            else:
+                pconf("Godcmd")["password"] = config.get("admin_password")
            PluginManager().instances["GODCMD"].reload()

        if config.get("group_app_map") and pconf("linkai"):
--- a/common/utils.py
+++ b/common/utils.py
@@ -1,8 +1,9 @@
 import io
 import os
+import re
 from urllib.parse import urlparse
 from PIL import Image
-
+from common.log import logger

 def fsize(file):
    if isinstance(file, io.BytesIO):
@@ -54,3 +55,24 @@ def split_string_by_utf8_length(string, max_length, max_split=0):
 def get_path_suffix(path):
    path = urlparse(path).path
    return os.path.splitext(path)[-1].lstrip('.')
+
+
+def convert_webp_to_png(webp_image):
+    from PIL import Image
+    try:
+        webp_image.seek(0)
+        img = Image.open(webp_image).convert("RGBA")
+        png_image = io.BytesIO()
+        img.save(png_image, format="PNG")
+        png_image.seek(0)
+        return png_image
+    except Exception as e:
+        logger.error(f"Failed to convert WEBP to PNG: {e}")
+        raise
+
+
+def remove_markdown_symbol(text: str):
+    # 移除markdown格式，目前先移除**
+    if not text:
+        return text
+    return re.sub(r'\*\*(.*?)\*\*', r'\1', text)
--- a/config-template.json
+++ b/config-template.json
@@ -1,37 +1,30 @@
 {
-  "channel_type": "wx",
-  "model": "",
-  "open_ai_api_key": "YOUR API KEY",
-  "claude_api_key": "YOUR API KEY",
-  "text_to_image": "dall-e-2",
+  "channel_type": "web",
+  "model": "claude-sonnet-4-5",
+  "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",
+  "zhipu_ai_api_key": "",
+  "minimax_api_key": "",
+  "dashscope_api_key": "",
  "voice_to_text": "openai",
  "text_to_voice": "openai",
-  "proxy": "",
-  "hot_reload": false,
-  "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"
-  ],
+  "voice_reply_voice": false,
  "speech_recognition": true,
  "group_speech_recognition": false,
-  "voice_reply_voice": false,
-  "conversation_max_tokens": 2500,
-  "expires_in_seconds": 3600,
-  "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。",
-  "temperature": 0.7,
-  "subscribe_msg": "感谢您的关注！\n这里是AI智能助手，可以自由对话。\n支持语音对话。\n支持图片输入。\n支持图片输出，画字开头的消息将按要求创作图片。\n支持tool、角色扮演和文字冒险等丰富的插件。\n输入{trigger_prefix}#help 查看详细指令。",
  "use_linkai": false,
  "linkai_api_key": "",
-  "linkai_app_code": ""
+  "linkai_app_code": "",
+  "feishu_bot_name": "",
+  "feishu_app_id": "",
+  "feishu_app_secret": "",
+  "dingtalk_client_id": "",
+  "dingtalk_client_secret":"",
+  "agent": true,
+  "agent_max_context_tokens": 40000,
+  "agent_max_context_turns": 20,
+  "agent_max_steps": 15
 }
--- a/config.py
+++ b/config.py
@@ -1,10 +1,10 @@
 # encoding:utf-8

+import copy
 import json
 import logging
 import os
 import pickle
-import copy

 from common.log import logger

@@ -15,9 +15,11 @@ available_setting = {
    "open_ai_api_key": "",  # openai api key
    # openai apibase，当use_azure_chatgpt为true时，需要设置对应的api base
    "open_ai_api_base": "https://api.openai.com/v1",
+    "claude_api_base": "https://api.anthropic.com/v1",  # claude api base
+    "gemini_api_base": "https://generativelanguage.googleapis.com",  # gemini api base
    "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, 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
+    "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，
    "use_azure_chatgpt": False,  # 是否使用azure的chatgpt
    "azure_deployment_id": "",  # azure 模型部署名称
@@ -27,6 +29,7 @@ available_setting = {
    "single_chat_reply_prefix": "[bot] ",  # 私聊时自动回复的前缀，用于区分真人
    "single_chat_reply_suffix": "",  # 私聊时自动回复的后缀，\n 可以换行
    "group_chat_prefix": ["@bot"],  # 群聊时包含该前缀则会触发机器人回复
+    "no_need_at": False,  # 群聊回复时是否不需要艾特
    "group_chat_reply_prefix": "",  # 群聊时自动回复的前缀
    "group_chat_reply_suffix": "",  # 群聊时自动回复的后缀，\n 可以换行
    "group_chat_keyword": [],  # 群聊时包含该关键词则会触发机器人回复
@@ -34,6 +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
    "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
@@ -69,10 +73,13 @@ available_setting = {
    "baidu_wenxin_model": "eb-instant",  # 默认使用ERNIE-Bot-turbo模型
    "baidu_wenxin_api_key": "",  # Baidu api key
    "baidu_wenxin_secret_key": "",  # Baidu secret key
+    "baidu_wenxin_prompt_enabled": False,  # Enable prompt if you are using ernie character model
    # 讯飞星火API
    "xunfei_app_id": "",  # 讯飞应用ID
    "xunfei_api_key": "",  # 讯飞 API key
    "xunfei_api_secret": "",  # 讯飞 API secret
+    "xunfei_domain": "",  # 讯飞模型对应的domain参数，Spark4.0 Ultra为 4.0Ultra，其他模型详见: https://www.xfyun.cn/doc/spark/Web.html
+    "xunfei_spark_url": "",  # 讯飞模型对应的请求地址，Spark4.0 Ultra为 wss://spark-api.xf-yun.com/v4.0/chat，其他模型参考详见: https://www.xfyun.cn/doc/spark/Web.html
    # claude 配置
    "claude_api_cookie": "",
    "claude_uuid": "",
@@ -95,8 +102,8 @@ available_setting = {
    "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，需要设置对应语音合成引擎的api key
    "always_reply_voice": False,  # 是否一直使用语音回复
-    "voice_to_text": "openai",  # 语音识别引擎，支持openai,baidu,google,azure
-    "text_to_voice": "openai",  # 语音合成引擎，支持openai,baidu,google,pytts(offline),ali,azure,elevenlabs,edge(online)
+    "voice_to_text": "openai",  # 语音识别引擎，支持openai,baidu,google,azure,xunfei,ali
+    "text_to_voice": "openai",  # 语音合成引擎，支持openai,baidu,google,azure,xunfei,ali,pytts(offline),elevenlabs,edge(online)
    "text_to_voice_model": "tts-1",
    "tts_voice_id": "alloy",
    # baidu 语音api配置， 使用百度语音识别和语音合成时需要
@@ -144,6 +151,7 @@ available_setting = {
    "feishu_app_secret": "",  # 飞书机器人APP secret
    "feishu_token": "",  # 飞书 verification token
    "feishu_bot_name": "",  # 飞书机器人的名字
+    "feishu_event_mode": "websocket",  # 飞书事件接收模式: webhook(HTTP服务器) 或 websocket(长连接)
    # 钉钉配置
    "dingtalk_client_id": "",  # 钉钉机器人Client ID 
    "dingtalk_client_secret": "",  # 钉钉机器人Client Secret
@@ -167,14 +175,23 @@ available_setting = {
    "zhipu_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
    "moonshot_api_key": "",
    "moonshot_base_url": "https://api.moonshot.cn/v1/chat/completions",
+    #魔搭社区 平台配置
+    "modelscope_api_key": "",
+    "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
    # LinkAI平台配置
    "use_linkai": False,
    "linkai_api_key": "",
    "linkai_app_code": "",
    "linkai_api_base": "https://api.link-ai.tech",  # linkAI服务地址
-    "Minimax_api_key": "",
+    "minimax_api_key": "",
    "Minimax_group_id": "",
    "Minimax_base_url": "",
+    "web_port": 9899,
+    "agent": True,  # 是否开启Agent模式
+    "agent_workspace": "~/cow",  # agent工作空间路径，用于存储skills、memory等
+    "agent_max_context_tokens": 50000,  # Agent模式下最大上下文tokens
+    "agent_max_context_turns": 30,  # Agent模式下最大上下文记忆轮次
+    "agent_max_steps": 15,  # Agent模式下单次运行最大决策步数
 }


@@ -189,16 +206,26 @@ class Config(dict):
        self.user_datas = {}

    def __getitem__(self, key):
-        if key not in available_setting:
-            raise Exception("key {} not in available_setting".format(key))
+        # 跳过以下划线开头的注释字段
+        if not key.startswith("_") and key not in available_setting:
+            logger.warning("[Config] key '{}' not in available_setting, may not take effect".format(key))
        return super().__getitem__(key)

    def __setitem__(self, key, value):
-        if key not in available_setting:
-            raise Exception("key {} not in available_setting".format(key))
+        # 跳过以下划线开头的注释字段
+        if not key.startswith("_") and key not in available_setting:
+            logger.warning("[Config] key '{}' not in available_setting, may not take effect".format(key))
        return super().__setitem__(key, value)

    def get(self, key, default=None):
+        # 跳过以下划线开头的注释字段
+        if key.startswith("_"):
+            return super().get(key, default)
+        
+        # 如果key不在available_setting中，直接返回default
+        if key not in available_setting:
+            return super().get(key, default)
+        
        try:
            return self[key]
        except KeyError as e:
@@ -216,7 +243,7 @@ class Config(dict):
        try:
            with open(os.path.join(get_appdata_dir(), "user_datas.pkl"), "rb") as f:
                self.user_datas = pickle.load(f)
-                logger.info("[Config] User datas loaded.")
+                logger.debug("[Config] User datas loaded.")
        except FileNotFoundError as e:
            logger.info("[Config] User datas file not found, ignore.")
        except Exception as e:
@@ -261,6 +288,15 @@ def drag_sensitive(config):

 def load_config():
    global config
+
+    # 打印 ASCII Logo
+    logger.info("  ____                _                    _   ")
+    logger.info(" / ___|_____      __ / \\   __ _  ___ _ __ | |_ ")
+    logger.info("| |   / _ \\ \\ /\\ / // _ \\ / _` |/ _ \\ '_ \\| __|")
+    logger.info("| |__| (_) \\ V  V // ___ \\ (_| |  __/ | | | |_ ")
+    logger.info(" \\____\\___/ \\_/\\_//_/   \\_\\__, |\\___|_| |_|\\__|")
+    logger.info("                          |___/                 ")
+    logger.info("")
    config_path = "./config.json"
    if not os.path.exists(config_path):
        logger.info("配置文件不存在，将使用config-template.json模板")
@@ -276,6 +312,9 @@ def load_config():
    # Some online deployment platforms (e.g. Railway) deploy project from github directly. So you shouldn't put your secrets like api key in a config file, instead use environment variables to override the default config.
    for name, value in os.environ.items():
        name = name.lower()
+        # 跳过以下划线开头的注释字段
+        if name.startswith("_"):
+            continue
        if name in available_setting:
            logger.info("[INIT] override config by environ args: {}={}".format(name, value))
            try:
@@ -294,6 +333,23 @@ def load_config():

    logger.info("[INIT] load config: {}".format(drag_sensitive(config)))

+    # 打印系统初始化信息
+    logger.info("[INIT] ========================================")
+    logger.info("[INIT] System Initialization")
+    logger.info("[INIT] ========================================")
+    logger.info("[INIT] Channel: {}".format(config.get("channel_type", "unknown")))
+    logger.info("[INIT] Model: {}".format(config.get("model", "unknown")))
+
+    # Agent模式信息
+    if config.get("agent", False):
+        workspace = config.get("agent_workspace", "~/cow")
+        logger.info("[INIT] Mode: Agent (workspace: {})".format(workspace))
+    else:
+        logger.info("[INIT] Mode: Chat (在config.json中设置 \"agent\":true 可启用Agent模式)")
+
+    logger.info("[INIT] Debug: {}".format(config.get("debug", False)))
+    logger.info("[INIT] ========================================")
+
    config.load_user_datas()


@@ -337,6 +393,14 @@ def write_plugin_config(pconf: dict):
    for k in pconf:
        plugin_config[k.lower()] = pconf[k]

+def remove_plugin_config(name: str):
+    """
+    移除待重新加载的插件全局配置
+    :param name: 待重载的插件名
+    """
+    global plugin_config
+    plugin_config.pop(name.lower(), None)
+

 def pconf(plugin_name: str) -> dict:
    """
--- a/docker/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -6,9 +6,9 @@ services:
    security_opt:
      - seccomp:unconfined
    environment:
-      TZ: 'Asia/Shanghai'
+      CHANNEL_TYPE: 'web'
      OPEN_AI_API_KEY: 'YOUR API KEY'
-      MODEL: 'gpt-3.5-turbo'
+      MODEL: ''
      PROXY: ''
      SINGLE_CHAT_PREFIX: '["bot", "@bot"]'
      SINGLE_CHAT_REPLY_PREFIX: '"[bot] "'
@@ -17,9 +17,10 @@ services:
      IMAGE_CREATE_PREFIX: '["画", "看", "找"]'
      CONVERSATION_MAX_TOKENS: 1000
      SPEECH_RECOGNITION: 'False'
-      CHARACTER_DESC: '你是ChatGPT, 一个由OpenAI训练的大型语言模型, 你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。'
+      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'
      USE_LINKAI: 'False'
+      AGENT: 'True'
      LINKAI_API_KEY: ''
      LINKAI_APP_CODE: ''
--- a/Show More
+++ b/Show More